Reviewerly/exHarmony huggingface dataset:
Design and Evaluation of a Heuristic Optimization Tool Based on Evolutionary Grammars Using PSoCs

Currently, the evolutionary computing techniques are increasingly used in different fields, such as optimization, machine learning, and others. The starting point of the investigation is a set of optimization tools based on these techniques and one of them is called evolutionary grammar [1]. It is a evolutionary technique derived from genetic algorithms and used to generate programs automatically in any type of language. The present work is focused on the design and evaluation of hardware acceleration technique through PSoC, for the execution of evolutionary grammar. For this, a ZYNQ development platform is used, in which the logical part is used to implement factory modules and independents hardware blocks made up of a soft-processor, memory BRAM, and a CORDIC module developed to perform arithmetic operations. The processing part is used for the execution of the algorithm. Throughout the development, the procedures and techniques used for hardware and software design are specified, and the viability of the implementation is analyzed considering the comparison of the algorithm execution times in Java versus the execution times in Hardware.
The Evolving Assessment Landscape and Adaptive Instructional Systems - Moving Beyond Good Intentions

Adaptive instructional systems (AIS) hold great promise for improving the efficiency and availability of quality learning experiences for lifelong learners. Some have speculated that the rich, authentic, and personalized evidence available from AIS in support of a learner’s competency may indeed become so good that the need for summative assessment will diminish. This paper suggests that to prepare for this eventuality, a number of actions may be taken to move from good intent to well understood and research best practices. Specifically, this paper examines aspects of fairness, equity of access, and security as they relate to the use of AIS evidence for eventual summative decision-making. It makes a case for minimally instrumenting AIS to allow for collection of contextual metadata in addition to learning evidence in support of robust research agendas in these areas and in preparation for proper evaluation of suitability of purpose of individual data elements during formative and summative decision-making processes.
Image Watermarking Based on Fourier-Mellin Transform

Geometric attacks are still challenging issues in image watermarking. In this paper, the robustness of different insertion position and shape of the watermark are evaluated in watermarking scheme based on Fourier-Mellin transform. We propose diagonal, rectangular, and circular insertion of the mark. The robustness of these techniques against geometric deformations such as rotation, scaling and translation (RST) is evaluated. Results show that the circular insertion performs better for translation and scaling attacks, while the diagonal insertion is better for rotations and RST attacks. The last point makes the diagonal insertion to be preferred in industrial applications since the combination of RST attacks often occurs in many applications such as printing the image on a physical support, and scanning it (print-scan attack).
Strategic Sustainability Plan In Denpasar 2040

Abstract By 2040, Denpasar will become a vital international city. There are traditions flourishing and unique cultural history alongside sustainability innovations, modern technology, and empowered communities. Its core is that Denpasar embraces traditional philosophy, harmony with God, people and the environment. This report discusses various strategies for making Denpasar a sustainable city in 2040. The region’s natural resources are managed fairly and sustainably to enhance social cohesion and improve resistance to environmental risks. The city will reduce waste through a variety of economic, extensive reuse and waste recycling projects and support a wide range of industries and opportunities for the Denpasar people. Tourism will continue to promote employment and the economy without compromising related ecosystems and biodiversity. Denpasar will not only thrive, but will also be sustainable.
End-User Programming Architecture for Physical Movement Assessment: An Interactive Machine Learning Approach

In this article, we propose an end-user adaptive architecture for movement assessment from RGB videos. Our method allows physiotherapists to add customized exercises for patients from only a few video training examples. The main idea is to take leverage of Deep learning-based pose estimation frameworks to track in real-time the key-body joints from the image data. Our system mimics the traditional physical rehabilitation process, where the therapist guides patients through demonstrative examples, and the patients repeat these examples while the physiotherapist monitors their movements. We evaluate our proposed method on four physiotherapeutic exercises for shoulder strengthening. Results indicate that our approach contributes both to reduce physiotherapist time needed to train the system, and to automatically assess the patients' movements without direct monitoring from the physiotherapist.
Empeiría*: Powering Future Education Training Systems with Device Agnostic Web-VR Apps

This paper presents Empeiría, which uses cutting-edge technologies and novel virtual reality systems to enhance future in-class education training. Empeiría incorporates JavaScript, WebGL, WebVR, and powerful web graphics engines like Babylon.js to create immersive training experiences. It demonstrates a useful application of computer science concepts and increases the dissemination of edge technologies across academic disciplines. Most importantly, Empeiría improves education technology and professional training through the creation of two major software products; an immersive experience editing system (Empeiría-E) and an immersive experience viewing system (Empeiría-V). We show how these virtual reality systems can lead to more effective training and improve our understanding of trainees.
An Exploration of Low-Fidelity Prototyping Methods for Augmented and Virtual Reality

