Reviewerly/exHarmony huggingface dataset:
Efficient Categorization of Document with J48 Multi-Class Classifiers

In the text categorization field number of feature selection metrics has been explored such as Chi-Square (CHI), Information Gain (IG), Odds Ratios (OR), and Correlation Coefficient (CC). Form this OR and CC are the one-sided metrics and CHI and IG are the two-sided metrics. In this one side feature selection metrics only select the most indicative features and in the two side metrics only implicitly features are selected. Automated feature selection is important for text classification to decrease the feature size and to speed the learning performances of classifiers. In this paper present the feature selection approach for the efficient categorization of document based on the J48 algorithm. In proposed system we are working on a data set named as a 20-Newsgroups dataset which contains the data related to different news category this data set is taken as input to the system and as an output, we will get documents classification in several categories (topic) as an output. Here, the compared performance of the proposed system with existing Naive Bayes algorithm and the proposed system gives more accurate result as compared to the existing.
Demo Paper: Monitoring and Evaluation of Ethical Behavior in Dialog Systems

In this demo paper, we describe a Multi Agent System (MAS) application for monitoring and evaluation of the ethical behavior of customer service agents in an online chat point w.r.t their institution/company's codes of ethics and conduct. This MAS is realized using the state-of-the-art JaCaMo framework, and can be practically demonstrated to an audience.
Achieving Lossless Audio Encoder through integrated approaches of Wavelet Transform, Quantization and Huffman encoding (LAEIWQH)

In this paper, a lossless compression technique has been proposed with the help of discrete 2-D wavelet transformation (single level) over audio signal. Compressed audio data is achieved using quantization followed by Huffman encoding. In this approach, audio is compressed without losing any information. Experimental results are supported with statistical parameters for justifying compression quality comparing with existing lossless techniques.
Thinking ‘The End of Times’: The Significance of Bioart|BioArt for Art|Education

This chapter explores the importance of bioart within the historical moment of the Anthropocene. It raises questions as to why artists and art educators need to pay attention to such developments, although they seem marginal and minor. To do so, I turn to the early works of Eduardo Kac and Tissue Culture and Art Project (TC&A) and Natalie Jeremijenko where the question of ‘life’ is explored in order to outline the impact that BioArt has made concerning the ethical and political issues surrounding bioengineering. This raises the difference between philosophy of biology and biophilosophy as raised by the theorist Eugene Thacker. I make the distinction between bioart and BioArt: the former is less political and is more concerned with biomimesis, while the latter is ‘minoritarian’ in Deleuze and Guattari’s terms, attempting to intervene in the dominant discourse of bioengineering.
An instrumentation based algorithm for stack overflow detection

Despite all the efforts made by the scientific community in terms of computer security, buffer overflow vulnerabilities continue being the biggest security flaw in applications, since they compromise the security of the system through memory corruption. To tackle this problem, there are different techniques based on the binary analysis of the application in question. With this objective in mind, the present paper proposes an algorithm based on the dynamic instrumentation of binaries, that is, dynamic local variables belonging to the functions of the program are detected, and a check is performed to see whether there is an overflow of memory between them. The results obtained show how the proposed algorithm is able to detect buffer overflow errors in the stack frames of a function.
IoT Forensic Science

IoT devices produce information that can be used in criminal investigations and cybersecurity incidents to make inferences about identities, locations, chronologies, and relationships between relevant entities. Before this information is relied upon to make critical decisions, its veracity must be assessed critically, and the link between virtual and physical worlds must be evaluated carefully. This chapter presents the forensic science principles needed to exploit the full potential of IoT traces, including uniqueness, exchange, provenance, integrity, reliability, repeatability, evaluating links between virtual and physical entities, and formally assessing alternative hypotheses. This chapter also discusses core forensic processes and activities, demonstrating their application to forensic analysis of IoT devices using practical examples. A typology of IoT traces is proposed and their usefulness during an investigation is discussed. Finally, an investigative scenario is presented to illustrate the opportunities and challenges of exploiting IoT devices and traces for investigative and forensic purposes.
Storycoding - Programming Physical Artefacts for Research Through Design

