Reviewerly/exHarmony huggingface dataset:
Implementing Lean Principles in Scrum to Adapt to Remote Work in a Covid-19 Impacted Software Team

Agile Frameworks are a mainstay of Software Engineering teams, with Scrum having risen to prominence over the last decade. Teams are experimenting more with variants of Scrum, with inspirations coming largely from other process improvement methodologies. However, the prevalent mode of executing Scrum assumes that the team are in colocation. In early 2020, a global pandemic has shifted the world unexpectedly into remote work. The heavyweight nature of Scrum is leading to fatigue within teams, as the remote nature of video conferencing and asynchronous communication bring an overhead not experienced before. The result is a weakening of the Scrum fundamentals in teams, with modifications being made to accommodate the new normal that teams are experiencing. Not all of the changes are positive and a lot of waste has emerged within teams. Lean principles can be applied to Scrum with minor adjustments and minimal friction. The end result is a variant of Scrum designed with remote teams in mind. This paper will explore some of the challenges remote teams are facing, the modifications proposed and the result of those changes in a remote Software Engineering Team.
Approximate normalization for gradual dependent types

Dependent types help programmers write highly reliable code. However, this reliability comes at a cost: it can be challenging to write new prototypes in (or migrate old code to) dependently-typed programming languages. Gradual typing makes static type disciplines more flexible, so an appropriate notion of gradual dependent types could fruitfully lower this cost. However, dependent types raise unique challenges for gradual typing. Dependent typechecking involves the execution of program code, but gradually-typed code can signal runtime type errors or diverge. These runtime errors threaten the soundness guarantees that make dependent types so attractive, while divergence spoils the type-driven programming experience. This paper presents GDTL, a gradual dependently-typed language that emphasizes pragmatic dependently-typed programming. GDTL fully embeds both an untyped and dependently-typed language, and allows for smooth transitions between the two. In addition to gradual types we introduce gradual terms , which allow the user to be imprecise in type indices and to omit proof terms; runtime checks ensure type safety . To account for nontermination and failure, we distinguish between compile-time normalization and run-time execution: compile-time normalization is approximate but total, while runtime execution is exact , but may fail or diverge. We prove that GDTL has decidable typechecking and satisfies all the expected properties of gradual languages. In particular, GDTL satisfies the static and dynamic gradual guarantees: reducing type precision preserves typedness, and altering type precision does not change program behavior outside of dynamic type failures. To prove these properties, we were led to establish a novel normalization gradual guarantee that captures the monotonicity of approximate normalization with respect to imprecision.
Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used in many engineering sciences and has attracted the attention of many scientists and researchers. In this issue, a salesman starts to move from a desired node called warehouse and returns to the starting place after meeting n customers provided that each customer is only met once. The aim of this issue is to determine a cycle with a minimum cost for this salesman. One of the major weaknesses of the PSO algorithm in the classical version is that it gets stuck in local optimizations. Therefore, in the proposed algorithm, called MPSO, the best solution in the current iteration is also used in the movement step. In addition, a variety of local search algorithms are provided that are used when better answers are generated than before. Also, a new method for moving the particle towards the best particle is presented, which, in addition to probably increasing the quality of the new answer, prevents the premature convergence of the algorithm due to consideration of the concept of random. The results evaluated with the results of several metaheuristic algorithms in the literature show the efficiency of the MPSO algorithm because it has been able to achieve excellent solutions in most of these instances.
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers

We introduce SELFEXPLAIN, a novel selfexplaining model that explains a text classifier's predictions using phrase-based concepts.SELFEXPLAIN augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label.Experiments across five text-classification datasets show that SELFEX-PLAIN facilitates interpretability without sacrificing performance.Most importantly, explanations from SELFEXPLAIN show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines. 1
360° Videos for Immersive Mental Health Interventions: a Systematic Review

Abstract Virtual reality is increasingly recognized as a powerful method for clinical interventions in the mental health field, but has yet to achieve mainstream adoption in routine mental healthcare settings. A similar, yet slightly different technology, immersive 360° videos might have the potential to cover this gap, by requiring both lower costs and less technical skills to construct and operate such virtual environments. This systematic review therefore aims to identify, evaluate, and summarize mental health interventions using immersive 360° videos to support an understanding of their implementation in daily clinical practice. The quality of the 14 selected studies was evaluated using a critical appraisal tool, addressing populations with clinical levels of psychopathological symptoms, somatic conditions associated with psychological implications, and other at-risk groups. Immersive 360° videos successfully increased users’ feelings of presence, given their realistic features, and therefore yielded positive outcomes in clinical interventions where presence is considered as an essential precondition. Because the technical skills required to create immersive 360° video footage are fairly limited, most of the interventions using this approach have been created by mental health researchers or clinicians themselves. Immersive 360° videos are still in an early phase of implementation as a tool for clinical interventions for mental health, resulting in high heterogeneity in focus, procedures, and research designs. An important next step for making use of this technology may therefore involve the creation of standardized procedures, as a means to increase the quality of research and evidence-based interventions.
Preserving Health Care Data Security and Privacy Using Carmichael's Theorem-Based Homomorphic Encryption and Modified Enhanced Homomorphic Encryption Schemes in Edge Computing Systems

