Reviewerly/exHarmony huggingface dataset:
Data Journalism in the Pacific: Practices and Perceptions

To date research about data journalism has been primarily focused on how it is practised in US and European contexts. This chapter will present the results of a study into the emerging practice of data journalism in the Pacific. It will be based on a study that combines content analysis of data journalism published in newspapers from six Pacific nations, documenting the extent and types of data journalism being practised and qualitative data sourced from interviews with editors and senior journalists working in the region. The study will also incorporate input from journalism educators and students at the University of the South Pacific based in Fiji, with campuses spread throughout Oceania. The existence of the Australian Broadcasting Corporation’s Pacific Media Assistance Scheme; the Melbourne-based not-for-profit Asia Pacific Journalism Centre (established in 2003 to strengthen the capacity of the news media sector in the region) and foreign aid programmes focused on media capacity building evidence widespread understanding that media organisations in the Pacific face a number of challenges. Evaluation studies have identified a number of these problems including low wages, poor working conditions and lack of access to appropriate technology, as well as social structures that can inhibit journalists from holding powerful community members to account (Wake, Pacific Journalism Review, 22(2), 35–48, 2016). Given these circumstances it is not surprising that uptake of data journalism in the region has been slow. Research on the uptake of journalism in university journalism programmes in Australia in 2016 found that key inhibitors to the incorporation of data journalism were the lack of familiarity with the required skills and a lack of time to upskill, in addition to difficulties caused by students having low or mixed numeracy skill levels. This study will test whether similar issues are limiting the uptake of data journalism in the Pacific in educational settings, as well as in newsrooms.
Keystroke- Level Model to Evaluate Chatbot Interface for Reservation System

The tour package reservation system is an important part of improving tourism services. Reservations must be able to meet the information needs of prospective customers and can serve the desired tour package bookings. A reservation system is usually a form that must be filled in sequence by prospective visitors. This paper discusses the evaluation of the application of the chatbot interface on the reservation system with the keystroke-level model. Changing the interaction design that previously did the task fills out the form into a conversation interaction. The aim is to increase the speed of the ordering process through the system. Prospective visitors do not need to fill in the form, they only need to have a conversation with the system while entering the order data. The evaluation results using the keystroke-level model show that the chatbot interface can increase the speed of the process by shortening steps.
Cache Side-Channel Attacks: Flush+Flush and the Countermeasures Time Gap

In cloud computing, profitability among others, is the driving force that encourages full utilization of computing resources. Hence, scenarios where one or more users are co-located on the same CPU but on different virtual machines are normal. Even though this setting brings profitability, unfortunately it also brings security and privacy issues. Software-based sand-boxing techniques are not perfect, as a result, users through the shared memory of an application, can leak secret information. To tackle this problem, some work has been published proving how it is possible to detect cache side-channel attacks using HPCs (Hardware Performance Counters). However, this particular method of detection may only be effective for cache side-channel attacks that cause a lot of cache activity, especially in the form of cache misses, among others. Using HPCs, we observe that the Firefox Web Browser (a benign application) causes more page-faults and cache misses among others, in comparison with the FLUSH+FLUSH, FLUSH+RELOAD and PRIME+PROBE in our attack. In this paper, we focus on using the FLUSH+FLUSH method of cache side-channel attacks, to distinguish between input against the Links text-based browser in order to gain private information about last visited websites. Because the FLUSH+FLUSH attack method causes little or no cache misses, as a result, the attack is more stealthy and in some cases more accurate compared to the state of the art FLUSH+RELOAD attack method. We make the assumption that both the victim and the adversary are co-located on the same CPU but on different virtual machines and different cores, and that they have the same application installed, then through the shared memory of the application, the attack is made possible. Our experiment results indicate that the shared memory of computer applications may be vulnerable to cache side-channel attacks and that more work needs to be done to protect users in the cloud computing system in this aspect.
A Data-Driven Framework for Optimal Placement of Grid-Connected Solar Generation

This work presents a decision-making approach for selecting an optimal placement of the grid-connected solar generation using Geographical Information System (GIS) as the decision-making tool. A terrain analysis for solar radiation assessment, as well as buildings and vegetation spatial data are analyzed in order to determine the shadow impact that can be anticipated for medium or large-scale photovoltaic (PV) installation. In addition, different historical weather conditions are considered and integrated into the model to show the impact of this variable on the solar generation output. Some details of the methodology, testbed development and results related to the selection of potential sites for PV installation are presented. To illustrate the process and proposed methodology, an example using large scale synthetic network is implemented.
Bulk Mode Encryption Devices Modification for Usage of Quantum Key Distribution

