Reviewerly/exHarmony huggingface dataset:
Empirically Investigating the Impact of Employee Engagement on Counterproductive Work Behavior of Academic Staff

This study aims to assess the impact of Employee Engagement (EE) on counterproductive work behavior. An adopted survey instrument comprised of EE and CWB was used. Data was composed from academic staff through self-administered questionnaire. The sample entailed 199 respondents randomly drawn from academic staff of the universities owned by private sector. The instruments were found reliable and valid. The statistical tools such as descriptive statistics score, ANOVA, Ftest, T-test, coefficient correlations and regressions were used to analyze the data. The upshots of the study proposed sufficient evidence for the correlation between EE and CWB and concluded a negative connection between these two variables. The study recommended that universities shall focus more on EE in order to reduce the CWB and hence increase the overall employees performance. Academics can incorporate results of this study to develop models that would promote the EE and CWB area.
Investigation of credit cards fraud detection by using deep learning and classification algorithms

Criminal financial behaviour is a problem for both banks and newly created fintech companies. Credit card fraud detection becomes a challenge for any such company. The aim of this paper is to com-pare ability to detect credit card fraud by four algorithmic methods: Generalized method of moments, K-nearest neighbour, Naive Bayes classification and Deep learning. The deep learning algorithm has been tuned to select key parameters so that fraud detection accuracy is the best. Five recognition accuracy parameters and a cost calcualtions showed that the deep learning algorithm is the best fraud detection meth-od compared to other classification algorithms. A financial company reduces losses and increases customer confidence by using fraud prevention technologies.
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing

We propose a novel method for analyzing and visualizing the complexity of standard reinforcement learning (RL) benchmarks based on score distributions. A large number of policy networks are generated by randomly guessing their parameters, and then evaluated on the benchmark task; the study of their aggregated results provide insights into the benchmark complexity. Our method guarantees objectivity of evaluation by sidestepping learning altogether: the policy network parameters are generated using Random Weight Guessing (RWG), making our method agnostic to (i) the classic RL setup, (ii) any learning algorithm, and (iii) hyperparameter tuning. We show that this approach isolates the environment complexity, highlights specific types of challenges, and provides a proper foundation for the statistical analysis of the task's difficulty. We test our approach on a variety of classic control benchmarks from the OpenAI Gym, where we show that small untrained networks can provide a robust baseline for a variety of tasks. The networks generated often show good performance even without gradual learning, incidentally highlighting the triviality of a few popular benchmarks.
Model AI Assignments 2020

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Artificial Neural Network Based Image Encryption Technique

Cryptography is used for the information transformation in safety manner. Encryption is the best method to convert the data one form to another. The cipher data cannot be understood by a third person. So to understand these various types of encryption schemes are available. In this present study, various encryptions, decryption techniques and they are proposed using chaotic neural network (CNN). CNN produces the same results in case of same input data. If the applied inputs are same, it is difficult to predict the system's behaviour, which will changes for any small change in input to that system. The objective is efficient use of artificial neural network with chaotic cryptography. With the help of chaotic sequence we found the weights of neural network. They are continuously updated on the basis of the generation of the key in the encryption algorithm. We used the Matlab tool/software to implement the encryption algorithm.
Non-Topical Coherence in Social Talk: A Call for Dialogue Model Enrichment

Current models of dialogue mainly focus on utterances within a topically coherent discourse segment, rather than new-topic utterances (NTUs), which begin a new topic not correlating with the content of prior discourse.As a result, these models may sufficiently account for discourse context of task-oriented but not social conversations.We conduct a pilot annotation study of NTUs as a first step towards a model capable of rationalizing conversational coherence in social talk.We start with the naturally occurring social dialogues in the Disco-SPICE corpus, annotated with discourse relations in the Penn Discourse Treebank (PDTB) and Cognitive approach to Coherence Relations (CCR) frameworks.We first annotate content-based coherence relations that are not available in Disco-SPICE, and then heuristically identify NTUs, which lack a coherence relation to prior discourse.Based on the interaction between NTUs and their discourse context, we construct a classification for NTUs that actually convey certain non-topical coherence in social talk.This classification introduces new sequence-based social intents that traditional taxonomies of speech acts do not capture.The new findings advocates the development of a Bayesian game-theoretic model for social talk. 1
Recoloring Interval Graphs with Limited Recourse Budget.

