Reviewerly/exHarmony huggingface dataset:
Feistel Network Assisted Dynamic Keying based SPN Lightweight Encryption for IoT Security

In the last few years Internet-of-Things (IoT) technology has emerged significantly to serve varied purposes including healthcare, surveillance and control, business communication, civic administration and even varied financial activities.Despite of such broadened applications, being distributed, wireless based systems, IoTs are often considered vulnerable towards intrusion or malicious attacks, where exploiting the benefits of loosely connected peers, the attackers intend to gain device access or data access un authentically.However, being resource constrained in nature while demanding time-efficient computation, the majority of the classical cryptosystems are either computationally exhaustive or limited to avoid attacks like Brute-Force, Smart Card Loss Attack, Impersonation, Linear and Differential attacks, etc.The assumptions hypothesizing that increasing key-size with higher encryption round can achieve augmented security often fail in IoT due to increased complexity, overhead and eventual resource exhaustion.Considering it as limitation, in this paper we proposed a state-of-art Generalized Feistel Network assisted Shannon-Conditioned and Dynamic Keying based SSPN (GFS-SSPN) Lightweight Encryption System for IoT Security.Unlike classical cryptosystems or even substitution and permutation (SPN) based methods, we designed Shannon-criteria bounded SPN model with Generalized Feistel Network (SPN-GFS) model that employs 64-bit dynamic key with five rounds of encryption to enable highly attack-resilient IoT security.The proposed model was designed in such manner that it could be suitable towards both data-level security as well as device level accesscredential security to enable a "Fit-To-All" security solution for IoTs.Simulation results revealed that the proposed GFS-SSPN model exhibits very small encryption time with optimal NPCR and UACI.Additionally, correlation output too was found encouragingly fair, indicating higher attack-resilience.
Detect Profane Language in Streaming Services to Protect Young Audiences

With the rapid growth of online video streaming, recent years have seen increasing concerns about profane language in their content. Detecting profane language in streaming services is challenging due to the long sentences appeared in a video. While recent research on handling long sentences has focused on developing deep learning modeling techniques, little work has focused on techniques on improving data pipelines. In this work, we develop a data collection pipeline to address long sequence of texts and integrate this pipeline with a multi-head self-attention model. With this pipeline, our experiments show the self-attention model offers 12.5% relative accuracy improvement over state-of-the-art distilBERT model on profane language detection while requiring only 3% of parameters. This research designs a better system for informing users of profane language in video streaming services.
Energy of taut strings accompanying random walk

We consider the kinetic energy of the taut strings accompanying trajectories of a Wiener process and a random walk.Under certain assumptions on the band width, it is shown that the energy of a taut string accompanying a random walk within a band satisfies the same strong law of large numbers as proved earlier for a Wiener process and a fixed band width.New results for Wiener processes are also obtained.
A Study of Operators on Fuzzy Sets

Overtime, mathematics had been used as a tool in modeling real life phenomenon.In some cases, these problems cannot fit-into the classical deterministic or stochastic modeling techniques, perhaps due system complexity arising from lack of complete knowledge about the phenomenon or some uncertainty.The uncertainty could either be due to lack of clear boundaries in the description of the object or perhaps due to randomness.In this article, we study a mathematical tool discovered in 1965 by Zadeh suitable for modeling real life phenomenon and examined operations on such a tool.Motivated by the work of Zadeh, we studied operators on Type-1 Fuzzy Sets (T1FSs) and Type-2 Fuzzy sets (T2FSs) and provided examples, one of which is a variant of the Yager complement function for which the complement operator was graphically illustrated.The joint and the meet operators were also studied and examples provided.Non-standard operators were defined on T1FSs and T2FSs and also classified into two groups; the triangular-norm (t-norm) and triangular-conorm (t-conorm).Using tnorm and t-conorm, an example was adopted from Castillo and Aguilar to illustrate the computation of the standard operation on T2FSs.Finally, future research direction was provided based on what is yet to be achieved in fuzzy set theory.
IMPACT OF FINANCIAL DEVELOPMENT ON ECONOMIC GROWTH IN SUDAN: SHORT-RUN AND LONG-RUN ANALYSIS

