Reviewerly/exHarmony huggingface dataset:
ECC Atomic Block with NAF against Strong Side-Channel Attacks on Binary Curves

Various side-channel attacks against elliptic curve cryptography (ECC) have been proposed so far, including simple power analysis, horizontal collision correlation analysis, improving the Big Mac attack, and differential power analysis. Developing countermeasures against such attacks is considered as an important research task in cryptography. They are executed by analyzing power consumption while a device implemented in a cryptosystem performs cryptographic processing. To address this problem, we propose using three atomic blocks serving as the countermeasures against such attacks on ECC over finite fields of characteristic two. Two of them are basic atomic blocks, while the third one is an improved version of these two, having lower computational cost. In this paper, concerning the possibility of more sophisticated side-channel attacks appearing in the future, we propose a threat model based on the atomic blocks that is constructed to be secure for strong side-channel attacks with more powerful abilities.
Study about the Aptitude-Treatment Interaction between Learning Using the e-Learning System and Learning Type of Learner

The low completion rate of e-learning, which has been considered as a problem since its inception, is recently attracting renewed attention as a problem yet to be solved.This study confirms the assumption that the effect of e-learning varies with the learner's learning type, and that a low e-learning completion rate results from the participation of learners who are unsuited to e-learning.It has been found that the differences between e-learning achievement rates can be classified into seven learning types.This suggests that e-learning may have little effect when the e-learning system does not match the learner's learning type.
Let the Data Speak: Using Rigour to Extract Vitality from Qualitative Data

Qualitative data can be gathered from an array of rich sources of research information. One of the popular ways to collect this data is by interviewing a range of experts on the topic, followed by transcription, resulting in a database of written documents, often supplemented by other documented data that informs the topic. Thematic or Content Analysis can then be used to explore the data and identify themes of meaning that enlighten the research topic, with the themes being gathered into nodes. The researcher now has an array of nodes, which needs to be organised into a coherent model, and more importantly, one that represents the views of the research informants. To do this with some degree of rigour, the researcher needs some way of ranking the nodes in terms of their relative importance. The node ranking can be based on experience, or on the literature, but neither of these approaches looks to the data itself. If the database contains new or unexpected knowledge, neither experience nor the literature will guide us to it, and vital new insights may easily be missed. The framework outlined in this paper aims to provide a sound first‑cut analysis of the data, based on the evidence in the research interviews themselves. Clearly the literature and research experience have an important role to play in shaping the results of any research. However this paper argues that one should proceed only after the data itself has been offered "the first chance to speak".The node classification matrix detailed here, identifies distinct node categories, each ranging in significance and with particular characteristics that reveal key aspects of the informants' views. In this way the researcher can use the nodes to reveal the voice of the experts, and build a scientifically rigorous set of results from a qualitative database.
Financing innovative economic development in Sub-Saharan Africa: the role of mobile payment systems

Electronic payments like mobile banking services have been associated with unprecedented access to financial services in economies, where a large section of the population was underfunded or underfunded due either due to lack of proximity to bank branches. However, the continuous development of electronic payment systems, especially mobile payment in sub-Saharan Africa has brought about a rise in volume of cashless transactions, thereby reducing the use of cash for payment. This therefore promotes financial integration among the segments of the population as it offers those without access to the formal banking system a safe and efficient payment alternative to cash. Also, electronic payment usage also has enormous benefits to governments as it will potentially help collect more tax revenue by providing a clear electronic trail and transparent transactions that can readily be taxed. Our study considers how mobile money services influence innovative economic growth. A linear regression analysis sub-Saharan African data for period 2011-2018 has been carried out. It has been revealed that mobile services and payments system have significant positive effect on economic growth, hence innovative development of the region. Based on these findings it is concluded that mobile payment services or systems are critical aspect of financial intermediation in sub-Saharan Africa as they offer them funds that can be drawn into the formal financial system from resources from both the population with bank accounts and the population without bank accounts, which can contribute to innovative economic growth in the region.
Black-Box Adversarial Attack against Deep Neural Network Classifier Utilizing Quantized Probability Output

