Reviewerly/exHarmony huggingface dataset:
Lightweight Protection of Cryptographic Hardware Accelerators against Differential Fault Analysis

Hardware acceleration circuits for cryptographic algorithms are largely deployed in a wide range of products. The HW implementations of such algorithms often suffer from a number of vulnerabilities that expose systems to several attacks, e.g., differential fault analysis (DFA). The challenge for designers is to protect cryptographic accelerators in a cost-effective and power-efficient way. In this paper, we propose a lightweight technique for protecting hardware accelerators implementing AES and SHA-2 (which are two widely used NIST standards) against DFA. The proposed technique exploits partial redundancy to first detect the occurrence of a fault and then to react to the attack by obfuscating the output values. An experimental campaign demonstrated that the overhead introduced is 8.32% for AES and 3.88% for SHA-2 in terms of area, 0.81% for AES and 12.31% for SHA-2 in terms of power with no working frequency reduction. Moreover, a comparative analysis showed that our proposal outperforms the most recent related countermeasures.
Driving Cybersecurity Policy Insights From Information on the Internet

Cybersecurity policy analytics quantitatively evaluates the effectiveness of cybersecurity protection measures consisting of both technical and managerial countermeasures and is inherently interdisciplinary work, drawing on the concepts and methods from economics, business, social science, and law.
Identification of How Health Information Security Awareness (HISA) Influence in Patient’ Health Information Protection Awareness (PHIPA)

Securing the confidency of health information in health care industry has been a notable matter. Nursing students are still inexperienced and are in progress to thriving their individual worths and trusts on the significance of Health Information Security Awareness (HISA) and Patient' Health Information Protection Awareness (PHIPA). The objectives of this study is to identify the influence of HISA in PHIPA, to measure the quality of HISA and PHIPA of nursing students in Baturaja and to provide recommendations on HISA and PHIPA improvement on nursing education. Thirty-two questions have been designed in the questionnaire and 94 respondents valid data were continued to analysed using Partial Least Squares (PLS). Based on the analysis results, HISA influences nursing students' communication, management and referral factors in PHIPA in positive ways. And the measurement results shown that nursing students in Baturaja has a well quality of HISA and PHIPA. Our discovery in this study also supply implications for practice the nursing schools and the health care industry about planning extended curriculum and clinical practice guiding principle to simplify students' health information security awareness and patient' health information protection awareness.
Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach

Dimensionality reduction is an interesting area of research in data mining. An effective way to reduce dimensions is feature selection that removes irrelevant information meanwhile helping to understand the learning model better and improving prediction accuracy. In this paper, we face a challenge of filter methods to determine number of significant features that achieves better performance since filters don't evaluate performance based on accuracy but use certain criteria to rank features based on some scores. To handle this challenge, we proposed an effective hybrid approach for feature selection that is a filter-based method inspired by concepts of chi-square, Relief-F, and mutual information. It provides a score for each feature then specifies threshold value automatically based on dataset in use to select important subset of features used to build model, which reduces required execution time and amount of memory. Our proposed approach was analyzed empirically and theoretically to demonstrate its efficiency.
Data Mining in Sales Data Grouping

Abstract The purpose of making data mining is to make sales data neatly organized and based on sales data can see sales potential that can increase company profits. Selling goods requires a new method for analyzing data in sales and data mining is one way to analyze sales data created in an increasingly advanced business world. This study used a qualitative method and data collection techniques using literature reviews and observations with secondary data types. The results of this study are when processing data, there are many ways in data processing both in terms of data and in terms of processing. The compilation in subsequent processing becomes information that is easy to read and the data becomes the beginning of the information that we previously processed from several existing methods and data developed into information that is easy to read.
Age Invariant Face Recogntion Using Stacked Autoencoder Deep Neural Network

