Reviewerly/exHarmony huggingface dataset:
Exploring Learning Capability of an Agent in SOAR: Using 8-Queens Problem

Cognitive architecture deals with describing the intelligent behavior of an agent.The description of intelligent behavior states how well an agent can solve and represent variety of problems of the domainindependent task.The intelligence of an agent is considered in terms of its learning capabilities.In this study, we are exploring solving 8-Queens combinatorial problem using SOAR symbolic cognitive architecture.An 8-Queens problem consists of various constraints which is expressed by Constraint Satisfaction Problem (CSP).The constraints are further generalized in the Fuzzy Constraint Satisfaction Problem (FCSP) (a sub domain of CSP), which simplifies the condition of constraints by providing the priority value to the location of queen.This paper provides a way to solve 8-Queens problem by using a heuristic search and backtracking.These concepts are implemented in SOAR to find an efficient solution of similar task.The implementation of 8-Queens in SOAR provides computation efficiency in solving and a way for an agent to learn their own production rules to solve similar domain problems.The 8-Queens problem is analyzed by two parameters.First parameter defines how an agent can learn and transfer rules to solve similar domain problem.The second parameter describes number of chunks required to solve a problem.
Heatmapping of Group People Involved in the Group Activity

This paper proposes a method for heatmapping people who are involved in a group activity. Such people grouping is useful for understanding group activities. In prior work, people grouping is performed based on simple inflexible rules and schemes (e.g., based on proximity among people and with models representing only a constant number of people). In addition, several previous grouping methods require the results of action recognition for individual people, which may include erroneous results. On the other hand, our proposed heatmapping method can group any number of people who dynamically change their deployment. Our method can work independently of individual action recognition. A deep network for our proposed method consists of two input streams (i.e., RGB and human bounding-box images). This network outputs a heatmap representing pixelwise confidence values of the people grouping. Extensive exploration of appropriate parameters was conducted in order to optimize the input bounding-box images. As a result, we demonstrate the effectiveness of the proposed method for heatmapping people involved in group activities.
Quantum Key Recycling with Optimal Key Recycling Rate based on Error Rate

We propose a new Quantum Key Recycling (QKR) protocol, which can tolerate the noise in the quantum channel. Our QKR protocol recycles the used keys according to the error rate. The key recycling rate of the pre-shared keys in our QKR protocol is optimized depending on the real error rate in the quantum channel. And our QKR protocol has higher efficiency than the exiting QKR protocol with error-tolerance. The security proof shows the security of the recycled keys is universal composable.
Scmhl5 at TRAC-2 shared task on aggression identification: bert based ensemble learning approach

This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification ('Overtly Aggressive', 'Covertly Aggressive' and 'Non-aggressive') and English Sub-task B on binary classification for Misogynistic Aggression ('gendered' or 'non-gendered'). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams.
Hypergeometric L-functions in average polynomial time

We describe an algorithm for computing, for all primes $p \leq X$, the mod-$p$ reduction of the trace of Frobenius at $p$ of a fixed hypergeometric motive in time quasilinear in $X$. This combines the Beukers--Cohen--Mellit trace formula with average polynomial time techniques of Harvey et al.
The Utilization of Blockchain for Enhancing Big Data Security and Veracity

Blockchain as one of technological hype in digital economy besides the Internet-of-Things (IoT) and Big Data Analytic, fills the need of a secured peer-to-peer connection with the concept of distributed database. However, it does not eliminate the centralized database on massive data storage which it is the core of Big Data. Blockchain is more suitable for information log, a kind of application that requires dynamic and updated information with hierarchical hash security features to support a distributed database system. The Blockchain features are prospective to enhance the security of Big Data from attacks to its CIA Triad, namely Confidentiality, Integrity, and Availability. As information has become a crucial and critical to business, meanwhile managing a huge-volume data is also challenging in terms of its security and veracity, therefore Blockchain technology can be considered as a prospective solution. Based on our study, we found that Blockchain can enhance the security of Big Data by strengthening the security of the data storage, enhancing the data integrity using digital certificate and chaining the block using hash of previous block, and enhancing data availability using peer-to-peer transmission, distributed nodes, and consensus method. Blockchain can also enhance the performance of Big Data Analytic by providing a better data veracity from token-based validation to enhance the truth discovery, and ID decentralization to prove the identity of data source.
Streaming Language-Specific Twitter Data with Optimal Keywords.

