Reviewerly/exHarmony huggingface dataset:
Building Most Effective Requirements Engineering Teams by Evaluating Their Personality Traits Using Big-Five Assessment Model

The requirement engineering team plays a vital role in alignment of software development in accordance with intended user requirements. The requirements engineering is considered as the most social and interactive process within the software development life cycle. The strong social and collaborative nature of RE process highlights and justifies its primary dependence on personality attributes/traits of its participants like requirements analysts and product stakeholders. The induction of right persons with right personality traits for RE teams greatly affects their work performance and consequently contributes positively in the improvement of whole software development process. This article presents a method to build most effective RE teams by considering their personality traits at the time of their induction. The job descriptions and soft skills announced by software industry for hiring requirements engineers have been analyzed critically and accordingly the most suitable personality traits have been predicted using a systematic method. The devised method initially maps different soft skills required to perform different tasks during RE phase and then re-maps desired soft skills with personality traits of software engineers. The big-five (also known as five-factor) personality assessment model has traditionally been used to identify the most suitable personalities for RE teamwork due to its wide acceptance in the software engineering domain. The obtained results show that the personality traits of openness, extraversion and conscientiousness are the most suitable personality traits for requirements engineering team members to perform RE tasks. These results are very much aligned with already established findings in domain of personality assessment of software engineering roles, which advocates the accuracy of devised method.
Estimates of the macroeconomic costs of cyber‐attacks

Abstract This paper estimates the macroeconomic losses related to the cyber‐attacks originating from the information and communications technology (ICT) and the financial sectors. The study accounts for the interdependency of various economic sectors and looks to the cascading effect of cyber‐attacks on production network in the United States and leading Organisation for Economic Co‐operation and Development countries with the help of the input–output methodology and the World Input–Output Database. Our results suggest that cyber‐attacks that affect the ICT and finance sectors result in losses which also impact different economic sectors, due to cascading effects.
Analysis of deep-ocean sediments from the TAG hydrothermal field (MAR, 26° N): application of short-wave infrared reflectance (SWIR) spectra for offshore geochemical exploration

The cost-efficient methods of analysis, such as rapid short-wave infrared (SWIR) spectral analysis, have been applied for the efficient exploration of critical raw materials (CRM), including mineral components and rare earth elements (REE) from the deep-ocean sediments. Gravity cored sediment samples were collected during an oceanographic mission to the Trans-Atlantic Geotraverse (TAG) hydrothermal field of the Mid-Atlantic Ridge (MAR, 26° N). SWIR reflectance spectra (dependent variable) of samples were mathematically tested against referent geochemical data (independent variable), obtained by conventional analysis (ICP/OES, ICP/MS), after applied full cross-validation multivariate partial least square regression (CVPLSR). Value of parameter-residual predictive deviation (RPD) was used for evaluation of CVPLSR modeling: RPD > 2.5 (satisfactory calibration model for the screening purposes) and RPD > 5.0 (model adequate for the quality control of the studied elements). The CVPLSR modeling provided significant results for the determination of several mineral components: major elements (Fe and Si) had the values of RPD equal to 3.65 and 2.84, respectively, which indicated a viable potential for their routine analysis, whereas RPD for Ca was equal to 5.51, thus assuring its quality control by SWIR analysis, in sediment samples of the studied location. Among the REE, Ce (RPD = 2.55) and Er (RPD = 2.59) yielded the most satisfactory results. The findings highlight the benefit of rapidly obtained empirical SWIR-reflectance data, which can be used for near-real-time exploration of geochemical deposits hosted in deep-ocean sediments.
An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.
Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.
Quantum algorithms for computational geometry problems

We study quantum algorithms for problems in computational geometry, such as Point-On-3-Lines problem. In this problem, we are given a set of lines and we are asked to find a point that lies on at least 3 of these lines. Point-On-3-Lines and many other computational geometry problems are known to be 3Sum-Hard. That is, solving them classically requires time Ω(n^{2-o(1)}), unless there is faster algorithm for the well known 3Sum problem (in which we are given a set S of n integers and have to determine if there are a, b, c ∈ S such that a + b + c = 0). Quantumly, 3Sum can be solved in time O(n log n) using Grover’s quantum search algorithm. This leads to a question: can we solve Point-On-3-Lines and other 3Sum-Hard problems in O(n^c) time quantumly, for c<2? We answer this question affirmatively, by constructing a quantum algorithm that solves Point-On-3-Lines in time O(n^{1 + o(1)}). The algorithm combines recursive use of amplitude amplification with geometrical ideas. We show that the same ideas give O(n^{1 + o(1)}) time algorithm for many 3Sum-Hard geometrical problems.
Quantum Computer: Hello, Music!