Making programmable physical artefacts and prototypes has inherent value for Research-through-Design (RtD) based HCI. Furthermore, the abstractions and representations within RtD and programming are vastly different, such as between observations, storyboards and the code. Studies have shown that the program of an artefact influences the RtD outcome, but there is also a disconnect between the observations of use and the abstractions involved in the programming. How can we program an artefact so that the code can be created, modified and reflected upon based on directly observable and non-technical abstractions? In this paper we present Storycoding, a computational-thinking based method for programming that focuses on bridging the representational abstractions. Using Storycoded artefacts, we examine programming in light of RtD. We discuss how Storycoding enables programming that is directly observable between the use and the abstractions, being respectful towards RtD. Finally, we conclude with implications towards HCI research and practice.
Cross-Modal Representation

Abstract Cross-modal representation learning is an essential part of representation learning, which aims to learn latent semantic representations for modalities including texts, audio, images, videos, etc. In this chapter, we first introduce typical cross-modal representation models. After that, we review several real-world applications related to cross-modal representation learning including image captioning, visual relation detection, and visual question answering.
Reed-Solomon codes over Galois fields of characteristic 3 for a VLC channel

In this paper, we show that the Reed-Solomon codes built on a Galois field of characteristic 3, can be used in transmission through a visible light communication (VLC) channel, due to the information can be encoded using the colors red, green and blue. This allows us to efficiently use a VLC channel, since we can directly use the arithmetic of a Galois field of characteristic 3, and we avoid the conversion of non-binary to binary operations. Further, we show the results of our simulations for Reed-Solomon codes built on Galois fields of characteristic 3 and 2, in which we estimate a difference of 1.7 dB between these codes.
Monitoring Call activity and Service Task Invocations for BPMN

Currently, Service-Oriented Architecture (SOA) is used to integrate existing services, as it can significantly reduce time and redundancy in service development. Therefore, most organizations have widely adopted Business Process Model and Notation (BPMN) [1] in their process designs and simulation to verify business processes of the integrated services. A previous research proposed an approach for monitoring partner link invocations under WS-BPEL [2]. However, none of their studies has been conducted on call activity in BPMN. Thus, this paper proposes an approach to monitoring call activity and service task invocations for BPMN. This approach recognizes all any services and call activities that have not been tested, which monitors the imported and exported data of the service calls by using listener [3]. Therefore, existing test cases are analyzed to check all paths coverage in BPMN. Our approach also ensures the consistency of messaging services and testing the coverage of call activities and service task.
Windows 10 Hibernation File Forensics

Memory forensics is an essential part of any computer forensics investigation. Main memory provides valuable evidences, which may otherwise not be retrievable from hard drive. In cases when capturing main memory image is not possible, hibernation files are good source of information. The aim of this research is to show the importance of hibernation file forensics in a computer forensics investigation. Specifically, we focus on retrieving evidential information related to the use of Facebook and Instagram. Firstly, we develop a process that can simplify the task of hibernation file forensics. The proposed process explores concepts, tools, techniques and methodologies most suitable for Windows 10 hibernation file acquisition and analysis. Subsequently, we use the proposed process to experimentally demonstrate the extraction of critical personal and confidential information related to Facebook and Instagram activities, from hibernation file. The extracted data can be used to establish a link between the suspect and the evidences.
Virtual Environment Systems for Simulating Robots in Manned Space Fligts.

The article considers computer-based prototyping as part of simulation stands and space simulators for future human-robotic space exploration, as well as features of using virtual environment systems (VES) for this purpose. The article describes the formulation of research tasks related to the use of VES-based simulation stands and systems in modeling the cosmonauts’ activities relying on the experience of designing orbital simulators. In prospect it will justify the composition and purpose of the VES modules for lunar projects and clarify the use of VR-technologies in the simulation of robotic operations during human exploration of the moon.
Advancing Learning Math Industry Era 4.0

Abstract The emergence of the industrial era 4.0 is a challenge in welcoming and preparing mathematics learning. The purpose of this study was to determine the components in welcoming mathematics learning in the industrial era 4.0. This type of research uses descriptive qualitative with the method used is the study of literature/literature review from 2012 to 2019. From the results of the literature review, it is concluded that there are 3 components in welcoming mathematics learning in the industrial era 4.0. 1) HR Literacy, covering aspects; critical thinking, creative thinking, reflective thinking, mathematical imagination, mathematical reasoning, mathematical communication, and problem-solving. 2) Data literacy, including system aspects cyber-physical, internet of things, cognitive computing, and cloud computing. 3)Technology literacy, including aspects; Technology for doing mathematics, Technology for practicing skills, and Technology for developing conceptual understanding.
Some Techniques on Digital Signal Processing in the Visual Programming Environment Max

