Reviewerly/exHarmony huggingface dataset:
Efficient hardware realization and high radix implementation of modular multi exponential techniques for public key cryptography

Modular multi exponentiation (MME) is a critical operation in most of the public-key cryptosystems and verification functions. Two novel algorithms: HRMME and SFWHRMME, are proposed to evaluate the MME efficiently. The evaluation process of the proposed algorithm has an inbuilt confusion mechanism, which increases the security level. The side-channel attacks and timing attacks are counteracted by algorithmic means instead of using only hardware measures. The high-radix implementation is efficiently reducing the required number of clock cycles. The performance of the proposed algorithms is analyzed in terms of power, throughput and energy. The experimental results show that the proposed techniques HRMME and SFWHRMME increase the throughput in the order of 23.55% and 23.67%, respectively and consume less power in the order of 21.78% and 21.88%. Xilinx Vivado 21.2 on Virtex-7 evaluation boards and ICARUS Verilog simulation & synthesis tools are used for FPGA for hardware realization. The hardware compatibility of proposed algorithms has also been checked using Cadence for ASIC.
Paradoxical tensions in the implementation of digital security governance: Toward an ambidextrous approach to governing digital security

Due to increasing numbers of cyberattacks, security is one of the leading challenges to contemporary organizations. As contradictory demands (e.g., tensions in digital organizations) intensify, organizations increasingly find it difficult to implement digital security governance (DSG) as part of their regular organizational change activities. Based on data from forty-two interviews with Dutch CISOs and CIOs of large organizations that are active in various sectors, we identify three key paradoxical tensions that affect DSG implementation. We found that in a digital context, paradoxical tensions are pressurized and become out of balance. Disbalance among tensions exposes friction that hinders the implementation of DSG mechanisms. Finally, we present a conceptual model that sets direction for ambidextrous digital security. Understanding how to engage with tensions in an ambidextrous way determines the success of DSG implementations in today's complex digital environments.
High-speed recursive-separable image processing filters

The development of modern technologies in the field of image formation leads to an increase in the size of the generated images, as a result the question of reducing the processing computational costs arises, and this is an important factor in the creation of real-time systems. The study provides a description of high-speed recursive-separable filters for improving the quality of images, which, due to the peculiarities of their implementation, can reduce the number of computational operations required for the image processing process. This type of filters is obtained from two-dimensional linear digital filters, which are modified by applying recursive and separable properties to them. The MATLAB environment computing method for implementation of these filters is described. An extensive performance research of the developed filters has been carried out at various sizes of the test image and on various experimental installations. The comparison with the classical two-dimensional convolution method of the developed filters is demonstrated, and it shows the time gain required for the image processing. The results obtained can be applied in biomedical image processing systems or in vision systems working in heavy weather conditions.
Hierarchical Interactive Network for joint aspect extraction and sentiment classification

Aspect-based sentiment analysis (ABSA) aims at identifying the opinion aspects (aspect extraction) and sentiment polarities toward corresponding aspects (sentiment classification) from a sentence. Recently, some span-based methods, which first extract aspects by detecting aspect boundaries and then predict the span-level sentiments, have achieved promising results. However, the correlations between aspect extraction and sentiment classification have not been explicitly explored. For example, sentimental expressions can be better understood if specific aspects are given. In contrast, aspects can be better detected if we know where the sentimental expressions are located. Therefore, we propose a novel H ierarchical I nteractive N etwork (HIN) to enhance the internal connections between aspect extraction and sentiment classification. To this end, the HIN jointly learns the aspect extractor and sentiment classifier across two layers hierarchically. The former learns some shallow-level interactions via a cross-stitch mechanism, and the latter learns deep-level interactions between two subtasks by using mutual information maximization technology. Extensive experiments on three real-world datasets demonstrate the HIN’s superior performance. • Aspect-Based Sentiment is an emerging area in both research and practice. • We proposed a novel span-based multi-task learning framework to enhance the correlations between two subtasks. • Extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed method.
Data-Driven Artificial Intelligence Recommendation Mechanism in Online Learning Resources