When it comes to computer vision, the most challenging and crucial field is Face recognition with latest technology system. There are plenty of research articles available, and had done lot of research in this domain to achieve the stability, but due to the timely Changes new challenges has been occurred with time and the research started towards to resolve the problem. As we have analyzed due to the certain changes of time and structure of face has reshaped with the effect of aging. So, various authors has discussed the face recognition without aging affect in plenty of articles, but all have some problems and low accuracy to identify the face. This research also describes face recognition with different face features like structure, shape, texture, size and many more. In this paper, we mainly considers two domains, face recognition and Age Invariant. The aim of this research is to include the face recognition work which does not achieved by previous researchers that does not discussed the significant methods, such as: Feature based, Appearance based and soft computing based method. To evaluate our technique, we used a large dataset known as Cross-Age Celebrity Dataset (CACD). This dataset contains more than 2000 Celebrities having 160,000 Images with age ranging from 16 to 62. We have used 1000 Images as input dataset for training and model evaluation. We proposed the Auto-encoder denoising deep neural Network, which extracts the 1000 features from image, and achieved 99.8% Accuracy.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.
Tightly Secure Chameleon Hash Functions in the Multi-user Setting and Their Applications

We define the security notion of strong collision resistance for chameleon hash functions in the multi-user setting (S-MU-CR security). We also present three specific constructions $$\mathsf {CHF}_{dl}$$ , $$\mathsf {CHF}_{rsa}$$ and $$\mathsf {CHF}_{fac}$$ of chameleon hash functions, and prove their tight S-MU-CR security based on the discrete logarithm, RSA and factoring assumptions, respectively. In applications, we show that tightly S-MU-CR secure chameleon hash functions can lift a signature scheme from (weak) unforgeability to strong unforgeability with a tight security reduction in the multi-user setting.
Side Channel Attack Resistance of the Elliptic Curve Point Multiplication using Gaussian Integers

Elliptic curve cryptography is a cornerstone of embedded security. However, hardware implementations of the elliptic curve point multiplication are prone to side channel attacks. In this work, we present a new key expansion algorithm which improves the resistance against timing and simple power analysis attacks. Furthermore, we consider a new concept for calculating the point multiplication, where the points of the curve are represented as Gaussian integers. Gaussian integers are subset of the complex numbers, such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this concept is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of a secure hardware implementation.
Estimating the performance of a Passive Multi-static Doppler-only Radar Network

Empirical large area testing is carried out to define the capability of a multi-static radar network originally presented in [1] for long-distance aircraft tracking. Local signal extraction is based on Doppler-only information from VHF illuminators of opportunity. The Extended Kalman Filter is used to track aircraft speed and position after track initialization. The validity of the tracks is verified with reference data based on Automatic Dependent Surveillance - Broadcast (ADS-B) information. The system can even detect space-borne tracks.
Employability Assessment of Agile Methods for Software Quality: An Empirical Case Study

The software industry is compelled to adapt with agile methodologies for their inherent software quality. This requires transition from traditional software engineering methods for an extended competitiveness which is successfully demonstrated in private sector; however, its potential benefits in the public sector are still unknown. In this paper, we assess employability of agile methods to improve software quality in large public sector software development companies. The choice of public sector is made due to its key role in sustainable e-Governance models to ensure uninterrupted services to the citizens. A survey of 28 questions, on software quality components (budget, time, faster debugging and correctness, changeability, easy testability and installability), is collected from the IT professionals of nine software development companies at WAPDA (water and power development authority), the largest public sector organization in Pakistan. The results conclude that these factors have more strong and positive relation to the software quality in agile development than traditional methods in public sector when the software product is to be developed, delivered, deployed and sustainably maintained in short span of time due to its inherent capability of easy debugging, testability and installability.
Mailto: Me Your Secrets. On Bugs and Features in Email End-to-End Encryption

