Reviewerly/exHarmony huggingface dataset:
Immersive Process Models

In many domains, real-world processes are traditionally communicated to users through abstract graph-based models like event-driven process chains (EPCs), i.e. 2D representations on paper or desktop monitors. We propose an alternative interface to explore EPCs, called immersive process models, which aims to transform the exploration of EPCs into a multisensory virtual reality journey. To make EPC exploration more enjoyable, interactive and memorable, we propose a concept that spatializes EPCs by mapping traditional 2D graphs to 3D virtual environments. EPC graph nodes are represented by room-scale floating platforms and explored by users through natural walking. Our concept additionally enables users to experience important node types and the information flow through passive haptic interactions. Complementarily, gamification aspects aim to support the communication of logical dependencies within explored processes. This paper presents the concept of immersive process models and discusses future research directions.
Visualization Criteria: supporting knowledge transfer in Incident Management Systems

Incident Management Systems (IMS) assist in managing resources in order to minimize fatalities and damage. Visual artifacts in an IMS can facilitate knowledge transfer between responders to an incident, however, evidence-based guidance on the design of these visualizations are lacking. The aim of this study is to propose evidence-based knowledge visualization criteria (KVC). Design Science Research (DSR) was the guiding methodology. We abstracted a set of KVC from the academic literature, and then applied said criteria to evaluate a cloud-based prototype IMS. The evaluation included interviews with content experts from the South African Fire Service to establish the relevance of the KVC. The KVC were also used in a heuristic evaluation of the IMS by usability experts. The theoretical contribution of the study is the validated set of KVC based on the triangulation of the findings from the content experts and the usability experts. The study also makes a practical contribution by demonstrating the use of evidence-based visualization criteria in IMS.
Taman Bacaan Masyarakat (TBMs): a global literacy potential in Bandung Smart City

Reading community, Taman Bacaan Masyarakat (TBMs) have a long story as the movement center of information literacy. At the beginning of its emergence, in the 1990s, TBMs stretched slowly but surely became the driving force for information literacy. In its development, TBMs emerged as a learning center, until there were several of them who were also leading the role of empowerment centers for the community. Using the case study method from qualitative research, this article tries to explore the other side of the role of TBMs in the midst of society in an effort to prepare an information society in Bandung Smart City to face global challenges. The study of several cases in previous studies with the topic of TBMs studies shows that there is a role for TBMs that has not been explored related to the challenges of globalization. This article seeks to reveal the unique side of TBMs as a global literacy center in Bandung Smart City. Hopefully the writing in this article will provide a discourse space for researchers of information literacy to continue to inspire TBMs that are actively growing in the midst of society to be part of community empowerment efforts, as well as global literacy locomotives for the environment.
Human Following Robot System That Integrates Global and Local Following for Smartphone Users

This paper introduces a mobile robot system that follows smartphone users. Especially, a human following robot system integrating “global” and “local” following is introduced. “Global human following” means that the robot follows the global trajectory of the target human. This is required to keep following when missing the targets in crowded environments. We propose a global human following system with Wi-Fi radio intensity and occupancy grid maps. Map representation named as “Wi-Fi-based waypoint map” is proposed for global following. The map representation consists of global waypoints based on Wi-Fi radio field strength and occupancy grid map of the robot. Rough positions of the target human with the smartphone are selected in the Wi-Fi-based waypoint map and the mobile robot travels to the positions autonomously. Then, global human following is achieved. This paper shows some experimental results for calling robot and following human using Wi-Fi-based waypoint map. Also, a method local following of the human with the smartphone is introduced in order to complete the system as a total human following system.
Reinforcement Learning Based Adaptive Bitrate Algorithm for Transmitting Panoramic Videos

Panoramic videos have become more and more popular now. 360-degree videos give users a better experience but put forward a higher request for network at the same time. Many kinds of solutions to meet the high need of bandwidth have been proposed, such as tiled-based transmission, layer-based transmission and so on. Then how to choose the most suitable bitrates to make full use of the network resource is the problem to be solved urgently. In this paper, we propose a method based on reinforcement learning(RL) algorithm to select the bitrates of the region-of-interest adaptively for panoramic videos. We also compare RL algorithm with three traditional algorithms when changing the Field of View(FOV) and find that RL algorithm proves to perform better in minimizing the rebuffer time and providing higher quality video contents under various network conditions.
Saliency Detection with Multi-Contextual Models and Spatially Coherent Loss Function

We have proposed a multi-contextual model architecture with color and depth information considered independently in this work. To utilize the feature maps of different levels better, short connection structures are used to integrate the knowledge from color and depth data separately. A novel loss function considering three criteria is proposed to improve the detection accuracy and spatial coherence of the detected results. The training process of the proposed network is divided into two stages, a pre-training phase and a refinement phase to increase the efficiency of the network.
Is reputational damage worse than a regulator's fine?

