Reviewerly/exHarmony huggingface dataset:
High Capability and Low-Complexity: Novel Fault Detection Scheme for Finite Field Multipliers over GF(2<sup>m</sup>) based on MSPB

Fault detection is becoming more and more essential to the cryptographic circuits protection (for the purpose of fighting against both natural and malicious faults). While finite field multiplier is regarded as the bottleneck arithmetic unit for cryptosystems such as elliptic curve cryptography, efficient implementation of finite field multiplier with high fault detection capability is still missing in the literature. In this paper, therefore, we propose a novel fault detection scheme for finite field multipliers over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ), where the proposed work aims at obtaining high fault detection performance for finite field multipliers and meanwhile maintain low-complexity implementation. To successfully carry out the proposed design strategy, we have used the modified shifted polynomial basis (MSPB) to represent the field and have conducted three coherent interdependent stages of efforts: (i) a novel 1-bit parity based detection scheme for bit-serial MSPB multiplier is presented after thorough mathematical derivation; (ii) a novel Toeplitz matrix-vector product (TMVP)-based multi-bit parity detection scheme for digit-serial MSPB multiplier is proposed then to obtain both high detection performance and low-complexity implementation; (iii) detailed complexity analysis and comparison show that the proposed designs have significantly better performance over the best of existing ones. For instance, for the bit-serial multipliers, the proposed design (using 1 parity bit) can achieve around 99.49% fault detection performance while the best existing one with 2-bit parity checking scheme achieves only 75.12% fault detection. The proposed scheme, because of its high fault detection capability and low-complexity, can be extended further in many cryptographic applications.
Securing password using dynamic password policy generator algorithm

It is proposed to tackle the problem of password leakage of popular websites like Linked-In, Adobe, Gmail, Yahoo, eHarmony, etc. by using dynamic password policy and enhanced hash algorithm. Here, an algorithm is developed that will generate password policies dynamically depending on the frequency of characters. Time complexity is computed, and it is found that the algorithm works fast. Since the algorithm creates password policies dynamically, it will be tough for the attacker to guess the characteristics of the password database.
Research on context-aware group recommendation based on deep learning

In the field of artificial intelligence, the development of many technologies requires technical support for relational classification. Recently, deep learning has been applied more and more to text-based entity relationship classification tasks, but most of the previous methods need to use syntax or dependency structure feature. However, due to the time and space complexity of syntactic parsing, the structural features are inconvenient to use directly in the pre-processing stage. In addition, structural features may have serious domain dependence problems. This paper studies the current recommendation algorithm, analyzes the current research status of the recommendation system, and deeply analyzes the research of deep learning in the field of recommendation systems, based on BPSO algorithm, the context complex segmentation method is applied, and then the deep convolutional neural network is applied for feature extraction. The extracted feature set is sent to WordEmbedding, and using the technology to generates the word vector, the input layer of the CBOW is used to represent the size of the training window. The experimental results show that the model has obvious advantages over the methods proposed in other literature. It can adapt to multi-category context semantic analysis, more accurate related recommendations, and obtain a better user experience.
Track-to-track data fusion for Unmanned Traffic Management System

The growing need of large scale sensor networks for tracking of Unmanned Aerial Systems (UAS) results in high demand of effective algorithms which will provide automatic data fusion and produce human readable results. This paper presents a track-to-track data fusion system, where data from two independent sources are used to track Unmanned Aerial Vehicles (UAVs). The first source of data is Cooperative Surveillance System (CSS) trackers for UAVs, and the second source is Independent Non-cooperative Surveillance (INCS) from a ground based staring radar. The paper provides details on the whole process including: data pre-processing, association, analysis, fusion and output processing. Metrics and their influence on tracking results are also explained. Results of track-to-track data fusion of real-life experimental flight tests are provided.
A tensor voting based fractional-order image denoising model and its numerical algorithm

