Reviewerly/exHarmony huggingface dataset:
Mobile Realities

We present two proof of concept experiences for a virtual reality (VR) game that draws on several medium-specific qualities of mobile, location-based, and tangible storytelling. In contemporary smartphone-VR, experiences are limited by short playtimes, limited interactions, and limited movement within a physical space. To address these limitations, we suggest a reconceptualization of smartphone-VR. Rather than design that deems the smartphone the least capable VR platform, we propose design that adds VR to an already rich mobile storytelling platform. We argue that by drawing on otherwise separate storytelling media, designers can circumvent limitations related to smartphone-VR while also extending the range of smartphone-based storytelling. We conclude by reflecting on possible implications of this extended design space.
What’s Most Broken? Design and Evaluation of a Tool to Guide Improvement of an Intelligent Tutor

Intelligent Tutoring Systems (ITS) have great potential to change the educational landscape by bringing scientifically tested one-to-one tutoring to remote and under-served areas. However, effective ITSs are too complex to perfect. Instead, a practical guiding principle for ITS development and improvement is to fix what's most broken. This paper presents SPOT (Statistical Probe of Tutoring), a tool that mines data logged by an ITS to identify 'hot spots' most detrimental to its efficiency and effectiveness in terms of its software reliability, usability, task difficulty, student engagement, and other criteria. SPOT uses heuristics and machine learning to discover, characterize, and prioritize such hot spots in order to focus ITS refinement on what matters most. We applied SPOT to data logged by RoboTutor, an ITS that teaches children basic reading, writing and arithmetic. A panel-of-experts experimental evaluation found SPOT's selected video clips of RoboTutor's hot spots as significantly more informative than video clips selected randomly.
Fast Dual Simulation Processing of Graph Database Queries

Graph database query languages feature expressive yet computationally expensive pattern matching capabilities. Answering optional query clauses in SPARQL for instance renders the query evaluation problem immediately PSPACE-complete. Light-weight graph pattern matching relations, such as simulation, have recently been investigated as promising alternatives to more expensive query mechanisms like, e.g., computing subgraph isomorphism. Still, pattern matching alone lacks expressive query capabilities: graph patterns may be combined by usual inner joins. However, including more sophisticated operators is inevitable to make solutions more useful for emerging applications. In this paper we bridge this gap by introducing a new dual simulation process operating on SPARQL queries. In addition to supporting the full syntactic structure of SPARQL queries, it features polynomial-time pattern matching to compute an overapproximation of the query results. Moreover, to achieve running times competing with state-of-the-art database systems, we develop a novel algorithmic solution to dual simulation graph pattern matching, based on a system of inequalities that allows for several optimization heuristics. Finally, we achieve soundness of our process for SPARQL queries including UNION, AND and OPTIONAL operators not restricted to well-designed patterns. Our experiments on synthetic and real-world graph data promise a clear gain for graph database systems when incorporating the new dual simulation techniques.
Low-Cost Road Traffic State Estimation System Using Time-Spatial Image Processing

Road traffic mobility can be described by its Level of Services (LoS). A major challenge in traffic state and LoS estimation is the limitation of observed traffic data. To derive the traffic state of a road network, a sensor network needs to be installed. Most stationary sensing techniques involve high investment in terms of the sensor installation, data communication and computational resources. This paper proposes a low-cost image processing system for road traffic state estimation using time-spatial image (TSI) processing. The TSI is an image processing technique for transforming a series of video images into a single image. Therefore, the TSI can reduce memory resources compared with the traditional methods. A camera can be exploited for traffic-state estimation through integration with TSI generating and processing modules. In addition, traffic state variables such as space-mean-speed, flow and density can be estimated. Empirical results are provided based on several experiments to show that TSI processing is a viable lowcost approach to traffic state estimation.
Watch Me from Distance (WMD)