In this paper we describe a concept for modification of high capacity bulk mode (layer 1) cryptographic device that is originally designed for pre-shared key distribution to adopt quantum key distribution system. The main goal is to achieve reliable session key agreement suitable to operate at extreme bit rates without unnecessary frequent key agreement protocols, like commonly used Diffie-Helman. The architecture of solution is designed to achieve high encryption speed and maximal level of security is described in details. It is designed by utilizing field programmable gate array (FPGA) technology at gate level, bios-only microcontroller and Linux based embedded computer.
Intervention-Aware Early Warning

How can we early warn against an impending student drop out or an adverse health condition in near real-time? More challengingly, how do we learn to early warn from data containing confounding interventions-e.g., tutoring or medicines-while remaining interpretable to the human decision maker? We consider the problem of learning to interpretably early warn from labeled data tainted by interventions. We first identify three principles that an early warning system should follow. We then propose SmokeAlarm which provably obeys these principles and produces early warning scores in an online manner. Notably, learned model is "bi-inspectable", i.e., it can be visualized both in the presence and in the absence of interventions. Experiments demonstrate the efficacy of SmokeAlarm over prior approaches.
Shaping the Curve

Research Background: The interpretation and representation of music via colour, form, and movement has been central to abstract animation since the invention of cinema. Animated representations in a live performance context have presented challenges to animators from Oskar Fischinger’s Raumlichtkunst of 1926 until the present day. Research Contribution: Advancements in 3D and 2D animation software and hardware drive experimentation into new techniques and creative approaches to creating animation for live performances. Shaping the Curve represents a series of animation experiments aimed at exploring and evaluating the creative possibilities afforded in the latest iterations of a selection of animation software (Autodesk Maya MASH plugins, and Red Giant Trapcode suite for Adobe After Effects) in the context of the visual representation of music in a live performance. This project makes an important contribution to the wider discourse of designing effective abstract animation to music. It provides further points of discussion and inspiration for animation practitioners and theorists as they continue to seek visual solutions to musical conundrums.Research Significance: The Melbourne International Saxophone Festival is Melbourne’s premiere world-class saxophone event, run annually by Melbourne Saxophone. Behind this organisation are renowned international saxophone performers and academics who are dedicated to furthering and showcasing saxophone performance and education. Screening at MISF, in front of an audience comprising primarily of musical professionals, forms an opportunity to prompt insightful discussion across the musical and animation disciplines, inciting further creative ideas and collaborations.
Tensor Completion based 3D Reconstruction of Binocular Stereo Vision

Binocular stereo vision is a common way for 3D reconstruction of objects, which is core technology in the fields of computer-aided geometric design, machine vision and so on. However, the conventional binocular stereo vision algorithms are very sensitive to the ambient lighting, restricting their further promotion. The method of tensor completion, applied to the progress of preprocessing the captured binocular image, provides a way to solve this problem. In this paper, a binocular stereo vision algorithm that uses low-rank tensor completion to perform 3D reconstruction is presented. In the pictures, the positions where the ambient illumination are strong are regarded as information that cannot be observed, and the others are regarded as observable information. The unobservable information will be completed using the observable information based on tensor completion. The results show that the 3D reconstruction results obtained using tensor completion are better than those without tensor completion.
Planning in Stochastic Environments with Goal Uncertainty

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem - a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution. GUSSPs introduce flexibility in goal specification by allowing a belief over possible goal configurations. The unique observations at potential goals helps the agent identify the true goal during plan execution. The partial observability is restricted to goals, facilitating the reduction to an SSP with a modified state space. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time using FLARES - a start-of-the-art probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results on a search and rescue mobile robot and three other problem domains in simulation.
Probability Density Computation Neural Network for Time Series Data

Traditional point prediction systems compute a most probable value without representing the uncertainty. The point prediction is a value close to the mean or the median. A person or an autonomous system may require a prediction corresponds to a different cumulative probability (CP), known as the uncertainty bound. Therefore, in this paper, we present a probability density computing neural network (NN) training procedure. To overcome the limitation of an effective cost function, example uncertainty bounds are constructed with the help of correlation. Similar occurrences are selected through the correlation and weights are assigned to each similar occurrence based on both shape-similarities and ratio based similarities. Then example results from similar samples are considered. The normalized weighted distribution of examples is the probability distribution. Finally, a shallow NN with the example probability density is trained. The NN receives input circumstances and the cumulative probability. The NN returns the value corresponds to the given circumstances and the cumulative probability. Proposed cumulative probability computation point from a shallow NN is less computation extensive compared to the correlation-based similarity analysis. Moreover, we propose a probability density computation NN for the first time. We also upload an example code to the GitHub.
An Efficient Non-Occluded Face Area Detection Method under Unconstrained Environment of Smartphone Database