Companies today have a huge task on their hands with the sheer volume of red tape required to demonstrate compliance. Such is the global nature of today's regulations that most organisations must adhere to them even if they are not physically in the markets covered. This is exposing them to greater compliance risk than ever before. Such is the global nature of today's regulations that most organisations must adhere to them even if they are not physically in the markets covered. This is exposing them to greater compliance risk than ever before. Data leaks, intentional or unintentional, happen. And when incidents occur, organisations may find themselves having to prove – both to the authorities and their customers – what measures were taken to secure information. This can leave firms struggling to manage the adverse side effects of data leaks, such as damage to customer loyalty, says Jesse Canada of ASG Technologies.
Multifeature Image Indexing for Robot Localization in Textureless Environments

Robot localization is an important task for mobile robot navigation. There are many methods focused on this issue. Some methods are implemented in indoor and outdoor environments. However, robot localization in textureless environments is still a challenging task. This is because in these environments, the scene appears the same in almost every position. In this work, we propose a method that can localize robots in textureless environments. We use Histogram of Oriented Gradients (HOG) and Speeded Up Robust Feature (SURF) descriptors together with Depth information to form a Depth-HOG-SURF multifeature descriptor, which is later used for image matching. K-means clustering is applied to partition the whole feature into groups that are collectively called visual vocabulary. All the images in the database are encoded using the vocabulary. The experimental results show a good performance of the proposed method.
Data Integrative Bayesian Inference for Mixtures of Regression Models

Summary Modern data collection techniques, which often produce different types of relevant information, call for new statistical learning methods that are adapted to cope with data integration. In the paper Bayesian inference is considered for mixtures of regression models with an unknown number of components, that facilitates data integration and variable selection for high dimensional data. In the approach presented, named data integrative mixture of regressions, data integration is accomplished by introducing a new data allocation scheme that summarizes additional data in the form of an informative prior on latent variables. To cope with high dimensionality, a shrinkage-type prior is assumed on the regression parameters, and a posteriori variable selection is conducted based on Bayesian credible intervals. Posterior estimation is achieved via a Markov chain Monte Carlo algorithm. The method is validated through simulation studies and illustrated by its performance on real data.
Exploiting entity information for stream classification over a stream of reviews

Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the prediction of a review's label is based more on reviews referring to other, more popular or simply more recently inspected entities, while reviews referring to the same entity might be ignored as too old. In this study, we enforce that the reviews to each entity are taken into account for learning, adaption and forgetting.
Patient Health Description using NFC-Tag-M-Health

Electronic medical recd systems (EMR) have been used in adopting primary care physicians; few also use such system in practice. It is said to be user friendly environment, secure in services, many new ways have been used in order to access health care data. With ought reducing of patient quality care mobile health care system is said to be implemented. By using Near Field Communication (NFC) a novel method is developed that uses basic architecture for m-health care services. For the welfare of the patients requirement the mobile health care system have been used globally. The work highlights fetching patient records by the doctor in a single tap with NFC tag to the NFC reader. With the help of Near Field Communication (NFC), these following steps need to be undertaken with high priority in order as well as provisioning the healthcare of the patients anytime and anywhere through wireless technologies. Introducing such technologies will avoid more paper usage for maintaining patient records physically.
Exploiting context and quality for linked data source selection

The traditional Web is evolving into the Web of Data, which gathers huge collections of structured data over distributed, heterogeneous data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection deserves special attention, because it allows us to identify the sources that most likely contain relevant content. Due to the semantic heterogeneity of the Web of Data, however, it is not always easy to assess relevancy. Context information might help in interpreting the user needs. Moreover, besides relevancy, another important criterion for selecting sources is the quality of data they contain. In this paper, we discuss how context and quality metadata can be exploited to improve source selection.
A Preliminary Study of the Role of Language in Home Network Troubleshooting