Preserving the privacy of people in video surveillance systems is quite challenging, and a significant amount of research has been done to solve this problem in recent times. Majority of existing techniques are based on detecting bodily cues such as face and/or silhouette and obscuring them so that people in the videos cannot be identified. We observe that merely hiding bodily cues is not enough for protecting identities of the individuals in the videos. An adversary, who has prior contextual knowledge about the surveilled area, can identify people in the video by exploiting the implicit inference channels such as behavior, place, and time. This article presents an anonymous surveillance system, called Watch Me from Distance (WMD), which advocates for outsourcing of surveillance video monitoring (similar to call centers) to the long-distance sites where professional security operators watch the video and alert the local site when any suspicious or abnormal event takes place. We find that long-distance monitoring helps in decoupling the contextual knowledge of security operators. Since security operators at the remote site could turn into adversaries, a trust computation model to determine the credibility of the operators is presented as an integral part of the proposed system. The feasibility study and experiments suggest that the proposed system provides more robust measures of privacy yet maintains surveillance effectiveness.
A Survey of Personalized Recommendation Algorithm Selection Based on Meta-learning

The purpose of algorithm selection is to automatically select the most suitable one for the current problem from among the many available optimization algorithms. For this problem, this paper describes the recommendation algorithm selection of meta-learning. Firstly, the related techniques based on meta-learning are analyzed. Then the algorithm selection framework and the extraction of meta-features are constructed. Finally, the problems of algorithm selection based on meta-learning ideas are proposed, and the future development direction is pointed out.
Named Entity Recognition in Clinical Text Based on Capsule-LSTM for Privacy Protection

Clinical Named Entity Recognition for identifying sensitive information in clinical text, also known as Clinical De-identification, has long been critical task in medical intelligence. It aims at identifying various types of protected health information (PHI) from clinical text and then replace them with special tokens. Along with the development of deep learning technology, lots of neural-network-based methods have been proposed to deal with Named Entity Recognition. As one of the state-of-the-art methods to address this problem, Bi-LSTM-CRF has become the mainstream due to its simplicity and efficiency. In order to better represent the entity-related information expressed in the context of clinical text, we design a novel Capsule-LSTM network that is able to combine the great expressivity of capsule network with the sequential modeling capability of LSTM network. Experiments on 2014 i2b2 dataset show that the proposed method outperforms the baseline and thus reveal the effectiveness of the newly proposed Capsule-LSTM network.
Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA.
Competitive Compliance with Blockchain

Authentication is essential for sharing information in IoT and its secondary use with AI-capable machines. The aim is to support humans in optimizing risk of supply chains for industrial manufacturing and service provisioning in a timely manner. The ultimate aim is sustainability. The problem for deciding on authentication is probably imperfect information on compliance. Its asymmetric implications of the meaning of contracts for secure information sharing may cause vulnerability of data breach and misuse. A traditional way to avoid harm of that asymmetry requires authentic and consistent sharing of audit information on violation of a certification policy to a centralized audit intelligence. This information sharing is, however, subject to the problem of single point of failure of the centralized audit intelligence. With our work on Security by Design, we show a non-central approach of clarifying accountability to reduce the risk caused by asymmetric implications of the meaning of contracts on authentication. Our signaling and screening scheme SK4SC provides personal digital evidences on compliance to multilateral policies on using information or in other words on trustworthiness. Blockchains are used to realize their symmetric distribution while users share risk on accountability with competition on incentives in a privacy-enhancing manner. Customer relationship management with royalty points, e.g., for eGovernment with taxation, is an example for using SK4SC as digital platform.
Towards Automated Generation of Bug Benchmark for Smart Contracts

Smart Contracts bring Ethereum transactions great convenience, meanwhile they can have potentially devastating financial consequences. To address the lack of security guarantee, we investigate state-of-art bug detection tools and conduct experiments to analyze their pros and cons. Finally, we conduct research to answer why they have false positives and a convincing bug benchmark is provided.
A Framework to Monitor Machine Learning Systems Using Concept Drift Detection

As more and more machine learning based systems are being deployed in industry, monitoring of these systems is needed to ensure they perform in the expected way. In this article we present a framework for such a monitoring system. The proposed system is designed and deployed at Mastercard. This system monitors other machine learning systems that are deployed for use in production. The monitoring system performs concept drift detection by tracking the machine learning system's inputs and outputs independently. Anomaly detection techniques are employed in the system to provide automatic alerts. We also present results that demonstrate the value of the framework. The monitoring system framework and the results are the main contributions in this article.
Students’ Metacognitive Weaknesses in Academic Writing: A Preliminary Research