For Digital Media Art, Max is a high-quality visual programming language with the features of visualization and modularization. The processing of the digital signal in Max can achieve a variety of effects. The related operations include numerical operations, numerical mapping, numerical automatic generation, and conditional branch programming, etc. In the process of artistic creation, information such as audio, video and motion can be input, processed, and output. This article summarizes some basic techniques from the above aspects, aiming to promote the communications in art and other fields, and to some extent inspire the transformation of relevant art education.
Evaluating Interpretability in Machine Teaching

Building interpretable machine learning agents is a challenge that needs to be addressed to make the agents trustworthy and align the usage of the technology with human values. In this work, we focus on how to evaluate interpretability in a machine teaching setting, a setting that involves a human domain expert as a teacher in relation to a machine learning agent. By using a prototype in a study, we discuss the interpretability definition and show how interpretability can be evaluated on a functional-, human- and application level. We end the paper by discussing open questions and suggestions on how our results can be transferable to other domains.
Effectively Evaluating the Accessibility of Electronic Monographs Using VPATs and Other Resources at the College of Staten Island Library- CUNY

(2020). Effectively Evaluating the Accessibility of Electronic Monographs Using VPATs and Other Resources at the College of Staten Island Library- CUNY. Serials Review: Vol. 46, Special Issue on Accessibility, pp. 98-113.
Systematic Review of Data Mining based Recommendation Methods Reference to Business to Business (B2B) Recommendation

In today's recommendation methods, the web-based approach is used for recommending the set of ranked and limited products from all available products of the same category without using direct inputs from end users. The Business to Business (B2B) services is one of the application areas in which recommendation techniques should be applied, but due to the complex items and users profiles, it becomes difficult to apply. In this paper, we presented the systematic review of novel fuzzy preference graph based B2B marketing campaign recommendation system to address the challenges of the complex and dynamic nature of the business buying process. The fuzzy approach used to address the challenges of vague or fuzzy online users preferences problem. The investigation and comparative analysis of recent studies related to the B2B recommendation system considered in this review. The outcome of this paper claims the various research gaps identified from the literature review.
The Meaning of Things as a Concept in a Strong AI Architecture

Artificial intelligence becomes an integral part of human life. At the same time, modern widely used approaches, which work successfully due to the availability of enormous computing power, based on ideas about the work of the brain, suggested more than half a century ago. The proposed model describes the general principles of information processing by the human brain, taking into account the latest achievements. The neuroscientific grounding of this model and its applicability in the creation of AGI or Strong AI are discussed in the article. In this model, the cortical minicolumn is the primary computing processor that works with the semantic description of information. The minicolumn transforms incoming information into its interpretation to a specific context. In this way, a parallel verification of hypotheses of information interpretations is provided when comparing them with information in the memory of each minicolumn of the cortical zone, and, at the same time, determining a significant context is the information transformation rule. The meaning of information is defined as an interpretation that is close to the information available in the memory of a minicolumn. The behavior is a result of modeling of possible situations. Using this approach will allow creating a strong AI or AGI.
Using Expert Evaluation to Assess the Usability of an Educational Mobile Game for Cognitive Skills Training

The labor market inclusion of people with intellectual dis- abilities is one of the main concerns of current societies. Non-profit associations make everyday efforts to improve the living conditions of these citizens. This contribution presents a heuristic evaluation of an educational mobile game helping social workers and health professionals in their training in order to incorporate into the labor market citizens with intellectual disabilities. The application provides technological support for cognitive skills training and is aimed at people with mild to moderate intellectual disability and who do not present physical difficulties that prevent the normal use of a mobile device. We use a development process that combines the iPlus methodology together with a collaborative user-centered approach. Suggested recommendations based on the results can improve the usability of the game developed.
A Submodular Approach for Reference Recommendation

Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach.
An Analytical Visualization of Local Running Activity on a Public Display

Wearable technology now enables casual runners to collect vast amounts of personal activity data. Although this data is typically reviewed privately to track personal goals, we believe that the public sharing of these sports statistics can lead to unique forms of social engagement. We thus deployed an analytical data visualization in the actual context that spawns physical activity data, i.e. at running events. Even though data visualization has already been used on public displays to inform passers-by, there exists limited knowledge about its real potential towards collaborative and analytical sense-making. We present the design process and evaluation of a multidimensional, interactive visualization of sports statistics on a public display. Our initial results indicate that analytical visualizations on public displays should include annotations to surface key insights and designers may consider offering introductory narratives to improve their accessibility. Multiple displays may better support comparison tasks in social and public contexts.
Recent Developments in Cryptography

