Reviewerly/exHarmony huggingface dataset:
GameViews

Various stakeholders in the sports domain rely on the analysis and presentation of sports data to derive insights. In particular, sportswriters construct game stories using statistical information; fans share their viewpoints based on the real-time stats while watching the game. In this paper, we explore how these stakeholders construct data-driven sports stories. We began by observing a sportswriter, then analyzed published sports stories, and characterized 1500 fan comments about particular sporting events. We found that their story needs were similar in some respects while quite different in others. Based on the findings, we implemented two exploratory prototypes: GameViews-Writers for sportswriters to quickly extract key game information and GameViews-Fans to support a real-time data-driven game-viewing experience for fans. We report insights from two user studies conducted with four professional sportswriters and eight sports fans, respectively. We discuss the results of these studies and present several avenues for future work.
Trends in the discovery of new minerals over the last century

Patterns in the discovery and description of new minerals over the last century emerge from a new database of 4046 mineral discovery reports (roughly ¾ of all known minerals). The number of new minerals discovered per year was steady over time from 1917 to the early 1950s, when it began a rapid increase punctuated by spikes in 1962–1969, 1978–1982, and 2008–2016, the last of which is probably still ongoing. A detailed breakdown of the technological, geographic, institutional, and other characteristics of mineral discovery in this data set elucidates factors leading to increases in mineral discovery. (1) The availability of instrumentation for a particular analytical technique has a far larger impact on the rate of its uptake in mineral discovery than the technique’s invention or computer automation. (2) Samples from mines, quarries, and resource exploration have produced around 2⁄3 of all new mineral discoveries due to geochemical peculiarity and good exposure; lunar and meteoritic samples have contributed relatively few new minerals. (3) Peralkaline intrusions and volcanic fumaroles are the next most productive sites of new mineral discovery. (4) Which countries host mineralogists who discover large numbers of new minerals have varied over time but is always a relatively small number (<20), and mineral discovery is highly concentrated in specific laboratories or workgroups. (5) Involvement of governmental organizations in new mineral discovery peaked in the aftermath of World War II and has since declined to almost nil, with new mineral discoveries now coming primarily from universities and similar academic institutions (75%) and from museums (25%). (6) The average number of authors on mineral discovery papers has risen from <1.5 in 1950 to >6 now and follows an exponential trend. (7) The average number of methods used to characterize new minerals has not changed significantly since 1960, and about half of new mineral descriptions are made using roughly the minimum of analyses required for a new mineral to be recognized. (8) A partial study of discredited or redefined minerals identified changes to nomenclature and classification as the primary causes for discreditation; failure to replicate analytical results is a distant second. Only five cases of fraudulent mineral discovery are known. This article presents the data underlying these analyses and discusses some possible reasons for the observed trends in the rate of new mineral discovery, as well as the implications for the history (and future) of mineralogy.
Reverse SQL Question Generation Algorithm in the DBLearn Adaptive E-Learning System

Using a traditional e-learning system, when teaching structured query language (SQL) queries in classical classrooms help instructors, to improve the students' SQL skills and learning effectiveness. However several problems in using e-learning as a teaching and learning assistant remain - such as difficulties in differences in learning ability and knowledge level. We solved these problems by applying an adaptation module to our e-learning system. However, we still found it required considerable effort to create enough exercises to make the adaptation effective enough. So, we developed a novel automatic question generating algorithm, named Reverse SQL Question Generation Algorithm (RSQLG), to automatically generate exercises (including both answer and question) from a source database. RSQLG reverses the traditional manual process used previously by instructors. Instead of creating questions and answers for them, RSQLG creates the answers first. The generated exercises are presented to students by applying question adaptation methodology based on student knowledge level in each supported learning objective. We evaluated the learning effectiveness of our approach by using outcome-based learning. After post-test to pre-test scores were compared, we found students using our system improved their scores by 26%. Consequently, the adaptive e-learning framework using RSQLG could be applied in any adaptive or traditional e-learning for a database course to benefit the instructors leading to less effort in exercise management and to improve the learning outcome from the students allowing as much practice as they need.
Component-Based Refinement and Verification of Information-Flow Security Policies for Cyber-Physical Microservice Architectures

