Oladosu focuses on ABU title - The Nation Newspaper
Akeem Lawal
West Africa Boxing Union (WABU) champion, Rilwan ‘Real One’ Oladosu has sent a strong warning to African Boxing Union champion, Oto ‘Joe Boy’ Joseph to be ready to lose his belt at the ABU title fight, NationSport reports.
Oladosu and Oto were earlier scheduled to clash in the ‘Fight of The Decade’ at the GOtv Boxing Night 20 on October 20 in Lagos, but the event could not hold following a shoulder injury sustained by the ABU champion while training with his sparring partner.
Oladosu, who defeated Ola “Take Blow” Adebakin (1-5) with a first-round TKO at the maiden GOtv Boxing Night Mini show held at Rowe Park Sports Centre in Lagos on Saturday, said his focus has always been on the African title.
“I’m so grateful for this victory. I did not train because of this fight rather I was training for the Africa title. That gave me the motivation to stop this guy as early as possible.
“I’m training for Oto Joseph. He is the one I want to fight. I don’t need this kind of boxer. All these while, my focus is on Joe Boy. I don’t want to repeat the same class every year. I am the national and West African champion, now I need the African title.”
I have the belief I can take the title because of my training, the coaches and my fans.
“I’m using this opportunity to thank my coach, family and my fans for their support. I don’t have any other job than boxing and I want to use the sport to build myself,” Oladosu told NationSport.