Purpose: The aim of this paper is to investigate the influence of banking sector on economic growth apart from other financial development tools.Four control variables are included in the analysis, precisely, domestic credit provided by banks to private sector, liquid liabilities, bank deposit and trade ratio.Design/Methodology/Approach: This study employs time series data of four financial indicators and economic growth in Sudan from 198 to 201 .Using Auto 0 5Regressive Distributed Lag ARDL and associated ECM.Findings: The study reveals positive link (relationship) between economic growth and the financial development indicators.Also, the empirical results indicate that change in economic growth is positively related to the most of financial development indicators coming from banking sector.In view of long-run results, the analysis pointed out, liquid liabilities, trade ratio and domestic credit provided to private sector bring to bear direct (positive) impact on economic growth, while bank deposit affects economic growth negatively.In the short-run, the local (domestic) credit by banks to private sector, liquid liability and trade ratio exert negative impact on economic growth, while bank deposit shows positive relationship.Practical Implications: The findings suggest that policy makers need to concentrate on bank credit to private sector, raise the liquidity by offering e-banks services and Impact of Financial Development on Economic Growth in Sudan: Short-run and Long-run Analysis ttp://iaeme.com/Home/
xFabLedger: Extensible Ledger Storage for Hyperledger Fabric

Blockchain technology is widely used in finance, supply chains, energy exchange, etc. To keep data immutable, all peers in blockchain must store the whole copy of the ledger data. With the growth of transaction volume, the storage burden of each peer increases. To solve the storage scalability issues, we propose a scheme called xFabLedger, which strips ledger storage from peers when using Hyperledger Fabric. The ledger data are separated from the peer and stored on a remote database, so a lightweight peer is conducted. The Vector Commitment (VC) is adopted to keep the integrity of remote data for peers. On the remote database, only one copy of ledger data should be retained. We integrate our solution into Hyperledger Fabric V2.2 and confirm its feasibility.
Detecting Deepfakes with Haralick&amp;#x2019;s Texture Properties

In the recent years, the detection of deepfake videos has become a major topic in the field of digital media forensics, as the amount of such videos circulating on the internet has drastically risen. Providers of content, such as Facebook and Amazon, have become aware of this new threat to spreading misinformation on the Internet. In this work, a novel forgery detection method based on the texture analysis known from image classification and segmentation is proposed. In the experimental results, its performance has shown to be comparable to related works.
ROBUST DETECTION AND RECOGNITION SYSTEM BASED ON FACIAL EXTRACTION AND DECISION TREE

Automatic face recognition system is suggested in this work on the basis of appearance based features focusing on the whole image as well as local based features focusing on critical face points like eyes, mouth, and nose for generating further details. Face detection is the major phase in face recognition systems, certain method for face detection (Viola-Jones) has the ability to process images efficiently and achieve high rates of detection in real time systems. Dimension reduction and feature extraction approaches are going to be utilized on the cropped image caused by detection. One of the simple, yet effective ways for extracting image features is the Local Binary Pattern Histogram (LBPH), while the technique of Principal Component Analysis (PCA) was majorly utilized in pattern recognition. Also, the technique of Linear Discriminant Analysis (LDA) utilized for overcoming PCA limitations was efficiently used in face recognition. Furthermore, classification is going to be utilized following the feature extraction. The utilized machine learning algorithms are PART and J48. The suggested system is showing high accuracy for detection with Viola-Jones 98.75, whereas the features which are extracted by means of LDA with J48 provided the best results of (F-measure, Recall, and Precision).
Analisis Perilaku Belajar Dan Minat Belajar Terhadap Pemahaman Akuntansi Dengan Kecerdasan Intelektual Sebagai Variabel Intervening (Studi Pada Mahasiswa S1 Akuntansi Periode 2017 – 2018 UPN “Veteran” Jawa Timur)

Higher education institutions are required to not only produce graduates who have mastered abilities in the academic field, but also have technical analytical skills in the field of humanistic skills and professional skills so that they have added value in competing in the world of work. The problem that arises in this research is the understanding of accounting that changes or fluctuates in the Accounting Students of UPN "Veteran" East Java during the period 2017 - 2018. The type of research used is the type of research using quantitative approaches. The results of this study indicate that learning behavior has a significant effect on understanding accounting, learning interest has a significant effect on accounting understanding, learning behavior has a significant effect on intellectual intelligence, learning interest has a significant effect on intellectual intelligence, intellectual intelligence has a significant effect on accounting understanding, learning behavior has no effect. on understanding of accounting through intellectual intelligence, and interest in learning has a significant effect on understanding of accounting through intellectual intelligence.
Reinforcement Learning Based Hierarchical Multi-Agent Robotic Search Team in Uncertain Environment