Low-fidelity prototyping is well-established method of developing concepts for traditional two-dimensional user interfaces. This paper considers the techniques and methods that can be used for simulating and early testing of augmented and virtual reality applications. It covers techniques such as body-storming, storyboarding, and mocking up prototypes using a variety of materials such as: sheets of paper, cardboard, sticky tape, straws, pin-boards, Lego bricks, etc. It describes the role of each method within the design process and provides practical advice for its application. The paper draws upon two student workshops that were organized to teach and practice several of these techniques for the design of augmented reality and virtual reality prototypes. Feedback from the participants is reported.
Automatic Spoken Language Identification Using Emotional Speech

Spoken language identification (LID) is the process of automatically recognizing the language from the uttered speech of an unknown speaker. Automatic recognition of language spoken is of vital importance in human-computer interaction and its applications. It can be applied in speech-to-speech translation systems, at call centers to re-route incoming calls to native speaker operators, and in speaker diarization in multilingual environments. The majority of studies which utilized LID systems focused solely on the use of neutral (i.e., normal) speech. However, in real applications and for comprehensive research investigations, the use of emotional speech in LID is crucial. The current study aims at investigating the effectiveness and performance of a deep neural networks (DNNs) based LID system when emotional speech is used.
Simulation Support for Explainable Cyber-Physical Energy Systems

Smart energy grids are evolving from static infrastructures to dynamic systems where Distributed Energy Resources (PV, eCars) are joining or leaving the system randomly. Cyber-Physical (Energy) Systems (CP(E)S) are therefore increasingly complex and dynamic, with several stakeholders (end users, system operators) requiring explanations of the system status/behaviour. The development of solutions for explainable CP(E)S algorithms is however challenging because the deployment and testing in vivo of these solutions is restricted, if not impossible, in terms of the risks of modifying a critical infrastructure. In this paper, we present a semantics based solution to explainable CP(E)S and show how its development is supported by being able to test it in in vitro settings enabled by the BIFROST simulation engine. We validate the proposed solution in a simulated e-mobility use case.
Securing Sensitive Data in Body Area Sensor Network Using Blockchain Technique

Body Area Network which comes under Sensor network is a continuously evolving technology, Which is very helpful in current health care system. One of the crucial specifications in the present health care system is to protect the prosperity parameters like pulse rate (BPM), Electro encephalogram(EEG), Blood pressure, respiration (SPO2) of an individual which are collected from body Sensors against potential adversaries. Thus, the collected sensor data is aggregated and stored in personal server. So, Its' important to have a secure data ingress mechanism. Introducing Blockchain to our concept maintains integrity of the system. Hence this paper considers Blockchain for securing the aggregated sensor data from being tampered.
Simulating probabilistic sampling on particle populations to assess the threshold sample sizes for particle size distributions

The primary objective in sampling is to acquire the smallest sample as a reliable estimate of a particle population. This study aims to assess the threshold (minimum) sample sizes for reliable estimation of particle size distributions. For that purpose, samples were simulated from particle populations artificially generated from the Gates-Gaudin-Schumann model. Then, two-sample Kolmogorov-Smirnov and Chi-Square Goodness-of-Fit tests were implemented between the number-weighted size distributions of samples and their parent populations. Results suggest that continuous size distributions can be estimated with at least 36–40% of the number of population particles. Corresponding masses to estimate continuous distributions varies between 34% and 68% population mass, where smaller populations may require larger samples. Results indicate that probabilistic sampling may be insufficient to estimate the discrete number-weighted size distributions. Probabilistic sampling seems insufficient to estimate the mass-weighted size distribution of widely sized population particles. Estimating mass-weighted distributions requires larger samples than their number-weighted equivalents. Mass-weighted size distributions of samples better fit at the finest or the coarsest size ranges than the mid-size range of their population. If a population is large, the percent population mass taken as a sample is nearly equal to the percent number of population particles.
Development and Technical Experience of Plastic Injection Machine for STEAM Education