Deep neural networks (DNNs) are vulnerable to welldesigned input samples, known as adversarial examples. In particular, an attack involving the generation of adversarial examples is called a black-box attack when an adversary attacks without any internal knowledge of the target network. In a simple black-box attack, adversarial perturbations are selected on the basis of changes in output probability when the input to the DNN is slightly changed. Output probability quantization has been proposed as a countermeasure against the simple black-box attack. In this work, we quantitatively evaluate the effectiveness of this protection method by using the image degradation index and propose a new black-box attack that can overcome the output probability quantization. We conducted experiments to generate adversarial examples using the MNIST public dataset. In the conventional method, if the fourth digit after the decimal point of the output probability is truncated, perturbations that can easily be recognized by humans appear in the adversarial example, and the attack ability decreases. With the new attack method, we find that adversarial examples can be generated with a sufficiently small degradation even if the output probability is truncated after the second decimal place. This demonstrates that the output probability quantization countermeasure against the simple black-box attack is not effective.
Dialect Diversity in Text Summarization on Twitter

Extractive summarization algorithms can be used on Twitter data to return a set of posts that succinctly capture a topic. However, Twitter datasets have a significant fraction of posts written in different English dialects. We study the dialect bias in the summaries of such datasets generated by common summarization algorithms and observe that, for datasets that have sentences from more than one dialect, most summarization algorithms return summaries that under-represent the minority dialect. To correct for this bias, we propose a framework that takes an existing summarization algorithm as a blackbox and, using a small set of dialect-diverse sentences, returns a summary that is relatively more dialect-diverse. Crucially, our approach does not need the sentences in the dataset to have dialect labels, ensuring that the diversification process is independent of dialect classification and language identification models. We show the efficacy of our approach on Twitter datasets containing posts written in dialects used by different social groups defined by race, region or gender; in all cases, our approach leads to improved dialect diversity compared to the standard summarization approaches.
An Architecture for Sharing Cyber-Intelligence Based on Blockchain

Cyber-intelligence sharing can leverage the development and deployment of security plans and teams within organizations, making infrastructures resilient and resistant to cyberattacks. To be efficient, information sharing should be performed in a trusted environment, ensuring both the integrity, privacy and confidentiality and the truthfulness and usefulness of the information. This paper addresses this issue with the development and deployment of an architecture based on blockchain technology. Each participant is granted a reputation level, that is used to assess and verify the information other actors produce. Each actor, then, is given an amount of credit, corresponding to the number and accuracy of the validation. Information is also organized in topics, instantiated in independent ledgers. The architecture was validated with a three organization scenario, for proof-of-concept.
Zonal integration of circular Hartmann and Placido patterns with non-rotationally symmetric aberrations

Placido disk methods for corneal topography use a target with concentric rings in order to obtain measurements of the corneal surface, codifying the topography from the deformations of the rings’ image. Knowing exactly how the corneal surface departs from a rotational symmetric shape is difficult by using Placido rings. This is due to the fact that any ray deviations in the angular direction (sagittal transverse aberrations) are not easily detected and measured. This is the so-called skew ray error. For that reason, this technique has been considered as limited, especially when one tries to measure corneal aberrations with large rotational symmetry errors. However, we considered that the Placido disk topography has the potential to obtain a full description of the corneal surface as long as the skew ray error is fixed. Here, we present a solution based in the assumption that a corneal topography calculated with the presence of the skew ray error has hidden information that can be extracted by applying some basis of the classical Hartmann test. To achieve that solution, we improve some aspects of the Hartmann test to be later applied in the processing of Placido disk images. Our solution gives us the ability to solve the skew ray error in a simple and direct method, with an effectiveness that is probed by the computing of some simulated representative surfaces without rotational symmetry and the performance of our algorithm.
An Efficient MQ-based Signature with Tight Security Proof

At PKC 2018, Chen et al. proposed SOFIA, the first MQ-based digital signature scheme having tight security in the quantum random oracle model (QROM). SOFIA is constructed by applying an extended version of the Unruh transform (EUROCRYPT 2015) to the mathcal MQ-based 5-pass identification scheme (IDS) proposed by Sakumoto et al. (CRYPTO 2011). In this paper, we propose an MQ-based 3-pass IDS with impersonation probability of 1/2 and apply the original version of the Unruh transform to it to obtain a more efficient MQ-based digital signature scheme tightly secure in the QROM. The signature size of our digital signature scheme decreases by about 35% compared with SOFIA in the level I of NIST PQC security category, and is supposed to be the shortest among that of MQ-based signatures tightly secure in the QROM.
Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization

Thanks to the rapid development of hyperspectral sensors, hyperspectral videos (HSV) can now be collected with high temporal and spectral resolutions and utilized to handle invisible dynamic monitoring missions, such as chemical gas plume tracking. However, using such sequential large-scale data effectively is challenged, because the direct process of these data requires huge demands in terms of computational loads and memory. This paper presents a key-frame and target-detecting algorithm based on cumulative tensor CANDECOMP/PARAFAC (CP) factorization (CTCF) to select the frames where the target shows up, and a novel super-resolution (SR) method using sparse-based tensor Tucker factorization (STTF) is used to improve the spatial resolution. In the CTCF method, the HSV sequence is seen as cumulative tensors and the correlation of adjacent frames is exploited by applying CP tensor approximation. In the proposed STTF-based SR method, we consider the HSV frame as a third-order tensor; then, HSV frame super-resolution problem is transformed into estimations of the dictionaries along three dimensions and estimation of the core tensor. In order to promote sparse core tensors, a regularizer is incorporated to model the high spatial-spectral correlations. The estimations of the core tensor and the dictionaries along three dimensions are formulated as sparse-based Tucker factorizations of each HSV frame. Experimental results on real HSV data set demonstrate the superiority of the proposed CTCF and STTF algorithms over the comparative state-of-the-art target detection and SR approaches.
Social and Environmental Competence as a Component of University Graduate’s Competitiveness

In the modern world of innovative and digital technologies, one of the priorities of the country’s strategic development is the transition to environmentally friendly and resource-saving energy, where research of the development of technological systems for climate and ecosystem management is particularly relevant. However, before starting a particular research in this scientific field, it is necessary to solve the main task of organizing professional training of specialists in the field of environmental education. It is important to create a set of measures for the effective formation of socio-environmental competence among students of higher education institutions. Based on our previous research of practical pedagogical experience in developing a competitive specialist with certain professional knowledge and skills in the field of their professional activities, we have developed and implemented in the educational process a model for the formation of socio-environmental competence of students. Within the framework of this model, a set of diagnostic measures was carried out to identify interdisciplinary knowledge in students, including environmental knowledge, as well as their further relationship with the formation of a professionally successful career.
Corrections to “Electronic Regulation of Data Sharing and Processing Using Smart Ledger Technologies for Supply-Chain Security” [doi: 10.1109/TEM.2020.2965991]

Presents corrections to author information in the above named article.
Collocated Wearable Interaction for Audio Book Application on Smartwatch and Hearables

This paper proposes a wearable audio book application using two wearable devices, a smartwatch and a hearables.We review requirements of what could be a killer wearable application and design our application based on these elicited requirements.To distinguish our application, we present 7 scenarios and introduce several wearable interaction modalities.To show feasibility of our approach, we design and implement our proof-of-concept prototype on Android emulator as well as on a commercial smartwatch.We thoroughly address how different interaction modalities are designed and implemented in the Android platform.Lastly, we show latency of the multi-modal and alternative interaction modalities that can be gracefully handled in wearable audio application use cases.
Assessing the quality of the requirements specification by applying GQM approach and using NLP tools

Software requirements are quite difficult to measure in terms of quality without reviews and subjective opinions of stakeholders. Quality assessment of specifications in an automated way saves project resources and prevents future latent defects in software. Requirements quality can be evaluated based on a huge variety of attributes, but their meaning is quite vague without any mapping to specific measurement metrics. Application of goal-question-metric (GQM) approach in the quality model helps to choose the most important quality attributes and create a mapping with metrics, which can be collected and calculated automatically. Text of software requirements written in natural language can be analyzed by NLP tools due to identify weak signle words and phrases, which make statements ambiguous. Metrics for such quality attributes as ambiguity, singularity, subjectivity, completeness, and readability are proposed in this work. The quality model was implemented in a prototype by adopting natural language processing techniques for requirements written in the Russian language with the support of external API.
APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK AND A KOHONEN NETWORK FOR ACCELERATED DETECTION AND RECOGNITION OF OBJECTS IN IMAGES