Field of robotics has been under the limelight because of recent advances in Artificial Intelligence (AI). Due to increased diversity in multi-agent systems, new models are being developed to handle complexity of such systems. However, most of these models do not address problems such as; uncertainty handling, efficient learning, agent coordination and fault detection. This paper presents a novel approach of implementing Reinforcement Learning (RL) on hierarchical robotic search teams. The proposed algorithm handles uncertainties in the system by implementing Q-learning and depicts enhanced efficiency as well as better time consumption compared to prior models. The reason for that is each agent can take action on its own thus there is less dependency on leader agent for RL policy. The performance of this algorithm is measured by introducing agents in an unknown environment with both Markov Decision Process (MDP) and RL policies at their disposal. Simulation-based comparison of the agent motion is presented using the results from of MDP and RL policies. Furthermore, qualitative comparison of the proposed model with prior models is also presented.
Le Bâton: A Digital Musical Instrument Based on the Chaotic Triple Pendulum

This paper describes Le Bâton, a new digital musical instrument based on the nonlinear dynamics of the triple pendulum.The triple pendulum is a simple physical system constructed by attaching three pendulums vertically such that each joint can swing freely.When subjected to large oscillations, its motion is chaotic and is often described as unexpectedly mesmerizing.Le Bâton uses wireless inertial measurement units (IMUs) embedded in each pendulum arm to send real-time motion data to Max/MSP.Additionally, we implemented a control mechanism, allowing a user to remotely interact with it by setting the initial release angle.Here, we explain the motivation and design of Le Bâton and describe mapping strategies.To conclude, we discuss how its nature of user interaction complicates its status as a digital musical instrument.
Benchmark of Similar Blocks Search under Noisy Conditions

A similarity search in images has become a typical operation in many applications. A presence of noise in images greatly affects the correctness of detection of similar image blocks, resulting in a reduction of efficiency of image processing methods, e.g., non-local denoising. In this paper, we study noise immunity of various distance measures (similarity metrics). Taking into account a wide variety of information content in real life images and variations of noise type and intensity. We propose a set of test data and obtain preliminary results for several typical cases of image and noise properties. The recommendations for metrics' and threshold selection are given. Fast implementation of the proposed benchmark is realized using CUDA technology.
INTEGRATION OF ARTIFICIAL INTELLIGENCE MARKETING TO GET BRAND RECOGNITION FOR SOCIAL BUSINESS

The purpose of this article is to conduct a detailed analysis of the integration of Artificial Intelligence (AI) marketing to get brand recognition for social business. It has been seen that AI is immensely popular with businesses these days. This article mainly focuses on the use of AI marketing elements to increase brand recognition for social businesses, which are not very well-known to consumers in Bangladesh. This study makes use of a quantitative research approach that includes a survey questionnaire used to explain the kind of impact AI may have on brand recognition for social businesses. The authors have approached 500 respondents and got close to an 81% response rate, 403 responses. With the support of SPSS, the collected answers from the sample were analyzed. The results show that integration of AI marketing strategies will play a significant role in leading to increase brand awareness and recognition for social business. This research will allow the professionals to integrate AI marketing elements to achieve improved brand recognition. This study will also encourage establishing of more social businesses ultimately benefiting society.Keywords: Artificial Intelligence Marketing, Brand Awareness, Brand Recognition, Social Business, Social Businesses in Bangladesh.JEL Classifications: M31, L31DOI: https://doi.org/10.32479/irmm.11542
Special Section: Mobile Robots and Unmanned Ground Vehicles

Inspired by the fifth-year anniversary celebration of the homonymous symposium at the International Mechanical Engineering Congress & Exposition (IMECE), this Special Section with 10 papers shares the latest research efforts in design, theory, development, and applications for Mobile Robots (MRs) and Unmanned Ground Vehicles (UGVs).
Increasing Academic Self-Efficacy Through Group Guidance in Class VIII Students of SMP Negeri 25 Banjarmasin

Academic self-efficacy is a determining factor in students' academic performance in school.The reality that occurs in the world of education, especially in Indonesia is that students get a lot of pressure, especially in academics.The academic value demands that must meet the minimum completeness criteria, without being accompanied by a pleasant learning ambiance are often experienced by students in Indonesia.This situation can trigger a decrease in the motivation and commitment of students to study.To avoid this, the counselor needs to make efforts, one of which is group guidance.This study aims to improve the academic self-efficacy of class VIII students at SMP Negeri 25 Banjarmasin, which is characterized by changes in student learning behavior.This research is qualitative research using a type of action research.The results of action research indicate a change in behavior in learning, namely students have self-confidence in learning so they have the belief to be able to complete difficult tasks and take on challenges in learning.
Reputation Measurement based on a Hybrid Sentiment Analysis Approach for Saudi Telecom Companies

