Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Twitter as a Tertiary Education Stakeholder Communication Tool: A Case of #FeesMustFall in South Africa

Twitter has been a prevailing proxy in activating South Africa's #FeesMustFall student movement. This research explores whether or not, social media enables effective student online activism. In contentious periods, it is crucial to determine an effective means of conflict resolution within tertiary education, via information and telecommunication technology. This case study analyses a gross total of 567,533 tweets, sampling the student movement's inceptional years of 2015 and 2016. Frameworking this enormous engagement using big data requires a mixed research approach. Using a big data conceptual framework, this paper prioritises trend lines over headlines. The findings suggests a methodological problem for South African researchers, university practitioners, and social science scholars to collaborate ensuring long-term success of a microblogging data management value chain within a tertiary education specific ecosystem. A South African higher education microblogging environment which collectively explores local inter-campus microblogging for public engagement. The Big Data V-Model can inform higher education stakeholders of public engagement effectiveness on five different qualitative and quantitative factors. In this process, key big data opportunities and issues can be addressed promptly and appropriately to the respective campus issues.
Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures.
A Study on the Effects of Exception Usage in Open-Source C++ Systems

Exception handling (EH) is a feature common to many modern programming languages, including C++, Java, and Python, that allows error handling in client code to be performed in a way that is both systematic and largely detached from the implementation of the main functionality. However, C++ developers sometimes choose not to use EH, as they feel that its use increases complexity of the resulting code: new control flow paths are added to the code, "stack unwinding" adds extra responsibilities for the developer to worry about, and EH arguably detracts from the modular design of the system. In this paper, we perform an exploratory empirical study of the effects of exceptions usage in 2721 open source C++ systems taken from GitHub. We observed that the number of edges in an augmented call graph increases, on average, by 22% when edges for exception flow are added to a graph. Additionally, about 8 out of 9 functions that may throw only do so by propagating a throw from another function. These results suggest that, in practice, the use of C++ EH can add complexity to the design of the system that developers must strive to be aware of.
BallTrack: Football ball tracking for real-time CCTV systems

The paper describes a deep network based system specialized for ball detection in long shot videos. System comprises of flexible detector and classical particle tracking. The core contribution is incorporation of hy-percolumn concept in the processing pipeline achieving real-time tracking on 12MPx videos. System achieves state-of-the-art results in ISSIA-CNR Soccer Dataset and its feasibility has been tested on 4 camera prototype system.
Trajectory Anonymization: Balancing Usefulness about Position Information and Timestamp

Movement trajectories can provide useful information for all fields. However, they may have high privacy parameters, sharing trajectory data with other operators without anonymization carries the risk of linking movement trajectories to individuals. Therefore, it is necessary to consider applying privacy protection to trajectory data. Anonymization indicators, such as k-anonymity are generally adopted for anonymization of trajectory data. Some studies modify position information to achieve anonymity. This modification sometimes produces inaccuracies in data sets. In this study, to reduce the modification distance of the position, we propose an algorithm that allows the mismatch of time when the position information is acquired within a certain range. Further, we define indicators that represent distortions of position and time information. As a result of comparing the proposed method and the existing method, the usefulness of the proposed method is shown.
Named Entity Enrichment Based on Subject-Object Anaphora Resolution

Named Entity Recognition (NER) is an early stage processing of an Information Extraction, which identifies and classify entities in text. The outcomes of the task have become the foundation of building complex Information Extraction applications. With the enormous amount of information available everywhere, the area has gained lots of attention from the research community. Currently, there are two main approaches to perform the Named Entity Recognition; rule-based approach and machine learning approach. In order to improve the accuracy of the classification and performance of the recognizer, some researchers have implemented a hybrid approach, which is the combination of both approaches. Even though many research and works have been done in the Named Entity Recognition, there is still room available for improvement. This paper proposed to increase the accuracy of entities detected by implementing anaphora resolution during the preprocessing phase and a hybrid approach to classify the detected tokens during the classification phase. The hybrid approach is combined the Conditional Random Field (CRF) classifier with a gazetteer and pattern rules to perform classification. The result has shown that the application of anaphora and gazetteer has increased 46% accuracy of the detected entities for the person class.
A New Method for Abbreviation Prediction via CNN-BLSTM-CRF

