Reviewerly/exHarmony huggingface dataset:
Solar Tracking System for Several Groups of Solar Panels

Solar tracking systems have proven their effectiveness and thereby justified their cost price. They orient solar panels and increase the conversion of solar energy into electricity. Positioning systems work based on different methods and principles. In most cases they are individual systems attached to a single solar panel array. This paper presents a proposal for a system to control the solar orientation of a virtually unlimited group of solar panels located in the same geographical area. It allows orientation along two axes, with reduced costs due to the own consumption of the orientation motors.
A Word Cloud Model based on Hate Speech in an Online Social Media Environment

Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acquisition and pre-processing, feature extraction, model development, visualization and viewing of word cloud model result. The results present an image in a series of text describing the top words. This model can be considered as a simple way to exchange high-level information without overloading the user's details.
Directing evolution

As computing power grows, the automated specialization and design of evolutionary algorithms (EAs) to tune their performance to individual problem classes becomes more attractive. To this end, a significant amount of research has been conducted in recent decades, utilizing a wide range of techniques. However, few techniques have been devised which automate the design of the overall structure of an EA. Most EA implementations rely solely on the traditional evolutionary cycle of parent selection, reproduction, and survival selection, and those with unique structures typically replace this with another static, hand-made cycle. Existing techniques for modifying the evolutionary structure use representations which are either loosely structured and highly stochastic, or which are constrained and unable to easily evolve complicated pathways. The ability to easily evolve complex evolutionary pathways would greatly expand the heuristic space which can be explored during specialization, potentially allowing for the representation of EAs which outperform the traditional cycle. This work proposes a methodology for the automated design of the evolutionary process by introducing a novel directed-graph-based representation, created to be mutable and flexible, permitting a black-box designer to produce reusable, high-performance EAs. Experiments show that our methodology can produce high-performance EAs demonstrating intelligible strategies.
A Design Method for Multiclass Classifiers

Logic minimization is used to design multiclass classifiers for machine learning. This can be an alternative to a neural network. A partially defined classification function f is derived from the training set. Our multiclass classifier correctly classifies not only all the samples in the training set, but also much of samples in the unseen test set. To improve the test accuracy, 1) minimization of variables in f;2) minimization of the number of products in a ternary SOP for f; and 3) maximization of the number of literals in a ternary SOP for f, are performed. Experimental results using MNIST and fashion MNIST data set show that logic minimization improves the test accuracy. Our classifiers can be easily implemented by LUTs and glue logic.
MRINet: Multilevel Reverse-Context Interactive-Fusion Network for Detecting Salient Objects in RGB-D Images

The use of RGB-D information for salient object detection (SOD) is being increasingly explored. Traditional multilevel models handle both low- and high-level features similarly, as they use the same number of features for blending. Unlike these models, in this paper, we propose multilevel reverse-context interactive-fusion (MRI) network (MRINet) for RGB-D SOD. Specifically, first, we extract and reuse different numbers of features depending on their level; the deeper the information, the more times do we perform the extraction. Deeper information contains more semantic cues, which are important for locating salient regions. Thereafter, we use an RGB MRI block (MRIB) to merge RGB information at different levels; furthermore, we use depth features as auxiliary information and an RGB-D MRIB for full merging with RGB information. RGB and RGB-D MRIBs can reconstruct the high-level feature map in high resolution and integrate the low-level feature map to enhance boundary details. Extensive experiments demonstrate the effectiveness of the proposed MRINet and its state-of-the-art performance in RGB-D SOD.
Expression Transfer Using Flow-based Generative Models

Among the different deepfake generation techniques, flow-based methods appear as natural candidates. Due to the property of invertibility, flow-based methods eliminate the necessity of person-specific training and are able to reconstruct any input image almost perfectly to human perception. We present a method for deepfake generation based on facial expression transfer using flow-based generative models. Our approach relies on simple latent vector operations akin to the ones used for attribute manipulation, but for transferring expressions between identity source-target pairs. We show the feasibility of this approach using a pre-trained Glow model and small sets of source and target images, not necessarily considered during prior training. We also provide an evaluation pipeline of the generated images in terms of similarities between identities and Action Units encoding the expression to be transferred. Our results show that an efficient expression transfer is feasible by using the proposed approach setting up a first precedent in deepfake content creation, and its evaluation, independently of the training identities.
Quantized Fully Convolution Neural Network for HW Implementation of Human Posture Recognition