We present the results of a preliminary study into the usability of troubleshooting terminology around home computer networks. Forty-seven participants classified 29 terms, selected from interview transcripts and online help forums, in an open card sort. We analyzed words participants explicitly indicated as unfamiliar as well as words that participants misclassified. The study serves as a proof of concept for a broader study to determine whether certain technical terms and/or their colloquial counterparts are understandable by technical novices and intermediates. Our findings indicate that participants found technical and colloquial terms equally problematic. These findings have implications for the design of troubleshooting tools and systems as well as the design of technical support scripts and training.
Multiplet selection

This paper presents a new approach, Multiplet Selection, for multi-faces recognition and its application in student attendance system. Instead of using a linear classifier such as SVM to classify face feature vectors, we adopt a "multiplet selection" approach such that Euclidean distances score between each identity's Anchor face [4] and a random input face are computed. Together with a pre-determined threshold parameter, this score is used for input face-identity pair association. We also develop a student attendance system based on the proposed multi-face recognition algorithm. And testing results video are available at the following URL: https://youtu.be/OZOgcw7B1YI.
Live Demonstration: Autoencoder-Based Predictive Maintenance for IoT

This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection.
Guidelines for Secure Cloud-Based Personal Health Records

Traditionally, health records have been stored in paper folders at the physician's consulting rooms - or at the patient's home. With the rise of technology there have been many offerings of ways to store health records in a safer and better way by using Personal Health Records (PHRs). Web-based PHRs are stored in various ways including the use of Cloud computing. This brings numerous benefits including accessibility and efficiency but there are also privacy and security concerns. This paper proposes guidelines that can assist PHR providers in choosing a Cloud Service Provider that will store PHRs in a secure manner.
Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine

The recent application of artificial intelligence（AI）to clinical medicine has confirmed the usefulness of AI for diagnostic imaging, histopathological examinations, and dermatologic screening. Clinical decision support systems are another promising area to which AI could contribute toward better clinical decisions. We have developed computer-assisted diagnostic support systems to reduce human diagnostic errors such as delayed diagnoses, misdiagnoses, and overdiagnoses. Our three Diagnosis Reminder（DR）systems include two AI systems that use machine learning in their diagnosis algorithms. Here, we compared the diagnostic accuracy of a DR-supported group with that of an unassisted physicians group, using three difficult patient cases provided by experts in general medicine. Our analyses revealed that the three AI diagnostic systems could not provide accurate differential diagnoses up to top 10 in all three patient cases because of incomplete data inputs for machine learning. However, the first DR system, which was developed by an experienced diagnostician over the last 35 years, showed very useful performance in reducing human diagnostic errors when it was used by an expert physician. The use of AI diagnostic systems by knowledgeable physicians will lead to better diagnostic performance. We also discuss the current scenario, future challenges, and prospects for AI diagnostic systems herein.
Statistically significant performance testing of Julia scientific programming language

In this article, we compare the support for SIMD instructions for Julia and Fortran. The comparison is carried out according to the methodology described in work of T. Kalibera, R. E. Jones. The first part of the article gives a brief description of this technique. We emphasize on the practical implementation using Python, NumPy and SciPy. The second part of the article briefly discusses the syntactic capabilities of Fortran and Julia to work with SIMD processor extensions. Specific code snippets are given. Next, the performance of Julia and Fortran is compared for arithmetic operations on arrays of small length. The results are presented in tabular and graphical form.
Covariance intersection fusion for visual tracking with hierarchical features

In recent years, several visual tracking methods have applied multilayer convolutional features to correlation filters, but they mostly use fixed weights to fuse the multilayer response maps, which is difficult to adapt to various scene changes. To address this problem, a robust tracking algorithm based on adaptive fusion of multilayer response maps is proposed. In this paper, we extract multilayer convolutional features from the target’s candidate area to improve the tracking robustness and the translation correlation filter is feed with CNN features extracted from each layer. Different from previous methods, we proposed a fast covariance intersection algorithm to adaptive fuse the multilayer response maps. After the final target center position is determined, we adopted a 1D scale filter through multi-scale sampling with HOG features to handle large scale variations. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion and error accumulation, we present a new random update mechanism to update the translation filters. The experimental results on some challenging benchmark datasets show that the proposed algorithm achieves the outstanding performance against the state-of-the-art tracking methods.
Mathematical Analysis of Algorithms: Part I