Traditional technical education exhibitions for high-school or elementary school students used static posters, demo tools or oral presentation ways to introduce different kinds of professional technical education. For Mechanical engineering and product design fields, limits by learning space, budget, safety issues that hadn’t similar industrial level machines for them to operate and experience those real-world technologies. Normally those exhibition results did not as their prediction, still had a gap between schools and industries. For solving this issue, we introduced how to implement an industrial level plastic injection machine to be education applications for STEAM (Science, Technology, Engineering, Arts and Mathematics). By a small machine structure and simple UI/UX design of HMI (Human-Machine-Interface) control panel indication, the visitors could operate the table type plastic injection machine and produces toys by themselves via play experience. This innovation STEAM teaching model could enhance learning interesting and motivation for younger students before them entrance the technical colleges or universities, let them understand the latest technology trends and assist them chose appropriate development fields and found career plan directions in the future.
Evaluation of Incongruent Feeling During Mouse Operation Using Eye Gaze and EEG

One of the most important issues in the design of a personal computer or a game is creating an interface that a user can operate without a feeling a sense of incongruity between their actions and what appears on the screen. Since the degree of incongruent feeling varies from person to person, it is important to focus on the physiological processes of the user in elucidating the mechanisms underlying the occurrence of this feeling. In this study, we recorded electroencephalography (EEG) and eye gaze from individuals while they completed a simple task using a mouse to move a cursor on a screen. By changing the setting of the mouse speed, we were able to create an environment where the psychological state of incongruent feeling was likely to occur. After each trial, we asked individuals whether they experienced a feeling of incongruency. We then, compared the EEG and eye gaze responses for when the feeling of incongruency occurred and when it did not occur.
Efficient Cumulative Probability Distribution Estimation for Arithmetic Coding

Cumulative probability estimates of binary symbols are performed by finite state machines selecting binary symbols from the predefined finite set, in order to feed state-of-the-art arithmetic coders in image compression devices. This paper discloses novel method for cumulative probability distribution estimation, based on low pass filtering with varying dominant pole, providing high estimation precision and short code length. This embodiment utilizes only integer arithmetic, without multiplication or division operations, thus providing significant reduction in necessary hardware resources of IoT devices.
A Method and Analysis to Elicit User-Reported Problems in Intelligent Everyday Applications

The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This article contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps, and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps’ algorithmic decision-making. We enriched this data with users’ coping and support strategies through a follow-up online survey (N = 286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output rather than processes.
Forensic Exchange Analysis of Contact Artifacts on Data Hiding Timestamps

When computer systems are increasingly important for our daily activities, cybercrime has created challenges for the criminal justice system. Data can be hidden in ADS (Alternate Data Stream) without hindering performance. This feature has been exploited by malware authors, criminals, terrorists, and intelligence agents to erase, tamper, or conceal secrets. However, ADS problems are much ignored in digital forensics. Rare researches illustrated the contact artifacts of ADS timestamps. This paper performs a sequence of experiments from an inherited variety and provides an in-depth overview of timestamp transfer on data hiding operations. It utilizes files or folders as original media and uses the timestamp rules as an investigative approach for the forensic exchange analysis of file sets. This paper also explores timestamp rules using case examples, which allow practical applications of crime scene reconstruction to real-world contexts. The experiment results demonstrate the effectiveness of temporal attributes, help digital forensic practitioners to uncover hidden relations, and trace the contact artifacts among crime scenes, victims, and suspects/criminals.
CommonsHood: A Blockchain-Based Wallet App for Local Communities

In this paper, we present CommonsHood, a Blockchain based Wallet app aiming to provide the general public with the tools to not only use, but also create new kinds of cryptographic tokens for their own purposes, something that currently resides only in the hands of technologists. It provides communities with instruments for financial inclusion to support the sustainability of the local economy. It provides commons and associations with instruments to help finance themselves with tokens representing prepaid cards, crowdfunding, complementary currencies, to share tools and infrastructures with tokens representing access rights. It provides small retailers with tokens representing loyalty tools and institutions with purpose-driven tokens to reward volunteers for, e.g., recycling. It allows the dematerialization of tickets and collectibles. All tokens can be distributed to the Wallet app of other users, via websites, QR codes, or proximity marketing using geolocalization. The use of Blockchain allows us to construct an open decentralized and disintermediated infrastructure to avoid the fees and data control of services such as group-buying or crowdfunding websites, and to have a general purpose single app, rather than different ones for different associations and retailers. Thanks to its flexibility, actually CommonsHood is used in different projects for supporting communities in the co-creation of local economy models.
Understanding How Visitors Interact with Voice-Based Conversational Systems