The main property of the MergeTree cloud tables is the absence of manual control over the sharding scheme of data on a cluster. The data in the cloud tables are distributed around the cluster on its own, while at the same time providing the locality property for a certain key.
Requirements
- Creating a cloud table makes it visible on all nodes of the cluster. No need to manually create a separate Distributed table and local tables on each node.
- When ingesting data to a cloud table, while the table is very small, data is distributed across several cluster servers, but as data grows, more servers are involved (for example, starting from gigabytes per server). The user can create a small table and it should not be too cumbersome; but when creating a table, we do not know in advance how much data will be loaded into it.
- The user specifies a sharding key (arbitrary tuple). Data for the sharding key range (in lexicographical order) is located on some servers. Very small ranges are located on several servers and to access it is enough to read data from a single server, while sufficiently large ranges are spread across all servers. For example, if we are talking about web analytics the sharding key might start with CounterID, the website identifier. Data on a large site like https://yandex.ru should be spread across all servers in the cluster, while data on a small site should be located on only a few servers. Physical explanation: the cluster should scale to simultaneously provide throughput for heavy queries and to handle high QPS of light queries, and for light queries, the latency should not suffer. In general, this is called data locality.
- The ability for heavy queries to use all the servers in the cluster, rather than 1 / N, where N is the replication coefficient. Thus, one server can contain multiple replicas of different shards.
- When replacing the server with an empty one (node recovery), the data restore must be parallelized in some way. At least the reads should be spread over different servers to avoid overloading individual servers.
- On each local server, reading the range of the primary key should be touching not a very large number of file ranges or not too small file ranges (minimizing disk seeks).
- (Optional) The ability to use individual disks instead of RAID, but at the same time preserving throughput when reading medium-sized primary key ranges and preserving QPS when reading small-sized ranges.
- The ability to create multiple tables with a common sharding scheme (co-sharding).
- Rebalancing data when adding new servers; creation of additional replicas with long unavailability of old servers.
- SELECT queries should not require synchronous requests to the coordinator. No duplicates or missing data visible by SELECT queries during data rebalancing operations.
- SELECT queries must choose large enough subset of servers considering conditions on sharding key and knowledge of the current sharding scheme.
- The ability to efficiently distribute data across servers with uneven available disk space.
- Atomicity of INSERT on a cluster.
Out of scope and will not be considered:
- Erasure data encoding for replication and recovery.
- Data storage on systems with different disks - HDD and SSD. An example is storing fresh data on an SSD.
General Considerations
A similar problem usually (in Map-Reduce or blob-storage systems) is solved by organizing data in chunks. Chunks are located on the nodes of the cluster. Mappings: table or file -> chunks, chunk -> nodes, are stored in the master, which itself can be replicated. The master observes the liveliness of nodes and maintains a reasonable replication level of all chunks.
Difficulties arise when there are too many chunks: in this case, the master does not cope with the storage of metadata and with the load. It becomes necessary to make complicated metadata sharding.
In our case, it may seem tempting to solve a problem in a similar way, where instead of a chunk, an instance of a MergeTree type table containing the data range is used. Chunks in other systems are called “tablets” or “regions”. But there are many problems with this. The number of chunks on one server cannot be large, because then the property is violated - minimizing the number of seeks when reading data ranges. The problem also arises from the fact that each MergeTree table itself is rather cumbersome and consists of a large number of files. On the other hand, tables with a size of one terabyte are more or less normal if the data locality property is maintained. That is if several such tables on one server begin to be used only for not too small data ranges.
A variety of options can be used for sharding data, including:
Sharding according to some formula with a small number of parameters. Examples are simple hashing, consistent hashing (hash ring, rendezvous hashing, jump consistent hashing, sumbur). The practice of using in other systems shows that in its pure form this approach does not work well, because the sharding scheme is poorly controlled. Fits fine, for example, for caches. It can also be used as part of another algorithm.
The opposite option is that the data is divided into shards using an explicitly specified table. The table may contain key ranges (or, in another case, hash ranges from keys) and their corresponding servers. This gives a much greater degree of freedom in choosing when and how to transfer data. But at the same time, to scale the cluster, the size of the table has to be dynamically expanded, breaking the existing ranges.
One of the combined options is that the mapping is made up of two parts: first, the set of various keys is divided into some pre-fixed not too few and not too many “virtual shards” (you can also call “logical shards”, “mini-shards”). This number is several times larger than the hypothetical cluster size in the number of servers. Further, the second mapping explicitly specifies the location of each mini-shard on the servers, and this second mapping can be controlled arbitrarily.
The complexity of this approach is that partitioning hash ranges gives uniformity, but does not give locality of data for range queries; whereas when splitting by key ranges, it is difficult to choose a uniform distribution in advance since we do not know what the distribution of data will be to the keys. That is, the approach with the choice of a pre-fixed split into mini-shards does not work if data locality is required.
It turns out that the only acceptable approach in our case is partitioning by key ranges, which can change dynamically (repartitioned). At the same time, for more convenience, manageability, and uniformity of data distribution, the number of partitioning elements can be slightly larger than the number of servers, and the mapping from the partitioning element into servers can be changed separately.
Possible Implementation
Each ClickHouse server can participate in a certain cloud. The cloud is identified by a text string. The membership of a node in the cloud can be ensured by creating a certain type of database on the node (IDatabase). Thus, one node can be registered in several clouds. Registry of the nodes registered in the cloud is maintained in the coordinator.
Cloud nodes are selected to accommodate the replicas of the shards of cloud tables. The node also sends some additional information to the coordinator for its selection when placing data: the path that determines the locality in the network (for example, data center and rack), the amount of disk space, etc.
The cloud table is created in the corresponding database registered in the cloud. The table is created on any server and is visible in all databases registered in the cloud.
Sharding key is set for cloud table on it“s creation, an arbitrary tuple. Sometimes it is practical that the sharding key matches the primary key (example - (CounterID, Date, UserID)), sometimes it makes sense that it is different (for example, the DateTime primary key, sharding key - UserID).
Sharding is a composition of several mappings:
- The set of all possible tuples, the values of the sharding key, is mapped onto many half-intervals that break the half-interval [0, 1). Initially, this number is the size of the partition, it is equal to 1. That is, all values are mapped into a single semi-interval, the whole set [0, 1). Then, as the amount of data in the table increases, the semi-intervals, the split elements, can be divided approximately in half by the median of the distribution of values in lexicographical order.
- For each half-interval splitting, several cloud servers are selected and remembered in some way, on which replicas of the corresponding data will be located. The choice is made based on the location of servers on the network (for example, at least two replicas in different data centers and all replicas in different racks), the number of replicas already created on this server (choose servers with the minimum) and the amount of free space (from various servers just select the server with the maximum amount of free space).
As a result, this composition forms a mapping from the sharding key into several replica servers.
It is assumed that in the course of work both parts of this mapping may change.
The result of mapping 1 can be called the “virtual shard” or “logical shard”. In the process of work, virtual shards can be divided in half. Going in the opposite direction is impossible - the number of virtual shards can only grow. It is assumed that even for tables occupying the entire cluster, the number of virtual shards will be several times larger than the number of servers (for example, it may be greater by 10 times the replication ratio). Data ranges occupying at least a tenth of all data should be spread across all servers to ensure throughput of heavy queries. The mapping as a whole is specified by the set of boundary values for the sharding key. This set is small (roughly kilobytes) and stored in the coordinator.
The mapping of virtual shards on real servers can change arbitrarily: the number of replicas can increase when servers are not available for a long time or increase and then decrease to move replicas between servers.
How to Satisfy All Requirements
List items below correspond to the requirement numbers above:
- IDatabase synchronously goes to the coordinator to get or change the list of tables. The list of cloud tables is stored in the coordinator in the node corresponding to the cloud. That is, all the tables in the cloud are visible on each server entering the cloud.
- It is ensured by the fact that initially the partition consists of a single element, but begins to break up further with increasing data volume. Each replica responsible for the local storage of this data can initiate the splitting, once the criterion for the data volume has been reached. Multiple replicas may decide to do this competitively, and the decision is made using atomic CAS. To have fewer problems, it is possible to randomize somewhat the moment of deciding repartition. The criterion when it is necessary to additionally break virtual shards turns out to be non-trivial. For example, you can break up to the number of servers * the replication rate quite soon, by growing a shard to several gigabytes. But it is already worth breaking shards even when shards are 1 / N in size from the server size (for example, around a terabyte). In coordinator, you should store the last and previous splits immediately and do not do the splitting too often.
- It is ensured by the fact that the number of virtual shards will be several times (user-defined) more than the number of servers. Note: for additional data spreading, you can impose some spreading transformation on the sharding key. Not thought out. For example, instead of a key (CounterID, Date, UserID) use for sharding (hash (UserID)% 10, CounterID, Date, UserID). But in this case, even small CounterIDs will fall into 10 ranges.
- Similarly.
- If several virtual shards are located on a single server, their replicas will be spread over a larger number of servers, and during recovery, there will be more fanout.
- Small requests will use one shard. While large requests will use several shards on the same server. But since each shard will be somewhat smaller, the data in the MergeTree table will probably be presented by a smaller set of parts. For example, we now have a maximum part size of 150 GiB, and for large tables, many such large chunks are formed in one partition. And if there are several tables, there will be a smaller number of large chunks in each. On the other hand, when inserting data, a larger number of small pieces will be generated on each server. And these small parts will cause an increase in the number of seeks. But not much, as the fresh data will be in the page cache. That is why too many virtual shards per server might not work well.
- Pretty hard. You can have groups of neighboring shards on different disks of the same server. But then reading of medium size ranges will not be parallelized (since the whole range will be on one disk). In RAID, the problem is solved by the fact that the size of the chunk is relatively small (typically 1 megabyte). It would be possible to come up with a separate distribution of data in different pieces on different disks. But it is too difficult to design and implement carefully. Probably it“s better not to do the whole thing, and as a minimum, make it so that when on the JBOD server, one server disk is selected for the location of one shard.
- It is possible to identify the sharding scheme with a string, which may be common to different tables. The criterion for splitting shards is determined based on the total amount of data for all tables with the same sharding scheme.
- It is solved completely by changing the mapping of virtual shards on the servers. This mapping can be controlled independently of everything else.
- Servers can cache the sharding map (both parts of it) for a while and update it usually asynchronously. When rebalancing data due to the splitting of virtual shards, you should keep the old data for a longer time. Similarly, when transferring replicas between servers. Upon request, the initiator server also asks if the remote server has the necessary data: data for the required shard according to the sharding scheme that is cached by the initiator server. For the query, one live replica of each shard is selected, on which there is data. If suddenly there were none, then it is worthwhile to update the sharding map synchronously, as for some reason all the replicas were transferred somewhere.
- It is trivial.
- It is solved on the basis that more than one shard accounts for one server and the fact that the distribution of shards replicas among servers is more or less arbitrary and can take into account the amount of disk space.
Issues
To ingest data into a table, you can send an INSERT query to any server. The data will be divided into ranges and recorded on the desired servers. At the same time, it is synchronously ensured that we use a fresh sharding map - it is requested before the data is inserted and it is checked that it is not out of date, simultaneously with the commit in ZK.
When a SELECT query is used, if the old sharding map was used, the latest data will not be visible. Therefore, the asynchronous update interval of the sharding map for SELECT should be made customizable, and an option should be added to synchronously use the latest sharding map.
 This should work since the probability of inaccessibility or inhibition of all replicas of one shard is still low. But it will work slowly and, possibly, unstable. With a lot of INSERTs, there will be a terrible load on the coordinator. Although it can withstand one INSERT per second normally. To achieve high throughput of INSERTs, it is sufficient to simply make them parallel, but with the same low frequency of INSERTs in general. However, this is still a big problem.
There are the following possible solutions:
- You can add something to the beginning of the sharding key. For example, Date % 10 or toMinute. Then INSERTs will touch fewer shards (in the typical case when recent data is inserted), but at the same time during some time intervals, some shards will be hotter than others. Normally, if it reduces the number of active shards, for example, from 5000 on INSERT to 500. It is also very inconvenient for users.
- You can come up with some kind of incomprehensible sharding scheme, where the fresh data first falls into some fresh shard where it is not clear where from where it is then lazily overwritten. A fresh shard is essentially a distributed queue. At the same time, a fresh shard with SELECT is always requested. Not so good. And still, it contradicts the atomicity of these transfers of data, visible at SELECT. Alternatively, you could relax the requirements if you allow SELECT not to see some of the fresh data.
It looks like it“s generally not working well at a cluster size of over 500 servers.
Another problem is that to properly spread the ranges of the primary key, the number of virtual shards must be no less than the number of servers squared. And this is too much.
How to Get Around These Issues
For sharding, you can add some more intermediate mappings. There are the following options: - Splitting each shard into a set of shards in an arbitrary way. For example, 10 pieces. This is equivalent to adding a random number 0.N-1 to the beginning of the sharding key, which means nothing. Then with INSERT, you can only insert into one randomly selected shard, or a minimum sized shard, or some kind of round-robin; and as a result, INSERT becomes easier. But this increases the fanout of all point SELECTs. For convenience, such a partition can be done dynamically - only large enough shards can be divided in such a way (this will help avoid excessive splitting of old shards in the case when the sharding key starts with Date and the data is inserted in the Date order) or do such a partition starting from the situation when the number of shards is large enough (restriction on top of fanout INSERT requests).
An additional advantage: in the case of servers with JBOD, it is possible to prefer to place such second-level shards on the disks of one server, which half emulates RAID-0.
But there is a serious drawback: there is no possibility to do local IN / JOIN. For example, this possibility is assumed if the sharding key is hash (UserID), and we do JOIN by UserID. It would be possible to avoid this drawback by always placing all the “symmetric” shards on one server. - A mapping that spreads the data while keeping the number of virtual shards. The essence of this mapping is as follows:
- The spreading factor is set, for example,
N = 10.
As the very first mapping, 10 times more ranges are generated. For example, if we want to end up with 7 shards, then we divide the data into 70 ranges. - Then these ranges are renumbered in a circle with numbers from 0.6 and the ranges with the same number will fall into one shard, as a result, there will be 7 shards again.
- The continuous analogue of this mapping:
x in [0, 1) -> fractional_part (x * N)
, multiplication by N on a circle.
- The spreading factor is set, for example,
If you draw it on the picture in Cartesian coordinates, you get a “saw” with 10 teeth.
After this, it becomes obvious that this mapping simultaneously spreads the data and preserves its locality.
See also: Arnold's cat map.
But what is described here does not exactly work. First, until a sufficient amount of data has been accumulated, it is impossible to create a uniform division into parts (there is no place to count quantiles). Secondly, according to such a simple scheme, it is impossible to divide the intervals.
There is an option in which, instead of dividing a range in half, it uses splitting into 4 parts, which are then mapped into two shards. It is also not clear how this will work.