Occlusion is one of the most challenging problems for recognizing faces captured in unconstrained conditions. This paper focuses on direct detect the useful part in the face which used for features extraction process. To solve occlusion problem and detect un occluded face parts, a Non-Occluded Face Area Detection (NOFAD) method based on geometric features is proposed. The performance of the proposed method is evaluated on the SFV and MOBIO databases, and the experimental results show the outperformance of the proposed method in extracting the non-occluded facial area, realising an accuracy rate of 99.4% and 99.1%, respectively.
Prototype of Seismic Alarm Based on Internet of Things

Seismic events have been responsible for several human catastrophes and Latin America has not been exempt from these events. High magnitude telluric movements have caused the collapse of infrastructures causing hundreds of deaths in different countries. Although these are unpredictable natural disasters, for years seismic alarm systems have been perfected to warn the population. Some of the most current designs have robust models capable of alerting seismic events before they are perceptible to people. Hence, this article presents an early notification seismic alarm system based in Internet of Things, focusing on the use of a SigFox network for the integration between an electronic prototype, data monitoring and seismic warning, in order to make it interoperable with existing systems. The system's tests demonstrated stable and reliable communication, along with a processing algorithm capable of detecting seismic signals that alerts the population 8 seconds after the arrival of the primary waves of an earthquake, giving more time to the evacuation process.
Parallel Algorithms for Mean, Variance, and Standard Deviation over a 2D-Mesh Network

Abstract: Due to advanced technology and usage of internet at worldwide scale, there has always been a challenge to handle and manage the huge amount of data in an efficient way. There are so many statistical and non-statistical methods applied to data to find result and conclusions that can later be used to solve many other complex problems in an easy and fast way, which also reduces the cost. In this study, we propose algorithm to parallely implement the process of finding mean, variance, standard deviation over a 2D-mesh network. Our proposed algorithm successfully finds standard deviation in 5√N+8 times. The algorithm can be compared with the conventional method for finding standard deviation in N2 times, whereas the standard deviation with parallel implementation takes 5(N-1)+4 times for the similar networks. Keywords: Fast All Sum, mean, variance, standard deviation etc. Cite this Article: Gaytri Kumari Gupta, Sudhanshu Kumar Jha. Parallel Algorithms for Mean, Variance, and Standard Deviation over a 2D-Mesh Network. Recent Trends in Parallel Computing. 2019; 6(3): 17–22p.
Study of Modified Chaotic Artificial Bee Colony Algorithm Based on Process Solutions

Artificial Bee Colony (ABC) has been wildly used in various global optimization problems. In order to avoid premature convergence, a modified chaotic artificial bee colony algorithm using process solutions (CABC-WTB) is presented. Chaos initialization increases population diversity, chaotic local search helps to get out of local minima, process solutions are used to improve convergence speed. Comparative studies have been performed using 800 randomly generated functions. The proposed algorithm raises the performance of ABC algorithm.
Performance-accuracy Optimization of Face Detection in Human Machine Interaction

In intelligence human machine interaction systems, performance and accuracy of face detector play a vital role. After building a particular system, the hardware and face detection algorithm are constants. As a result, the image resolution becomes the tool to adjust the accuracy. However, the image resolution impacts the performance of such systems. Especially, in real-time systems, detection delay can degrade the overall performance drastically. If the resolution is reduced, the system responses faster. However, the accuracy of the detection degrades. On the other hand, if the resolution increases, the accuracy increases but the performance degrades. Trial and error basis approach is an intuitive solution to gain optimum performance and accuracy. However, it is a tedious method and does not exploit all of the possibilities. In this paper, a mathematical model has been derived from experimental data using curve fitting method for performance-accuracy optimization of face detection in human machine interaction.
Ontology Based Decision Support in Career Choice

