Reviewerly/exHarmony huggingface dataset:
Corporate Social Responsibility: A Legal Perspective with Reference to Banks in Pakistan

Purpose: The term "Corporate Social Responsibility" is not new in the business world. Over time, several academics and researchers have evolved their definitions of CSR, basing them on the political, environmental, economic, and social circumstances of the moment. Since the 1950s, no solitary, recognized definition of CSR among academics and researchers has existed. Companies that engage in CSR see a rise in customer loyalty. No doubt, CSR a new-fangled idea in Pakistan intensifies the significance of CSR for banks in Pakistan. The researchers will try to determine whether the banks operating in Pakistan are obeying the standards of CSR and, if so, how closely those rules are being adhered to.&#x0D; Design/Methodology/Approach: To achieve that, we will try to comprehend the idea of CSR, look into where it came from, and examine its constituent parts and primary motivators. affect the environment and people.&#x0D; Findings: Additionally, it is important to consider how CSR compliance may The study will attempt to look out for pertinent laws, rules, regulations, and recommendations in the local and international legal frameworks that highlight the importance of CSR and its application.&#x0D; Implications/Originality/Value: If banks do not execute the CSR laws, we will attempt to determine if the State and people of Pakistan have any legal remedies against the banks&#x0D;
Developing Interactive Design for Educating Childhood Cancer Awareness

Current websites specific to childhood cancer still lack approachable design. The interface and layout are unattractive with inappropriate and limited multimedia elements, making the information dissemination to the user ineffective. Hence, this study aims to develop an interactive infographic website to educate childhood cancer named i-Hope. The objectives are to identify and implement suitable multimedia and interactive features, integrate them with the appropriate interaction design and approach in producing the website. The methods comprise five phases: analysis, design, development, implementation and evaluation. This paper discusses the development and evaluation of the website in attracting users' interest in childhood cancer.
Data Integrity on the Cloud Computing using Algebraic Signature

Abstract: The Trends of Cloud Computing, the cloud security work must be important because the huge number of data with specialized connections to distribute data processing among the various servers. Client stores their data on cloud server to maintain their data privacy and data security. The popular data security method which is called cryptography taking more time and space to encrypt and decrypt for data auditing processes. The existing method is Provable Data Possession which is dynamically operates the data and gives the high computation space and time complexity. So to avoid that complexity the proposed method is called as Algebraic Signature to used low computation performance time and low data space for large data set. It is based on data integrity method for providing good data security on the cloud for large organization also. This proposed method is used the Third Party Auditing method which is to provide large data file security with the Hashing technique using Algebraic Signature method. The conclusion of this paper is providing the data integrity and time efficiency of the process using TPA method also provides the dynamic operation.
Purdue University Students' Perceptions of Cybersecurity

An investigation into Purdue University students’ attitudes about cybersecurity was conducted. Students were asked about their thoughts on cybersecurity, cyber-terrorism, and how they protect themselves. The data indicated that students were both aware of cyber-threats and participated in potentially unsafe internet activities. A variety of misconceptions regarding online security exposed, highlighting the need for greater education for college students regarding staying safe during online activities.
An Analytical Study on the Concentration(Samādhi)

This Study deals with meaning, names, numbers of Dhamma and development(bhāvanā) of Concentration(Samādhi) analytically.Samādhi should be translated into concentration, right concentration of mind, concentration of mind etc. Samādhi could be said one pointedness of skilled mind and the subject of concentration is called good mind, mind or belonging to mind and concentration established in one point.The method is samely, perfectly, calm down and not distracted.Samādhi, Samatha, Jhāna and Samāpatti are both the same meaning and different meaning.Therefore we should interpret the meaning of the word samādhi differently according to contexts.Compounds of the word samādhi consists of three parts, connected in before or end of a word samādhi and the both of the word.According to the analysis by number of Dhammas, in two Dhammas, Samādhi has a symmetry structure in threefold dhammas has a stage structure, in over fourfold Dhammas has a elemental structure.And Samādhi related with and belongs to the category of buddhist practices of every Dhamma.It is known that the samādhi has also subcategory.The first meditation involved with the concentration which has initial and sustained application, the concentration which has only sustained application without initial application.The lower stages of concentration helps make the higher concentration.It should be considered that there is the difference of the concentration's meaning according to the stages of buddhist practice like as in content of buddhist practice appeared in the first and second meditation.There are differences among concentration of the Buddha, Arhan, the learned and normal person.As a result, we need to study and analysis the aim, benefit and stages of the concentration in detail.
Autonomous Driving Systems for Decision-Making Under Uncertainty Using Deep Reinforcement Learning