The Twitter Streaming API has been used to create language-specific corpora with varying degrees of success. Selecting a filter of frequent yet distinct keywords for German resulted in a near-complete collection of German tweets. This method is promising as it keeps within Twitter endpoint limitations and could be applied to other languages besides German. But so far no research has compared methods for selecting optimal keywords for this task. This paper proposes a method for finding optimal key phrases based on a greedy solution to the maximum coverage problem. We generate candidate key phrases for the 50 most frequent languages on Twitter. Candidates are then iteratively selected based on a variety of scoring functions applied to their coverage of target tweets. Selecting candidates based on the scoring function that exponentiates the precision of a key phrase and weighs it by recall achieved the best results overall. Some target languages yield lower results than what could be expected from their prevalence on Twitter. Upon analyzing the errors, we find that these are languages that are very close to more prevalent languages. In these cases, key phrases that limit finding the competitive language are selected, and overall recall on the target language also decreases. We publish the resulting optimized lists for each language as a resource. The code to generate lists for other research objectives is also supplied.
Stochastic Artificial Intelligence: Review Article

Artificial intelligence (AI) is a region of computer techniques that deals with the design of intelligent machines that respond like humans. It has the skill to operate as a machine and simulate various human intelligent algorithms according to the user’s choice. It has the ability to solve problems, act like humans, and perceive information. In the current scenario, intelligent techniques minimize human effort especially in industrial fields. Human beings create machines through these intelligent techniques and perform various processes in different fields. Artificial intelligence deals with real-time insights where decisions are made by connecting the data to various resources. To solve real-time problems, powerful machine learning-based techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization have been used in recent years. This chapter explains artificial neural network-based adaptive linear neuron networks, back-propagation networks, and radial basis networks.
Batch and Incremental Kinodynamic Motion Planning using Dynamic Factor Graphs

This paper presents a kinodynamic motion planner that is able to produce energy efficient motions by taking the full robot dynamics into account, and making use of gravity, inertia, and momentum to reduce the effort. Given a specific goal state for the robot, we use factor graphs and numerical optimization to solve for an optimal trajectory, which meets not only the requirements of collision avoidance, but also all kinematic and dynamic constraints, such as velocity, acceleration and torque limits. By exploiting the sparsity in factor graphs, we can solve a kinodynamic motion planning problem efficiently, on par with existing optimal control methods, and use incremental elimination techniques to achieve an order of magnitude faster replanning.
Augmented reality to aid retention in an African university of technology engineering program

Whenever complex threshold concepts are covered facilitators need to take extra care that students retain the new knowledge for future application. The problem encountered was that certain concepts, like digital logic gates with its layers of information are harder to retain. The purpose of this paper is to investigate the students’ perceptions on the possible use of a custom design Quick Response code and Augmented Reality application in an Electrical Engineering subject. Evaluation was done on Logic Design III a 2nd year subject where the threshold concepts should have been imprinted. An exploratory design is implemented with data collected using an online survey tool. The results were promising as 88.89% of students felt that they will remember the content better with the use of the application and 83.33% were positive about adopting the technology in class. The paper shows that technology like Quick Response code and Augmented Reality in the classroom is not only beneficial for improving retention but is also welcomed by students
Assessing the possibility of a country's economic growth using static bayesian network models

This article is devoted to the use of Bayesian networks to analyze the possibility of economic growth in Ukraine.It was found that at the maximum level of external investment, direct investment in Ukraine will increase and this creates the conditions for increasing the country's economic growth.It has also been shown that Noisy-max nodes, compared to General nodes, provide a relatively high initial accuracy.General nodes require retesting.However, Noisy-max nodes entail an increase in time and computational cost.
Relaxing global-as-view in mediated data integration from linked data