With the tremendous growth of technology, providing data security to critical applications such as smart grid, health care, and military is indispensable. On the other hand, due to the proliferation of external data threats in these applications, the loss incurred is incredibly high. Standard encryption algorithms such as RSA, ElGamal, and ECC facilitate in protecting sensitive data from outside attackers; however, they cannot perform computations on sensitive data while being encrypted. To perform computations and to process encrypted query on encrypted data, various homomorphic encryption (HE) schemes are proposed. Each of the schemes has its own shortcomings either related to performance or with storage that acts as the barrier for applying in real-time applications. With that conception, our objective is to design HE schemes that are simple by design, efficient in performance, and highly unimpeachable against attacks. Our first proposed scheme is based on Carmichael's Theorem, referred to as Carmichael's Theorem-based Homomorphic Encryption (CTHE), and the second is an improved version of Gorti's Enhanced Homomorphic Encryption Scheme, referred to as Modified Enhanced Homomorphic Encryption (MEHE). For brevity, the schemes are referred to as CTHE and MEHE. Both the schemes are provably secure under the hardness of integer factorization, discrete logarithm, and quadratic residuosity problems. To reduce the noise in these schemes, the modulus switching method is adopted and proved theoretically. The schemes' efficiency is proven by collecting the data from cardiovascular dataset (statically)/blood pressure monitor (dynamically) and is homomorphically encrypted in the edge server. Further analysis on encrypted data is carried out to identify whether a person has hypotension or hypertension with the aid of parameters, namely, mean arterial pressure. As the schemes are probabilistic in nature, breaking the schemes by a polynomial time adversary is impossible and is proven in the article.
VolcanoML

End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VolcanoML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VolcanoML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VolcanoML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. Our evaluation demonstrates that, not only does VolcanoML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.
Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Abstract Background Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. Methods This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.
Multimodal Dialog System: Relational Graph-based Context-aware Question Understanding

Multimodal dialog system has attracted increasing attention from both academia and industry over recent years. Although existing methods have achieved some progress, they are still confronted with challenges in the aspect of question understanding (i.e., user intention comprehension). In this paper, we present a relational graph-based context-aware question understanding scheme, which enhances the user intention comprehension from local to global. Specifically, we first utilize multiple attribute matrices as the guidance information to fully exploit the product-related keywords from each textual sentence, strengthening the local representation of user intentions. Afterwards, we design a sparse graph attention network to adaptively aggregate effective context information for each utterance, completely understanding the user intentions from a global perspective. Moreover, extensive experiments over a benchmark dataset show the superiority of our model compared with several state-of-the-art baselines.
LCD and ACD codes over a noncommutative non-unital ring with four elements

We study LCD (linear complementary dual) and ACD (additive complementary dual) codes over a noncommutative non-unital ring E with four elements. This is the first attempt to construct LCD codes over a noncommutative non-unital ring. We show that free LCD codes over E are directly related to binary LCD codes. We introduce ACD codes over E. They include free LCD codes over E as a special case. These facts imply that LCD and ACD codes over E are worth studying. In particular, we characterize a free LCD E-code C in terms of a binary generator matrix G. We also define an ACD code over E, called a left-ACD code. We give several conditions for the existence of left-ACD codes.
Improved Path Planning for Indoor Patrol Robot Based on Deep Reinforcement Learning

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable.
Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms

One of the prominent uses of Predictive Analytics is Health care for more accurate predictions based on proper analysis of cumulative datasets. Often times the datasets are quite imbalanced and sampling techniques like Synthetic Minority Oversampling Technique (SMOTE) give only moderate accuracy in such cases. To overcome this problem, a two-step approach has been proposed. In the first step, SMOTE is modified to reduce the class imbalance in terms of Distance-based SMOTE (D-SMOTE) and Bi-phasic SMOTE (BP-SMOTE) which were then coupled with selective classifiers for prediction. An increase in accuracy is noted for both BP-SMOTE and D-SMOTE compared to basic SMOTE. In the second step, Machine learning, Deep Learning and Ensemble algorithms were used to develop a Stacking Ensemble Framework which showed a significant increase in accuracy for Stacking compared to individual machine learning algorithms like Decision Tree, Naïve Bayes, Neural Networks and Ensemble techniques like Voting, Bagging and Boosting. Two different methods have been developed by combing Deep learning with Stacking approach namely Stacked CNN and Stacked RNN which yielded significantly higher accuracy of 96-97% compared to individual algorithms. Framingham dataset is used for data sampling, Wisconsin Hospital data of Breast Cancer study is used for Stacked CNN and Novel Coronavirus 2019 dataset relating to forecasting COVID-19 cases, is used for Stacked RNN.
Securing E-Healthcare Images Using an Efficient Image Encryption Model