We consider the problem of coloring an interval graph dynamically. Intervals arrive one after the other and have to be colored immediately such that no two intervals of the same color overlap. In each step only a limited number of intervals may be recolored to maintain a proper coloring (thus interpolating between the well-studied online and offline settings). The number of allowed recolorings per step is the so-called recourse budget. Our main aim is to prove both upper and lower bounds on the required recourse budget for interval graphs, given a bound on the allowed number of colors.For general interval graphs with n vertices and chromatic number k it is known that some recoloring is needed even if we have 2k colors available. We give an algorithm that maintains a 2k-coloring with an amortized recourse budget of 𝒪(log n). For maintaining a k-coloring with k ≤ n, we give an amortized upper bound of 𝒪(k⋅ k! ⋅ √n), and a lower bound of Ω(k) for k ∈ 𝒪(√n), which can be as large as Ω(√n).For unit interval graphs it is known that some recoloring is needed even if we have k+1 colors available. We give an algorithm that maintains a (k+1)-coloring with at most 𝒪(k²) recolorings per step in the worst case. We also give a lower bound of Ω(log n) on the amortized recourse budget needed to maintain a k-coloring.Additionally, for general interval graphs we show that if one does not insist on maintaining an explicit coloring, one can have a k-coloring algorithm which does not incur a factor of 𝒪(k ⋅ k! ⋅ √n) in the running time. For this we provide a data structure, which allows for adding intervals in 𝒪(k² log³ n) amortized time per update and querying for the color of a particular interval in 𝒪(log n) time. Between any two updates, the data structure answers consistently with some optimal coloring. The data structure maintains the coloring implicitly, so the notion of recourse budget does not apply to it.
Comparative Study of 3D Reconstruction Methods from 2D Sequential Images in Sports

The process of 3D reconstruction is a basic problem in Computer Vision. However, recent researches have been successfully addressed by motion capture systems with body worn markers and multiple cameras. To recover 3Dreconstruction from fully-body human pose by single camera still remains a challenging problem. For instance, noisy background, variation in human appearance and self-occlusion were among these challenges. This thesis investigated methods of 3D reconstruction from monocular image sequences in dynamic activities such as sports. Six recent methods were selected based on they focused on recovery fully automated system for estimating 3D human pose for 2D joint location. These researches have been developed the algorithm that be able to solve ill-posed problem. Evaluation of the methods was divided in two sections. First, the theoretical and comparative study of each method was disclosed to identify the technique used, the problems that enquired and the results achieved in their approach. After that, the advantages and disadvantages of each method were listed. Also, several factors such as accuracy, self-occlusion and so on have been compared amongst these methods. In Second stage, based on the advantages found in the first stage of evaluation, three methods were chosen to be evaluated using specific data set. Initially, the codes of the three methods on PennAction dataset (tennis) were run and the performance of the methods in 3D reconstruction is showed. Then, the methods were tested on a mixed activities sequence from the CMU motion capture database. The novel of this study is evaluation of recent methods based on the accuracy of their performance on the specific dataset of tennis player. Also, we proposed a technique which combining specific advantages of each method to create a more efficient method for 3D reconstruction of 2D sequential images in the context of outdoor activities.
THE EFFECT OF MOTIVATION ON ARABIC COLLOCATION KNOWLEDGE: THE MEDIATING ROLE OF COLLOCATION LEARNING STRATEGIES

Background and Purpose: The role of collocation learning strategies is less of a concern as mediator. Although several correlation studies of bivariate factors have provided the relationship between the variables, many cannot answer the question of how the relationship exists. Also, a lot of studies have taken into account the variables of collocation learning strategies as mediator factors and have not illustrated clearly the relationship between independent variables (motivation) and dependent variables (collocation knowledge). As such, the aim of the present study is to identify the knowledge of Arabic collocation by taking several factors, namely; motivation and collocation learning strategies that have the potential to increase the knowledge of Arabic collocation in Malaysia. &#x0D; &#x0D; Methodology: In this study, a cross-sectional design was applied. Simple random sampling was used, where a total of 344 final year Arabic language students from eight public universities in Malaysia took part in the study by completing a set of tests and questionnaires. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.&#x0D; &#x0D; Findings: The findings show there is a direct and indirect relationship between motivation variable, collocation learning strategies and collocation knowledge. In addition, the learning strategies factor also serves as a partial mediator.&#x0D; &#x0D; Contributions: This study suggests that collocation learning strategies play a role in the relationship between motivation and collocation knowledge in the context of Arabic language at higher education level in Malaysia.&#x0D; &#x0D; Keywords: Collocation learning strategies, motivation, collocation knowledge, Arabic language, public universities.&#x0D; &#x0D; Cite as: Asbulah, L. H., Aladdin, A., &amp; Sahrim, M. (2020). The effect of motivation on Arabic collocation knowledge: The mediating role of collocation learning strategies. Journal of Nusantara Studies, 5(2), 1-18. http://dx.doi.org/10.24200/jonus.vol5iss2pp1-18
Factors Influencing Information Systems Quality From The System Developers Perspective