In this paper, a very tiny HW design of a Quantized Fully Convolutional Neural Network is proposed which demonstrates that accurate Human Posture Recognition can be designed by exploiting only pressure sensors and keeping the computation close to the data sources, according to the edge computing paradigm. The custom design of the QFCN exploits a base-2 quantization scheme to achieve state-of-the-art performances in terms of classification accuracy, together with a very reduced number of mapped physical resources and power consumption. Trained and validated on a public dataset for in-bed posture classification, the QFCN exhibits an accuracy up to 96.77% in recognizing 17 different postures. When prototyped on a Xilinx Artix 7 FPGA the design achieves less than 7 mW dynamic power dissipation and a maximum operation frequency of 26.6 MHz, compatible with an Output Data Rate (ODR) of the sensors of 9.13 kHz.
From 2-Sequents and Linear Nested Sequents to Natural Deduction for Normal Modal Logics

We extend to natural deduction the approach of Linear Nested Sequents and of 2-sequents. Formulas are decorated with a spatial coordinate, which allows a formulation of formal systems in the original spirit of natural deduction -- only one introduction and one elimination rule per connective, no additional (structural) rule, no explicit reference to the accessibility relation of the intended Kripke models. We give systems for the normal modal logics from K to S4. For the intuitionistic versions of the systems, we define proof reduction, and prove proof normalization, thus obtaining a syntactical proof of consistency. For logics K and K4 we use existence predicates (following Scott) for formulating sound deduction rules.
Equivalence class selection of categorical graphical models

Learning the structure of dependence relations between variables is a pervasive issue in the statistical literature. A directed acyclic graph (DAG) can represent a set of conditional independencies, but different DAGs may encode the same set of relations and are indistinguishable using observational data. Equivalent DAGs can be collected into classes, each represented by a partially directed graph known as essential graph (EG). Structure learning directly conducted on the EG space, rather than on the allied space of DAGs, leads to theoretical and computational benefits. Still, the majority of efforts has been dedicated to Gaussian data, with less attention to methods designed for multivariate categorical data. A Bayesian methodology for structure learning of categorical EGs is then proposed. Combining a constructive parameter prior elicitation with a graph-driven likelihood decomposition, a closed-form expression for the marginal likelihood of a categorical EG model is derived. Asymptotic properties are studied, and an MCMC sampler scheme developed for approximate posterior inference. The methodology is evaluated on both simulated scenarios and real data, with appreciable performance in comparison with state-of-the-art methods.
A 3D medical image registration method based on multi-scale feature fusion

Abstract Deformable medical image registration refers to finding a certain transformation so that the corresponding points of two medical images can be aligned in space. This has important clinical applications. In this article, we propose an unsupervised end-to-end medical image registration method. In this method, the fixed image and the moving image are concatenated in series and input into the convolution neural network to obtain the feature images of different scales. In order to improve the ability of neural network to capture global and local information, we fuse feature maps of different scales. The spatial transformation network uses the deformation field to deform the moving image, so as to realize the registration of the two pairs. We validate our method in the ABIDE data set and compare it with some classic state-of-the-art methods. The experimental results show that our method improves the registration accuracy of image pairs.
Virtual Reality Applications for the Treatment of Anxiety and Mental Disorders

Virtual Reality (VR) environments are computer-generated mediums that try to provide the user a sense of presence. The use of VR term in science has a long history, but an increasing number of commercial cost-effective VR devices are creating new possibilities and applications for it. Regardless of entertainment, as the most widespread market for VR, the application of VR in medicine is attracting great attention in the scientific field. VR can be used as a tool for public healthcare, as a teaching environment for medical students or to improve the skills of experts, together with a well-accepted method for physical rehabilitation and mental therapies. The focus of this review paper is on the use of VR for psychiatric and anxiety disorders. Several empirical studies show the effectiveness of VR in improving the conditions of patients in a manner comparable to traditional methods and with long-lasting effects in their daily lives. However, a small number of case studies or the lack of a detailed comparison between VR and conventional methods is the shortcoming of current studies. In this paper, we review different VR applications focusing on the identification of the technologies used, the evaluation methods applied, and the results of the studies.
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer

Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.
Power-Law Distributed Graph Generation With MapReduce

A graph generator is a tool which allows to create graph-like data whose structural properties are very similar to those found in real world networks. This paper presents two methods to generate graphs with power-law edge distribution based on the MapReduce processing model that can be easily implemented to run on top of Apache Hadoop. The proposed methods allow the generation of directed and undirected power-law distributed graphs without repeated edges. Our experimental evaluation shows that our methods are efficient and scalable in terms of both graph size and cluster capacity.
SDD: A trusted display of FIDO2 transaction confirmation without trusted execution environment

The FIDO2 protocol allows users to perform online authentication by setting a public key and avoids the shortcomings of the traditional password authentication mechanism in terms of security. During transaction confirmation with the FIDO2 protocol, users must confirm the transaction message and then sign this message using a cryptographic signature scheme. However, it is a challenge to show that transaction messages are correct or trusted in practice. No available authenticator that supports the FIDO2 protocol uses trusted display hardware to guarantee the correctness of transaction messages. This paper proposes a trusted display of transaction messages by developing a lightweight and trusted base on hardware without a trusted execution environment (TEE). The proposed trusted display is easily applied in the FIDO2 protocol and resists four types of well-known attacks, such as malicious process tampering with display and occupying an authenticator. The experimental results indicate that the improved FIDO2 protocol slightly increases the processing overhead compared to the traditional protocol.
Dark web traffic detection method based on deep learning

Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.
Performance Monitoring for Exercise Movements using Mobile Cameras

Despite numerous devices targeted to fitness tracking, the strength training domain has often been overlooked and understudied. In this paper, we propose a smartphone camera based approach to track users' strength training workouts, as well as metrics pertaining to their form and performance. Our goal is to detect the repetitions in a workout without requiring user intervention or any training data from the user. Unlike many existing systems, our proposed system is scalable, low-cost, and widely accessible. We gather data from two sources for 5 exercises across 25 subjects. We compute performance metrics such as range of motion, velocity, and duration from each repetition with median errors less than 10%. These results demonstrate that commercial off the shelf smartphone cameras can be used to accurately detect and count repetitions in user movements, as well as to compute rep-by-rep user performance.
Underwater image enhancement algorithm combining color correction and multi-scale fusion

Due to the absorption of light by water and the scattering effect of suspended particles on light, underwater optical images often have many problems, such as noise interference, blurred details, uneven illumination, low contrast and color distortion. In order to solve those problems, a novel underwater image enhancement algorithm which combines color correction and multi-scale fusion is proposed in this paper. First, the color of the underwater images are corrected by the improved multi-scale retinex with color restoration algorithm. After that, the contrast and edge details of the color-corrected image are enhanced by the contrast limited adaptive histogram equalization algorithm and sharpening algorithm. Then, we calculate multiple weights of the contrast-enhanced image and edge-enhanced image to construct normalized weight maps. Finally, we input the images into the Laplacian pyramid, and input the normalized weight maps into the Gaussian pyramid for multi-scale decomposition and fusion reconstruction layer-by-layer . Through multi-scale fusion, we combine the advantages of color-corrected image, contrast-enhanced image and sharpened image without introducing Manmade noise. Comparative experiment results show that the underwater image enhancement algorithm proposed in this paper can significantly improve the contrast of the underwater images, correct the color deviation, enriches the edge details and make the image clearer.
Provably Accurate Memory Fault Detection Method for Deep Neural Networks