If you can read this your Caddy container on Docker is working.
More information about this image here.
Caddy Documentation here.


DONATE NOW
To Help Keep Our Lifesaving Work Going Strong
Since the pandemic began, we've had to do more with less. But fighting heart disease and stroke as we expand to confront the challenges of COVID-19 has stretched our resources to their limits. We need your help now more than ever to protect the hearts you love through innovative research, CPR and cardiovascular care training for first responders and support for those who have survived a heart attack or stroke. Give a gift that saves lives today!
DONATE NOW
To Help Keep Our Lifesaving Work Going Strong
Processing Donation...


'He is our present and future' - Marco Silva keeping Moise Kean from Everton exit
by Richie McCormackEverton manager Marco Silva insists Moise Kean is staying with the Merseyside club.
The Italian striker was dropped from the Toffees squad for their 2-1 win away to Southampton before the international break. The 19-year old had been late for a team meeting on two occasions.
Kean has since apologised to Silva and his Everton team-mates, but that hasn't stopped speculation that he was being lined up for a return to Serie A.
Speaking ahead of Saturday's Premier League visit of Norwich to Goodison Park, Silva insisted his €27.5m summer signing was very much still in his plans, "Moise has all the support here. We bought him, we believe in him.
The Portuguese boss confirmed Kean will be in his squad this weekend, "He has all the support from me, from the club and his team-mates to adapt to the Premier League. He is our present and future."
Silva insists there are no communication or attitude issues, "What is the most important is what I speak to him directly and everything is so clear for him.
"He's working really hard, he wants to play.
"After the Brighton game, where [he] didn’t play, on Sunday morning he was here [in Finch Farm] to work really hard and gave me the answer I want."
Kean hasn't played for Everton since being taken off at half-time of their 2-0 win over Watford in the Carabao Cup on October 29.
But Silva has drawn comparisons with another big-money signing who was eased into life on Merseyside - Liverpool midfielder Fabinho, "There’s many many examples of players who have come to the Premier League and not played.
"An example not too far from here is Fabinho. He plays a lot in the Liverpool team, but for the first six, seven months he didn’t play.
"And for most of the games he was in the stands.
"The most important thing is to keep teaching him, for him to adapt as fast as he can.
"We are talking about a talent with quality and he has all the support from myself and my staff and he is working hard to play because that is what he wants."
Having been dropped by Roberto Mancini for a similar breach of discipline with the Italian national side, Kean found the net for the Azzurri's U21s in midweek.
The 19-year old scored twice in a 6-0 thrashing of Armenia in the Republic of Ireland's European Championship qualifying group.
Silva hopes the goals, and the position from where they were scored, gives Kean impetus to knuckle down for the rest of Everton's season, "It was really good. Maybe important for some of you (to note) he scored twice (for Italy) from the right-wing.
“Most of the games when he goes to the national team he plays there as well.
"Like all players on international duty, it was very important for him to score."


PubSub Messenger
Click on the button to send a message.
See the console for additional information
Send Message


Registration
Information
(current)
Planning
Registration
Search
Log In
« Previous
Long-Term Planning
Semester 1
Semester 2
Semester 3
Add New Semester
Course Catalog
Courses
Add
AMST 116 | Asian American Fiction
AMST 266 | American Literature from the Civil War to the 1930s
ENG 113 | Studies in Fiction
ENG 208 | Poetry
ENG 208 | Writing for Television
Choose Semester
Semester 1
Semester 2
Semester 3
Add to Semester Schedule


THE INHERITORS A film review by Steve Rhodes Copyright 1998 Steve Rhodes RATING (0 TO ****): * 1/2
What's left for peasants to do when the owner of the farm they worked all their lives has died? Quite a lot actually, in writer and director Stefan Ruzowitzky's morality tale, THE INHERITORS (DIE SIEBTELBAUERN), set in a 1930's Austria that looks like the 1730s. They've inherited the farm, and they've never worked so hard in their lives now that they are both employers and employees.
Their owner has died of mysterious circumstances, angering the local police, who like their murderers to be caught red-handed. The story isn't a murder mystery per se, the crime's investigation being shuttled off to a cinematic sidetrack.
The setup concerns the effect of the owner's will. Hating basically everyone in the town, he gives his farm to the peasants who have worked it. Not some noble gesture, his bequest is done as a kind of a lark, and he says in the will that he expects them to fight among themselves. Among the ten peasants, the two leaders are played by Sophie Rois as Emmy and Simon Schwarz as Lukas.
The formula film rigidly dichotomizes the local inhabitants into the good and the bad. The good are the free-spirited, lithe peasants dressed in cheerful, plaid clothes. The bad can be just as easily recognized. They are the farmers with their abundant girth and black, somber suits, hats and ties. The farmers get lines certain to draw our wrath. ("God wants things the way they are, and the way they have always been," the lead farmer argues as to why it isn't right for peasants to own their own farm.)
The characters lack much depth or complexity. Looking at their clothes is sufficient information to predict their behavior. Made in interchangeable parts, the peasants are downtrodden and hence worthy of our sympathy. The farmers, rich and vicious, are deserving of our indignation. It is all rather like vaudeville plays in which they hold up "applause" and "hiss" signs to instruct the audience on how they are supposed to feel when the various characters are on stage.
As a director, Stefan Ruzowitzky strives to set a whimsical tone, but his script doesn't provide much humorous material to back up his intentions.  The problem with this and other similar scenes is that the characters are not genuine enough for us to care as we should.
The body of the movie has the farmers conniving to get the farm from the peasants while the peasants work hard to save it. Lacking much complexity, the movie exists on scenes like the recurring one around the large kitchen table. The peasants scarf down their soup while grimacing at each other. They always keep a place set for the dead owner. Why is never adequately explained, since they loathed him.
Ultimately it isn't what happens in THE INHERITORS that is the problem, but what doesn't. The story is so thin that it is barely there, and the formula film runs most of the time on autopilot.
THE INHERITORS runs 1:32. It is in German with English subtitles. The film isn't rated but would be an R for sex, nudity, violence, profanity and rape and would be acceptable for teenagers only if they are older and mature.
Email<EMAIL_ADDRESS>Web: www.InternetReviews.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


WaMDaM_v1.05 [WaMDaM_DBWrench_Schema_v1.05.xml]
A Method refers to how a quantitative or qualitative data values of an attribute where created or generated and by whom. A Method helps users understand the procedure that was used to create data and realize its limitations.
| PK | Name | Data Type | NULL | Auto | Default | Comment | FK |
|
| MethodID | Integer |
| Primary key identifier | |||
| MethodName | VarChar(255) | Full text name of the method | |||||
| MethodWebpage | VarChar(255) |
| A URL to a full description of the method | ||||
| MethodCitation | VarChar(500) |
| a text of the proper citation of the method | ||||
| MethodTypeCV | VarChar(255) |
| CV term of the method type used to report or generate a data value |
|
|||
| DataQuality | Text |
| Document the quality of data and any associated uncertainty | ||||
| PersonID | Integer | Foreign key identifier for a person |
|
||||
| Description | Text |
| Full text description of the method |
(none)
| Name | Parent Table | Child Table | Parent Columns | Child Columns |
| fk_Methods_CV_MethodType | CVs.CV_MethodType | Metadata.Methods | Name | MethodTypeCV |
| fk_Methods_People | Metadata.People | Metadata.Methods | PersonID | PersonID |
(none)
| Diagram Name |
| 01_WaMDaM |
| 02_WaMDaM_WCVs |