In scenarios where many different, independent and dynamic data sources need to be brought together, mediated data integration at runtime is rapidly gaining interest. In a global-as-view approach, schema mappings express how to get data from each data source according to the global schema of the mediator. Key issues include the effort required to include and map new data sources, and the very need of data sources for the global schema to be expressed. It has been argued that the principles of Linked Data can be used to spread the cost of adding new sources in a pay-as-you-go model. We contribute by describing a data integration framework able to mitigate these issues, by relating data sources under a global schema which is implicit and only partly known at the time a new data source joins. Mappings over a data source only require partial knowledge of it and of the part of the global schema that it will affect. Pay-as-you go can then be employed to guarantee eventual schema compliance. This approach was adopted in a large-scale data integration system for Smart Cities, where it allowed short time-to-publish for new data and iterative schema refinements.
Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation

Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.
The corporate library activities in the organization structure

The paper is based on the revealed domestic publications and analysis of vast publications in the foreign professional periodicals. Internet-based resources, e. g. www-sites of diverse organizations were analyzed, and the data on efficient Russian corporate libraries was identified. The national library studies exclude corporate libraries from the traditional official library typology. The national classification system (Library Bibliographic Classification, LBC) as well, does not envisage typology for the corporate libraries. The author does not possess statistical data. Some experts relate the concept to “corporations”, others see the corporate libraries as former “technical” (sectoral, industrial) libraries. However, the analysis findings demonstrate that this term can be primarily related to the concept of “corporate culture”. The corporate libraries make part of corporations, financial and trade companies, banks, foundations, joint-stock companies, etc. The prerequisites of extensive uprise of these libraries in Russia emerged in 1990s. The few publications on the topic are discussed along with the information available from Internet resources. The author concludes that the corporate libraries belong to special libraries, and within the latter group, the corporate libraries should be separated. Both state-funded and non-government organizations have got the libraries within their structure. The author attempts to formulate the definition for the new type of libraries, examines their functions and characteristic features, staff and users. The examples of the place of these libraries within the organizational structure are provided.
Lexemes in Wikidata: 2020 status.

Wikidata now records data about lexemes, senses and lexical forms and exposes them as Linguistic Linked Open Data. Since lexemes in Wikidata was first established in 2018, this data has grown considerable in size. Links between lexemes in different languages can be made, e.g., through a derivation property or senses. We present some descriptive statistics about the lexemes of Wikidata, focusing on the multilingual aspects and show that there are still relatively few multilingual links.
The future for the libraries: Information or communication

The findings of two sociological surveys held in Arkhangelsk in 2017-2018 are discussed: expert interviews “The thoughts on the future of libraries” and the poll “What are the libraries needed for?”. The administrators and experienced librarians, professors in library sciences and other specialists of the library sphere in several Russian regions took on the role of experts. The author summarizes their views on the current status of libraries, and suggests forecasts and recommendations for the future to all interested players: i.e. librarians, library administration, the ministry, and users. Over 500 Arkhangelsk residents participated in the online poll. The respondents are over 18 year-old age, and the survey appears to be representative in terms of gender and age. The topics under discussion are: frequency of visits to the library, relevancy of library services, user pre­ferences. The Arkhangesk residents’ satisfaction with library services is evaluated; their attitude toward closing libraries is revealed, residents’ reviews are cited. Based on the survey findings, the collective portrait of opposite groups — frequent-goers and non-goers is described. The professional library community and the users contend that the libraries are highly demanded in the modern society.
Evaluasi Adaptive Hypermedia System Berbasis Moodle Pada Pendidikan Vokasi