OpenPGP and S/MIME are the two major standards for email end-to-end encryption. We show practical attacks against both encryption schemes in the context of email. First, we present a design flaw in the key update mechanism, allowing a third party to deploy a new key to the communication partners. Second, we show how email clients can be tricked into acting as an oracle for decryption or signing by exploiting their functionality to auto-save drafts. Third, we demonstrate how to exfiltrate the private key, based on proprietary mailto parameters implemented by various email clients. An evaluation shows that 8 out of 20 tested email clients are vulnerable to at least one attack. While our attacks do not target the underlying cryptographic primitives, they raise concerns about the practical security of OpenPGP and S/MIME email applications. Finally, we propose countermeasures and discuss their advantages and disadvantages.
Cuckoo inspired stacking ensemble framework for content‐based cybercrime detection in online social networks

Abstract In recent times, the topic of content‐based cybercrime has gained significant attention. It is the requirement of today's era for social media companies to meet the challenges faced in differentiating the oppressive content in both a precise and proficient manner, thereby securing their clients. This article proposes a novel cuckoo inspired stacking ensemble framework that is the integration of Cuckoo Search and several machine learning models. The proposed framework automatically seeks for near‐optimal combinations of classification techniques along with their tuning parameters for efficiently solving the problem of content‐based cybercrime detection. Four datasets obtained from Formspring, ASKfm, and Twitter are used for testing purposes. The experimental results showcased significant improvement in the performance of classification on all the datasets in comparison to state‐of‐art classification models. The success rate of the proposed model with the excellent recall is 0.984 via 10‐fold cross‐validation demonstrates its high efficiency and effectiveness.
Directional wavelet packets originating from polynomial splines

The paper presents a versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Discrete Fourier transforms (DFT) of qWPs are located in either positive or negative half-band of the frequency domain. Consequently, the DFTs of 2D qWPs, which are derived by the tensor products of 1D qWPs, occupy one of quadrants of 2D frequency domain. Such a structure of the DFT spectra of the 2D qWPs results in the directionality of their real parts. Due to the fact that the spectra of qWPs are well localized in the frequency domain, the shapes of real parts of the qWPs are close to windowed cosine waves oscillating in a variety of different directions with a variety of frequencies. For example, a set of the fourth-level qWPs comprises 314 different directions and 256 different frequencies. The above properties combined with a fast transform implementation make directional qWPs a strong tool for the application to a variety of image processing tasks such as restoration of degraded images and extraction of characteristic features from images to use them in deep learning. A few illustrations of successful application of the designed qWPs to image denoising, inpainting, and classification are given in the paper.
Influence of different fuzzy operators on analytical structure and variable gains of typical interval type-2 fuzzy PI controller

The fuzzy operator is one of the most important elements affecting the control performance of interval type-2 (IT2) fuzzy proportional-integral (PI) controllers. At present, the most popular fuzzy operators are product fuzzy operator and min() operator. However, the influence of these two different types of fuzzy operators on the IT2 fuzzy PI controllers is not clear. In this research, by studying the derived analytical structure of an IT2 fuzzy PI controller using typical configurations, it is proved mathematically that the variable gains, i.e., proportional and integral gains of typical IT2 fuzzy PI controllers using the min() operator are smaller than those using the product operator. Moreover, the study highlights that unlike the controllers based on the product operator, the controllers based on the min() operator have a simple analytical structure but provide more control laws. Real-time control experiments on a linear motor validate the theoretical results.
WHO IS THE CHASER IN CRYPTOCURRENCIES?

This paper explores the interactions between the Bitcoin (BTC) prices in the US and Chinese markets, by employing the bootstrap rolling window causality test. The results reveal that BTC prices behave differently across markets, and also vary with time, which subjects to the theory of price discovery. In other words, the BTC price in one market could precede the other, and vice versa, based on the information advantage. Markets that are more flexible (US) respond sensitively to information, thus, in order to induce the price changes in Chinese markets. The improvements in the economic conditions of the emerging markets have exerted an influential role in global markets. Since the Chinese market possesses a considerable amount of trading volumes, the BTC price in the US can be assumed to chase the BTC price in China. The lead–lag relationship between these two markets also reflects the acknowledgement of the aversion towards the risks involved in accepting BTC as a currency. However, knowing which market reacts the most quickly to new information could prove to be beneficial to regulators who aim to implement a particular BTC price, and, as a result, prevent any arbitrary prices, and eventually stabilize the financial market.
Role and task allocation framework for Multirobot collaboration with latent knowledge estimation