To take structure information into consideration, we propose a fractional-order image denoising model based on structure tensor. The existence and uniqueness of solution is proved. Furthermore, we also propose an alternating direction implicit (ADI) scheme of this model to find numerical solution and keep the stability of algorithm. Unconditional stability and convergence of this scheme is proved. Moreover, several numerical tests show that the proposed model can improve the denoising results of some published denoising models.
On the Relation Between the Concepts of Irreducible Testor and Minimal Transversal

This paper aims at studying the relationship between two rather relevant theoretic fields such as Graph Theory and Testor Theory, deepening in the unexploited relation between the concepts of Minimal Transversal and Irreducible Testor.First, the classic definitions of each concept are provided, and then the relation between them is shown and formalized.Some of the immediate consequences of this relation, in terms of the duality property of transversals and about the equivalence of the result of two specific algorithms, one from each field, are discussed.Finally, we also discuss several future research directions that arise from the relationship between the concepts of Minimal Transversal And Irreducible Testor, and the way in which those directions can potentially benefit the development of theory and algorithms for solving different practical problems in both areas.
Drone Flight Planning for Safe Urban Operations: UTM Requirements and Tools

This paper describes the requirements of a flight planning tool for safe urban operations, which may be used to collaboratively design flights considering traffic constraints and limitations according to an unmanned traffic management system. Representative examples of flight planning are described, as calculated by a prototype flight planning tool.
Deep learning and blockchain fusion for detecting driver's behavior in smart vehicles

Studies have been actively conducted on analyzing the driver's behavior inside the vehicle premises. Moreover, the transmission of the tempered proof multimedia content is also a major point of interest for the research community. At present, most of the techniques for detecting the distracted behavior of the driver is based on the detection of different face attributes like eyes and head posture etc, by using the traditional hand crafted features. In this paper we propose the deep learning based algorithm using the Convolution Neural Network. The proposed algorithm is independent of feature extraction of the specific parts, instead, it automatically picks the best features specific to the problem. We have utilized the State Form Distracted Driver Detection dataset to train our proposed algorithm. Furthermore, this paper also proposes a secure and tempered proof multimedia transaction. Original video data may be edited and fabricated with the false information. Multimedia blockchain can be helpful in tackling this problem. We have used Secure Hashing Algorithm (SHA‐256) algorithm for extracting the hashes of multimedia content. By utilizing the blockchain, we safely transmit the tempered proof video data coming from inside the vehicle, automatically detecting abnormal activities with our deep learning based algorithm. So, this paper combines the deep learning algorithms with blockchain techniques which is novel in research. Comparison between the results of proposed algorithm with the current state of the art work shows that proposed algorithm outperforms by achieving 86.02% accuracy on the test data.
A conceptual model and empirical assessment of HR security risk management

Purpose This study aims to develop a conceptual model and assess the extent to which pre-, during- and post-employment HR security controls are applied in organizations to manage information security risks. Design/methodology/approach The conceptual model is developed based on the agency theory and the review of theoretical, empirical and practitioner literature. Following, empirical data are collected through a survey from 134 IT professionals, internal audit personnel and HR managers working within five major industry sectors in a developing country to test the organizational differences in pre-, during- and post-employment HR security measures. Findings Using analysis of variance, the findings reveal significant differences among the organizations. Financial institutions perform better in employee background checks, terms and conditions of employment, management responsibilities, security education, training and awareness and disciplinary process. Conversely, healthcare institutions outperform other organizations in post-employment security management. The government public institutions perform the worst among all the organizations. Originality/value An integration of a conceptual model with HR security controls is an area that is under-researched and under-reported in information security and human resource management literature. Accordingly, this research on HR security management contributes to reducing such a gap and adds to the existing HR security risk management literature. It, thereby, provides an opportunity for researchers to conduct comparative studies between developed and developing nations or to benchmark a specific organization’s HR security management.
Progressive Image Enhancement under Aesthetic Guidance