Quantum computing is emerging as a promising technology, which is built on the principles of subatomic physics. In spite of the continuous progress in the development of increasingly more sophisticated hardware and software, research in quantum computing has been focusing primarily on developing scientific applications. Up till now, there has been virtually no research activity aimed at widening the range of applications of this technology beyond science and engineering in particular applications for the entertainment industry and creative economies. This chapter introduces a new field of research, which we refer to as Quantum Computer Music. The research is aimed at the development of quantum computing tools and approaches to creating, performing, listening to and distributing music. Two examples of preliminary work combing quantum computing and music technology are presented: a singing voice synthesizer and a musical sequencer. A primer on quantum computing is also given.
Distinguishing between paradigmatic semantic relations across word classes: human ratings and distributional similarity

This article explores the distinction between paradigmatic semantic relations, both from a cognitive and a computational linguistic perspective. Focusing on an existing dataset of German synonyms, antonyms and hypernyms across the word classes of nouns, verbs and adjectives, we assess human ratings and a supervised classification model using window-based and pattern-based distributional vector spaces. Both perspectives suggest differences in relation distinction across word classes, but easy vs. difficult class–relation combinations differ, exhibiting stronger ties between ease and naturalness of classdependent relations for humans than for computational models. In addition, we demonstrate that distributional information is indeed a difficult starting point for distinguishing between paradigmatic relations but that even a simple classification model is able to manage this task. The fact that the most salient vector spaces and their success vary across word classes and paradigmatic relations suggests that combining feature types for relation distinction is better than applying them in isolation.
Sharing of Cyber Threat Intelligence between States

Novel environmental invasive biotechnologies, such as gene drives and Horizontal Environmental Genetic Alteration Agents exceed the classical applications of genetically modified organisms. The reason for this is that they are designed to transform wild organisms into genetically modified organisms which express desired traits. Instead of in a laboratory, this transformation takes place in the environment. The far-ranging effects that may be triggered by gene drive and Horizontal Environmental Genetic Alteration Agents require an extension of risk assessment to include socio-political consequences. The present article offers a first brief examination whether regulation is prepared for possible conflicts caused by benevolent or by hostile use of these novel technologies.
Image recoloring using specular decomposition and in-painting

Image recoloring using wavelet transform, in-painting and dichromatic reflection model is proposed. Detailed wavelet bands are passed through a sharpening filter whereas the approximation band is passed through dichromatic reflection model to extract body and specular coefficients. In-painting and white illumination minimization using erosion and Gaussian blur further enhance the image quality. The proposed technique produces better results, both qualitatively and quantitatively, in terms of edge preservation and minimization of noise, artefacts in contrast to state-of-art existing techniques.
Measurement-induced nonlocality quantified by Hellinger distance and weak measurements

In this article, we propose measurement-induced nonlocality (MIN) quantified by Hellinger distance using von Neumann projective measurement. The proposed MIN is a bonafide measure of nonlocal correlation and is resistant to local ancilla problem. We obtain an analytical expression of the Hellinger distance MIN for general pure and $2 \otimes n$ mixed states. In addition to comparing with similar measures, we explore the role of weak measurement in capturing nonlocal correlation.
On Vietnamese Sentiment Analysis: A Transfer Learning Method

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing (NLP) and text analysis to systematically identify, extract and quantify states and subjective information. Transfer learning (TL) [1] is a study in machine learning focusing on storing knowledge gained while solving one problem and applying it to a different but related problem. In NLP, recent results also demonstrated the effectiveness of models using pre-training on a language modeling task [2], [3]. The transfer learning based models help to rapidly increase understanding of words and sentences arrangement in which semantics and connections are easily grasped. In this paper, we present the results from applying BERT [16], a transfer learning method, in Vietnamese benchmark [17] for one of text classification problems, the Aspect Based Sentiment Analysis problem. The experiments were conducted on two data sets, named Hotel and Restaurant [17], in two task (A) Aspect Detection and (B) Aspect Polarity. The obtained results have outperformed some previous systems [18]-[20] in precision, recall, as well as the F1 measures.
AutoPath: Image-Specific Inference for 3D Segmentation