Contents
clc,clear;
GetRideData;
Part 1
disp('Part 1...................................Climb is independent')
xclimb = climb;
y1Calories = calories;
Mean_of_Climb = mean(climb);
Variance_of_Climb = var(climb);
disp(['Mean of Climb is ',num2str(Mean_of_Climb)])
disp(['Variance of Climb is ',num2str(Variance_of_Climb)])
disp('----------------------------------------------------------------')
Mean_of_Calories = mean(calories);
Variance_of_Calories = var(calories);
disp(['Mean of Calories is ',num2str(Mean_of_Calories)])
disp(['Variance of Calories is ',num2str(Variance_of_Calories)])
p=polyfit(xclimb,y1Calories,1);
a=p(1);
b=p(2);
disp(['a is ', num2str(a)]);
disp(['b is ', num2str(b)]);
yLfit1E1=a*xclimb+b;
n=numel(y1Calories);
Mean1E1 = sum(y1Calories)/n;
S1E1 = sum((y1Calories-Mean1E1).^2);
A1E1= sum((yLfit1E1-y1Calories).^2);
r21E1 = 1-A1E1/S1E1;
pp=polyfit(xclimb,log(y1Calories),1);
d=pp(1);
C=exp(pp(2));
disp(['C is ', num2str(C)]);
disp(['d is ', num2str(d)]);
yLfitE12=C*exp(d*xclimb);
Mean1E2 = sum(y1Calories)/n;
S1E2 = sum((y1Calories-Mean1E2).^2);
A1E2= sum((yLfitE12-y1Calories).^2);
r21E2 = 1-A1E2/S1E2;
figure(1)
plot(xclimb,y1Calories,'o',xclimb,yLfit1E1)
legend('Measured data','Fitted data','Location','northwest')
title(['Calories against Climb with y=ax+b', ' r2 = ', num2str(r21E1)])
xlabel('Climb (feet)')
ylabel('Calories (kcal)')
hold on
newCfeet = 6500;
newCal = a*newCfeet+b;
disp('********************************************************************')
disp(['At 6500 Climbing feet,', num2str(newCal), ' kcal is the Calories predicted by the choosen model: y=ax+b'])
Part 1...................................Climb is independent
Mean of Climb is 1573.0167
Variance of Climb is<PHONE_NUMBER>
----------------------------------------------------------------
Mean of Calories is 2249.15
Variance of Calories is<PHONE_NUMBER>
a is 1.0189
b is 646.4203
C is 1105.7362
d is 0.00041186
********************************************************************
At 6500 Climbing feet,7269.2 kcal is the Calories predicted by the choosen model: y=ax+b
Part 2
disp('********************************************************************')
disp('********************************************************************')
disp('Part 2...................................Distance is independent')
xdistance=distance;
y2calories=calories;
pL=polyfit(xdistance,y2calories,1);
a2=pL(1);
b2=pL(2);
yLfit2E1calories=a2*xdistance+b2;
disp('For Part 2')
disp(['a is ', num2str(a2)]);
disp(['b is ', num2str(b2)]);
p3=polyfit(log(xdistance),log(y2calories),1);
f=p3(1);
G=exp(p3(2));
yLfit2E2 = G*(distance.^f);
disp('----------------------------------------------------------------')
disp('For Part 2')
disp(['G is ', num2str(G)]);
disp(['f is ', num2str(f)]);
n=numel(y2calories);
Mean2E1 = sum(y2calories)/n;
S2E1 = sum((y2calories-Mean2E1).^2);
A2E1= sum((yLfit2E1calories-y2calories).^2);
r22E1 = 1-A2E1/S2E1;
Mean2E2 = sum(y2calories)/n;
S2E2 = sum((y2calories-Mean2E2).^2);
A2E2= sum((yLfit2E2-y2calories).^2);
r22E2 = 1-A2E2/S2E2;
figure(2)
plot(xdistance,y2calories,'o',xdistance,yLfit2E1calories,'b')
legend('Measured data','Fitted data','Location','northwest')
title(['Calories against Distance Measured Data y=ax+b ','r2 =', num2str(r22E1)])
xlabel('Distance (miles)')
ylabel('Calories (kcal)')
hold on
********************************************************************
********************************************************************
Part 2...................................Distance is independent
For Part 2
a is 77.945
b is -13.8536
----------------------------------------------------------------
For Part 2
G is 78.1008
f is 0.99695
Part 3
disp('********************************************************************')
disp('********************************************************************')
disp('Part 3...................................Speed is independent')
xspeed=speed;
y3calories=calories;
pL3=polyfit(xspeed,y3calories,1);
a3=pL3(1);
b3=pL3(2);
yLfit3E1calories=a3*xspeed+b3;
disp('For Part 3')
disp(['a is ', num2str(a3)]);
disp(['b is ', num2str(b3)]);
Mean_of_Speed = mean(speed);
Variance_of_Speed = var(speed);
disp(['Mean of Speed is ',num2str(Mean_of_Speed)])
disp(['Variance of Speed is ',num2str(Variance_of_Speed)])
disp('----------------------------------------------------------------')
Mean_of_Calories = mean(calories);
Variance_of_Calories = var(calories);
disp(['Mean of Calories is ',num2str(Mean_of_Calories)])
disp(['Variance of Calories is ',num2str(Variance_of_Calories)])
figure(3)
plot(xspeed,y3calories,'o',xspeed,yLfit3E1calories,'g')
legend('Measured data','Fitted data','Location','northeast')
title('Calories against Speed Measured Data y=ax+b')
xlabel('Distance(mile)')
ylabel('Calories(kcal)')
hold on
********************************************************************
********************************************************************
Part 3...................................Speed is independent
For Part 3
a is -264.3182
b is 6157.5344
Mean of Speed is 14.7867
Variance of Speed is 0.3727
----------------------------------------------------------------
Mean of Calories is 2249.15
Variance of Calories is<PHONE_NUMBER>


Hello, My name is Kashyap Patel, also known as KP. I am a Developer and FreeLancer.
I currently work with my two teammates. Dhanraj (Web Technologies) and Mrunali (Team and Community Manager). We are looking forward to help others in the tech feild and specially girls. So we made a community to help everyone out who are interested in Programming or Developing. So to join our community enter your email below.
Apart from being community, we are your friends. We will help you guide your carrer if you are not sure what to do. Here you will share your work with others and have chance to work with like minded people. Start some project others will join and together we will create something new and better. We will share knowledge about developer groups of Google, Facebook, Microsoft, and many more. You can be part of them but first you will have to start coding. SO join us on this journey!
Before joining. here are some thing that you should read.
First you will have to submit yout email then we will contact you by email and then if you are confirmed then we will add you to your slack commnunity.Then you can talk about any new projject or make your new one with others.
Be nice. We're all part of the same community, so be friendly, welcoming, and generally a nice person. Be someone that other people want to be around.
Be respectful and constructive. Remember to be respectful and constructive with your communication to fellow members. Don't get into flamewars, make personal attacks, vent, or rant unconstructively. Everyone should take responsibility for the community and take the initiative to diffuse tension and stop a negative thread as early as possible.
Be collaborative. Work together! We can learn a lot from each other. Share knowledge, and help each other out.
Participate. Join in on discussions, show up for in-person meetings regularly, offer feedback, and help implement that feedback.
Step down considerately. If you have some form of responsibility in your community, be aware of your own constraints. If you know that a new job or personal situation will limit your time, find someone who can take over for you and transfer the relevant information (contacts, passwords, etc.) for a smooth transition.
Basic etiquette for online discussions. Don’t send messages to a big list that only need to go to one person. Keep off topic conversations to a minimum. Don’t be spammy by advertising or promoting personal projects which are off topic.


Created by Volha Yasinskaya
Unit tesing - you test the smallest part of the code - function in JS.
Jira - bug tracking system by Atlassian


$7m scam: Lebanese for arraignment Thursday - The Nation Newspaper
by https://www.facebook.com/CheerfulpraiseRobert Egbe
The Federal High Court in Lagos Monday rescheduled till Thursday, the arraignment of a suspected Lebanese money launderer, Hamza Koudeih, and his alleged Nigerian accomplice Kayode Philip, alias Voice of the King.
Philip and Koudeih are facing 24 counts filed against them by the Economic and Financial Crimes Commission (EFCC).
Their arraignment, which was scheduled for yesterday before Justice Muslim Hassan, could not go on because Koudeih’s lawyer, Abdulamid Mohammed, said he had yet to see the charge sheet against his client.
The lawyer, who said he was based in Abuja, said he was only told on Sunday that the matter would be coming up in Lagos on Monday and he had to rush down.
He said on getting to court yesterday morning, his client told him that the EFCC served the charge sheet on him on Friday and he gave it to his wife who took it home.
Mohammed prayed Justice Hassan to adjourn the case to enable him to study the charge sheet, the three-volume proof of evidence attached to it and to prepare for his client’s defence.
He cited Section 36 of the Constitution, which provides that an accused must be given adequate time and facility to prepare for his defence.
Read Also; Suspected killers of Kogi PDP woman leader arrested
But EFCC counsel Rotimi Oyedepo opposed the prayer for an adjournment.
Following arguments between the lawyers, Justice Hassan, in a short ruling, said he would like to exercise caution and adjourn the case for Koudeih’s lawyer to study the 24 counts and the three volumes of proof of evidence.
He adjourned till Thursday, for arraignment.
In the first count, the EFCC alleged that Philip and Koudeih conspired to convert aggregate sums of $7.06m and €80,000, “which sums you reasonably ought to have known form part of proceeds of fraud.”
The prosecution said the duo acted contrary to sections 18(a) and 15(2)(d) of the Money Laundering (Prohibition) Act and were liable to be punished under Section 15(3) of the same Act.
The EFCC had on October 26 arrested Koudeih in his multi-million dollar Victoria Island luxury apartment in Lagos.
The EFCC claimed that Koudeih pays an annual rent of $5m for the apartment in the 33-storey Eko Atlantic Pearl Tower, VI.


Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities.
| Version: | 2.3-1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, graphics, expint |
| LinkingTo: | expint |
| Suggests: | MASS |
| Published: | 2018-03-19 |
| Author: | Vincent Goulet [cre, aut], Sébastien Auclair [ctb], Christophe Dutang [aut], Xavier Milhaud [ctb], Tommy Ouellet [ctb], Alexandre Parent [ctb], Mathieu Pigeon [aut], Louis-Philippe Pouliot [ctb] |
| Maintainer: | Vincent Goulet <vincent.goulet at act.ulaval.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | actuar citation info |
| Materials: | NEWS |
| In views: | Distributions, Finance |
| CRAN checks: | actuar results |
| Reference manual: | actuar.pdf |
| Vignettes: |
Introduction to actuar Complete formulas used by coverage Credibility theory Additional continuous and discrete distributions Loss distributions modeling Risk and ruin theory Simulation of insurance data |
| Package source: | actuar_2.3-1.tar.gz |
| Windows binaries: | r-devel: actuar_2.3-1.zip, r-release: actuar_2.3-1.zip, r-oldrel: actuar_2.3-1.zip |
| OS X binaries: | r-release: actuar_2.3-1.tgz, r-oldrel: actuar_2.3-1.tgz |
| Old sources: | actuar archive |
| Reverse depends: | lbiassurv, stratifyR |
| Reverse imports: | AnnuityRIR, BTSPAS, ChainLadder, CompDist, episensr, fitur, IPSUR, kendallRandomWalks, mbbefd, OpVaR, ReliabilityTheory, RobExtremes, robmixglm, teachingApps |
| Reverse suggests: | extraDistr, fitdistrplus, fitteR, flexmix, GeneralizedHyperbolic, HyperbolicDist, PhaseType, PredictionR, raw, ruin |
Please use the canonical form https://CRAN.R-project.org/package=actuar to link to this page.


Makes the following keyboard shortcuts work the same or similarly in both macOS and terminal apps:
| Shortcut | Action | Bash/Readline | Zsh |
|---|---|---|---|
| ⌘Z | Undo | ✅ | ✅ |
| ⇧⌘Z | Redo | ❌ | needs bindkey '^[e' redo |
| ⌘D | Duplicate the word left of the cursor | ❌ | ✅ |
| ⇧⌘D | Duplicate the last word of the previous command line | ✅ | ✅ |
| ⌫ | Delete one character backward | ✅ | ✅ |
| ⌦ or fn⌫ | Delete one character forward | ✅ | ✅ |
| ⌥← | Go one natural word backward | ✅ | needs WORDCHARS='' |
| ⌥→ | Go one natural word forward | ✅ | needs WORDCHARS='' |
| ⌥⌫ | Cut one natural word backward | ✅ | needs WORDCHARS='' |
| ⌥⌦ or ⌥fn⌫ | Cut one natural word forward | ✅ | needs WORDCHARS='' |
| ⌘← | Go one shell word backward | ✅ | needs bindkey '^[^B' vi-backward-blank-word |
| ⌘→ | Go one shell word forward | ✅ | needs bindkey '^[^F' vi-forward-blank-word |
| ⌘⌫ | Cut one shell word backward | ✅ | needs zsh-edit |
| ⌘⌦ or ⌘fn⌫ | Cut one shell word forward | ✅ | needs zsh-edit |
| ⌃A | Go to previous line break | ✅ | ✅ |
| ⌃E | Go to next line break | ✅ | ✅ |
| ⌃U | Cut to previous line break | ✅ | needs bindkey '^U' backward-kill-line |
| ⌃K | Cut to next line break | ✅ | ✅ |
| ⌥↑ | Go to first line | ✅ first line of history | ✅ first line of buffer or, if already there, of history |
| ⌥↓ | Go to last line | ✅ last line of history | ✅ last line of buffer or, if already there, of history |


Experienced big data and software engineer with 8+ years of experience in the field of data science. Currently specializing in deep learning and natural language processing (NLP). For past years, I have extensively used frameworks of the big data stack. I am fluent in Python and R programming languages. I am seeking for career opportunities in the big data industry, where I can fully use my problem solving skills. I would like to work with projects oriented on the "4 V's of data", as well as gain more experience with deep learning. Scientist and entrepreneur Clive Humby once said, that "Data is the new oil" - I share his opinion, that understanding data makes for tangible profits in modern organizations. For more info, check out my writing, speaking, certifications, projects, and Kaggle profile.