In this short note, we briefly describe cryptosystems that are believed to be quantum-resistant and focus on isogeny-based cryptosystems. Recent SIDH (Supersingular Isogeny Diffie-Hellman) developments have focused on (2,2)-reducible Jacobians, where addition is executed via the Kummer surface. While elliptic curve isogenies are easy, explicit, and fast to compute thanks to Velús formulas, this is not the case for higher genus curves. The case of (2,2)-isogenies in genus 2 curves are an exception thanks to the work of Richelot. In addition, some explicit work has been completed in the case of (3,3) and (5,5)-isogenies, which are much more complicated than the case of Richelot isogenies. In this paper, we further investigate the case of (4,4)-reducible Jacobians and explicitly compute the locus ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> .
Evaluation of Different Feature Descriptor Algorithms on Classification Task

In this paper, we have done an assessment to evaluate the efficiency of different feature descriptor algorithms like Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) on gender classification problem. The evaluation is done with and without using the feature descriptors and the results are compared to get a better understanding of the algorithms. Experiments are carried out on CAS-PEAL FRONTAL database.
Improvement in Localization of a Moving Vehicle using K-means Clustering

Automotive radar uses multiple sensors to locate and determine the movement of the vehicles. Frequency Modulated Continuous Wave (FMCW) is the prevailing waveform used in this field. The proposed method estimates the vehicle location and speed. It tracks the vehicle(s) using multiple sensors arranged in a Uniform Linear Array (ULA). Vehicle identification and assigning tracks to a vehicle in case of a multi-target scenario is based on three estimated parameters: vehicle range, speed, and range-to-track association. Both single and multiple target scenarios are examined in the proposed method. The study is further extended to a uni-directional multi-lane road scenario to establish the universality of the method.
Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI

Many benchmarks and challenges for AI and AGI exist, which help to reveal both short- and long-term topics and directions of research. We analyze elementary school Olympiad math tasks as a possible benchmark for AGI that can occupy a certain free niche capturing some limitations of the existing neural and symbolic systems better than other existing both language understanding and mathematical tests. A detailed comparison and analysis of implications of AGI is provided.
Impact of Field of Study Trend on Scientific Articles

The volume and diversity of scientific literature are escalating every day and millions of new scientific articles are published every year. Researchers work and publish in their respective fields of interest. A major portion of the scientific community publishing in the same field of interest forms a trend in the field which could be deemed as relatively more popular than other trends. A pioneering researcher picks a field of interest by depending upon its popularity. This may have a positive impact on the acceptance of a study or high count of citations in future. This study identifies how significant it is to follow a research trend and the impact of the field of study (FoS) trend on research paper citations. For this purpose, we have chosen the field of Computer Science and Microsoft Academic Graph dataset from the 2007-2015 time period. In the dataset, every paper has a list of fields of study. The FoS provided in MAG is systematized hierarchically into 4 levels; level-0 - level-3. In this study, we apply the clustering technique to the FoS and citations pattern separately. Likewise, we also analyze how papers following a FoS trend, gain citations over the time. We also introduce a novel method Field of Study Multigraph (FoM) using graph centrality measures degree, betweenness and closeness to analyze the FoS trend, citation trend, and the relation between research areas in scientific articles from the domain of Computer Science. The experimental results show that the FoS has a certain impact on citation count. Furthermore, the results depicts that if papers belong to the same FoS, then there are 66% of the chances of having a similar citation pattern and that they have the same citation trend as they also achieved a high correlation value. This proves that a FoS has a certain impact on the citation count of a paper and researchers should contemplate the FoS trend before selecting a particular research area.
Anomaly Detection with Pipeline Structure using Joint Distribution

We presented a novel way for the problem of anomaly detection, where, given a set of examples, the goal is to judge if the type of a query example is out of the training dataset. The key contribution of our work is to improve the accuracy of GAN-based anomaly detection and extend the use cases of this type of method (such as audio). In order to accomplish this goal, firstly, we replaced generator of standard GAN with pipeline structure consisting of autoencoder. Secondly, using joint contribution to improve the quality of reconstruction. Thirdly, we used Self-Attention to capture the long-range dependence of the time series for detecting anomalies in our network. The effectiveness of the proposed method is measured across three available datasets including image as well as audio datasets, and the desired results are achieved.