Deep reinforcement learning has achieved human-level and even beyond performance on complex tasks like Atari games and Go. However, this performance is not easy to adapt to autonomous driving since real world state spaces are extremely complex and have continuous action spaces. Besides, autonomous driving tasks often require decision making under uncertainty. Hence, the autonomous driving problem can be formulated as a partially observable Markov decision process (POMDP).In this paper, we propose a new approach to solve the autonomous driving problem based on decision making under uncertainty as a partially observable Markov decision process, using Guided Soft Actor-Critic (Guided SAC). Self driving car has been trained for the scenario where it encountered with a pedestrian crossing the road. Experiments show that the control agent exhibits desirable control behavior and performed close to the fully observable state under various uncertainty situations.
Hybrid Recommendation System for Forum based Social Network Platforms

With the widespread use of recommendation systems, it has become a need to determine and present content specifically for each user on social networks. Within the scope of the study, a personalized feed system is being produced by looking at the past interactions of users for forum based social network platforms. A Collaborative Filtering Based Model (CFBM) and a Content Based Model (CBM) are used. In CBFM, item-item based approaches such as Item2Vec and TF-IDF weighted Item2Vec have been developed, taking into account the topics that the user enters in a particular session. In the CBM, BERT architecture and FastText model are used for vectorization of forum messages. In the evaluation, it was seen that CFBM is more successful than the CBM. When the interaction numbers of the suggested contents are compared, it is seen that CFBM recommends the contents that receive more interaction, while CBM is more prone to recommend newly produced contents.
The Impact of Pre-trained Language Models on Turkish Semantic Role Labelling

Semantic role labeling (SRL) is the task of finding the argument structures of verbs in a sentence. Previous studies for Turkish SRL have focused mostly on syntactic features; context-oriented approaches have not been explored in this area yet. In this paper, we investigate the impact of pre-trained neural language models, which are strong in context representations, on the semantic role labeling task of Turkish. BERT, ConvBERT and ELECTRA language models are adapted to Turkish SRL with parameter tuning. We report a 10 percentage points improvement over the morphology focused results, which relies on gold-standard morphological tags and thus does not contain the errors propagated due to a previous morphological analysis layer. Since our model does not have any such dependencies, the performance increase will be even higher in the actual scenario.
Using Deep Compression on PyTorch Models for Autonomous Systems

Applications of artificial neural networks on low-cost embedded systems and microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed compared to workstations, employment of current deep learning algorithms on MCUs becomes more practical with the help of model compression. This makes MCUs common and practical alternative solution for autonomous systems. In this paper, we add model compression, specifically Deep Compression, to an existing work, which efficiently deploys PyTorch models on MCUs, in order to increase neural network speed and save electrical power. First, we prune the weight values close to zero in convolutional and fully connected layers. Secondly, the remaining weights and activations are quantized to 8-bit integers from 32-bit floating-point. Finally, forward pass functions are compressed using special data structures for sparse matrices, which store only nonzero weights. In the case of the LeNet-5 model, the memory footprint was reduced by 12.5x, and the inference speed was boosted by 2.6x.
Cyber Threat Modeling Framework

The Cyberspace has reshaped the boundaries of the dispute fields used by organizations and governments. Organizational security doctrines and strategies have been adapted to face the evergrowing complexity of cyber threats. Part of the success in surviving in that environment depends on the ability one has to understand the nature of the threats aiming the systems to be protected. In this work, we propose a framework to map high complex cyber threats to help technical and management personnel with the task of generating useful knowledge, both in corporate and government cyber security sectors.
Can We Detect Malicious Behaviours in Encrypted DNS Tunnels Using Network Flow Entropy?