The objective of many research works in educational field of studies is the discovery of the natural student's motivation to study, timely personal development planning in order to make the best and individually adequate educational choice. For that purpose, the following studies have been performed: statistical analysis of the first year performance for a cohort of more than ten thousand students of the University of Alicante, data intellectual analysis, decision on alternative educational alternatives ranging for every students including the competence management (aptitudes, cognitive and social models). The ontology has been developed in order to rationalize, organize and automate the processes, procedures of the decision support system, based on the rules and precedents within the semantic net context notions of the study area.
Autonomous pilot drones for detecting objects in a space

Abstract The rapid development of technology in the field of air transportation technology has made Drone a widely developed flying vehicle. Drones have many uses for example for hobbyists, navigation tools, and transportation equipment. Drones have also been developed to monitor environments that are difficult for humans to reach. The ability to control manually with limited distance becomes an obstacle. This study describes the Mission Definition System (MDS) which is designed to help in defining the Drone mission easily and efficiently and to support the application of autonomous flight. Under the direct supervision of the pilot line, the selection of Drone control will be possible if in an unsafe situation. After the user defines the mission, it is then forwarded to the Mission Calculation Machine, which is tasked with carrying out the exact calculations to get a compatible and predictable flight plan. A Flight Plan is a list of orders that must be completed by a Drone to completing the designed mission. Test results and measurement of the degree of accuracy of the autonomous system have been tested 5 times for each object and have failed the command once because of natural factors when tested. In terms of the image mapping, drones succeed in detecting objects with only one failure on Saturday by mapping several locations that are targets. For sensors that are used only have one failure to detect obstacles.
Grid Monitoring Alarm Event Recognition Method Integrating Expert Rules and Deep Learning

SCADA collects a large amount of alarm information during the power system operation. If the controller cannot quickly judge the monitoring alarm events, it may cause the expansion of the accident. Therefore, we propose a grid monitoring alarm event recognition method combining expert rules and deep learning. The method includes two stages: expert knowledge base determination and deep learning recognition model determination. In the first stage, the characteristics and rules related to monitoring alarm events are sorted, the expert knowledge base is constructed, and the alarm events are initially identified. In the second stage, the preliminary judgment results and the alarm information are combined into the convolutional neural network (CNN) recognition model, thus the final recognition result is obtained.
Identification of Software Problem Report Types Using Multiclass Classification

Users often experience failures, have problems, or have further requests with regard to the software they use. Software companies provide customer care service or customer support to handle such issues or problems which sometimes can be resolved right away and sometimes have to be forwarded to responsible persons. Efficiency of problem handling is very important to software companies to maintain customer satisfaction. This paper reports a case of a software company in Thailand whose derivatives trading software is used by a large number of broker companies and their customers. The software company has experienced problems where the reported software problems are classified incorrectly and hence are directed to the wrong persons and have to be reclassified. Assigning the problem reports to the responsible persons in a timely and correct manner is crucial especially for the nature of the trading software. This paper presents a multiclass classification method to classify 11 problem report types that are found in this trading software. Machine learning algorithms that are applied include Multinomial Naïve Bayes, Linear SVC, Random Forest, and Logistic Regression, and consider both lexical features and metadata of the problem reports. In an experiment, Linear SVC performed best, having the F1 score of 91.69% and accuracy of 91.79% when using unigram and trigram features of the problem report text which is written in Thai and English. The paper presents a support tool for classifying new problem reports and providing a dashboard of the problems found in this derivatives trading software for the software team to manage its maintenance.
A Multi-view Images Classification Based on Shallow Convolutional Neural Network

Multi-view images represent the target object from multiple perspectives. Learning the target object information from different viewpoints helps to improve the accuracy of multi-view images classification. We propose a multi-view recognition method (SCNN-a) based on shallow convolutional neural network. In order to improve the generalization capability and classification performance of model, we develop a new multi-view images classification method (SCNN), which adds Dropout (after each max-pooling layer) technology to SCNN-a. SCNN-a and SCNN regard the images from predefined views as latent variables, and extract the high-order features of multi-view images with two convolutional layers. Experimental results show that SCNN achieves similar accuracy to the state-of-the-art result of [7] with less layers and time complexity.
Face Detection and Natural Language Processing System Using Artificial Intelligence

The objective of this paper is to look at a system based on artificial intelligence that recognizes the faces and the system that processes the natural language. The process of face recognition has three phases: face representation, removal of features, and classification. But the most important phase-out of every phase is extraction. At this stage, the image is extracted from the unique features of the face image. However, variation in appearance remains one of the main factors affecting the accuracy of face recognition systems.