Abstract It is a crucial problem to process abbreviation in the field of natural language processing. The most commonly used way to cope with this problem is to construct the reference database by predicting the abbreviation through its fully expanded form. Previous work on abbreviation prediction mostly rely on traditional machine learning algorithms, which inevitably requires a large number of manual annotations or expert knowledge to establish a feature system. In this paper, a neural network model based on CNN-BLSTM-CRF is proposed, which can predict Chinese abbreviations better without relying too much on the feature system: Firstly, convolutional neural network extracts phrase and Chinese character information from the fully expanded form, and then BLSTM-CRF deep network is constructed to annotate the fully expanded form, so as to extract its corresponding abbreviation form. The experimental results show that the method in this paper can perform better than the state-of-art method in traditional machine learning, and the results provide a reference for abbreviation research and the construction of resource repository.
Smart Contract-based Miner Registration and Block Validation

Rewriting-history attacks are devastating for blockchain-based cryptocurrency systems. To deter such attacks, this paper introduces a new model utilizing a smart contract to effectively prevent such attacks from succeeding. Each node who creates a new block is required to register with the smart contract to get a voucher required for the subsequent block validation. We explain the flow of the proposed model and the algorithms of the smart contract. We have implemented this smart contract in the Solidity language and simulated on the Ethereum test network. We also present security and simulation results of the proposed model.
An Augmented Reality Shared Mission Planning Scenario: Observations on Shared Experience

The proliferation of immersive technologies has accentuated their potential utilization across a broad range of operational situations from strategic planning to the tactical edge. However, little is known about individual and team behavior associated with immersive technologies. We begin to address this challenge by conducting a user-based experiment comparing a 3D augmented reality (AR) device (HoloLens) to a traditional 2D flat screen display. Participant dyads used both technologies to interactively plan a mission to retrieve a repository of intelligence documents located within enemy-held territory. Survey and performance data were collected during the mission planning task. A survey instrument called the Shared Augmented Reality Experience (SARE) survey was developed to measure some aspects of “presence” as defined by factor categories from early virtual environment research. Tested during the mission planning task, a correlation analysis reveals several significant relationships between the survey items and the SARE total score. All relationships between the SARE total score and performance metrics are in the predicted direction; however, only one relationship is significant. Based on the limited data collected to date, experimental outcomes indicate that the level of shared AR experience should be associated with better performance and larger samples should be pursued.
Construction of Knowledge Graph Based on Geographic Ontology

Here are massive archival data in the geography of surveying and mapping industry, which provides important guarantee for the national economy and social development. Efficient storage and retrieval systems for archival data must be built, in order to make effective use of these data. In this paper, OWL language is used to establish the ontology relation model of computer language, and then the ontology reasoning mechanism is studied on this basis. The reasoning model is educed with open source project Jena inference engine. Then the knowledge map of regional geographic ontology is constructed with the powerful ontology graphical representation ability of graph database. Finally, the search system is developed based on the java language, achieving the required retrieval requirements.
Minimum distance histograms with universal performance guarantees

Abstract We present a data-adaptive multivariate histogram estimator of an unknown density f based on n independent samples from it. Such histograms are based on binary trees called regular pavings (RPs). RPs represent a computationally convenient class of simple functions that remain closed under addition and scalar multiplication. Unlike other density estimation methods, including various regularization and Bayesian methods based on the likelihood, the minimum distance estimate (MDE) is guaranteed to be within an $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> distance bound from f for a given n , no matter what the underlying f happens to be, and is thus said to have universal performance guarantees (Devroye and Lugosi, Combinatorial methods in density estimation. Springer, New York, 2001). Using a form of tree matrix arithmetic with RPs, we obtain the first generic constructions of an MDE, prove that it has universal performance guarantees and demonstrate its performance with simulated and real-world data. Our main contribution is a constructive implementation of an MDE histogram that can handle large multivariate data bursts using a tree-based partition that is computationally conducive to subsequent statistical operations.
Predicting Search Intent Based on In-Search Context for Exploratory Search