This study attempts to reveal the intelligent recommendation mechanism of online learning resources. The “multimodal data fusion” aspect is adopted, illustrating online learning resource modeling, learning preference modeling, learning preference evolution, and referral engine smart design. Multimodal learning resources are key constituents of multidimensional intelligent recommendation services. The learner model that integrates multimodal preference data is the key to implementing smart recommendations. Learning preference evolution is a key factor in sustaining learner model robustness. The intelligent recommendation engine is the technical guarantee for developing an intelligent recommendation. Providing learning resources is an important part of online learning services, which mediate online learning processes. Intelligent recommendation, as an effective personalized servicing strategy, recommends differentiated, personalized, and precise learning resources to learners, thereby promoting the effectiveness of online learning performance. This study constructs a framework that reveals an intelligent recommendation mechanism for online learning resources.
What is robust control in quantum technology?

Technology is the product of understanding physical processes and learning how to control them in order to achieve desired objectives. To be practically usable, technology requires a degree of reliability, which can be achieved by robust system design and control [1].
Image Segmentation

Image segmentation is the process that partitions a digital image into disjoint regions that correspond to the objects in the field of view. It can be approached from two different perspectives. The region-based approach identifies connected sets of pixels that correspond to individual objects. Boundary-based techniques locate and connect edge pixels to define the boundaries that separate the objects. Thresholding, region growing, and morphological processing are useful region-based techniques for image segmentation. Edge detection, boundary tracking, edge linking, and active contours are useful boundary-based image segmentation techniques. The segmentation of an image can be specified by an object label map or by boundary chain codes.
Challenges and Opportunities of Blockchain for Cyber Threat Intelligence Sharing

The emergence of the Internet of Things (IoT) technology has caused a powerful transition in the cyber threat landscape. As a result, organisations have had to find new ways to better manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing platforms. However, most existing solutions are highly centralised and do not provide a way to exchange information in a distributed way. In this chapter, we subsequently seek to evaluate how blockchain technology can be used to address a number of limitations present in existing CTI sharing platforms. To determine the role of blockchain-based sharing moving forward, we present a number of general CTI sharing challenges, and discuss how blockchain can bring opportunities to address these challenges in a secure and efficient manner. Finally, we discuss a list of relevant works and note some unique future research questions.
Identification of Four Major Dialects of Assamese Language Using GMM with UBM

The Assamese language is spoken by the people of Assam, which is located in India's north-east corner. The Indo-European language family includes the Assamese language. The pronunciation, grammar, and vocabulary of Assamese are vary in different sections of the state, resulting in different regional dialects of the language. There are four major regional dialects of the Assamese language, namely Central Assamese spoken in and around Nagaon district, Eastern Assamese dialect spoken in the Sibsagar and its neighboring districts, Kamrupi dialect spoken in Kamrup, Nalbari, Barpeta, Kokarajhar and some parts of Bongaigaon district and Goaplari dialect spoken in the Goaplara, Dhuburi and part of Bongaigaon district. Therefore, to develop a universal Assamese speech recognition system that seamlessly recognizes the words spoken in the Assamese language and its dialects, the identification of the dialect is a necessary condition. Using the Gaussian Mixture Model (GMM) and the Gaussian Mixture Model with Universal Background Model, this research proposes a novel technique for recognizing Assamese dialects (GMM-UBM). To extract spectral information from collected voice sample, the Mel-Frequency Cepstral Coefficient (MFCC) is used. Modeling is done using the GMM and GMM-UBM modeling techniques.
Digitization Through SNS: Issues, Challenges, and Recommendations—A Case Study

In today’s digital era, everything relies on the Internet which poses so many concerns on security. Growth of the Internet results in danger for transferring information online through multimedia devices. Information transmitted through these digital devices can be hacked by hackers. The objective of this study is to throw light on problems that arise due to a lack of digital awareness for social networking sites among users and propose an automated solution to minimize these crimes. Communication sites have many areas of exploitation such as progressive promoting, online commercials and marking, etc. Many users are not much skilled in digital terms so these users are not aware of the negative side of these social network sites. The safety concerns and complications on social network sites such as character misuse, malware, phishing attacks, and unknown request risks have been discussed. Though introducing the management actions to governor these major concerns, the proposed paper also suggests some appropriate measures which could be convenient to every user as the assembly in the collaboration by private area for providing digital secure electronic world.
Phrase-Based English–Nyishi Machine Translation