Deep Neural Networks (DNNs) have been widely deployed in real-world systems, many of which have strict safety constraints. Soft errors on memory acceleration platforms for DNNs can degrade their inference accuracy and result in silent data corruption, which can have severe consequences in safety-critical applications. No doubt to say, efficient and effective techniques to detect and mitigate memory faults are needed. In this paper, we propose a novel methodology to diagnose the presence of faults in the memory of DNN accelerators. Our method queries the protected DNN with a set of specially crafted test cases that can accurately reveal if model parameters stored in the hardware are faulty. We provide a theoretical guarantee for the performance of our method and conduct systematic proof-of-concept experiments by simulating memory faults on computer vision models. Our empirical evaluations corroborate the effectiveness and efficiency of our approach. Detecting faults with our method requires simple decision-based access to the inference capability of the DNN, and does not require any additional functionality from the accelerator, which makes our method ideal for legacy systems.
Users’ Error Recovery Strategies in the Interaction with Voice Assistants (VAs)

Errors in interactions with Voice Assistants (VAs) are still recurrent, but evidence shows that users try to repair interactions by applying error recovery strategies and that such tactics are affected by VA responses. Although previous studies have addressed this matter in voice-based interfaces, VAs have specific characteristics that demand new investigations. Thus, this study aimed to understand the relationship between different types of VA responses and users' error recovery strategies. We conducted usability tests followed by debriefing sessions with VA users and identified categories of VA responses and user behavior. Our findings echo previous studies that pointed to VA responses as a source of understanding about the VA. While speech recognition issues were dealt with changes in pronunciation and repetition, participants approached unintended feature execution with exploratory behavior. Finally, when users received instructions on how to proceed, they followed recovery paths, indicating the importance of support for error handling. However, our findings show that HF/E specialists need to carefully design such guidance to accommodate users' preferences and achieve successful recoveries.
RawVis: A System for Efficient In-situ Visual Analytics

In-situ processing has received a great deal of attention in recent years. In in-situ scenarios, big raw data files which do not fit in main memory, must be efficiently handled on-the-fly using commodity hardware, without the overhead of a preprocessing phase or the loading of data into a database system. This paper presents RawVis, an open source data visualization system for in-situ visual exploration and analytics over big raw data. RawVis implements novel indexing schemes and adaptive processing techniques allowing users to perform efficient visual and analytics operations directly over the data files. RawVis provides real-time interaction, reporting low response time, over large data files, using commodity hardware.
QoS for Dynamic Deployment of IoT Services

This paper introduces RVAF, a runtime verification (RV) extension of the Arrowhead Framework (AF) with container-based service-deployment and runtime-enforcement of a desired quality of service (QoS). AF is a service-oriented middleware architecture for IoT-applications, consisting of a set of core and auxiliary services and systems, respectively. The QoS manager (QoSM) is one AF's most important auxiliary systems, which can be used to guarantee the application's QoS for a wide set of parameters. In RVAF the QoS offered to a particular IoT-application is specified in signal temporal logic, and is continuously monitored by the RVAF-QoSM. In case of an imminent violation, RVAF automatically initiates a container-based reconfiguration, which is ensured to maintain the desired QoS. RVAF is beneficial to large IoT-applications, where the use of continuous-integration and continuous-deployment tools, is not only a recommended practice but also a necessity. Moreover, the use of RVAF is advantageous both during the development of an IoT application, and after its deployment. We describe the architecture of RVAF, provide its formal underpinning, and demonstrate the usefulness of RVAF supported by an industrial IoT application. The main contribution of this work is to show what it takes to incorporate RV concepts into modern SOA frameworks supporting the development of IoT applications.
Biometrics based access framework for secure cloud computing

This paper is focused on the topic of the use of biometrics framework and strategy for secure access identity management of cloud computing services. This paper present's a description of cloud computing security issues and explored a review of previous works that represented various ideas for a cloud access framework. This paper discusses threats like a malicious insider, data breaches, and describes ways to protect them. It describes an innovative way portrayed a framework that fingerprint access-based authentication to protect Cloud services from unauthorized access and DOS, DDoS attacks. This biometrics-based framework as an extra layer of protection, added then it can be robust to prevent unauthorized access to cloud services.