Modern information systems are expected to assist users with diverse goals, via exploiting the topical dimension (‘what' the user is searching for) of information needs. However, the intent dimension (‘why' the user is searching) has preferred relatively lesser for the same intention. Traditionally, the intent is an ‘immediate reason, purpose, or goal' that motivates the user search, and captured in search contexts (Pre-search, In-search, Pro-Search), an ideal information system would be able to use. This article proposes a novel intent estimation strategy; based on the intuition that captured intent, and proactively extracts likely results. The captured ‘Pre-search' context adapts query term proximities within matched results beside document-term statistics and pseudo-relevance feedback with user-relevance feedback for In-search. The assessment asserts the superior performance of the proposed strategy over the equivalent on tradeoffs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs exploration.
Cognitive and Hierarchical Fuzzy Inference System for Generating Next Release Planning in SaaS Applications

The next release planning is considered as a cognitive decision-making problem where many stakeholders provide their judgments and opinions about the set of features that shall be included in the next release of the software. In multi-tenant Software as a Service (SaaS) applications, planning for the next release is a significant process that plays important roles in the success of SaaS applications. SaaS providers shall fulfill the evolving needs and requirements of their tenants by continuously delivering new releases. The first step in a release development lifecycle is the release planning process. This paper proposes a novel approach for the next release planning for multi-tenant SaaS applications. This approach is a prioritization approach that employs a hierarchical fuzzy inference system (HFIS) module to deal with the uncertainty associated with human judgments. The main objectives of the proposed approach are maximizing the degree of overall tenants' satisfaction, maximizing the degree of commonality, and minimizing the potential risk, while considering contractual, effort, and dependencies constraints. The performance of the proposed approach is validated against a one from the literature and shows better results from the perspective of overall tenants' satisfaction and adherence to the risk.
Towards Explainable AI Using Similarity: An Analogues Visualization System

AI Systems are becoming ubiquitous and assuming different roles in our lives: they can act as recommendation systems in multiple contexts, they can work as personal assistants, they can tag images, etc. Whilst their contributions are clear, the reasoning behind them are not so transparent and may need explanations. This need for interpretability created new challenges for developers and designers from different communities. Visualizing multidimensional data and exploring the objects’ similarities can help with the explainability of an AI system. In this work, we discuss the visual inspection of high-dimensional objects being complementary to machine learning techniques. We present RAVA (Reservoir Analogues Visual Analytics), a system that employs machine learning and visual analytics techniques to empower geoscientists in the task of finding similar reservoirs.
Examination of Digital Forensics Software Tools Performance: Open or Not?

The performance evaluation in terms of digital forensics tools and software can be characterized as challenging research area due to constant development of technology in the digital world and rise of various manners in which it can be utilized for illegal purposes. There are many developed tools and software for digital forensics, some of them available for a license, and some of them free of charge. However, given that some practitioners from this field argue for commercial while others for open-source software, the reliability of the digital evidence which is collected, analyzed, and presented by both is constantly questioned. Motivated by the dilemma which tool or software for extracting digital evidence to use, we have conducted the review of the existing studies which directed us towards the examination of the performance of two different types of digital forensics tools: open-source (Linux Autopsy Sleuth Kit) and commercial (Magnet Axiom). The results of the research showed that the open-source digital forensics tool has better performance in comparison to the commercial one. In addition to this conclusion which can be useful for further investigations and research in both practical digital forensics and academic community, we also provide open issues to be addressed in the future.
Using Disposable Domain Names to Detect Online Card Transaction Fraud