Museums and Art exhibition spaces are adopting Artificial Intelligence (AI) systems to engage and attract visitors in several contexts. The use of AI can boost visitors’ attention, promote informal learning through conversations in front of the exhibits, and motivate visitors to act socially. In this paper, we describe a voice-based conversational system Iris+, in which visitors are inquired to answer questions to an agent. It is a proactive agent that invites visitors to reflect and take action to improve future world. First, we will describe how this system works. Second, we will show the outcomes of evaluation studies with visitors in situ and, a survey shows how visitors engaged in social action after interacting with IRIS+ and public demographics. Based on those visitors’ studies, we propose a set of challenges to design conversational systems in public spaces and the improvements were incorporated into the redesign of IRIS++.
Placing the suspect at a PC: A preliminary study involving fingerprints on keyboards and mice

Digital devices now play an important role in the lives of many in society. Whilst they are used predominantly for legitimate purposes, instances of digital crime are witnessed, where determining their usage is important to any criminal investigation. Typically, when determining who has used a digital device, digital forensic analysis is utilised, however, biological trace evidence or fingerprints residing on its surfaces may also be of value. This work provides a preliminary study which examines the potential for fingerprint recovery from computer peripherals, namely keyboards and mice. Our implementation methodology is outlined, and results discussed which indicate that print recovery is possible. Findings are intended to support those operating at-scene in an evidence collection capacity.
A Heterogeneous Ensemble Learning-Based Acoustic Fall Detection Method for Elderly People in Indoor Environment

Falling is a severe hazard among elderly people, which always follows unpredictable consequences such as a permanent disability or even death especially for the people living alone. Accurate and reliable automatic fall detection based on sound in an indoor environment enables elderly people to receive instant treatment and can alleviate the severe consequences of falls. Aim to achieve this purpose, we propose a novel heterogeneous ensemble learning (HEL) method for indoor acoustic fall detection. Firstly, we pre-process the indoor fall acoustic in the dataset and extract the fall-like sound signals by short-time energy. Then, the MFCC and spectrogram is obtained as features. Finally, we adopt the HEL method to classify the daily fall-like signals and the actual human fall, which use bagging ensemble selection method and stacking method to combine three independent base classifiers. This paper demonstrates the stacking-based HEL method can improve the accuracy from 87.83% to 94.17% compared to the single component base classifier and it can recognize falling sounds with better performance, which greatly avoids the safety hazards of the elderly due to falls in an indoor environment.
Single-Image Super-Resolution based on Steering Kernel and Gaussian Process Regression

Single-image super-resolution (SR) imaging is a fundamental problem in image processing; it is important in entertainment, video surveillance, remote sensing, medicine, and other fields. Gaussian process regression (GPR) is a kernel method whereby nonlinear mapping relationships in data can be learned. However, the traditional Gaussian kernel function used in GPR is isotropic and fails to capture complex image structures. Accordingly, the structure information of image patches, termed steering kernel coefficients (SKCs), is extracted by a steering kernel function. After patches with similar structure are clustered according to their SKCs, an anisotropic automatic-relevance-determination (ARD) kernel function is used to learn the model for each cluster. Aiming at learning a structure-sensitive GPR model, we integrate the SKCs and ARD to achieve improved performance for GPR-based SR. Experiments demonstrate that the proposed method can effectively capture the structural relevance of image patches and yield promising results.
The Effect of Multiple Visual Variables on Size Perception in Geographic Information Visualization

Flow maps are a common form of geographic information visualization to show the movement of information from one location to another. Visual variables are essential elements of information representation. Previous studies have evaluated the perceived effectiveness of individual visual variables in a flow. However, the composition of flows usually includes multiple visual variables, and how to evaluate the usability of multiple visual variables has not been well documented. In this study, we investigated the influence of four visual variables (Thickness, Length, Brightness, and Hue) in flows on size perception. The results showed that length and brightness had a significant influence on the accuracy of size perception, while hue had no significance. Furthermore, multiple visual variables might involve a higher visual complexity and have a significant effect on response efficiency. In addition, we found that different references significantly affect the results of comparison judgments. The basic study could help improve the usability of flow maps in geographic information visualization.
Analysis of Closed- form Wigner Distribution in Linear Canonical Domains for Continuous-time Noisy Signals

Closed-form Wigner distribution in linear canonical domains (CICFWD) has been recently proposed for time-frequency (TF) analysis of non-stationary signals. The CICFWD represents entire class of transformations offering, at the first glance, significant flexibility in designing particular TF representation. In this paper we are studying noise influence to the CICFWD for continuous-time environment by deriving the mean value and variance of transform for Gaussian noise environment.