With the introduction of some of the major nature-inspired algorithms and the brief outline of mathematical foundations, now we are ready to analyse these algorithms in great detail. Obviously, we can analyse these algorithms from different angles and perspectives. Let us first analyse the basic components and mechanisms of most algorithms so as to gain some insights.
Target tracking in over the horizon radar

This paper describes the application of the Deep Target Extractor (DTE), developed from the Maximum Likelihood Probabilistic Multiple Hypothesis Tracker (ML-PMHT), to measurements from an over-the-horizon radar (OTHR) observing a highly maneuvering target (HMT). We describe the motion model for HMTs that start at very large speeds and makes extremely sharp turns. We then present a measurement model for OTHR that uses ray-tracing software to produce detections across multiple signal paths resulting from ionospheric refractions. Next, we describe the DTE operation in the multiple signal path framework of the OTHR. Finally, we present a test scenario where the DTE performance shows low root-mean-square error (RMSE) for HMT motion parameter estimation.
A New Improved Simplified Particle Swarm Optimization Algorithm

A new simplified particle swarm optimization algorithm is proposed for the question that the basic particle swarm optimization algorithm is easy to fall into the local optima. The algorithm introduces new parameters on the basis of simplifying particle swarm, adjusts parameters by adaptive method, and coordinates the relationship between various parameters, which increases the use of particle information and ensures the difference between particles. Through the test function of eight, the improved algorithm can effectively avoid the premature convergence and greatly improve the convergence speed and convergence precision.
Study on Vulnerability Rating of the Intelligent and Connected Vehicle’s Cybersecurity

Based on the automobile vulnerability data, the rating system of automobile cybersecurity vulnerabilities was constructed by referring to CIA, HEAVENS et al. First of all, basic rules such as naming, coding, classification and description of vulnerabilities are developed to ensure the uniqueness, normalization and compatibility of vulnerabilities. Next, the vulnerability rating system was developed by taking the three elements of scene, threat and impact and the 13 sub-elements of subdivision as evaluation indexes. The evaluation of the existing vulnerabilities and the comparison with the professional evaluation by using the established rating system show that the system is reasonable and effective.
Video Tutoring System with Automatic Facial Expression Recognition

In the past decades, new technologies were introduced and adopted in the e-learning environment to facilitate the learning process. It is commonly acknowledged that emotions are significant factor in gaining cognition which influences information processing, memory and performance. Up until now, affective computing and computer vision are hot research topics in the industries and especially in the field of education. This paper presents the level of performances for two group of students at the same class enrolled in programming course. A Video Tutoring System with Facial Expression Recognition (VTSFER) was developed as teaching tool to facilitate instructions in programming course. The system is intelligent enough to identify emotions through face recognition and was used by BSIT 3rd year students. The students was group into two in which second half of students utilized the VTSFER. An assessment was conducted and determined the level of performances to both group of students. A statistical tool t-test was used to determine the significant difference. The t-value showed a highly significant with a value of 2.1827 and a probability of 0.0382. The result presented that the second half of the students gain more knowledge in using the video tutoring system with facial expression recognition compare to the first half of students who uses a traditional video tutorial. With the intervention of face expression recognition embedded in video tutorial, students were engaged and gained more knowledge in learning.
A provable secure key-escrow-free identity-based signature scheme without using secure channel at the phase of private key issuance

The identity-based cryptosystems furnish us with simplest key management procedures. Yet, they have a very slow adoption in cryptography due to the key escrow problem and the necessity of a secure channel between the user and the Private Key Generator (PKG) to transmit the created private key to the user. In this paper, we propose an identity-based signature scheme that not only solves the key escrow problem but also eliminates the requirement of the secure channel. The proposed scheme is secure against existential forgery under adaptively chosen message and ID attacks in the random oracle model assuming the hardness of the Computational Diffie–Hellmann Problem (CDHP). Furthermore, we compare the efficiency of our scheme to that of a similar established scheme.
The Mechanism of Confirming Big Data Property Rights Based on Smart Contract

The confirmation of data property rights is one of the most important functions of big data trading institutions. This paper builds a specialized classifier which aims to the normalization process of data property confirmation in the process of big data transaction and proposes the mechanism of confirming big data property rights based on Smart Contract.