Since cyber-physical systems are inherently vulnerable to information leaks, software architects need to reason about security policies to define desired and undesired information flow through a system. The microservice architectural style requires the architects to refine a macro-level security policy into micro-level policies for individual microservices. However, when policies are refined in an ill-formed way, information leaks can emerge on composition of microservices. Related approaches to prevent such leaks do not take into account characteristics of cyber-physical systems like real-time behavior or message passing communication. In this paper, we enable the refinement and verification of information-flow security policies for cyber-physical microservice architectures. We provide architects with a set of well-formedness rules for refining a macro-level policy in a way that enforces its security restrictions. Based on the resulting micro-level policies, we present a verification technique to check if the real-time message passing of microservices is secure. In combination, our contributions prevent information leaks from emerging on composition. We evaluate the accuracy of our approach using an extension of the CoCoME case study.
Multilabel Feature Extraction Algorithm via Maximizing Approximated and Symmetrized Normalized Cross-Covariance Operator

Multilabel feature extraction (FE) is an effective preprocessing step to cope with some possible irrelevant, redundant, and noisy features, to reduce computational costs and even improve classification performance. Original normalized cross-covariance operator represents a kernel-based nonlinear dependence measure between features and labels, whose empirical estimator is formulated as a trace operation including two inverse matrices of feature and label kernels with a regularization constant. Due to such a complicated expression, it is impossible to derive an eigenvalue problem for linear FE directly. In this paper, we approximate this measure using Moore-Penrose inverse matrix, linear kernel for feature space, and delta kernel for label space, and then symmetrize the entire matrix in the trace operation, resulting in an effective approximated and symmetrized representation. According to orthonormal projection direction constraints, maximizing such a modified form induces a novel eigenvalue problem for multilabel linear FE. Experiments on 12 data sets illustrate that our proposed method works the best, compared with seven existing FE techniques, according to eight multilabel classification performance metrics and three statistical tests.
Security in Cyber-Physical Systems

In this manuscript, we explore the network and cyber security challenges furthermore, issues of cyber or digital physical frameworks. (1) We epitomize the general work process of cyber or digital physical frameworks, (2) identify the conceivable vulnerabilities, assault issues, foes qualities and an arrangement of difficulties that are required to be addressed. A framework has been proposed for setting situation-apprehensive security structure for general digital or cyber physical frameworks with the implementation of biometrics.
Steered Microaggregation as a Unified Primitive to Anonymize Data Sets and Data Streams

As data grow in quantity and complexity, data anonymization is becoming increasingly challenging. On one side, a great diversity of masking methods, synthetic data generation methods, and privacy models exists, and this diversity is often perceived as unsettling by practitioners. On the other side, most of the anonymization methodology was designed for static, structured, and small data, whereas the current landscape includes big data and, in particular, data streams. We explore here a unified and conceptually simple anonymization approach, by presenting a primitive called steered microaggregation that can be tailored to enforce various privacy models on static data sets and also on data streams. Steered microaggregation is based on adding artificial attributes that are properly initialized and weighted in order to guide the microaggregation process into meeting certain desired constraints. To demonstrate the potential of this type of microaggregation, we show how it can be used to achieve kanonymity, t-closeness, l-diversity, and E-differential privacy in the context of static data sets; furthermore, we discuss how it can be used to achieve k-anonymity of data streams while controlling tuple reordering. Beyond its flexibility and theoretical appeal, steered microaggregation can drastically reduce information loss, as shown by our experimental evaluation.
Exploring Parallel Coordinates Plots in Virtual Reality

Parallel Coordinates Plots (PCP) are a widely used approach to interactively visualize and analyze multidimensional scientific data in a 2D environment. In this paper, we explore the use of Parallel Coordinates in an immersive Virtual Reality (VR) 3D visualization environment as a means to support the decision-making process in engineering design processes. We evaluate the potential of VR PCP using a formative qualitative study with seven participants. In a task involving 54 points with 29 dimensions per point, we found that participants were able to detect patterns in the dataset compared with a previously published study with two expert users using traditional 2D PCP, which acts as the gold standard for the dataset. The dataset describes the Pareto front for a three-objective aerodynamic design optimization study in turbomachinery.
Personas Changing Over Time

One of the critiques of personas is that the underlying data that they are based on may stale, requiring further rounds of data collection. However, we could find no empirical evidence for this criticism. In this research, we collect monthly demographic data over a two-year period for a large online content publisher and generate fifteen personas each month following an identical algorithmic approach. We then compare the sets of personas month-over-month, year-over-year, and over the whole two-year period. Findings show that there is an average 18.7% change in personas monthly, a 23.3% change yearly, and a 47% change over the entire period. Findings support the critique that personas do change over time and also highlight that changes in the underlying data can occur within a relatively short period. The implication is that organizations using personas should employ ongoing data collection to detect possible persona changes.
Methodology and validation for identifying gait type using machine learning on IMU data