Machine translation (MT) is the sub-domain of natural language processing (NLP). It process and analyze natural language data which eliminates the language inconceivable matters and help to interact among people of different linguistic backgrounds. In our work, we used the statistical machine translation (SMT) approaches to comprehensively explore the translation accuracy, fluency, and adequacy with the context of low resources Indian dialect (language) Nyishi. SMT needs less training time and works well with highly complex long sentences than other methods, but it requires adequate parallel corpus, which is troublesome in the context of low-resource language like Nyishi. In this paper, we train 30,000 newly collected pairs of corpora and measured the translation accuracy in both directions forward and backward. Finally, results of individual n-gram of BLEU and NIST with and without tuning are calculated, and by using these results, we find out the effectiveness of translation accuracy in respect of fluency and adequacy.
Culture knowledge graph construction techniques

This article describes the development process of the Thai cultural knowledge graph, which facilitates a more precise and rapid comprehension of the culture and customs of Thailand. The construction process is as follows: First, data collection technologies and techniques were used to obtain text data from the Wikipedia encyclopedia about cultural traditions in Thailand. Second, entity recognition and relationship extraction were performed on the structured text set. A natural language processing (NLP) technique was used to characterize and extract better textual resources from Wikipedia to support a deeper understanding of user-generated content by using automatic tools. Regarding entity recognition, a BiLSTM model was used to extract relationships between entities. After the entities and their relationships were obtained, triple data were generated from the semistructured data in the existing knowledge base. Then, a knowledge graph was created, knowledge bases were stored in the Neo4j Desktop, and the quality and performance of the created knowledge graph were assessed. According to the experimental findings, the precision value is 84.73%, the recall value is 82.26%, and the F1-score value is 83.47%; therefore, BiLSTM-CNN-CRF can successfully extract entities from the structured text.
A survey on cloud computing scheduling algorithms

Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.
Challenges and review of goal-oriented requirements engineering based competitive non-functional requirements analysis

Modelling and analysis in software system development can be especially challenging in early requirements engineering (RE), where high-level system non-functional requirements are discovered. In the early stage, hard to measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is key to system success. Goal-oriented requirements engineering (GORE) has been successful in dealing with the issues that may arise during the analysis of requirements. While assisting in the analysis of requirements, i* goal model is the only framework available among the many GORE models, emphasising socio-technical domains such as stakeholders/actors/players, goals/objectives, dependencies and design options/alternatives. Most current approaches to goal-model analysis use quantitative methods or formal information that is hard to gather in early RE, or produce analysis results automatically over models. In real-time competitive applications, the goals of various stakeholders are conflicting in complex systems. Also, each of the system goals have various alternative design options for the systems and optimal selection of goal-oriented requirements faces several challenges in requirements-based engineering. Hence, effective decision-making frameworks are necessary to capture the real issues to achieve multi-objective optimisation of interdependent actors. To obtain an optimum strategy for interdependent actors in the i* goal model must balance the opposing goals reciprocally. To achieve this, the model needs to go beyond the analytical decision-making tools such as sensitivity analysis tasks, cost-effective analysis process, game-theoretic concepts and analytical hierarchical process. To address these requirements, this paper discusses the design of novel frameworks for an agent-based goal model analysis in requirements engineering. The objective of this paper is to provide a brief and comprehensive review of the major efforts undertaken along this line of research. In this paper we have prepared literature review of the concepts, terminology, significance and techniques of Goal oriented requirements engineering in the context of non-functional requirements analysis.
Blockchain security in virtual environment

This paper is to improve the privacy and security in the distributed virtual environment using blockchain technology. One of the feature it provides is greater security in the decentralized virtual environment. A key aspect of this technology is used for various fields like healthcare, finance, business and cloud environment. Key issue of the virtual environment is to protect the data privacy and security which is difficult to handle. To overcome this issue, a new security model to protect the virtual environment is created and will focus on different types of attacks in blockchain technology.
They May Look and Look, Yet Not See: BMDs Cannot be Tested Adequately

