Reviewerly/exHarmony huggingface dataset:
Tighter constraints of multiqubit entanglement for negativity

We provide a characterization of multiqubit entanglement monogamy and polygamy constraints in terms of negativity. Using the square of convex-roof extended negativity (SCREN) and the Hamming weight of the binary vector related to the distribution of subsystems proposed in Kim (Phys Rev A 97:012334, 2018), we provide a new class of monogamy inequalities of multiqubit entanglement based on the $$\alpha $$th power of SCREN for $$\alpha \ge 1$$ and polygamy inequalities for $$0\le \alpha \le 1$$ in terms of squared convex-roof extended negativity of assistance (SCRENoA). For the case $$\alpha <0$$, we give the corresponding polygamy and monogamy relations for SCREN and SCRENoA, respectively. We also show that these new inequalities give rise to tighter constraints than the existing ones.
Information Theoretic Security for Shannon Cipher System under Side-Channel Attacks †

We consider the secure communication problem for broadcasting of two encrypted sources. The sender wishes to broadcast two secret messages via two common key cryptosystems. We assume that the adversary can use the side-channel, where the side information on common keys can be obtained via the rate constraint noiseless channel. To solve this problem we formulate the post encryption coding system. On the information leakage on two secrete messages to the adversary, we provide an explicit sufficient condition to attain the exponential decay of this quantity for large block lengths of encrypted sources.
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks

In neural networks with binary activations and or binary weights the training by gradient descent is complicated as the model has piecewise constant response. We consider stochastic binary networks, obtained by adding noises in front of activations. The expected model response becomes a smooth function of parameters, its gradient is well defined but it is challenging to estimate it accurately. We propose a new method for this estimation problem combining sampling and analytic approximation steps. The method has a significantly reduced variance at the price of a small bias which gives a very practical tradeoff in comparison with existing unbiased and biased estimators. We further show that one extra linearization step leads to a deep straight-through estimator previously known only as an ad-hoc heuristic. We experimentally show higher accuracy in gradient estimation and demonstrate a more stable and better performing training in deep convolutional models with both proposed methods.
Audit Opinion Prediction: A Comparison of Data Mining Techniques

ABSTRACT This study compares the ability of four data-mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' various financial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by the RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent. JEL Classifications: M42.
Brand conversation: Linguistic practices on social media in the light of face-work theory

The rise of social media has resulted in brand–consumer interaction and more frequent conversations between consumers and brand representatives. To better understand how this ‘brand conversation’ occurs, our research employs face-work theory and explores ambivalent consumer perceptions towards brand linguistic practices. Specifically, our results show how ‘threatening’ messages (according to face-work theory) can be accepted by consumers, while ‘flattering’ messages can be rejected. These paradoxical situations can be explained by brand relationships and the commercial and symbolic brand status. By identifying the specific features of brand–consumer interactions in the light of face-work, we propose a decision-making tool for brand management and community management practices.
Identification of phishing websites through hyperlink analysis and rule extraction

Purpose The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately. Design/methodology/approach Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F -measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model. Findings Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance. Originality/value Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.
Improving Robustness via Risk Averse Distributional Reinforcement Learning.

One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies. Robustness is critical when the policies are trained in simulations instead of real world environment. In this work, we propose a risk-aware algorithm to learn robust policies in order to bridge the gap between simulation training and real-world implementation. Our algorithm is based on recently discovered distributional RL framework. We incorporate CVaR risk measure in sample based distributional policy gradients (SDPG) for learning risk-averse policies to achieve robustness against a range of system disturbances. We validate the robustness of risk-aware SDPG on multiple environments.
Learning to Predict Superquadric Parameters From Depth Images With Explicit and Implicit Supervision

Reconstruction of 3D space from visual data has always been a significant challenge in the field of computer vision. A popular approach to address this problem can be found in the form of bottom-up reconstruction techniques which try to model complex 3D scenes through a constellation of volumetric primitives. Such techniques are inspired by the current understanding of the human visual system and are, therefore, strongly related to the way humans process visual information, as suggested by recent visual neuroscience literature. While advances have been made in recent years in the area of 3D reconstruction, the problem remains challenging due to the many possible ways of representing 3D data, the ambiguity of determining the shape and general position in 3D space and the difficulty to train efficient models for the prediction of volumetric primitives. In this article, we address these challenges and present a novel solution for recovering volumetric primitives from depth images. Specifically, we focus on the recovery of superquadrics, a special type of parametric models able to describe a wide array of 3D shapes using only a few parameters. We present a new learning objective that relies on the superquadric (inside-outside) function and develop two learning strategies for training convolutional neural networks (CNN) capable of predicting superquadric parameters. The first uses explicit supervision and penalizes the difference between the predicted and reference superquadric parameters. The second strategy uses implicit supervision and penalizes differences between the input depth images and depth images rendered from the predicted parameters. CNN predictors for superquadric parameters are trained with both strategies and evaluated on a large dataset of synthetic and real-world depth images. Experimental results show that both strategies compare favourably to the existing state-of-the-art and result in high quality 3D reconstructions of the modelled scenes at a much shorter processing time.
Lumen: A Case Study of Designing for Sustainable Energy Communities through Ambient Feedback