Thousands of active people on social media daily share their thoughts and opinions about different subjects and different issues.Many social media platforms used to express the feeling or opinion and at top of it is Twitter.On Twitter, many opinions are expressed in many fields such as movies, events, products, and services; this data considered a valuable resource for companies and decision-makers to help in making decisions.This study was based on using a hybrid approach to extract the opinions from an Arabic tweet to measuring service providers' reputation.In this study, the Saudi telecom companies used as a case study.This research concentrates on determining peoples' opinions more accurately by utilizing the Retweet and Favorite.The number resulting from positive and negative tweets after applying the polarity equation was used to estimate reputation scores.The result indicated that the STC company represents a high reputation compared to other companies.The proposed approach shows promising results to expand existing knowledge of sentiment analysis in the domain of measure reputation.
An Ontological Framework for Healthcare Web Applications Security

The current era of digitization and transformation causes various issues and advantages both at the same time in the healthcare sector.The beneficial advantages are exceptionally good but the issues that are gardening the business of attackers is a serious issue and requires effective prevention.Current statistics and attack vector analysis portray that technical breaches have the most common and highest priority in numbers.This type of information opens a need to prevent and develop an effective model that helps to exerts and healthcare practitioners in security management.In order to achieve this desired goal, we adopt and apply an ontology-based approach of security and development methodology and provide a model that effectively produces systematic secure pathways to design healthcare web applications.The conceptual framework discussed in the study has many effective and beneficial advantages namely, it gives a unified pathway to future developers; the model also attains focus on requirement identification during development and portrays its significance.
A Model of Cloud Forensic Application With Assurance of Cloud Log

The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.
Generating a Condensed Representation for Positive and Negative Association Rules

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.
BEHAVIORAL HIDDENTESTING OF DISTRIBUTED INFORMATION SYSTEMSTAKING INTO ACCOUNTOF ENERGY

The introduction of new energy-consuming properties for positions and transitions into the checked properties of the extended reference Petri net, for which the deviations of the tested Petri net are determined and a testing model is developed, provides new diagnostic possibilities. Keeping the class of checked properties in the composition of deviations of incidence relations, correspondences and marking functions of positions and transitions for the checked and reference Petri nets, the new properties make it possible to record the appearance of critical temperature regimes that are a consequence of errors or directly leading to their appearance. Thisversatility of testing helps to increase its completeness, accuracy and efficiency. The energy-heavy testing model is based on verification of incidence, correspondence, and markup functions. Checking the markup functions when generating events in positions, performing actions in transitions, as well as the proposed checking of the energy consumption indicators accumulated in the monitor tokens, is performed when checking the incidence, correspondences. The features of the testingmodel include the input of generalized energy-loaded Petri nets recorders, accumulating information about energy consumption in the behavior of positions/transitions, topological componentsand subnets, the entire Petri net in the process of its functioning. The testingmodel is also distinguished by the recognition of the reference energy-loaded behavior when checking the Petri net based on behavioral identification and coincidenceof subsets of positions/transitions, the determination of behavior, the use of checkprimitives and transactions. The behavioral testingmodel defines the formal conditions for behavioral testingprocedures, including the analysis of the correctness of energy consumption. The dimensionality of the testingmodel was estimated using the representation of Petri net graphs, special graphs of attainable states, including Rabin-Scott automata, using list structures. These estimates define the limits of applicability of the formal testingmodel.
Media Culture as the Information Age Phenomenon

The article raises the issues of scientific reception of such a phenomenon as media culture. The authors offer their interpretation of media culture as a special type of culture of the information society in the broadest understanding of this phenomenon. The authors consider the concepts of media and culture and establishes their functional corresponddence. The contemporary stage of media development is characterized by a combination of communication and information intentions: classical media and mass communication media, including new media, blogs, social networks, as well as digital copies of non-network artifacts and their network modifications. The result of these media communications is a media text in the broadest interpretation of this concept. According to the authors concept, contemporary media culture realizes itself in two main aspects. In the applied sense, a media culture is a form of representation and digitalization of classical and network cultural units. In the global sense, media culture is understood as an aesthetic and axiological sphere of societys life, in which culture combines the value and artistic heritage, using the information and communication channels of the media for its representation in politics, education, and culture itself.