Yi Du
I am an associate professor of Computer Network Information Center (CNIC), Chinese Academy of Sciences (CAS).
I currently lead Big Data Knowledge Engineering Lab that aims to fill the gap between data and knowledge.
My research and development interests focus on Visual Analytics and Data Mining.
(Information Last Updated: Aug.30th, 2021)| EDUCATION&EXPERIENCE | |
| Jul.2019 - now | Director of
Big Data Knowledge Engineering Laboratory, Computer Network Information Center, Chinese Academy of Sciences
Research Interest: Visual Analytics, Data Mining |
| Dec.2015 - now | Associate Professor at
Big Data Application Service Technology Laboratory, Computer Network Information Center, Chinese Academy of Sciences
Research Interest: Visual Analytics, Data Mining |
| Sep.2015 - Sep.2016 | Visiting Scholar at
VACCINE, Purdue University
Research Interest: Information Visualization, Visual Analytics Collaborator: David Ebert |
| Jul.2013 - Dec.2015 | Assistant Professor at
Scientific Data Center, Computer Network Information Center, Chinese Academy of Sciences
Research Interest: Information Visualization, Human Computer Interaction |
| Sep.2008 - Jul.2013 | Ph.D. Computer Science at
Institute of Software, Chinese Academy of Sciences
E-UIDL: A User Interface Description Language on Mobile Computing Advisor: Guozhong Dai Committee: Qingyi Hua, Yuanchun Shi, Guoping Wang, Zhiliang Wang, Shiguang Shan |
| Sep.2004 - Jul.2008 | B.S. Software Engineering at
School of Software, Shandong University
Research and Application of Pen Based Graphical Editor Framework Advisor: Zhongmin Yan, Dongxing Teng |
| PROJECTS | |
| Think Tank System——Academic Division of the Chinese Academy of Sciences (Oct.2020-)
Develop a think tank system to help Academic Division of the Chinese Academy of Sciences improve finding strategy scientists. Techniques:Bigdata, Data governance, Knowledge graph Demos:demo(Internal Network),screenshot1,screenshot2 |
|
| Data Convergence and Analysis System——Bureau of International Cooperation Chinese Academy of Sciences (Oct.2019-)
Build a system to help analyze international cooperation of Bureau of International Cooperation Chinese Academy of Sciences. Techniques:Bigdata, Data governance, Knowledge graph Demos:demo(Internal Network),screenshot1,screenshot2 |
|
| Intelligent System——Bureau of Science&Technology for Development Chinese Academy of Sciences (Aug.2019——Jun.2020)
Build a system to help analyze S&T of Bureau of Science&Technology for Development Chinese Academy of Sciences. Techniques:Bigdata, Graph database, Knowledge graph Demos:demo(Internal Network),screenshot1 |
|
| National Space Science Center (NSSC) Knowledge Management System (Apr.2019-Apr.2020)
Build a knowledge management system for NSSC. Techniques:Bigdata, Graph Database, Network Analysis Demos:demo(Internal Network),screenshot1,screenshot2,screenshot3 |
|
| China Association for Science and Technology (CAST) Knowledge Graph System (Jan.2019-Jan.2020)
Build a big data knowledge graph system to help CAST decision makers to get current research and development situations. Techniques:Crawler, Bigdata, Graph Database Demos:demo(Internal Network),screenshot1,screenshot2,screenshot3 |
|
| Tobacco Scientific Knowledge Graph System (Sep.2018-Dec.2019)
Build a big data knowledge graph system to help researchers of tobacco domain. Techniques:PiFlow(big data pipline), Neo4J Demos:demo(Internal Network),screenshot |
|
| Management and Service Platform of Big NSFC Knowledge Data (Jan.2017-now)
Based on the long-collected data from NSFC (National Scientific Fundation of China), the platform re-organize researcher, achievement and project into a BigGraph model. Then, network analyzing and mining algorithms are used to get knowledge behind data. Techniques:Hadoop, Ambari, Titan, GraphX, Apache Nifi, Apache Kylin Demos:screenshot,,demo1,demo2,demo3 |
|
| Optimization of Alarm System about Navigation Mark (Oct.2016－Jan.2017)
This project proposed a optimization system about Navigation Mark. Techniques:Python、RandomForest Demos:TBD |
|
| Visual Analytics System of Correlation about Air Quality
The system can help domain experts and analysts find potential cause and effect between multiple spatial regions and temporal scopes. Techniques:Javascript, D3.js demos:screenshot |
|
| Visual Analytics System of Air Quality
This system can help analyze the air quality by integrating multiple visualization techniques. Besides, this system also proposed an adaptive trend visualization technique. Techniques:Javascript, D3.js demos&papers:paper |
|
| Data Visualization Platform(DVIZ)
This is a visualization platform, similar to ManyEyes, but has more functions. This platform supports more than 9 data format, including 5 file and 4 database. Further more, it supports create one visualization with two or more charts, changes of one chart can affect the changes of other charts. Techniques:Java, ECharts, D3.js Demos&Papers:online screenshot gallary videos |
|
| H7N9 Data Visualization System
Using heatmap and paralle coordinate to visulizing the explosion of H7N9 bird flu. techniques:Java, D3.js Demos&Papers: online screenshot |
|
| Radiation-hardened Orbit Design Demonstration System
Desc. techniques:Java, WorldWind Java SDK Demos&Papers:screenshot1 screenshot2 screenshot3 videos |
|
| Visualization System of a Food Factory
Desc. techniques:Java, ECharts, MongoDB Demos&Papers: |
|
| Orbit Data Visualization System
Desc. techniques:Java, WorldWind Java SDK Demos&Papers:screenshot1 screenshot2 screenshot3 screenshot3 |
|
| The Taxi GPS Visualization System
Desc. techniques:Java, MongoDB, OpenLayers Demos&Papers:screenshot1 screenshot2 screenshot3 |
|
| Research on User Interface Description Language under Mobile Environment
Desc. techniques:Java, C Demos&Papers:toolsScreenshot1 toolsScreenshot2 generatedScreenshot1 generatedScreenshot2 generatedScreenshot3 generatedScreenshot4 |
|
| Direct Social Network Importer of Gephi
This is a sub project of Google Summer of Code 2010. It is a graph visualization software, and can import, visualize and export graph. Besides these. My responsibility: Design and implement five data export plugins. These plugins can export data from twitter, facebook and NYT, and convert these data into gephi format. techniques:Java, Netbeans Demos&Papers:screenshot1 screenshot2 screenshot3 |
|
| Circle Pack for File System
Desc. techniques:C++,OpenGL Demos&Papers:youtube |
|
| Visual Analytics System for a Company using Daisy
It is a visual analytics system built by Daisy. techniques:Java, RCP, Prefuse Demos&Papers:youtube |
|
| Daisy
It is a visualization platform for building a visualization quickly. techniques:Java, RCP, Prefuse Demos&Papers:youtube |
| RESEARCH PROJECTS | |
| A study of key technologies for visual analysis of large-scale space-time data correlation. Beijing Natural Science Foundation projects (project leader,2021-2023) | |
| The talent system of science and technology think tanks. Academic Division of the Chinese Academy of Sciences (project leader,2020-2021) | |
| Application and dissemination of trademark data analysis. National Intellectual Property Office of China (project leader,2020-2021) | |
| Intelligent management service platform of scientific and technological cooperation based on knowledge graph. CAS STS (project leader,participant,2020-2023，Two projects.) | |
| Research of Output Evaluation Methods Based on Technique Domain Knowledge Graph. Beijing Nova program of 2019 (project leader,2020-2022) | |
| Design and Research of Open Sharing Policy and Platform of NSFC. National Natural Science Foundation of China (project leader,2020-2021) | |
| Research and Application of New Innovation Method System based on CrowdIntelligent. Innovation Method Special Fund (executive leader,2020-2021) | |
| Identity Resolution Public Service Support Platform（Industrail Knowledge Graph）. Industrial Internet Innovation and Development Project (project participant,2020-2022) | |
| Tobacco Scientific Knowledge Graph. CNTC (China National Tobacco Corporation ) Science and Technology Major Project (project leader,2018-2020) | |
| Research on the Technology of Intelligent Governance of Cross-domain Heterogeneous Data in Manufacturing Industry. National Key Research and Development Plan (task participant,2018-2020) | |