In recent years, deep convolutional neural networks (CNNs) has made great achievements in the field of medical image segmentation, among which residual structure plays a significant role in the rapid development of CNN-based segmentation. However, the 3D residual networks inevitably bring a huge computational burden to machines for network inference, thus limiting their usages for many real clinical applications. To tackle this issue, we propose AutoPath, an image-specific inference approach for more efficient 3D segmentations. The proposed AutoPath dynamically selects enabled residual blocks regarding different input images during inference, thus effectively reducing total computation without degrading segmentation performance. To achieve this, a policy network is trained using reinforcement learning, by employing the rewards of using a minimal set of residual blocks and meanwhile maintaining accurate segmentation. Experimental results on liver CT dataset show that our approach not only provides efficient inference procedure but also attains satisfactory segmentation performance.
Photonic quantum data locking

Quantum data locking is a quantum phenomenon that allows us to encrypt a long message with a small secret key with information-theoretic security. This is in sharp contrast with classical information theory where, according to Shannon, the secret key needs to be at least as long as the message. Here we explore photonic architectures for quantum data locking, where information is encoded in multi-photon states and processed using multi-mode linear optics and photo-detection, with the goal of extending an initial secret key into a longer one. The secret key consumption depends on the number of modes and photons employed. In the no-collision limit, where the likelihood of photon bunching is suppressed, the key consumption is shown to be logarithmic in the dimensions of the system. Our protocol can be viewed as an application of the physics of Boson Sampling to quantum cryptography. Experimental realisations are challenging but feasible with state-of-the-art technology, as techniques recently used to demonstrate Boson Sampling can be adapted to our scheme (e.g., Phys. Rev. Lett. 123, 250503, 2019).
Lived Experiences and Technological Literacy of Heart Failure Patients and Clinicians at a Cardiac Care Centre in Uganda

Digital health could serve as a low-cost means of enabling better self-care in patients living with heart failure (HF) in resource-limited settings such as Uganda. However, digital health interventions previously deployed in such settings have been unsuccessful due to a lack of local patient and clinician engagement in the design process.To engage Ugandan HF patients and clinicians regarding their experiences with HF management and technology, so as to inform the future design of a digital health intervention for HF patients in Uganda.The study employed a convergent parallel mixed-methods design. Data collection was completed at the Uganda Heart Institute in Kampala, Uganda. Data were ascertained through a patient survey and semi-structured interviews completed with HF patients, caregivers, physicians, and nurses. A conventional content analysis approach was used to qualitatively examine interview transcripts.Survey data were collected from 101 HF patients (62 female/39 male, aged 54.2 ± 17.5 years). Nearly half (48%) disagreed that they knew what to do in response to changes in their HF symptoms. Almost all patients (98%) had access to a mobile device. Many patients (63%) identified as comfortable in using mobile money - a local set of services that use Unstructured Supplementary Service Data (USSD). Interviews were completed with 19 HF patients, three caregivers, seven physicians, and three nurses. Qualitative analysis revealed four clusters of themes: overdependence of patients on the clinic, inconvenience associated with attending the clinic, inconsistent patient self-care behaviours at home, and technological abilities that favoured USSD-based services.Ugandan HF patients possess unmet information needs that leave them ill-equipped to care for themselves. Future digital health interventions for this population should empower patients with HF-specific information and reassurance in their self-care abilities. Based on patient preferences, such systems should harness USSD technology with which most patients are already comfortable.
The Memory-Tightness of Authenticated Encryption

This paper initiates the study of the provable security of authenticated encryption (AE) in the memory-bounded setting. Recent works – Tessaro and Thiruvengadam (TCC ’18), Jaeger and Tessaro (EUROCRYPT ’19), and Dinur (EUROCRYPT ’20) – focus on confidentiality, and look at schemes for which trade-offs between the attacker’s memory and its data complexity are inherent. Here, we ask whether these results and techniques can be lifted to the full AE setting, which additionally asks for integrity. We show both positive and negative results. On the positive side, we provide tight memory-sensitive bounds for the security of GCM and its generalization, CAU (Bellare and Tackmann, CRYPTO ’16). Our bounds apply to a restricted case of AE security which abstracts the deployment within protocols like TLS, and rely on a new memory-tight reduction to corresponding restricted notions of confidentiality and integrity. In particular, our reduction uses an amount of memory which linearly depends on that of the given adversary, as opposed to only imposing a constant memory overhead as in earlier works (Auerbach et al., CRYPTO ’17). On the negative side, we show that a large class of black-box reductions cannot generically lift confidentiality and integrity security to a joint definition of AE security in a memory-tight way.
Formation Control and Target Tracking for a Class of Nonlinear Multi-Agent Systems Using Neural Networks