One of the most effective ways to improve the accuracy and speed of algorithms for searching and recognizing objects in images is to pre-select areas of interest in which it is likely to detect objects of interest. To determine areas of interest in a pre-processed radar or satellite image of the underlying surface, the Kohonen network was used. The found areas of interest are sent to the convolutional neural network, which provides the final detection and recognition of objects. The combination of the above methods allows to speed up the process of searching and recognizing objects in images, which is becoming more expedient due to the constantly growing volume of data for analysis. The process of preliminary processing of input data is described, the process of searching and recognizing patterns of aircraft against the underlying surface is presented, and the analysis of the results is carried out. The use of the Kohonen neural network makes it possible to reduce the amount of data analyzed by the convolutional network by 18–125 times, which accordingly accelerates the process of detection and recognition of the object of interest. The size of the parts of the input image fed into the convolutional network, into which the zones of interest are divided, is tied to the image scale and is equal to the size of the largest detectable object, which can significantly reduce the training sample. Application of the presented methods and centering of the object on training images allows accelerating the convolutional network training by more than 5 times and increasing the recognition accuracy by at least 10%, as well as minimizing the required minimum number of layers and neurons of the network by at least halving, respectively increasing its speed
From graph theory to nano topology

The basic objective of this paper is to study and investigate some properties of nano-topological space induced by a graph theory .We discuss a new measure generated by nano topology.In addition, we apply the connections between a digraph theory and a nano topology in the urinary system as one medical application.
Cluster Usage Policy Enforcement Using Slurm Plugins and an HTTP API

Managing and limiting cluster resource usage is a critical task for computing clusters with a large number of users. By enforcing usage limits, cluster managers are able to ensure fair availability for all users, bill users accordingly, and prevent the abuse of cluster resources. As this is such a common problem, there are naturally many existing solutions. However, to allow for greater control over usage accounting and submission behavior in Slurm, we present a system composed of: a web API which exposes accounting data; Slurm plugins that communicate with a REST-like HTTP implementation of that API; and client tools that use it to report usage. Key advantages of our system include a customizable resource accounting formula based on job parameters, preemptive blocking of user jobs at submission time, project-level and user-level resource limits, and support for the development of other web and command-line clients that query the extensible web API. We deployed this system on Berkeley Research Computing’s institutional cluster, Savio, allowing us to automatically collect and store accounting data, and thereby easily enforce our cluster usage policy.
SEMANTIC SIMILARITY MEASUREMENT FOR MALAY WORDS USING WORDNET BAHASA AND WIKIPEDIA BAHASA MELAYU: ISSUES AND PROPOSED SOLUTIONS

What is the similarity between ‘car’ and ‘automobile’? How many similarities are shared by these two words? This equation can be easily evaluated by humans but not by computers. Human language is very complicated and ambiguous. This ambiguity is a barrier that separates human understanding with computer comprehension. Semantic similarity between words is a very important task and widely practiced in the field of natural language processing. In this article, some issues regarding semantic similarity for Malay language using two Malay lexical resources (WordNet Bahasa and Wikipedia Bahasa Melayu) are discussed. Then, some solutions to solve the arising issues are proposed. An experiment was done to evaluate the performance of WordNet Bahasa and Wikipedia Bahasa Melayu on the coverage of semantic information for 150 Malay translated words (75 word-pairs). The result showed that the WordNet Bahasa and Wikipedia Bahasa Melayu are capable to be adapted to literature techniques. For WordNet Bahasa, we tested the coverage of WordNet Bahasa based on three word-levels (stem level, root level and mix level) to find the most applicable word level as our dataset. This is because WordNet Bahasa is a limited resource and some of the compound words cannot be match with the lemma in its database. The test indicated that the mix level of translated words outperformed the stem level and root level with 86.7% compared to stem level (78.7%) and root level (68.0%). For Wikipedia Bahasa Melayu, we evaluated the coverage of three main features in its article (gloss definitions, hyperlinks and categories) where these features are important in some previous techniques. The result of this test was used to choose the best technique based on the coverage of these features. The results of the experiment revealed that the gloss definition feature gave full coverage (100%) for our 75 word-pairs input compared to hyperlinks and categories (88.0%).
Tracking Down Sources of Spoofed IP Packets

The lack of authentication in the Internet’s data plane allows hosts to falsify (spoof) the source IP address in packet headers, which forms the basis for amplification denialof-service (DoS) attacks. Current approaches to locate sources of spoofed traffic lack coverage or are not deployable today. We propose a mechanism that a network with multiple peering links can use to coarsely locate the sources of spoofed traffic in the Internet. More precisely, the network can monitor and map spoofed traffic arriving on a peering link to the set of sources routed toward that link. We propose mechanisms the network can use to systematically vary BGP announcement configurations to induce changes to Internet routes and to the set of sources routed to each peering link. A network using our technique can correlate observations over multiple configurations to more precisely delineate regions sending spoofed traffic. Evaluation of our techniques on the Internet shows that they can partition the Internet into small regions, allowing targeted intervention.