| Building of Domain Specific Big Data Knowledge Graph. National Natural Science Foundation of China (executive leader,2019-2023) | |
| Monitor, Tracing and Early Warning Techniques of Foodborne Diseases.. National Key Research and Development Plan (task leader,2018-2021) | |
| Study on the method of interactive spatio-temporal data visualization based on Overview+Detail. Knowledge Innovation Program of Chinese Academy of Sciences (project leader,2015-2016,100,000RMB) | |
| Research on Development Method of Spatio-temporal Data Visualizations Based on Model Driven Approach. National Natural Science Foundation of China (project leader,2015-2017,260,000RMB) | |
| Research on Spatial-temporal Visualization Service of Big Data Based on Unconventional Emergency Management. National Natural Science Foundation of China (project participant) | |
| Research on Metaphor-based Interactive Learning Environment for Children. National Natural Science Foundation of China (project participant) | |
| By blending pen input document analysis and recognition theory and method of research. National Natural Science Foundation of China (project participant) | |
| Based on the research on man-machine interaction device attributes fusion. National Natural Science Foundation of China (project participant) | |
| Personal information management research for more equipment. National Natural Science Foundation of China (project participant) | |
| For video semantic description and interaction of the sketch interface key technology research. National Natural Science Foundation of China (project participant) | |
| Reality - -based Interaction model and user interface evaluation method research. National Natural Science Foundation of China (project participant) | |
| Based on handwritten equipment on-line handwriting intelligent understanding and interactive technology research. National Natural Science Foundation of China (project participant) |
| PUBLICATIONS | |
| Journal Articles |
Farah Deeba, Fayaz A. Dharejo, Muhammad Zawish, Fida H. Memon, Kapal Dev, Rizwan A. Naqvi, Yuanchun Zhou*, Yi Du, A novel image dehazing framework for robust vision-based intelligent systems, International Journal of Intelligent Systems, 2021.(SCI IF:8.7) Yi Du*, Yunchang Guo, Machine learning techniques and research framework in foodborne disease surveillance system, Food Control, 2021.(SCI IF:5.55) Yi Du*, Hanxue Wang, Wenjuan Cui, Hengshu Zhu, Yunchang Guo, Fayaz Ali Dharejo, Yuanchun Zhou, Foodborne Disease Risk Prediction using Multi-Graph Structural LSTM: Algorithm Design and Validation Study, JMIR Medical Informatics, 2021. Accepted (SCI IF:2.58) Weijun Wang, Chang Yao, Ziyue Qiao, Wenjuan Cui, Yi Du*, Yuanchun Zhou,Method of Discovering Interdisciplinary Knowledge of the National Natural Science Foundation of China Based on Word Embedding: A Case Study on Artificial Intelligence and Information Management, Journal of the China Society for Scientific and Technical Information, 2021. Accepted（FMS T1) Yimeng Wang, Ye Tian*, Shanpeng Sun, Yuanchun Zhou, Yi Du,Community Detection of Researchers Based on ORCID and Weighted Cross-layer Edge Clustering Coefficients, Computer Systems & Applications, 2021. Accepted Peng Zhang, Wenjuan Cui, Hanxue Wang, Yuanchun Zhou, Yi Du* ,High-efficiency machine learning method for identifying foodborne disease outbreaks and confounding factors, Foodborne Pathogens and Disease, 2021. Accepted(SCI IF:2.6) Farah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Yi Du, Xuezhi Wang*, She Kun ,Multi-scale Single Image Super-Resolution with Remote-Sensing Application Using Transferred Wide Residual Network, Wireless Personal Communications, 2021. (SCI IF:1.07) Fayaz Ali Dharejo, Farah Deeba, Yuanchun Zhou*, Das Bhagwan, Jatoi, Munsif Ali, Zawish Muhammad, Yi Du*, Xuezhi Wang,Single Image Remote Sensing Super-Resolution Combined with the Transferred Generative Adversarial Network and Wavelet Transformation, ACM Transactions on Intelligent Systems and Technology, 2021.Accepted (CCF Rank B) Fayaz Ali Dharejo, Yuanchun Zhou, Farah Deeba*, Munsif Ali Jatoi, Muhammad Ashfaq Khan, Ghulam Ali Mallah, Abdul Ghaffar, Muhammad Chhattal, Yi Du, Xuezhi Wang*,A deep hybrid neural network for single image dehazing via wavelet transform, Optik,2021. (SCI IF:2.2) Farah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Muhammad Ashfaq Khan, Bhagwan Das, Xuezhi Wang*, Yi Du,A Plexus-Convolutional Neural Network Framework for Fast Remote Sensing Image Super-Resolution in wavelet Domain, IET Image Processing,2021. (SCI IF:2.0) Hanxue Wang, Wenjuan Cui, Yunchang Guo, Yi Du*, Yuanchun Zhou,Prediction of Foodborne Diseases Pathogens: A Machine Learning Approach, JMIR Medical Informatics,2020. (SCI IF:2.58) Hanxue Wang,Wenjuan Cui,Yuanchun Zhou,Yi Du*, A Method for Identifying Pathogens of Foodborne Diseases based on Machine Learning, Data Analysis and Knowledge Discovery,2020. (FMS T2) Fayaz Ali Dharejo, Yuanchun Zhou, Farah Deeba, Munsif Ali Jatoi, Yi Du, Xuezhi Wang, A remote‐sensing image enhancement algorithm based on patch‐wise dark channel prior and histogram equalisation with colour correction, IET Image Processing,2020. (SCI IF:2.0) Yongsheng Wang, Weihua Feng, Xinzhang Zheng, Yali Liu, Nan Jia, Rui Wang, Guohao Zong, Di Wang, Yi Du Design and implementation of tobacco science and technology knowledge graph service platform, Computer Applications and Softwares(Accepted),2020. Xiaojie Zhu, Zihao Zhao, Yi Du* PiFlow: model driven big data pipeline framework. Journal of Computer Applications, Journal of Computer Applications,2020. Yuanchun Zhou, Weijun Wang, Ziyue Qiao, Meng Xiao, Yi Du* A survey on the construction methods and applications of scitech big data knowledge graph, SCIENCE CHINA Information Sciences,2020. Chengzan Li,Jianhui Li,Xuezhi Wang, Zhihong Shen,Yi Du, Research on Data Recommendation based on Community Detection of Citation Network, Journal of the China Society for Scientific and Technical Information,2019. (Accepted) Dharejo Fayaz Ali, Zhou Yuanchun, Deeba Farah, Du Yi, A Color Enhancement Scene Estimation Approach for Single Image Haze Removal, Geoscience and Remote Sensing Letters,2019. (SCI IF:3.5) TiNle Chen,Jun Pu,Xiaojie Zhu,Wenjuan Cui,Weihua Feng,Rui Wang, Yi Du*,Yuanchun Zhou A Hierarchical Splitting and Merging Task Management Method in Big Data Dataflow Processing, e-Science Technology & Application,2019. Yuanchun Zhou, Qingling Chang, Yi Du*, SKS: A Platform for Big Data Based Scientific Knowledge Graph, Frontiers of Data and Computing,2019. Jianghua Zhao, Xuezhi Wang, Hongqing Song, Yi Du, Wenjuan Cui and Yuanchun Zhou*, Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016, Atmosphere,2019. (SCI) Chang Yao, Xiaofan Wang, Yi Du, Zhaotian Zhang, Jianjun Li, Yanni Hao*, Architecture and Key Techniques of NSFC Big Data Knowledge Management Platform, Science Foundation in China,2019. Junjun FAN, Feng TIAN,*, Yi DU, Zhengjie LIU, Guozhong DAI, Thoughts on human-computer interaction in the age of artificial intelligence, SCIENTIA SINICA Informationis,2018. Yi Du, Abish Malik, Lianke Zhou, Yuanchun Zhou, A Correlation Visual Analytics System for Air Quality, Chinese Journal of Electronics,2018. (SCI)(Accepted) Yi Du, Lei Ren, Yuanchun Zhou, Jianhui Li, Feng Tian, Guozhong Dai, Banded Choropleth Map, Personal and Ubiquitous Computing,2017. (SCI) Fei Lv, Feng Tian, Yi Du, Xiangkai Chen, Wenjun Hou , Lei Ren, Guozhong Dai, A Natural User Interface Evaluation Method Based on Reality Framework, Journal of Computer-Aided Design & Computer Graphics,vol. 29, no. 11, pp. 2076-2082, 2017. Lei Ren, Yongchang Wei, Jin Cui, Yi Du, A Sliding Window based Multi-stage Clustering and Probabilistic Forecasting Approach for Large Multivariate Time Series Data, Journal of Statistical Computation and Simulation (GSCS),2017. (SCI) Wenjuan Cui, Pengfei Wang, Xin Chen, Yi Du, Danhuai Guo, Yuanchun Zhou, Jianhui Li, An Algorithm for Event Detection Based on Social Media Data, Neurocomputing,2017. (SCI) Yi Du, Cuixia Ma, Chao Wu, Xiaowei Xu, Yike Guo, Yuanchun Zhou, Jianhui Li, A Visual Analytics Approach for Station-based Air Quality Data, Sensors (Big Data and Cloud Computing for Sensor Networks),17(1), 30, 2017. (SCI) Yi Du, Danhuai Guo, Xin Chen, Lei Ren, Guozhong Dai, DVIZ: A Model-driven Visualization System, Journal of Software, vol. 27, no. 5, pp. 1199-1211, 2016（In Chinese) Yi Du, Feng Tian,Guozhong Dai, A Development Approach Based on Extensible User Interface Description Language, Journal of Software, vol. 26, no. 7, pp. 1772-1784, 2015.