With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. An inertial measurement unit (IMU) with accelerometer and gyroscope was affixed to their left ankle. Collected acceleration and angular velocity data were partitioned into individual time-normalised strides. These data were used as features in the artificial neural network (ANN) that classified the type of stride. Several ANN models were tested: using only acceleration, only angular velocity and both. Using primarily acceleration data in the trained ANN yielded the best results (>94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.
Building a hierarchical structure model of enablers that affect the software process improvement in software SMEs—A mixed method approach

In the present study authors developed a mixed approach based on qualitative and interpretive approaches to model and analyze software process improvement (SPI) enablers in software small and medium-sized enterprises (SMEs). In phase-I after listing the research questions, the data is synthesized using the observations of stakeholders in their work domain followed by semi-structured interviews. The results of the qualitative analysis, as key learning's formed the basis for grouping of SPI indicators into nine key SPI enablers. Open, focused and theoretical coding procedures in grounded theory were used to create categories, subcategories, and thematic relationships among these enablers. Further, in phase-II, the hierarchical structure model of SPI enablers is build using the interpretive structural modeling (ISM) approach and influencing dynamics among the enablers is analyzed using MICMAC analysis. From the results of the study, it is concluded that organization, people, and technology issues are important drivers, on which SMEs shall proactively focus for successful implementation of SPI initiatives.
Joint Crowd Management and Structural Health Monitoring Using Fiber Optic and Wearable Sensing

Dense gatherings such as sports events, festivals, and pilgrimages usually include a high risk of tragic incidents such as stampedes and structural failures. In this article, we present a framework for simultaneous crowd and structural monitoring based on a unique combination of sensing technologies with processing using machine learning, for intelligent crowd management decision making. This will enhance efficiency and minimize associated safety risks. A main novelty is the utilization of fiber optic sensors based on fiber Bragg grating for strain measurements combined with wearable biometric sensing devices for location, activity, and physiological measurements. We highlight key research and implementation challenges and propose ideas to address them. Moreover, we present supporting results based on measurements from a prototype experimental testbed in order to demonstrate the effectiveness of the proposed framework and to extract practical insights for future research extensions.
ALDONA

Sentences containing several different polarity aspects cause one of the main problems in sentiment analysis. Depending on an aspect, the same context words can have different effects on its sentiment value. Additionally, the polarity can be influenced by the domain-specific knowledge, showing the necessity to incorporate it into the sentiment classification. In this paper we present a hybrid solution for sentence-level aspect-based sentiment analysis using A Lexicalised Domain Ontology and Neural Attention (ALDONA) model to handle the problems mentioned above. To measure the influence of each word in a given sentence on an aspect's polarity, we introduce the bidirectional context attention mechanism. Moreover, the classification module is designed to handle the sentence's complex structure. Finally, the manually created lexicalised domain ontology (represented in OWL) is integrated to exploit the field-specific knowledge. Computational results obtained on a benchmark data set based on Web reviews have shown ALDONA's ability to outperform several state-of-the-art models and stress its contribution to aspect-based sentiment classification.
Effects of Influence on User Trust in Predictive Decision Making

This paper introduces fact-checking into Machine Learning (ML) explanation by referring training data points as facts to users to boost user trust. We aim to investigate what influence of training data points, and how they affect user trust in order to enhance ML explanation and boost user trust. We tackle this question by allowing users check the training data points that have the higher influence and the lower influence on the prediction. A user study found that the presentation of influences significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts.
CRYPTOCURRENCIES IN FINANCE: REVIEW AND APPLICATIONS

The literature has recently begun to investigate the properties of cryptocurrency markets to identify key drivers for the use of cryptocurrencies in investment strategies. This paper provides a comprehensive review on the financial applications of Bitcoin. The focus is on three lines of research: price formation, detection of market inefficiency, and diversified portfolio construction. Topics such as market micro-structure and the interplay between different cryptocurrencies are only touched on briefly. We observe that many empirical studies find that Bitcoin markets are inefficient, with huge price fluctuations and long-range memory, and that these markets are heavily influenced by news and sector-specific events, or by infrastructure conditions such as volume trading and market liquidity. Nevertheless, astonishing price appreciations and modest correlation values versus other asset classes have contributed significantly to motivate applications of Bitcoin to investment and diversification. Future research may address practical implementations of such solutions and investigate the long-term sustainability and viability of these investment strategies.
Improving The Efficiency of E-Healthcare System Based on Cloud