This study evaluates moodle-based adaptive hypermedia systems in the form of e-learning which is implemented in Vocational Education in Mechanical Engineering concentration of the Automotive, Faculty of Science and Technology, Ivet University in Semarang. The evaluation method used in this study was a formative evaluation of the Tessmer model. The evaluation stages include: 1) self evaluation, 2) expert review, 3) one to one evaluation, 4) small group evaluation, 5) field test.In this study the benchmarks used are the LORI 2.0 instrument with 8 aspects of assessment including: 1) Content Quality Aspect, 2) Learning Goal Alignment Aspect, 3) Feedback and Adaptation Aspect (Feedback and Adaptation) ), 4) Motivation Aspect, 5) Presentation Design Aspect, 6) Interaction Usability Aspect, 7) Accessibility Aspect, 8) Standards Compliance Aspect. The results showed that: 1) one-to-one evaluation resulted in an average respondent's answer was 3.74 and was in the good category with a percentage of eligibility of 74.75%; 2) small group evaluation results in an average of 3.80 respondents' answers and is in the good category with a eligibility percentage of 76.00%; 3) field evaluation yields an average of 3.86 respondents' answers and is in the good category with a percentage of eligibility of 77.10%. 4) Based on the evaluation series, it can be concluded that the adaptive hypermedia system or Evodu is appropriate.
An Improved Consensus Mechanism for the Blockchain Based on Credit Rewards and Punishments

In the traditional Proof of Stake (PoS), the share of the currency owned by the node, that is, the share of the equity, determines the probability that the node is selected as the representative node. In each round of the block generation, a node is selected as the creator of the next block by the verification, and the selected creator will pack appropriate transactions into the block and publish it on the blockchain. However, in the network with poor synchronization, the traditional PoS produces multiple verified representative nodes in each round, which generates multiple blocks. Then the blockchain is easy to produce the bifurcation. Aiming at the bifurcation problem of PoS, we propose an improved voting method based on credit rewards and punishments to vote on multiple blocks. The block is selected by considering the credibility of the creator, the count of the obtained comprehensive votes and the transaction value of the block. Thereby it can ensure the consistency and the fairness of the blockchain network. In the same time, the malicious node is found and voted to be removed in time by counting the number of invalid blocks generated by the nodes. It can ensure the security of the blockchain network.
A Receiver-Initiated Approach with Fuzzy Logic Control in Load Balancing

This paper proposes a new approach for load balancing by using receiver-initiated load transfer method.Usually data transfer for load balancing starts from a sender node.This is what called a sender-initiated method.In this paper, instead, a load balancing action starts from a receiver node; the receiver node initiates load balancing for asking a sender node for load transfer.Fuzzy logic control is applied in this approach to avoid using a fixed threshold value in load balancing in ad-hoc networks.Performance for the receiver-initiated approach is evaluated and compared with other load balancing approaches-BID protocol, fuzzy logic sender-initiated algorithm and non-fuzzy (threshold based) receiver-initiated algorithm.The results show that the receiver-initiated approach improves network performance by comparing with the other three.
Sound Regular Corecursion in coFJ

The aim of the paper is to provide solid foundations for a programming paradigm natively supporting the creation and manipulation of cyclic data structures. To this end, we describe coFJ, a Java-like calculus where objects can be infinite and methods are equipped with a codefinition (an alternative body). We provide an abstract semantics of the calculus based on the framework of inference systems with corules. In coFJ with this semantics, FJ recursive methods on finite objects can be extended to infinite objects as well, and behave as desired by the programmer, by specifying a codefinition. We also describe an operational semantics which can be directly implemented in a programming language, and prove the soundness of such semantics with respect to the abstract one.
iCapsNets: Towards Interpretable Capsule Networks for Text Classification

Many text classification applications require models with satisfying performance as well as good interpretability. Traditional machine learning methods are easy to interpret but have low accuracies. The development of deep learning models boosts the performance significantly. However, deep learning models are typically hard to interpret. In this work, we propose interpretable capsule networks (iCapsNets) to bridge this gap. iCapsNets use capsules to model semantic meanings and explore novel methods to increase interpretability. The design of iCapsNets is consistent with human intuition and enables it to produce human-understandable interpretation results. Notably, iCapsNets can be interpreted both locally and globally. In terms of local interpretability, iCapsNets offer a simple yet effective method to explain the predictions for each data sample. On the other hand, iCapsNets explore a novel way to explain the model's general behavior, achieving global interpretability. Experimental studies show that our iCapsNets yield meaningful local and global interpretation results, without suffering from significant performance loss compared to non-interpretable methods.