Abstract Bugs, misconfiguration, and malware can cause ballot-marking devices (BMDs) to print incorrect votes. Several approaches to testing BMDs have been proposed. In logic and accuracy testing (LAT) and parallel or live testing, auditors input known test votes into the BMD and check whether the printout matches. Passive testing monitors the rate at which voters “spoil” BMD printout, on the theory that if BMDs malfunction, the rate will increase noticeably. We provide lower bounds that show that these approaches cannot reliably detect outcome-altering problems, because: (i) The number of possible voter interactions with BMDs is enormous, so testing interactions uniformly at random is hopeless. (ii) To probe the space of interactions intelligently requires an accurate model of voter behavior, but because the space of interactions is so large, building a sufficiently accurate model requires observing an enormous number of voters in every jurisdiction in every election—more voters than there are in most U.S. jurisdictions. (iii) Even with a perfect model of voter behavior, the required number of tests exceeds the number of voters in most U.S. jurisdictions. (iv) An attacker can target interactions that are intrinsically expensive to test, e.g., because they involve voting slowly; or interactions for which tampering is less likely to be noticed, e.g., because the voter uses the audio interface. (v) Whether BMDs misbehave or not, the distribution of spoiled ballots is unknown and varies by election and possibly by ballot style: historical data do not help much. Hence, there is no way to calibrate a threshold for passive testing, e.g., to guarantee at least a 95% chance of noticing that 5% of the votes were altered, with at most a 5% false alarm rate. (vi) Even if the distribution of spoiled ballots were known to be Poisson, the vast majority of jurisdictions do not have enough voters for passive testing to have a large chance of detecting problems but only a small chance of false alarms.
An Analysis of the Security and Privacy Issues of the Neovote Online Voting System

Abstract This article provides the first security and privacy analysis of the Neovote voting system, which was used for three of the five primaries in the French 2022 presidential election. We show that the demands of transparency, verifiability and security set by French governmental organisations were not met, and propose multiple attacks against the system targeting both the breach of voters’ privacy and the manipulation of the tally. We also show how inconsistencies in the verification system allow the publication of erroneous tallies and document how this arrived in practice during one of the primary elections.
Programmable money: next-generation blockchain-based conditional payments

Abstract Conditional payments allow the transfer of money only when pre-defined rules hold. Example uses could include welfare payments, employee expenses, insurance payouts, or tied donations. Normally, conditions are checked manually in reimbursement or pre-approval/audit processes, either at accounts before funds are distributed, or using account records after distribution. Blockchain’s capabilities for smart contract and digital assets can be used to implement next-generation conditional payments, on a decentralized ledger. We conducted a project with an industry partner where we conceptualized, implemented, and evaluated a novel programmable money concept using blockchain. In the system, programmed policies are not attached to accounts, but instead to money itself. Policies here specify the conditions when money may be spent, and can be automatically checked by the money as it is spent. Policies can also define auxiliary actions to be taken as part of payment transactions, including side-payments. Policies can be dynamically added to and removed from money as it flows through an economy. These policies could be budget rules for tied funds, but could also enable new forms of “values-based money” that respect ethical or other rules which relate to societal values or social norms. We report on some of our experiences and insights regarding blockchain architecture, software engineering with blockchain, and blockchain-based programmable money. We also identify opportunities and open research questions in these areas.
Human Latent Metrics: Perceptual and Cognitive Response Correlates to Distance in GAN Latent Space for Facial Images

Generative adversarial networks (GANs) generate high-dimensional vector spaces (latent spaces) that can interchangeably represent vectors as images. Advancements have extended their ability to computationally generate images indistinguishable from real images such as faces, and more importantly, to manipulate images using their inherit vector values in the latent space. This interchangeability of latent vectors has the potential to calculate not only the distance in the latent space, but also the human perceptual and cognitive distance toward images, that is, how humans perceive and recognize images. However, it is still unclear how the distance in the latent space correlates with human perception and cognition. Our studies investigated the relationship between latent vectors and human perception or cognition through psycho-visual experiments that manipulates the latent vectors of face images. In the perception study, a change perception task was used to examine whether participants could perceive visual changes in face images before and after moving an arbitrary distance in the latent space. In the cognition study, a face recognition task was utilized to examine whether participants could recognize a face as the same, even after moving an arbitrary distance in the latent space. Our experiments show that the distance between face images in the latent space correlates with human perception and cognition for visual changes in face imagery, which can be modeled with a logistic function. By utilizing our methodology, it will be possible to interchangeably convert between the distance in the latent space and the metric of human perception and cognition, potentially leading to image processing that better reflects human perception and cognition.