Information systems are seen as one of the agents in launching an organization's operations. In fact, organizational performance is often associated with the quality of the system used. Thus, various efforts and activities are made to ensure that the system being developed is of quality and effective in terms of its use. There are various factors that can be used to measure the quality of a system, and even more studies have been done to identify these factors. Often, factors that influence the quality of the system are evaluated from the user's perspective but are often overlooked by the system developers. System developers are individuals who are directly involved in development, where systems need to be developed based on the specifications identified. They are able to make sure that the systems built are usable, effective and error free. Therefore, their views on determining factors that influence the quality of the system being developed should be taken into account. Ironically, in the past research, system developers’ view is often overlooked in determining the quality of the system, so this is the objective of this study. Quantitative methods were used in this study, with 43 respondents who involved in systems development. The data were analyzed using correlation and the findings showed that data quality, user satisfaction and organizational needs are positively related to determining system quality in the view of system developers. The findings of this study can serve as a guideline to the system development team in producing quality systems that able to enhance organizational performance.
Aplib: An Agent Programming Library for Testing Games

Testing modern computer games is notoriously hard. Highly dynamic behavior, inherent non-determinism, and fine grained interactivity blow up their state space; too large for traditional automated testing techniques. An agent-based testing approach offers an alternative as agents' goal driven planning, adaptivity, and reasoning ability can provide an extra edge. This paper provides a summary of aplib, a Java library for programming intelligent test agents, featuring tactical programming as an abstract way to exert control on agents' underlying reasoning based behavior. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL) while still staying in Java, and hence aplib programmers will keep all the advantages that Java programmers get: rich language features and a whole array of development tools.
Rancang Bangun Sistem Informasi Perpustakaan Sekolah Berbasis Web Guna Meningkatkan Efektivitas Layanan Pustakawan

The library is a facility that serves as a support for student and teacher learning activities. The Library's collection is a textbook that is made a reference to the learning process. In managing data and information, the Library of SMP Negeri 28 Tangerang uses conventional way. This makes the librarian difficult to process data and to display effective information. The purpose of this research makes designing a Web-based library information system to help librarians solve problems in school libraries. Observations, interviews and literature studies are methods used to collect data (need analysis). Meanwhile, prototypes were used to build the school library information System. The result of designing the school library information system is tested using black box testing. The results of this system activity's data, designed the school library information System and the results of information system evaluation. Activity Data in the show in system analysis is running. Design builds an information system is displayed with business process, UML diagram and screen view of a web-based information system. The results of the evaluation indicate that the school library information system can perform fast browsing of data and the information shown in the form of reports is accurate.
SATNet: Symmetric Adversarial Transfer Network Based on Two-Level Alignment Strategy towards Cross-Domain Sentiment Classification (Student Abstract)

In recent years, domain adaptation tasks have attracted much attention, especially, the task of cross-domain sentiment classification (CDSC). In this paper, we propose a novel domain adaptation method called Symmetric Adversarial Transfer Network (SATNet). Experiments on the Amazon reviews dataset demonstrate the effectiveness of SATNet.
CUNI Neural ASR with Phoneme-Level Intermediate Step for~Non-Native~SLT at IWSLT 2020

In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.
Nigeria’s 2019 electioneering discourse

The paper interrogates the various discourse strategies adopted by the electorate on social media (SM) to delegitimize political opponents and engender rivalry in the build-up to the February 2019 presidential elections in Nigeria. A total of forty-two (42) Facebook (FB) posts on Nigerian political discourse were purposively selected between November 2018 and January 2019, and subjected to pragmatic and evaluative analysis, adopting Mey’s (2001) pragmatic acts theory and Martin and White’s (2005) appraisal theory.From our analysis, fourteen (14) different delegitimization strategies were found to have been utilized by the writers to achieve the pragmatic acts of discrediting political opponents as unworthy of being voted into powers, while concurrently persuading the electorate to support their side instead. These strategies include the following categories: facts and evidence, issue-based rhetorical question, frank issue-based opinion, contrastive engagement, and dysphemism. Others are implicit antithesis, direct verbal attack, allegations, irony, demonization, self-indictment, lexical gymnastics, sarcasm, and prophetism. The findings show overt and covert persuasive strategies and interactivity towards the pragmatic act of delegitimizing political opponents, showing words as the real weapons on the battlefield of politics. The analysis further reveals that people tend to support a SM post not necessarily because it is true, but because it supports the chances of their candidate or hurts the opposing side. Thus a platform has been provided for implicit and explicit political stance taking, which may be difficult outside SM due to certain insecurity and face threats. SM has become a site for a running, fierce, and open political discourse, the new voice for the voiceless, as well as for issue-based campaigns in Nigeria. It may well become a space where general elections are won and lost even before a vote is cast.
Windowing as a Sub-Sampling Method for Distributed Data Mining