（In Chinese) Yi Du, Danhuai Guo, Yuanchun Zhou,Jianhui Li, A Data Processing and Visualization Platform for Large-scale Spatio-temporal Data, Journal of Computer Research and Development, vol. 51, no. s2, pp. 10-17, 2015.（In Chinese) Lei Ren, Yi Du,Guozhong Dai, Human-Computer Interaction Based on Semantic Focus+Context For Big Data Visualization in Small Interface, Chinese Journal of Computers, vol. 38, no. 12, pp. 2488-2498, 2015.（In Chinese) Lei Ren, Yi Du, Shuai Ma,XiaoLong Zhang, Visual Analytics Towards Big Data, Journal of Software, vol. 25, no. 19, pp. 1909-1936, 2013.（In Chinese) Yi Du, Feng Tian, Cuixia Ma,Guozhong Dai, A User Interface Generation Framework Based on Multi-scale Description Method, Chinese Journal of Computers, vol. 36, no. 11, pp. 2179-2190, 2013.（In Chinese) Yi Du,Lei Ren, DaisyVA:An Intelligent Interactive Visualization Platform for Visual Analysis of Multi-Facet Information, Journal of Computer-Aided Design & Computer Graphics, vol. 25, no. 8, pp. 1177-1182, 2013.（In Chinese) Yi Du, Changzhi Deng, Feng Tian,Guozhong Dai, Extensible user interface description language, Journal of Software, vol. 24, no. 5, pp. 1127-1142, 2013.（In Chinese) Yi Du, Fei Lv, Feng Tian, Wenjun Hou, Cuixia Ma,Guozhong Dai, Sketch-Based interface for creation and visual representation of hyper videos, Journal of Software, vol. 24, no. Suppl.(2), pp. 32-41, 2013.（In Chinese) Yi Du, Feng Tian, Guozhong Dai, Feng Wang,Hongan Wang, A User Model Based on Mobile Environment, Journal of Software, vol. 22, pp. 120-128, 2011.（In Chinese) Yi Du, Cuixia Ma, Dongxing Teng,Guozhong Dai, CONCEPT-SKETCH: A Tool for Cooperative Visual Analytics, International Journal of Advanced Intelligence (IJAI), vol. 3, no. 1, pp. 95-113, 2011. |
| Conference Papers/Posters |
Kaichun Yao, Chuan Qin, Hengshu Zhu, Chao Ma, Jingshuai Zhang, Yi Du, Hui Xiong. (2021) An Interactive Neurual Network Approach to Keyphrase Extraction in Talent Recruitment, 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). (CCF Rank B） Zhengzheng Tang, Ziyue Qiao, Xuehai Hong, Yang Wang, Fayaz Ali Dharejo, Yuanchun Zhou, Yi Du. (2021) Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification, The 5th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM). (CCF Rank C） Zhiyuan Ning, Ziyue Qiao, Hao Dong, Yi Du*, Yuanchun Zhou. (2021) LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding, The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021),2021. (CCF Rank C） Ziyue Qiao, Zhiyuan Ning, Yi Du*, Yuanchun Zhou. (2020) Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion, AAAI Conference on Artificial Intelligence (AAAI),2020. （Poster, CCF Rank A） Ziyue Qiao, Pengyang Wang, Yanjie Fu, Yi Du*, Pengfei Wang, Yuanchun Zhou. (2020) Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning, The 20th IEEE International Conference on Data Mining (ICDM),2020. （CCF Rank B） Ziyue Qiao, Yi Du, Yanjie Fu, Pengfei Wang, Yuanchun Zhou. (2019) Unsupervised Author Disambiguation using Heterogeneous Graph Convolutional Network Embedding, IEEE International Conference on Big Data,2019. Yi Du, Jia Li, Yuanchun Zhou Controlled Experiment of Banded Choropleth Map, HHME,2018 . Wenjuan Cui, Yi Du, Zhihong Shen, Yuanchun Zhou, Jianhui Li. (2017) Personalized Microblog Recommendation Using Sentimental Features. IEEE International Conference on Big Data and Smart Computing (IEEE BigComp 2017). Fei Lyu, Lei Ren, Yi Du (2017) An Optimization Method for User Interface Components Based on Big Data. In: Zhang L., Ren L., Kordon F. (eds) Challenges and Opportunity with Big Data. Monterey Workshop 2016. Lecture Notes in Computer Science, vol 10228. Springer, Cham Yi Du, Lei Ren, Yuanchun Zhou, Jiahui Li(2017) A Model-Driven Visualization System Based on DVDL. In: Zhang L., Ren L., Kordon F. (eds) Challenges and Opportunity with Big Data. Monterey Workshop 2016. Lecture Notes in Computer Science, vol 10228. Springer, Cham Wenjuan Cui, Pengfei Wang, Xin Chen, Yi Du, Danhuai Guo, Yuanchun Zhou, Jianhui Li, How to Use the Social Media Data in Assisting Restaurant Recommendation. In H. Gao, J. Kim & Y. Sakurai (Eds.), Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings (pp. 134-141). Cham: Springer International Publishing. Yi Du, Qianyu Liu, Yuanchun Zhou, Jianhui Li, DVIZ: A Model-driven Visualization Generation System (Poster), in IEEE Symposium on Visual Analytics Science and Technology (VAST) 2015, Chicago, USA, 2015. preview Danhuai Guo, Yi Du,A Visualization Platform for Spario-Temporal Data: a Data Intensive Computation Framework, in the 23rd International conference on Geoinformatics 2015, Wuhan, China, 2015. Lei Ren,Yi Du, A Sketch+Fisheye Interface for Visual Analytics of Large Time-Series (Poster), in IEEE Symposium on Visual Analytics Science and Technology (VAST) 2014, Paris, France, 2014, pp. 265-266. Lei Ren, Jin Cui, Yi Du,Guozhong Dai, Multilevel Interaction Model For Hierarchical Tasks In Information Visualization, in The 6th International Symposium on Visual Information Communication and Interaction (VINCI 2013) Tianjin, China, 2013, pp. 11-16. （EI） Yingying Jiang, Feng Tian, Guang Li, Xiaolong (Luke) Zhang, Yi Du, Guozhong Dai,Hongan Wang, SpeechTouch: Precise Cursor Positioning on Touch Screen Mobiles, in 15th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2013), Munich, 2013.（EI） Yi Du, Cuixia Ma, Dongxing Teng,Guozhong Dai, Cooperative Concept Map Based on Cognitive Model for Visual Analysis, in The 3rd Visual Information Communication - International Symposium (VINCI 2010), Beijing, China, 2010, pp. 76-83.（EI） Cuixia Ma, Yi Du, Dongxing Teng, Jia Chen, Hongan Wang, Guozhong Dai, An Adaptive Sketching User Interface for Education System in Virtual Reality, in Proceedings 2009 IEEE International Symposium on IT in Medicine and Education (ITME 2009), Jinan, China, 2009, pp. 796-802.（EI） |
| Thesis |
Yi Du, E-UIDL: A User Interface Description Language on Mobile Computing, Doctor's Thesis, Chinese Academy of Sciences, China, 2013 Yi Du, Research and Application of a Pen Based Graphical Editor Framework, Bachelor's Thesis, Shandong University, China, 2008. |
| Patents |
More Chinese Pattents：see here. Chinese Pattents：A spatio-temporal lead/lag correlation visualization method. Yi Du, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>53.6 Chinese Pattents：A visualization method on spatio-temporal lead/lag correlation based on Theme River. Yi Du, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>43.0 Chinese Pattents：A spatio-temporal lead/lag correlation calculation method based on meteorological data. Yi Du, Wenjuan Cui, Yuanchun Zhou, Jianhui Li. App. No.2016104650296 Chinese Pattents：A spatial clustering medhod based on time series correlation. Yi Du, Wenjuan Cui, Fei Lv, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>36.1 Chinese Pattents：A dispatch method of spatio-temporal data service based on access heat. Yi Du, Danhuai Guo, Yi Du, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>284 Chinese Pattents：A visualization development method and platform based on web environment. Yi Du, Danhuai Guo, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>971 Chinese Pattents：A interactive spatial scene search method. Danhuai Guo, Yi Du, Yuanchun Zhou, Jianhui Li. App. No.<PHONE_NUMBER>24X |
| Software Corprights |
Spatio-temporal visual analytics system of epidemic disease. Reg. No. :2015SRBJ0246 Online visualization system based on taxi data. Reg. No. :2015SRBJ0205 Satallite visualization system based on lla.. Reg. No. :2014SRBJ0295 |
| Book Chapters |
Guozhong Dai,Feng Tian, Pen Based User Interface (2nd Edition). Press of University of Science and Technology of China, 2014. Chapter 6 & 7. |
| Social Activities |
Young Scientist of Internet Society of China Member of CCF、ACM and ACM SIGCHI CHINA Chapter Reviewer of Journal of Computer Application (In Chinese) PC member of ISMC 2016 （The 2016 International Simulation Multi-Conference） Reviwer of Challenge of Visual Analytics at ChinaVis 2016 Member of Gephi Open Research Network IBM certificated expert on DB2 Administrator and XML Technology) |