Within sustainable HCI research, we see a growing interest to study how designing interactive technology can improve the utilisation of renewable energy resources. In this case study, we explore the concept of energy communities and how technology can be designed to support people to cooperate around transitioning to a more sustainable use of electricity. To do so, we designed the Lumen prototype, which aims to support a small energy community in shifting domestic energy-consuming practices to align with times of high availability of sustainable energy. By creating awareness of current and future sustainable energy availability through an ambient feedback display, the Lumen prototype informs households about the community's consumption patterns. To obtain insights into how people understand and experience an energy community, we conducted a qualitative field study with three Danish households. Through our study, we found sustainable awareness and incentives materialised in the ambient display were amplified by the dynamics of the community. We conclude by discussing future directions for exploring how to design technology for energy communities.
Electronic Health Record Integration

The advancement in Information Technology has increased the adoption of Electronic Medical Record (EMR) systems in the healthcare industry. The improved access of readily available information in EMR provides a well-coordinated and collaborative effort that contributes greatly to the timeliness of care and better quality of healthcare delivery. However, the high volume of healthcare data that are being produced and shared have led to a need to condense data so that they are easily manageable and perceivable with higher performance and better storage efficiency than currently possible in traditional relational databases. Linked data is one of the many use cases which uses universal RDF/JSON format to describe things and to connect data from heterogeneous systems or open data for theoretical and advanced analytics. Recent literatures showed that the emerging NoSQL (Not Only SQL) databases offer significant advantages such as simple and automatic scaling, greater performance with high availability which address the limitations of relational databases. Yet, there are limited studies that have evaluated the applicability of NoSQL database systems in healthcare domain. The present study aimed to propose a novel way of visualizing and linking EMR data by developing a NoSQL graph database using Neo4j. Overall, the study demonstrated that NoSQL systems such as Neo4j graph database have many technical and financial advantages over relational database systems and are more suitable for data visualization as well as better data storage and management alternative for large scale data-intensive applications such as EMR database systems.
Investigating the Impact of Functional Size Measurement on Predicting Software Enhancement Effort Using Correlation-Based Feature Selection Algorithm and SVR Method

The software Functional Size Measurement (FSM) is one of the major factors affecting the effort estimation. Several FSM methods have been proposed since they are useful when development/ enhancement effort must be estimated. However, the greatest challenge for the project managers and other stakeholders is how to identify the effectiveness of an FSM method and select an accurate enhancement effort prediction model. There is only one 2nd generation FSM method- the Common Software Measurement International Consortium (COSMIC) method and four first-generation FSM, including the International Function Point Users Group (IFPUG) method. The main goal of this paper is to investigate the effectiveness of the first and the second FSM generations, respectively, IFPUG and COSMIC methods for sizing functional changes, and their use for predicting software enhancement maintenance effort. In this paper, the Correlation-based-Feature Selection (CFS) algorithm is combined with the Support Vector Regression (SVR) model. The dataset used for training and testing the prediction model is obtained from the International Software Benchmarking Standards Group (ISBSG) Release 12. To make comparisons between the impact of enhancement functional size generated from COSMIC and IFPUG methods on the enhancement effort prediction, two types of experiments were conducted, one with the use of the IFPUG and the other with the use of the COSMIC. Results show that the use of COSMIC functional change size measurement as input for predicting enhancement effort provides significantly better results than IFPUG in terms of MAE (Mean Absolute Error) = 0.0382 and Root Mean Square Error (RMSE) = 0.1082.
Unifying execution of imperative generators and declarative specifications

We present Deuterium---a framework for implementing Java methods as executable contracts. Deuterium introduces a novel, type-safe way to write method contracts entirely in Java, as a combination of imperative generators and declarative specifications (written in a first-order relational logic with transitive closure). Existing approaches are typically based on encoding both the specification and the program heap into a constraint language, and then using an off-the-shelf constraint solver---without any additional guidance---to search for a new program heap that satisfies the specification. Deuterium takes advantage of user-provided generators to prune the search space and reduce incurred overhead of constraint solving. Deuterium supports two ways of solving declarative constraints: SAT-based and search-based with in-memory state exploration. We evaluate our approach on a suite of data structures, established as a standard benchmark by prior work. Furthermore, we use random and sequence-based test generation to create a new benchmark designed to mimic realistic execution scenarios. Our results show that generators improve the performance of executable contracts and that in-memory state exploration is the algorithm of choice when heap sizes are small.
An ontology-improved vector space model for semantic retrieval

Purpose The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords. Design/methodology/approach In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model. Findings The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce. Originality/value This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.
The Influence of Technical and Social Factors in Mitigating Threats in a BYOD-Enabled Environment