Online card transaction fraud is one of the major threats to the bottom line of E-commerce merchants. In this paper, we propose a novel method for online merchants to utilize disposable (“one-time use”) domain names to detect client IP spoofing by collecting client's DNS information during an E-commerce transaction, which in turn can help with transaction fraud detection. By inserting a dynamically generated unique hostname on the E-commerce transaction webpage, a client will issue an identifiable DNS query to the customized authoritative DNS server maintained by the online Merchant. In this way, the online Merchant is able to collect DNS configuration of the client and match it with the client's corresponding transaction in order to verify the consistency of the client's IP address. Any discrepancy can reveal proxy usage, which fraudsters commonly use to spoof their true origins. We have deployed our preliminary prototype system on a real online merchant and successfully collected clients DNS queries correlated with their web transactions; then we show some real instances of successful fraud detection using this method. We also address some concerns regarding the use of disposable domains.
A Declarative Approach for an Adaptive Framework for Learning in Online Courses

Online courses have gained popularity and seen a surge in enrollment with a reported 58 million students. Adaptive learning is an educational method that is applicable to online courses. Computers adapt the presentation of educational material according to students' learning needs. One of the major challenges with existing systems is that learners are not able to keep up with the instructions in the course that leads to a very low course completion rate. Personalization of the course materials based on the needs of a student is of great value. We propose an adaptive framework for learning that groups students and charts a course plan with the end goal of helping the learner complete all topics in the course. The system also provides feedback about the learner's strong and weak topics with a view to help them learn better. We present a declarative approach that is quite different from existing approaches and provides the user flexibility to specify the constraints and actions as well as consequence of each action instead of having the user encode how to find the solution.
Moving Object Detection in Noisy Images

The new method for detecting moving objects in an image distorted by white Gaussian noise is proposed. The method is based on the representation of the video sequence by a three-dimensional discrete Markov process and includes two stages. The first step is a three-dimensional nonlinear filtering of images which allows keeping the contours of objects. The second stage comprises detecting the objects of interest based on the assessment of their boundaries and brightness. The method requires small timing and computational expenditures which makes it possible to use this method in real-time data processing.
A novel Map-Scan-Reduce based density peaks clustering and privacy protection approach for large datasets

Large dataset clustering is a major research issue due to huge processing time. Clustering methods goal at split large dataset into number of groups and each group consists of similar data points. Numerous clustering methods such as partition-based clustering, hierarchical clustering, density-based clustering, spectral clustering, and subspace clustering are presented. These methods failed to produce true (accurate) clusters in less response time. To mitigate the issues of existing methods, in this paper, we propose a novel Map-Scan-Reduce based density peaks (DP) clustering approach to cluster the large datasets. MapReduce is a popular distributed processing framework that has several advantages: it has the ability to resolve any issues that arise with large data volume and it partitioned data in a distributed way, but native MapReduce has certain drawbacks including high communication and computation overhead. In this paper, the Map-Scan-Reduce process is divided into three steps: MAP, SCAN, and REDUCE, which solve the problems of native MapReduce. Users scheduling and data preprocessing is implemented using adaptive neuro fuzzy scheduler and improved version of M-Z-D-S (Max–Min, Z-score, and decimal scaling). Furthermore, clusters privacy is protected using differential privacy (α,β) method and clusters quality is validated using two matrices such as Silhoutte and Dunn for inter-cluster and intra-cluster validations, respectively. Finally, we conduct experiments to analyze the performance of the proposed work in terms of clustering accuracy, speedup ratio (execution time), and efficiency (precision, adjusted Rand index, and normalized mutual information).
Study on Spatiotemporal Characteristics of Gaze Gesture Input

The gaze gesture input has the advantages of high bandwidth, high efficiency, no misoperation and high customization, but it also has the disadvantages of high cognitive load and high fatigue degree. At present, few researches have thoroughly explored the regularity of gaze gesture input, which cause designers lack of design guidance, and it is difficult to reasonably apply gaze gesture input in interaction. This paper focuses on the study of the spatiotemporal features of the gaze gesture input. An experiment was designed so that the input region size, the feedforward type and the input gesture shape were within-subject experimental variables. By analyzing 1200 trajectory data of gaze gestures to exploring the effects of these three variables on the performance of and the subjective satisfaction of the gaze gesture input, the details of the three major characteristics of gaze gesture-based interaction were summarized, which are the input continuity, the implicit interaction and the real-time feedback. These may provide design guidance for gaze gesture interaction designers.