The e-healthcare management system could be enchanced extremelyby connecting tocurrent trend technology. This paper suggested a model of grouping adaptable e-healthcare services administration framework dependent on Cloud Computing. Along these lines, this paper prescribed a model of planning adaptable e-healthcare services administration framework dependent on distributed computing. The recommended framework has been enhanced and incorporates different divisions to create healthcare services framework. Health Data Management System based on the client side, simple healthcare cloud and application side creates a readily attainable network. Biometric based confirmation system is appropriate in this condition since it defeats the constraints of nominalcrime and forgetpasswords in the regular nominal id secret key instrument utilized for giving security. It additionally has high correctness rate for secure information access and recovery. At long last, the framework proposed enhances cost administration, time, cost, putting away patient profile.
Efficient Multi-Key FHE With Short Extended Ciphertexts and Directed Decryption Protocol

Multi-key full homomorphic encryption (MKFHE) can perform arbitrary operations on encrypted data under different public keys (users), and the final ciphertext can be jointly decrypted by all involved users. Therefore, MKFHE has natural advantages and application value in security multi-party computation (MPC). The MKFHE scheme based on Brakerski-Gentry-Vaikuntanathan (BGV) inherits the advantages of BGV FHE scheme in aspects of encrypting a ring element, the ciphertext/plaintext ratio, and supporting the Chinese remainder theorem (CRT)-based ciphertexts packing technique. However some weaknesses also exist such as large ciphertexts and keys, and complicated process of generating evaluation keys. In this paper, we present an efficient BGV-type MKFHE scheme. First, we construct a nested ciphertext extension for BGV and separable ciphertext extension for Gentry-Sahai-Waters (GSW), which can reduce the size of the extended ciphertexts about a half. Second, we apply the hybrid homomorphic multiplication between RBGV ciphertext and RGSW ciphertext to the generation process of evaluation keys, which can significantly reduce the amount of input/output ciphertexts and improve the efficiency. Finally, we construct a directed decryption protocol which allows the evaluated ciphertext to be decrypted by any target user, thereby enhancing the ability of data owner to control their own plaintext, and abolish the limitation in current MKFHE schemes that the evaluated ciphertext can only be decrypted by users involved in homomorphic evaluation.
Visual Cross-Image Fusion Using Deep Neural Networks for Image Edge Detection

Edge detection is a fundamental computer vision problem and has wide applications, Convolutional neural networks (CNN) has been a good fundamental component in many image edge detection systems. But the edge detection accuracy from the detection and extraction of edge features by CNN is still below human visual perception, the edges of lines are fuzzy and the process is time-consuming. Based on the convolutional architecture for fast feature embedding (Caffe) framework and Visual Geometry Group (VGG16) template, a new accurate edge detector is presented in this paper by using visual cross fusion (VCF) network to detect features first. Our VCF model extracts the features of the multi-level hierarchy by parameter dimension reduction and cross-fusion of the fully connected layers respectively to achieve the end-to-end image edge detection. Second, for further improvement, a custom grading weighted cross-entropy loss function is to maximize the use of image pixels set and organize the disputed pixels rationally. Finally, the cross-network fusion layer is used to refine the edge features of images. The experimental results show that the VCF algorithm achieves an ODSF-measure as 0.808 on the Berkeley Segmentation Data Set (BSDS500) classic dataset, which is 2.5% higher than the Holistically-Nested Edge Detection (HED) algorithm, while maintaining fast 30+ FPS. On the NYU Depth Dataset V2 (NYUD V2) dataset, the ODSF-measure is 0.783, improved by 5.66% compared with the HED algorithm, and the FPS is increased by 10+. Therefore, the VCF algorithm is closer to the average human visual perception.
Identification of credulous users on Twitter

Social networks can quickly propagate information to large audiences and can be used to spread fake news or to provide false figures of popularity. Social bots, i.e., software robots that automatically interact with human users and produce content under a fictive identity, are used for such harmful activities. In this paper, we study the relationship between bots and genuine human users with the aim of identifying those "credulous" users who are particularly exposed, and unintentionally contribute, to the activities planned by a network of bots. Spotting credulous users is useful to service providers to display warnings on their dashboards, scan their activities for early signs of attacks, or take more active measures to prevent or limit the negative effects of their activities.