As the adoption of bring your own device (BYOD) phenomenon by businesses and other sectors continues to grow. Employees find it desirable to use their personally owned mobile devices for work without the need to differentiate between their carrier services and their organizations' network. However, this practice makes such businesses vulnerable to various security threats. Nigeria banking institutions, like other businesses globally, have adopted the BYOD phenomenon. However, BYOD trend constitutes heterogeneity, and there is large diversity in mobile devices, hence the risk of corporate data being exposed to threats increases. Thus, this paper investigates the influence of technical and social threats as it relates to BYOD phenomenon in the banking institutions. Data was collected from the employees of four banks in Nigeria that supported BYOD trend. The study found out that there are some security threats that are integrated, and the existing security measures are not sufficient to mitigate those threats. Hence, this study has presented an integrated solution to curb these threats.
Moment Features based Violence Action Detection using Optical Flow

Instantaneous detection of violence is still an unsolved research problem although artificial intelligence lives its prosperous years.The severity of injury causes due to violence can be minimized by detecting violence in real time demands for effective violence detection.Various methods were previously proposed for violence detection which could not provide robust results due many challenges, i.e. noise, motion estimation, lack of appropriate feature selection, lack of effective classification approach, complex background and variations in illumination.This research proposes an efficient method for violence detection using moment features to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest.This means probability for extraction of motion intensity is getting lost because of same colored object in the background is reduced and thus minimizes background complexity.After that, proposed method uses optical flow to calculate angles and linear distances in each frame.In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise.Finally, decision for violence is determined using random forest classifier from single feature vector by generating a set of probabilities for each class.Proposed research performed extensive experimentation where accuracy rate of 99.12% was achieved using frame rate of 35 fps which is higher comparing with previous research results.Experimental results reveal the effectiveness of the proposed methodology.
Digital marketing effect to intention to domestic tourism during COVID-19 in Jordan

The pandemic COVID-19 proved as a great challenge for today world. The “Hashemite Kingdom of Jordan” timely control it due it’s aggressive actions against pandemic COVID-19, in contrast with other “MENA” countries. Where the government encouraged domestic tourism to reduce the burden on this sector. The purpose of this study is to lay emphasis on the Jordan domestic tourism in light of the investigation as to how digital marketing influence intention to domestic tourism with the moderating role of price, and the role of mediator perceived ease of use (PEOU). In order to achieve the objective, the study has adopted quantitative technique of analysis. The study has initially collected the primary data through questionnaire. The data were collected from 377 respondents (customers want to do domestic tourism) by using a convenience sampling technique. In the second step the collected data is analyzed by using statistical tools i.e. SPSS and AMOS. The outcomes of the “structural equation modeling”, concluded that digital marketing has significant relationship with PEOU and intentions to domestic tourism. Moreover; PEOU has a significant relationship with intentions to domestic tourism. Additionally, the PEOU also work as a mediating factor in association between the digital marketing, and intention to domestic tourism. Hypothesis on moderation effects were not justified. The outcomes of such study are useful to the domestic tourism industry for identifying tourists' needs during COVID-19 pandemic.
Flipped classroom and digitization: an inductive study on the learning framework for 21st century skill acquisition

While increasingly demands have been made on Higher Education Institutions for revamping curriculum designs and instructional pedagogies to adapt to the 21st century digitized learning and skill environment, the COVID-19 pandemic underscored this adaption as the singular solution to maintain the learning continuum.The inverted or flipped classroom has emerged as an enabling learning framework offering a convergence of technological advancements with active and collaborative learning.This study investigates the correlation between the flipped learning pedagogy in higher education and the acquisition of 21st century skills in the current digitized environment to meet Industry 4.0 readiness landscape.Primary data for this study was collected using a scientifically deployed mixed methods survey research and in-depth interviews from undergraduate students across universities to get their input on what they experienced and perceived as learners through the classroom flip experience.Until today, there has been lack of focus on Higher Education in the literature on an effective pedagogy for cultivating 21 st century skills.This is an original research work which presents a holistic view of what elements are offered by 'flipped learning' as key to the development of 21 st century skills and competences considering the demands of the fourth industrial revolution (IR 4.0) from Higher Education Institutions, for creation of a job resilient workforce.The findings of the study showed that the flipped classroom model was experientially and perceptibly preferred by undergraduates for the acquisition of 21 st century skills and competencies needed for industry 4.0 readiness amongst other reasons.The results of this study have implications for students, faculty and higher education institutions.
Casting the Dark Web in a New Light: A Value-Chain Lens Reveals a Growing Cyber Attack Ecosystem and New Strategies for Combating It.

Cyber attacks are increasing in frequency, sophistication, and impact. Defending against them requires a new perspective on the attacks and the attackers. By applying a value-chain lens to the problem, we can better understand the dark web as an ecosystem in which well-orchestrated attacks are assembled by entrepreneurs and supported by well-organized service offerings. This casts new light on the dark web and suggests more effective and proactive responses to cyber attacks.