Most existing image enhancement methods function like a black box, which cannot clearly reveal the procedure behind each image enhancement operation. To overcome this limitation, in this paper, we design a progressive image enhancement framework, which generates an expected "good" retouched image with a group of self-interpretable image filters under the guidance of an aesthetic assessment model. The introduced aesthetic network effectively alleviates the shortage of paired training samples by providing extra supervision, and eliminate the bias caused by human subjective preferences. The self-interpretable image filters designed in our image enhancement framework, make the overall image enhancing procedure easy-to-understand. Extensive experiments demonstrate the effectiveness of our proposed framework.
Efficient identity-based multi-bit proxy re-encryption over lattice in the standard model

Proxy re-encryption (PRE) can share ciphertext data without leaking the decryption key of the data owner in cloud computing. Because of the special property, PRE achieves good reliability and secrecy. Nevertheless, most of the proposed proxy re-encryption schemes are based on the number theoretic problem and cannot resist quantum attacks. In addition, most existing researches over lattice focus on the single bit encryption, which leads to low efficiency. Therefore, according to these problems, an efficient identity-based multi-bit proxy re-encryption over lattice in the standard model is constructed, which enlarges the plaintext space and improves the efficiency. What's more, the proposed multi-use and bidirectional scheme is proved to be CPA secure in the standard model.
Global-local divides and ontological politics: feminist STS perspectives on mobile learning for community health workers in Kenya

This theoretical paper argues that Feminist Science and Technology Studies (FSTS) can help advance the emancipatory project in critical Ed Tech research. To support this claim, we deploy Tsing’s concept of ‘scale-making projects’ (2005. Friction: An Ethnography of Global Connection. Princeton, NJ: Princeton University Press) to connect ‘global’ narratives to ‘local’ users in a mobile learning project for Kenyan health workers. Drawing from this exemplar case, we discuss more broadly how FSTS provides useful theory and methods for tracing the trans-national power relations of digital technologies ‘on the ground’. The paper concludes by advocating for new forms of emancipatory Ed Tech research – ones framed not only within oppositional pairings such as ‘global’ versus ‘local’, but which elucidate how binaries themselves are constituted through far-flung trans-national arrays of sociomaterial practice.
Self-reinforced diffusion for graph-based semi-supervised learning

Graph construction is the essential component of graph-based semi-supervised learning. Gaussian kernel weighted graph is widely used in this field. The problem of using such kind of graphs is that they are susceptible to data noise. They cannot approximate the geodesic distance on the underlying manifold appropriately, especially for high-dimensional data. Diffusion process has shown its effectiveness of learning pair-wise affinities which is equivalent to a graph, due to its capability of revealing the geometry structure of the manifold. However, data density distribution and label information are ignored in this process, limiting its application for semi-supervised problems. To address these issues, we propose a variant of the diffusion process, named Self-Reinforced Diffusion, which can make use of the label information. As for data density distribution, we introduce an intuitive affinity term, called self-affinity, which can well approximate density distributions and can be directly diffused on the graph. Extensive experiments on noisy synthetic data and various real-world data have demonstrated the effectiveness of the proposed method on semi-supervised learning.
A Comparative Application of Multi-criteria Decision Making in Ontology Ranking

The number of available ontologies on the web has increased tremendously in recent years. The choice of suitable ontologies for reuse is a decision-making problem. However, there has been little use of decision-making on ontologies to date. This study applies three Multi-Criteria Decision Making (MCDM) algorithms in ontology ranking. A number of ontologies/alternatives and the complexity metrics or attributes of these ontologies are used in the decision. The experiments are carried out with 70 ontologies and the performance of the algorithms are analysed and compared. The results show that all the algorithms have successfully ranked the input ontologies based on their degree/level of complexity. Furthermore, the results portray a strong correlation between the ranking results of the three MCDM algorithms, thereby, providing more insights on the performance of MCDM algorithms in ontology ranking.
Seeker

This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.
Talk to Me Intelligibly

Conversational interfaces (CIs) have the potential to empower a broader spectrum of users to independently conduct visual analysis. Yet, recent approaches do not fully consider the user's characteristics. In particular, the objective of matching the user's language has been understudied in visual analysis. In order to close this gap, we introduce an answer space motivated by Grice's cooperative principle for framing personalized communication in complex data situations. We conducted both an online survey (N=76) to analyze communication preferences and a qualitative experiment (N=10) to investigate personalized conversations with an existing CI. In order to match the user's language properly, our results suggest to consider additional user characteristics along with their knowledge level. While mismatching communication preferences triggers negative reactions, a preference-aligned communication evokes positive reactions. As our analysis confirms the importance of matching the user's language in visual analysis, we provide design implications for future CIs.
White-Box cryptography based data encryption-decryption scheme for IoT environment