This article aims to explain the weaknesses of metacognition that affect writing skills. Weaknesses of writing like content development, the organization of writing, compatibility of content with themes and audience awareness are assumed from the weaknesses of student metacognition. By using a qualitative approach, data is collected through questionnaires and interviews. Using the questionnaire found the level of student metacognitive awareness. A total of 22 male and female students were randomly selected. Responses given through self-report questionnaires showed that as many as 15 students had high metacognitive awareness and as many as 7 students had low metacognitive awareness. Furthermore, through interviews found metacognitive skills in academic writing. The results of the data analysis show that there are three weaknesses of student metacognitive, namely: students are too dependent on feedback from lecturers and highly dependent on lecturers and colleagues when writing, students cannot assess their own understanding of the information they receive for writing assignments, students are not aware benefit from the strategies used during writing. Students need to be trained to plan, monitor and evaluate writing activities so that they are skilled in arranging words, concepts, and terminology used in writing. In addition, through the writing they produce, it can be seen how the process of produces the meaning and thinking skills of students in writing.
Nonlinear Algorithms for Combining Conflicting Identification Information in Multisensor Fusion

One of the most difficult problems in advanced systems today is handling all the identification information that is received from an array of sensors. Dealing with all this information becomes even more of an issue when there is conflicting information. Historically, evidence theory has been used to combine information from different sensors, but handling conflicting information in an intuitive manner continues to be a challenging problem. Often when conflicting information is detected the set of information is processed for outliers in order to remove the conflicting information before fusing the remaining set. This paper describes the use of a nonlinear algorithm in conjunction with the foundations of evidence theory to handle the combination of sensor data in an intuitive manner while also managing conflicting information. The results from a novel nonlinear algorithm is contrasted and compared with results from other modern day evidence theory algorithms.
Trajectory Planning in Time-Varying Adverse Weather for Fixed-Wing Aircraft Using Robust Model Predictive Control

The avoidance of adverse weather is an inevitable safety-relevant task in aviation. Automated avoidance can help to improve safety and reduce costs in manned and unmanned aviation. For this purpose, a straightforward trajectory planner for a single-source-single-target problem amidst moving obstacles is presented. The functional principle is explained and tested in several scenarios with time-varying polygonal obstacles based on thunderstorm nowcast. It is furthermore applicable to all kinds of nonholonomic planning problems amidst nonlinear moving obstacles, whose motion cannot be described analytically. The presented resolution-complete combinatorial planner uses deterministic state sampling to continuously provide globally near-time-optimal trajectories for the expected case. Inherent uncertainty in the prediction of dynamic environments is implicitly taken into account by a closed feedback loop of a model predictive controller and explicitly by bounded margins. Obstacles are anticipatory avoided while flying inside a mission area. The computed trajectories are time-monotone and meet the nonholonomic turning-flight constraint of fixed-wing aircraft and therefore do not require postprocessing. Furthermore, the planner is capable of considering a time-varying goal and automatically plan holding patterns.
A Vision for Pervasive Information Visualisation to Support Passenger Navigation in Public Metro Networks

This paper presents a vision of how pervasive information visualization can be used to support passenger navigation in public metro systems. The system we propose combines mobile devices with large display interfaces in order to make the process of metro system navigation more convenient by allowing a passenger to plan their route in the station using a large-display interface and follow the route using notifications on a smart-watch or similar wearable device. This system will reduce the need for passengers to search in-station maps for their destination, rely on their view of in-carriage route maps, or work a metro navigation app on their smart-phone on crowded carriages. This aims to make the process of metro travel less of a hassle for most travellers, and especially travellers who are not familiar with the metro network. Our system acts also acts as an early stage case-study for testing the concept of pervasive information visualization.
Feature Selection of Parallel Binary Moth-flame Optimization Algorithm Based on Spark