With the advancements in e-healthcare services, it is possible to provide remote medical services to patients and swifter first aid. Medical images play an essential role in e-healthcare applications for providing quick and better remote diagnosis and treatment to patients. Medical images generally comprise secret details about the patients and are therefore prone to various security threats during their transmission over public networks. Thus, it is required to secure these images prior to their communication over public networks. But due to distinctive properties of medical images, like higher correlation and redundancy among the pixels, and larger size, it is required to design an efficient encryption model to resist against various security threats. In this paper, an efficient encryption model for medical images is proposed. To obtain the secret keys, six-dimensional hyperchaotic map (SDHM) is proposed. Firstly, plain medical image is divided into three channels such as red, green, and blue. Secret keys are used to diffuse these channels. Lastly, encrypted channels are concatenated and final encrypted medical image is obtained. Extensive experiments are drawn by considering the benchmark medical images. Also, comparisons are performed among the proposed SDHM and competitive techniques by considering various performance metrics. Comparative analysis reveals that the proposed SDHM achieves remarkably good performance than the existing encryption models.
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization

In large-scale recommender systems, the user-item networks are generally scale-free or expand exponentially. For the representation of the user and item, the latent features (a.k.a, embeddings) depend on how well the embedding space matches the data distribution. Hyperbolic space offers a spacious room to learn embeddings with its negative curvature and metric properties, which can well fit data with tree-like structures. Recently, several hyperbolic approaches have been proposed to learn high-quality representations for the users and items. However, most of them concentrate upon developing the hyperbolic similitude by designing appropriate projection operations, whereas many advantageous and exciting geometric properties of hyperbolic space have not been explicitly explored. For example, one of the most notable properties of hyperbolic space is that its capacity space increases exponentially with the radius, which indicates the area far away from the hyperbolic origin is much more embeddable. Regarding the geometric properties of hyperbolic space, we bring up a Hyperbolic Regularization powered Collaborative Filtering (HRCF) and design a geometric-aware hyperbolic regularizer. Specifically, the proposal boosts optimization procedure via the root alignment and origin-aware penalty, which is simple yet impressively effective. Through theoretical analysis, we further show that our proposal is able to tackle the over-smoothing problem caused by the hyperbolic aggregation and also brings the models a better discriminative ability. We conduct extensive empirical analysis, comparing our proposal against a large set of baselines on several public benchmarks. The empirical results show that our approach achieves highly competitive performance and surpasses both the leading Euclidean and hyperbolic baselines by considerable margins. Further analysis verifies the rationality and effectiveness of the proposal for robust, deeper, and lightweight neural graph collaborative filtering.
Factors influencing cloud computing adoption for higher educational institutes in India: a fuzzy AHP approach

Information and communication technologies (ICT) are changing the ways of imparting education, providing learning opportunities and enhancing institutional performance. Cloud computing (CC) helps in lightening the burden of massive technology investments required for improving performance. However, CC adoption in higher educational institutes in India is still in nascent phase. Therefore, the objective is to identify and rank the factors enabling CC adoption in the context. Fuzzy analytic hierarchy process (FAHP) has been applied. Time to cater IT demand, security and relative advantage are found as the most critical factors. The findings will aid other institutes in adoption decision and gain competitive advantage.
Blockchain in accounting, accountability and assurance: an overview

Purpose This study aims to present an overview of topics addressed in the papers appearing in this AAAJ special issue: Blockchain in accounting, accountability and assurance. Design/methodology/approach The authors present a review focussing on the papers published in this special issue. The authors imported the eight accepted papers into NVivo, coding them according to the research topics posed in the call for papers. Then, the authors conducted an in vivo coding for the emerging themes found in the papers. Findings Blockchain is a multifaced topic with multiple implications for accounting, auditing and accountability, the accounting professions, and governance. However, blockchain is still a developing topic. Blockchain research traditionally has four stages. More recently, a new research stage deserving more investigation is emerging based on the interaction of blockchain with other technological developments such as virtual reality and the metaverse. Originality/value The review not only uncovers and systematises the multiple implications of blockchain for accounting research. It also unveils the dark side of blockchain, focusing on the technology's negative environmental and social implications. Last, the authors highlight why accounting research should more extensively examine contemporary issues.
Robots and public libraries

Purpose Humanoid robots that are artificially intelligent have infiltrated almost every aspect of life, including libraries, and can do things that humans are incapable of doing with greater efficiency in the library. It is on that note that this study has examined artificial intelligence (AI) humanoid robots for public libraries reference services. This study aims to discuss AI and draw the link between it and robotic technology. Design/methodology/approach This study adopted conceptual analysis and review of literature on AI humanoid robots for public libraries reference services. Findings This study focused not only on humanoid robots but also discovered that there are other types of robots relevant to libraries including chatbot, telepresence and shelve-reading robots as well as general robots such as aerospace robots, drones and army robots, commercial entertainment robots, industrial robots and medical-surgical robots. This paper also reported the usefulness of robots in libraries, and zero in on robots and reference services in the library. Lastly, this study reported the impact of humanoid robots on reference services in public libraries. Originality/value This paper is the original idea from the authors and does not reflect on any copyrighted materials. This study recommended among others that public libraries (in collaboration with their parent organisations) devise a strategic plan for new and emerging technologies such as humanoid robots.