The economic progress of the Internet of Things (IoT) is phenomenal. Applications range from checking the alignment of some components during a manufacturing process, monitoring of transportation and pedestrian levels to enhance driving and walking path, remotely observing terminally ill patients by means of medical devices such as implanted devices and infusion pumps, and so on. To provide security, encrypting the data becomes an indispensable requirement, and symmetric encryptions algorithms are becoming a crucial implementation in the resource constrained environments. Typical symmetric encryption algorithms like Advanced Encryption Standard (AES) showcases an assumption that end points of communications are secured and that the encryption key being securely stored. However, devices might be physically unprotected, and attackers may have access to the memory while the data is still encrypted. It is essential to reserve the key in such a way that an attacker finds it hard to extract it. At present, techniques like White-Box cryptography has been utilized in these circumstances. But it has been reported that applying White-Box cryptography in IoT devices have resulted in other security issues like the adversary having access to the intermediate values, and the practical implementations leading to Code lifting attacks and differential attacks. In this paper, a solution is presented to overcome these problems by demonstrating the need of White-Box Cryptography to enhance the security by utilizing the cipher block chaining (CBC) mode.
When Agile Means Staying

The design of software development methodologies primarily focuses on improving processes including accommodating changing user requirements, accelerating product delivery and productivity. However, we know little about why and how modern development methodologies impact individual IT professional outcomes. Drawing on concepts from the agile development literature and job characteristics theory, we hypothesize a relationship between agile development usage and IT professionals' stay intention. We examine job satisfaction as mediating the effect of agile development usage on stay intention. We test our hypotheses using a sample of 18,755 software developers drawn from the 2018 Stack Overflow developer survey. We find a positive relationship between agile development usage and stay intention. Also, job satisfaction fully mediates the effect of agile development usage on stay intention. We discuss the implications our findings have on research and practice.
Behavioral Evolution of Design Patterns: Understanding Software Reuse Through the Evolution of Pattern Behavior

Design patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying instances of pattern decay, or pattern grime, is valuable because it allows for proactive attempts to extend the longevity and reuse of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We constructed a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral grime, as well as pattern quality and size, across pattern evolutions pertaining to four design pattern types. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. We identified statistically significant relationships between behavioral grime and quality metrics, as well between behavioral grime and pattern size.
PROOF-THEORETIC ANALYSIS OF THE QUANTIFIED ARGUMENT CALCULUS

Abstract This article investigates the proof theory of the Quantified Argument Calculus (Quarc) as developed and systematically studied by Hanoch Ben-Yami [3, 4]. Ben-Yami makes use of natural deduction (Suppes-Lemmon style), we, however, have chosen a sequent calculus presentation, which allows for the proofs of a multitude of significant meta-theoretic results with minor modifications to the Gentzen’s original framework, i.e., LK. As will be made clear in course of the article LK-Quarc will enjoy cut elimination and its corollaries (including subformula property and thus consistency).
Detecting Topics in Documents by Clustering Word Vectors

The automatic detection of topics in a set of documents is one of the most challenging and useful tasks in Natural Language Processing. Word2Vec has proven to be an effective tool for the distributed representation of words (word embeddings) usually applied to find their linguistic context. This paper proposes the use of a Self-Organizing Map (SOM) to cluster the word vectors generated by Word2Vec so as to find topics in the texts. After running SOM, a k-means algorithm is applied to separate the SOM output grid neurons into k clusters, such that the words mapped into each centroid represent the topics of that cluster. Our approach was tested on a benchmark text dataset with 19,997 texts and 20 groups. The results showed that the method is capable of finding the expected groups, sometimes merging some of them that deal with similar topics.