Windowing is a sub-sampling method, originally proposed to cope with large datasets when inducing decision trees with the ID3 and C4.5 algorithms. The method exhibits a strong negative correlation between the accuracy of the learned models and the number of examples used to induce them, i.e., the higher the accuracy of the obtained model, the fewer examples used to induce it. This paper contributes to a better understanding of this behavior in order to promote windowing as a sub-sampling method for Distributed Data Mining. For this, the generalization of the behavior of windowing beyond decision trees is established, by corroborating the observed negative correlation when adopting inductive algorithms of different nature. Then, focusing on decision trees, the windows (samples) and the obtained models are analyzed in terms of Minimum Description Length (MDL), Area Under the ROC Curve (AUC), Kulllback–Leibler divergence, and the similitude metric Sim1; and compared to those obtained when using traditional methods: random, balanced, and stratified samplings. It is shown that the aggressive sampling performed by windowing, up to 3% of the original dataset, induces models that are significantly more accurate than those obtained from the traditional sampling methods, among which only the balanced sampling is comparable in terms of AUC. Although the considered informational properties did not correlate with the obtained accuracy, they provide clues about the behavior of windowing and suggest further experiments to enhance such understanding and the performance of the method, i.e., studying the evolution of the windows over time.
Predicting Persuasive Effectiveness for Multimodal Behavior Adaptation using Bipolar Weighted Argument Graphs

Research states that persuasion is subjective. Moreover, people use behavioral cues all the time, very often even without noticing and are often not aware of being persuaded by non-rational cues. In order to draw attention to these effects, we want to enable virtual agents to adapt their behavior during interaction to the listener in order to increase their perceived power of persuasion. In this paper, we introduce a novel multi-modal persuasive AI system that presents arguments from an underlying logical argument structure to a user by means of a virtual agent and synthetic speech. In doing so, the agent is able to adapt its multimodal behavior to the user, based on his or her explicit feedback. To this end, the feedback is used to predict the current user's stance by considering the underlying argument structure using bi-polar weighted argument graphs to later optimize the adaptation of the multimodal presentation by means of Reinforcement Learning. We report on results of a user study with 48 participants showing the validity and practical potential of the proposed prediction model and conclude by providing limitations and implications in detail.
THE MULTIPLE SURVEY OF AVACHINSKY VOLCANO ACTIVITY IN 2013 – EARLY 2020: GROUND, VIDEO AND SEISMIC OBSERVATION

The results of monitoring at high-temperature fumarolic fields (Eastern and Western) of Avachinsky volcano in 2013–2020 are presented. Those were visual observations, temperature measurement and gas sampling, supplemented with video surveillance data. In order to identify the relationship between fumarolic activity and deep processes, the analysis of the volcano area seismicity for the specified period was carried out. Two periods of increased seismic activity have been identified. The first period, November 2014 – January 2015, is associated with changes in the character of gas emission, morphology and temperature of Western Fumarole observed in the field, reflected in the ratio of gas fluxes of the two high-temperature fumarolic fields. The second seismic increase, in October – December 2019, was accompanied in the dark by glowing, sometimes by bright flashes in the area of Western Fumarole, and as a result of ground survey in 2020 further changes in its morphology were revealed.
Detecting k-(Sub-)Cadences and Equidistant Subsequence Occurrences

The equidistant subsequence pattern matching problem is considered. Given a pattern string P and a text string T, we say that P is an equidistant subsequence of T if P is a subsequence of the text such that consecutive symbols of P in the occurrence are equally spaced. We can consider the problem of equidistant subsequences as generalizations of (sub-)cadences. We give bit-parallel algorithms that yield o(n²) time algorithms for finding k-(sub-)cadences and equidistant subsequences. Furthermore, O(nlog² n) and O(nlog n) time algorithms, respectively for equidistant and Abelian equidistant matching for the case |P| = 3, are shown. The algorithms make use of a technique that was recently introduced which can efficiently compute convolutions with linear constraints.