In view of the good classification ability of Moth-Flame Optimization (MFO) in reducing feature redundancy, this paper applied MFO algorithm to feature selection. However, the MFO algorithm is easy to fall into local optimum and has a weak search ability, which severely limits the classification performance and dimensional reduction ability of the algorithm. Therefore, this paper combined MFO algorithm with distributed parallel computing Spark platform distributed, and proposed a feature selection method based on Spark Parallel Binary Moth-Flame Optimization (SPBMFO) algorithm. The experimental results show that compared with the classical particle swarm optimization algorithm(PSO), the genetic algorithm(GA) and the cuckoo search algorithm(CS), when using the binary MFO algorithm for feature selection, the selected features are improved by 12.5%, 15% and 2.5%, respectively. SPBMFO algorithm avoids the search process falling into local optimum and improve the classification performance of the algorithm, which minimizes the number of features while maximizing the classification performance.
Improved Movie Recommendations Based on a Hybrid Feature Combination Method

Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.
A fidelity-restricted distributed principal component analysis compression algorithm for non-cable seismographs

This paper proposes a distributed principal component analysis seismic data compression algorithm based on fidelity restriction for non-cable seismic exploration systems. The proposed method compresses data effectively according to a customized fidelity. It first determines the cumulative contribution rate, then calculates the global eigenvector matrix, generates a projection in the seismographs and finally transmits the projected data. The numerical simulation results show that when the total waveform fidelity reaches 92%, the compression ratio still stands at 77 times or more. Furthermore, the field experiment also verifies the performance of the algorithm.
Multilinear Weighted Regression (MWE) with Neural Networks for trend prediction

The ability to define accurate linear models to find patterns or relationships between variables is one of the most challenging fields in Computer Science. In particular, extrapolative applications are widely used to predict values in Biological, Behavioral and Social Sciences. Analysts usually focus on reducing the approximation error in order to ensure fairly reliable predictions. Using pattern detection models such as Multilayer Artificial Neural Networks can offer good results in predicting possible outcomes, however they might not be the optimal fit for predicting evolutionary trends due to their inherent overfitting characteristics. Regression models, on the other hand are more often used to predict trending behaviors and therefore they can be more helpful when studying the evolution of a given instrument, symbol or series. However, a simple regression model can be very inaccurate with an unacceptable prediction error rate. This paper shows an automatic method to find trends in known instruments, by using a massive linear regression technique combined with a conventional machine learning proposal that works on optimizing the weights of the linear regressive structures. In this paper the authors show an application of the model in finance (stocks market instruments), however the proposal is designed to work for to any discipline that studies the trends of any evolutionary object.
Exploring Rural Community Practices in HIV Management for the Design of Technology for Hypertensive Patients Living with HIV

Information communication technologies for development (ICTD) can support people with chronic illnesses living in rural communities. In Kenya, ICTD use in areas where undetected cases of hypertension and high HIV infection rates exist is underexplored. Partnering with a health facility in Migori, Kenya, we report on the uses of technology in managing HIV. We see the use of technology to manage HIV was influenced by the roles and routines of patients and clinicians, trust between practitioners and patients, and sources of data that clinicians use for patient examination. We use these results to inform the design of technologies that can support patients living with comorbid HIV and hypertension, as well as their care providers, to manage their care in similar settings. We also reiterate the important mediatory role that community health volunteers (CHVs) can play in the adoption of technology as patients manage their condition(s) once out of hospital.
Adapting Scrum Process with 7C Knowledge Management Model

Today, Agile software development is one of the main approaches of software development. In this area adherence to the Agile principles and values has led to the removal of comprehensive documentation in the development process. This has replaced the explicit knowledge with implicit knowledge. In fact, the foundation of Agile software development is based on implicit knowledge. This has led knowledge management in the Agile software development as a challenging point. However, the nature of implicit knowledge is such that it can be used beneficially with optimal management. Considering this challenge, this study attempts to adapt the Scrum framework as the most popular Agile method with 7C model as one of the well-known implicit knowledge management models. The results of this study showed that this model would be successful in Scrum framework.