This paper explores the concept of entropy of a flow to augment flow statistical features for encrypted DNS tunnelling detection, specifically DNS over HTTPS traffic. To achieve this, the use of flow exporters, namely Argus, DoHlyzer and Tranalyzer2 are studied. Statistical flow features automatically generated by the aforementioned tools are then augmented with the flow entropy. In this work, flow entropy is calculated using three different techniques: (i) entropy over all packets of a flow, (ii) entropy over the first 96 bytes of a flow, and (iii) entropy over the first n-packets of a flow. These features are provided as input to ML classifiers to detect malicious behaviours over four publicly available datasets. This model is optimized using TPOT-AutoML system, where the Random Forest classifier provided the best performance achieving an average F-measure of 98% over all testing datasets employed.
Deep Learning Approach for Unmanned Aerial Vehicle Landing

It is one of the biggest challenges to land an unmanned aerial vehicle (UAV). Landing it by making its own decisions is almost impossible even if progress has been made in developing deep learning algorithms, which are doing a great job in the Artificial Intelligence sector. But these algorithms require a large amount of data to get optimum results. For a Type-I civilization collecting data while landing UAV on another planet is not feasible. But there is one hack all the required data can be collected by creating a simulation that is cost-effective, time-saving, and safe too. This is a small step toward making an Intelligent UAV that can make its own decisions while landing on a surface other than Earth's surface. Therefore, the simulation has been created inside gaming engine from which the required training data can be collected. And by using that training data, deep neural networks are trained. Also deployed those trained models into the simulation and checked their performance
Accurate Real-Time Labeling of Application Traffic

In this paper, we present the design and implementation of ATLAS, a novel tool for automatically labeling network packets with the process responsible for them. Our tool is able to label all kinds of outbound packets based on Windows events and TCP stream information with ground-truth accuracy. Additionally, it is able to label DNS packets with the correct process name instead of just the DNS resolver. Using ATLAS, it is possible to create large datasets, e.g., to create software fingerprints or train machine learning classifiers. Another use-case is to inspect the network traffic of a machine to determine which application is communicating with whom. We evaluate the performance considering different load scenarios to demonstrate the real-time capacity of ATLAS. Additionally, we analyze the communication endpoints of a Windows 10 host and compare the results before and after disabling all privacy related settings.
An <i>Intermediate-Centric</i> Dataflow for Transposed Convolution Acceleration on FPGA

Transposed convolution has been prevailing in convolutional neural networks (CNNs), playing an important role in multiple scenarios such as image segmentation and back-propagation process of training CNNs. This mainly benefits from the ability to up-sample the input feature maps by interpolating new information from the input feature pixels. However, the backward-stencil computation constrains its performance and hindered its wide application in diverse platforms. Moreover, in contrast to the efforts on accelerating the convolution, there is a rare investigation on the acceleration of transposed convolution that is identically compute-intensive as the former. For acceleration of transposed convolution, we propose an intermediate-centric dataflow scheme, in which we decouple the generation of the intermediate patch from its further process, aim at efficiently performing the backward-stencil computation . The intermediate-centric dataflow breaks the transposed convolution into several phases/stages, achieving feeding the input feature maps and performing the backward-stencil computation in a pipelining manner. It also provides four-degree computation parallelism and efficient data reuse of input feature maps/weights. Furthermore, we also theoretically analyze the irregular data dependence leveraging the polyhedral model, which constrains the parallel computing of transposed convolution. Additionally, we devise an optimization problem to explore the design space and automatically generate the optimal design configurations for different transposed convolutional layers and hardware platforms. By selecting the representative transposed convolutional layers from DCGAN, FSRCNN, and FCN, we generate the corresponding accelerator arrays of intermediate-centric dataflow on the Xilinx Alveo U200 platform and reach the performance of 3.92 TOPS, 2.72 TOPS, and 4.76 TOPS, respectively.
Text Summarization Approaches Under Transfer Learning and Domain Adaptation Settings—A Survey

Data explosion is becoming a well-known concern as a result of rapid improvements in Internet-related technologies. The ease with which people can share and access information over the Internet has resulted in an abundance of data on any topic. Text summarization powered by deep neural networks can analyse vast volumes of text input and create a short summary on any topic to address the problem of information overload. However, deep neural networks require a lot of labelled data, and therefore, training them using tiny labelled text datasets is inefficient. Transfer learning and domain adaptation are possible solutions to this problem, and researchers have recently begun to investigate them in text domains. In this study, we analyse a number of recent articles that use transfer learning and domain adaptation to tackle the text summarization problem. The methodology for text summarization in transfer learning and domain adaption settings is explained in this survey. The application of transfer learning in sequence-to-sequence- (Seq2Seq) and non-Seq2Seq-based techniques for text summarization is discussed, with emphasis on crucial unique methodology and contributions from different works. Latest approaches for text summarization under different configurations of domain adaptation are discussed and compared. Issues, challenges and opportunities pertaining to this domain have also been identified and discussed for the benefit of the readers.
A Study on a DDH-Based Keyed Homomorphic Encryption Suitable to Machine Learning in the Cloud

Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al.
Demand Analysis of Air Defense Unit Training Evaluation System

The construction of digitalization and informationization has put forward higher requirements for the training of air defense unit in scientific evaluation, correct decision-making and rapid response. At present, the manual evaluation method is not easy to query, classify, summarize and analyze the data information scientifically, and it cannot meet the needs of the modernization of the army. It is of great practical significance to study and establish the training and evaluation system of air defense unit to guide the training of troops and make scientific decision.
Virtual Reality as a Teaching Resource in Higher Education: Professors’ Assessment

In this paper, a quantitative research is carried out on the perception of a group of Spanish professors about the didactic use of virtual reality in higher education classrooms. Thirty-six Spanish university professors with no previous experience in the use of virtual reality participated in the study. These professors attended a talk-workshop on VR and expressed their assessment of the didactic use of this technology before and after the workshop through a survey of our own creation. The answers were statistically analyzed to compare the mean answers before and after the workshop and to identify gender and age differences in the valuations The results allow us to conclude that the training received positively increased the participants’ perception of all aspects of VR studied and that males and older professors increased their assessment of the technical aspects of virtual reality to a greater extent. In addition, the longest-serving professors positively increase their perception of the ease of use of virtual reality after the workshop.
Mobile-Assisted Language Teaching: A Systematic Review with Implications for Southeast Asia

Like the rest of the world, COVID-19 created a sudden move to online modes of learning in Southeast Asia that highlights the need for updated teacher training to adopt computer- and mobile-assisted learning/teaching techniques. The changes in technology provide a multimedia platform that revolutionizes how people can interact through ICT, including for education. The results of mobile-assisted language learning (MALL) on student performance have not been overwhelmingly positive. Yet, the use of technology-based instruction tools seems inevitable. The proliferation of ICT technologies including the internet, broadband, and mobile technologies will continue to increase and offer advantages to tap into. How are teachers—the workers in the educational service sector—affected by technological change? Education systems in Southeast Asia can benefit from mobile-assisted language teaching (MALT) in ways that address the specific obstacles that are faced by countries in the region. We systematically review the topics that have been covered in the literature on MALT for content analysis and consider the implications for educational contexts in Southeast Asia. The different strategies that are employed with ICT and/or mobile technology differ from traditional classroom learning. The findings of the systematic review suggest that the existing research is constrained by the type of technologies studied, with a bias toward existing technologies.
A Twin Kernel Ridge Regression Classifier for Binary Classification

Kernel ridge regression (KRR) is a popular machine learning technique for tasks related to both regression and classification. To improve the generalization ability of the KRR model, this paper suggests a twin KRR model for binary classification. The twin KRR (TKRR) solves two sets of linear equations rather than solving quadratic programming problems (QPPs) unlike support vector machine (SVM) and twin SVM (TWSVM). The conventional KRR learns only one large hyperplane while the TKRR learns two small hyperplanes. The proposed TKRR is novel, fast, and intuitive. Experimental simulations have been carried out on an artificial and a few interesting real-world datasets. The obtained classification accuracies of TKRR are statistically compared with SVM, TWSVM, and KRR models. The experimental outcomes demonstrate the efficacy and usability of the proposed TKRR model.
Developing Teaching Competencies for Implementing Blended Learning in Higher Education: Experiences of Faculty of Science, University of Kragujevac

One of the invariants for the education through the time is the effort for improvement and changes caused by different cultural, economic, and overall civilization circumstances. Nowadays, the huge impact of digital technology on educational systems and everyday practice is evident. The didactic theory still seeks guidelines for adequate use of new information communication technology. Higher education in Serbia also is a part of this process, as activities of ERASMUS+ project TeComp (Strengthening Teaching Competencies in Higher Education in Natural and Mathematical Sciences, 598434-EPP-1-2018-1-RS-EPPKA2-CBHE-JP) testified. In this paper, we present some outcomes and experiences collected during the project with an aim to disseminate results and to contribute broad discussion in the HE community. We give a quick overview of teachers’ attitudes about ICT in their teaching practice as well as some important information and conclusions conducted from evaluations of trainings the most visited by teachers from the Faculty of Science, Kragujevac, concerning aspects of incorporating blended learning in HE in fields of natural sciences, mathematics, computer science, and informatics as a way for strengthening teacher competences