This paper proposes a neural network-based backstepping controller to address the distance-based formation control problem and target tracking for a class of nonlinear multiagent systems in Brunovsky form using rigid graph theory. The radial basis function neural network (RBFNN) is used to ensure the system stability in the presence of unknown nonlinearity and disturbance in the system dynamics. A Lyapunov function is used to derive the neural network (NN) weights tuning law. The uniform ultimate boundedness (UUB) of the formation distance errors is rigorously proven based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is shown using the simulation results on a class of nonlinear multi-agent systems. A comparison between the proposed distance-based method and the existing displacement-based method is conducted to evaluate the performance of the proposed method.
Asynchronous SGD for DNN training on Shared-memory Parallel Architectures

We present a parallel asynchronous Stochastic Gradient Descent algorithm for shared memory architectures. Different from previous asynchronous algorithms, we consider the case where the gradient updates are not particularly sparse. In the context of the MagmaDNN framework, we compare the parallel efficiency of the asynchronous implementation with that of the traditional synchronous implementation. Tests are performed for training deep neural networks on multicore CPUs and GPU devices.
InterCriteria Analysis Implementation for Exploration of the Performance of Various Docking Scoring Functions

The present study describes an implementation of InterCriteria Analysis (ICrA) in the field of the computer-aided drug design and computational toxicology. ICrA strives to go beyond the nature of the criteria involved in a process of evaluation of multiple objects against multiple criteria, and, thus to discover some dependencies between the criteria themselves. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. In this study new software capabilities, implemented in order to apply ICrA to in silico drug design, are presented. As a case study, ICrA is implemented to explore the performance of various scoring functions in docking. Docking, which is the most commonly used structure-based drug design method, has been applied to predict the binding mode and to provide a measure for the ligand binding affinity to the protein. In particular, ligands of the peroxisome proliferator-activated nuclear receptor gamma (PPAR $$\gamma $$ ), involved in the control of a number of physiologically significant processes, have been investigated towards prediction of their binding to the protein. A dataset of 160 tyrosine-based PPAR $$\gamma $$ agonists with experimentally determined binding affinities has been used in this investigation. Docking combined with the in-house developed pharmacophore model as a filter has been applied. Various scoring functions and docking protocols have been tested in the molecular modelling platform MOE (v. 2019.01). ICrA has been applied to assess dependencies among the scoring functions. The analysis has demonstrated high positive consonance for two of the scoring functions – London dG and Alpha HB. None of the functions could be distinguished as a good predictor of the experimental binding affinity.
The Impact of the Advanced Technologies over the Cyber Attacks Surface

The article explores the changes are occurring in the security of the information and communication systems as a result of a state-of-the-art-technologies usage. The research is centered over the impact of new technologies on the cyber attack surface. The focus is on the synergy of a set of advanced technologies with existing digital environment and the consequences for cyber security particularly on the cyber attack surface extension. The study summarizes the evidences for increasing the surface area for attacks due to the advent of the listed advanced technologies. Explores how emerging technologies complicate cyber protection, introducing new risks as consequence from increasing possibilities for malicious threats. Indicate guidelines about future research related to refinement of legislation that is intended to regulate the cyber-physical world.
Free Rides in Logical Space Diagrams Versus Aristotelian Diagrams

In this paper we compare two types of diagrams for the representation of logical relations such as contradiction and contrariety, namely Logical Space diagrams (LSD) and Aristotelian diagrams (AD). The cognitive potential of Free Ride – defined in terms of tracking by consequence (Shimojima 2015) – is shown to hold for LSDs but not for ADs. The latter, however, do exhibit a greater inspection potential – defined in terms of tracking by correlation. The translational or informational equivalence between LSDs and ADs is contrasted to their lack of computational equivalence and their different degrees of iconicity.