Summary In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi‐Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV).
Multi‐label learning on principles of reverse <scp>k‐nearest</scp> neighbourhood

Abstract In this article, we present a novel neighbourhood based multi‐label classifier, Multi‐label Learning on principles of Reverse k‐Nearest Neighbourhood (ML‐RkNN) where we estimate the neighbourhood of the points on the basis of their reverse k ‐nearest neighbourhood (RkNN). Through RkNN, for the same value of k , we get different number of neighbours for different instances and this happens adaptively according to the neighbourhood configuration of the points. The automatically adaptive neighbourhood helps us in better learning of the local configurations around the points. Our scheme also facilitates implicit handling of the local imbalances prevailing in the datasets by comparing the class distributions of the test points and their reverse nearest neighbours. This implicit and adaptive handling is particularly useful for multi‐label label datasets, whose labels are differentially imbalanced. Empirical study is performed on 10 real‐world multi‐label datasets considering five neighbourhood based multi‐label learners. Macro F 1 is used as the evaluating metric. The proposed method has given statistically superior and statistically comparable performances with respect to three and two comparing methods respectively. Additionally, we have explored the use of two different distance metrics, Euclidean and Jaccard in our scheme for nominal datasets.
Team building as an essential component of team-based learning

When studying at a higher educational institution, a Bachelor must acquire competencies that will allow them to be successful in their future profession. According to the Federal state educational standard, one of these competencies is the ability to work and realize their role in the team, to carry out social interaction. The article raises the issue of the relevance of the use of command-oriented training as a way of developing the designated competence with an undergraduate taking the “Applied Informatics” course, the stages of teamoriented training are considered. Special attention is paid to the “team building” stage, and the goals and objectives of roleplaying, goal-setting, interpersonal, taskoriented approaches to the organization of this stage are discussed. The examples of implementation of each approach in practice of the Bachelor Degree training course “Applied Informatics” are given.
A Bi-directional Relation Aware Network for Link Prediction in Knowledge Graph

Knowledge graph embedding technique aims to represent elements in knowledge graph, such as entities and relations, with numerical embedding vectors in semantic spaces. In general, an existing knowledge graph has relatively stable number of entities and directional relations before being updated. Though existing research has utilized relations of entities for link predication in knowledge graph, the relational directivity feature has not been fully exploited. Therefore, this paper proposes a bi-directional relation aware network (BDRAN) for representation learning, mining information based on directivity of relations in existing knowledge graphs. BDRAN leverages an encoder to capture features of entities in different patterns with diverse directional relations in entity representation level and semantic representation level. Besides, decoder is used to simulate interactions between entities and relations for precise representation learning. Experiments are conducted with widely used standard datasets including WN18RR, FB15k-237, NELL-995 and Kinship. The results present the improvement of BDRAN on the datasets, demonstrating the effectiveness of our model for link prediction.
An Instrumented Mobile Language Learning Application for the Analysis of Usability and Learning

Mobile applications for language learning (MALL) is a field that is at large dominated by translation-based learning approaches. Moreover, MALL feature a number of common practices that may not effectively address learning or may even increase the number of user errors. In this tool paper, we introduce a language learning application equipped with instrumentation code to collect data about user behavior and use such data in different ways. The most obvious use is to provide statistics and patterns of learning of the users, which can be used by users to adjust their learning approaches and by researchers to study learning processes and attitudes. For the benefit of the user collected data can be also exploited to drive the synthesis of exercises that best suit the user’s language level and learning approach and are not likely to cause usability errors. The main use of the application is, however, as a tool for research purposes. In fact, it is a tool for testing new forms of exercises and their combination on samples of users, thus providing valuable information for research in language learning as well as supporting the software development process of new MALL. Finally, an additional feature of the tool is the conversion of the collected data into a formal description of the user’s behaviour to be used for formal verification and validation purposes.