Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Performance Portability

Exascale computing brings with it diverse machine architectures and programming approaches which challenge application developers. Applications need to perform well on a wide range of architectures while simultaneously minimizing development and maintenance overheads. In order to alleviate these costs, developers have begun leveraging portability frameworks to maximize both the code shared between platforms and the performance of the application. We explore the effectiveness of several such frameworks through applying them to small production codes. Throughout the process, we apply a logging tool to gather data on the development process. We use this information to develop metrics of application development productivity, which can be used to holistically assess how productively a performance-portable application was developed.
Fractional Weight Moving Average Based Thresholding Scheme for VLC With Mobile-Phone Camera

In this paper, we propose and demonstrate three weight moving average based thresholding schemes for mobile-phone camera based visible light communication, including equal weight moving average (EWMA) based thresholding scheme, fractional weight moving average (FWMA) based thresholding scheme and equal ratio weight moving average (ERWMA) based thresholding scheme. The conventional third order polynomial fitting (OPF) based thresholding scheme and three proposed thresholding schemes are evaluated and compared. The experimental results show that compared with the third OPF based scheme, our proposed schemes better resist the grayscale value fluctuation and have lower computational complexity. For 2 Kb/s data rate, thresholding schemes based on the third OPF, EWMA, FWMA, and ERWMA can achieve the transmission distances of 50 cm, 175 cm, 200 cm, and 175 cm, respectively. Moreover, we measure the average running time of these thresholding schemes, which are 1051.957 ms, 2.773 ms, 14.372 ms, and 88.271 ms, respectively. Considering the system performance and computational complexity comprehensively, FWMA-based thresholding scheme is optimal, which has great potential in practical applications.
A New Algorithm for Repairing Web-Locators using Optimization Techniques

The importance of test automation in the software industry has received a growing attention in recent years and it is continuously increasing. Unfortunately, the maintenance of the test cases is a major problem that faces those who use test automation. This problem becomes bigger when dealing with Graphical User Interface (GUI) tests.In this paper, an algorithm is introduced to maintain GUI tests for web applications. The Genetic algorithm is adopted to automatically repair the locators used to select elements from web pages. The algorithm is evaluated using several applications and results show that the proposed algorithm improves the repair percentage to 87% of the used locators compared to the previous result which was 73%.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

Classification and regression in brain–computer interfaces (BCIs) suffer from performance variations across subjects as well as across sessions within individual subjects due to various technical and biological reasons. Such variations limit the amount of data that can be used to train machine learning models, rendering BCIs highly subject specific and requiring tedious calibration and retraining before each usage in order to achieve optimal performance. This chapter presents state-of-the-art approaches to calibration-free and improved decoding through transfer learning techniques from multiple subjects and sessions. Within the feature space, a generalized framework for the popular common spatial patterns is described in terms of covariance regularization with multiple subjects. Recent methods using Riemannian features offer invariance to spatial characteristics between subjects and sessions. Finally, a probabilistic Bayesian multitask learning framework is presented that works on top of feature spaces and learns prior distribution over decision rules of different subjects and sessions. The mathematical details are left in for more technically inclined readers
Hybrid mmWave MIMO-OFDM Channel Estimation Based on the Multi-Band Sparse Structure of Channel

This paper presents a hybrid channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing mmWave systems based on the sparse nature of the angular channel and leveraging the compressed sensing (CS) tools. The angular support is recovered without any discretization by treating the angular channel in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS-based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage caused by the limited antenna array length. The RF combiner is implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the low reconstruction error and good performance of the proposed channel estimator.
A 28-GHz transceiver front-end with T/R switching achieving 11.2-dBm OP1dB, 33.8% PAEmax and 4-dB NF in 22-nm FD-SOI for 5G communication

A 28-GHz transceiver front-end is implemented in 22-nm FD-SOI technology for 5G communication. The front-end consists of a PA and an LNA, including the functionality of T/R switching. The OP<inf>1dB</inf>, PAE<inf>max</inf> and PAE<inf>6dB</inf> of the PA are 11.2 dBm, 33.8% and 13.1%, respectively. The NF, gain, and power consumption of the LNA are 4 dB, 17 dB, and 14.5 mW, respectively.
Effects of Suspended Moisture Particles on AC Breakdown Voltage and Electric Field Distribution of Vegetable Insulation Oil

Vegetable insulating oils are gaining importance worldwide to be used in power transformers as an alternative to petroleum based mineral oils because of their biodegradable nature and higher fire points than conventional mineral oils. However, these oils (whether natural or synthetic esters) easily absorb moisture and hence tend to affect the dielectric and physiochemical properties of insulation oils. This paper presents the effect of moisture absorbed by commonly used rapeseed oil on its AC breakdown voltage. A mathematical relationship was developed between breakdown voltage and percentage moisture absorbed to access the values of BDV at any moisture level and this relationship holds good for any moisture content in the oil. Moreover, effect of suspended water molecules on breakdown voltage was simulated using COMSOL Multiphysics. Empirical relationship developed was tested and verified and can be used to find out the BDV of these oils as the percentage moisture increases.
Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities

Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted.
Harmonic-recycling Rectifier Design for Localization and Power Tuning

This work introduces a new rectifier for wireless power transfer (WPT) with harmonic-recycling capability for enhanced functionality beyond RF-DC conversion. By taking advantages of nonlinearity of the diode, in the proposed rectifier circuits, the second harmonic is utilized for communication with the transmitter. It can be used as a feedback signal to the transmitter to (1) control the transmitting beam direction for maximum efficiency and (2) to determine the status of the attached load, whether the load is turned on. With the design of a multi-functional rectifier, it is possible to realize a close-loop system to achieve efficient WPT.
Video Streaming Energy Consumption Analysis for Content Adaption Decision-Taking

Over recent years, rapid growth of smartphone technology and capabilities makes it an important tool in our daily activities. Despite increasing processing power and capabilities as well as decreasing price, these consumer smartphones are still limited in term of batteries capacity. The heterogeneity properties of these devices, subscribed network as well as its users also lead to mismatch problem. Usage in power-hungry multimedia applications such as streaming video players and 3D games, the limited battery capacity motivates smartphone energy aware content adaptation research to address these problems. This paper present experiments of energy consumption of video streaming in various video encoding properties as well as different network scenarios. The result of the experiments shows that energy savings up to 40% can be achieved by using different encoding property.
Optimizing Gradual SDN Upgrades in ISP Networks

Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel software-defined network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end networks of Internet service providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when we consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives: 1) the maximization of the traffic that traverses at least one SDN node, and 2) the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using real-world network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other. We also study the dual upgrading problem, i.e., minimizing the upgrading cost for the ISP while ensuring specific performance goals. Our analysis shows that our proposed algorithm can achieve up to 2.5 times lower cost to ensure performance goals over state-of-the-art methods.
Simplifying Component Interaction with Actors and Databases

Component-based software systems are of the majority nowadays. When it comes down to implementation, the interaction between components is just as important as the functionality itself. When invoking a function of a component, how to pass data arguments, how to receive return results, and when to expect a return are all the questions to ask. There are so many ways in which these can be done, that it introduces the complexity in the usability of a component. We present in this article an architecture for integrating heterogeneous components across different language and binary platforms, different processes and even different machines, based on a simple message passing mechanism — the actor model, and the sharing of common and global data structures only through databases. We show the simplicity and flexibility of this architecture by re-structuring a slot machine framework, which consists of components to drive modern and legacy hardware, follows a static workflow, and changes the visual appearance quite often. We argue that this architecture with restricted implementation choices can be used in a much broader range of software systems, helping produce software components that are more independent and easy to adopt.
Distinct Influences of Land Cover and Land Management on Seasonal Climate

Anthropogenic land use and land cover change is primarily represented in climate model simulations through prescribed transitions from natural vegetation to cropland or pasture. However, recent studies have demonstrated that land management practices, especially irrigation, have distinct climate impacts. Here we disentangle the seasonal climate impacts of land cover change and irrigation across areas of high agricultural intensity using climate simulations with three different land surface scenarios: (1) natural vegetation cover/no irrigation, (2) year 2000 crop cover/no irrigation, and (3) year 2000 crop cover and irrigation rates. We find that irrigation substantially amplifies land cover‐induced climate impacts but has opposing effects across certain regions. Irrigation mostly causes surface cooling, which substantially amplifies land cover change‐induced cooling in most regions except over Central, West, and South Asia, where it reverses land cover change‐induced warming. Despite increases in net surface radiation in some regions, this cooling is associated with enhancement of latent relative to sensible heat fluxes by irrigation. Similarly, irrigation substantially enhances the wetting influence of land cover change over several regions including West Asia and the Mediterranean. The most notable contrasting impacts of these forcings on precipitation occur over South Asia, where irrigation offsets the wetting influence of land cover during the monsoon season. Differential changes in regional circulations and moist static energy induced by these forcings contribute to their precipitation impacts and are associated with differential changes in surface and tropospheric temperature gradients and moisture availability. These results emphasize the importance of including irrigation forcing to evaluate the combined impacts of land surface changes for attributing historical climatic changes and managing future impacts.
Generative Adversarial Networks Based Industrial Protocol Construction in the Fog Computing

Industrial protocols are critical communication elements for networking components in industrial applications, and plays core rule for security audit and anomalous detection. The emerging of fog computing paradigm makes that intelligence moves down to the industrial devices for enhanced local computational capability. In this work, deep convolutional generative adversarial networks (GAN) is applied for designing industrial protocol construction scheme, in which a discriminator and a generator are respectively established to achieve collaborative training and optimization. Siemens S7 protocol payloads are transformed into gray scale images for texture feature extraction, and a data set of industrial protocols are adopted for implementation. The adversarial training model could be uploaded in the industrial cloudlets, and the proposed protocol construction scheme will launch a perspective for establishing honeypots or honeynets in the fog computing.
Carbonate Adsorption to Ferrihydrite: Competitive Interaction with Phosphate for Use in Soil Systems

Carbonate (CO3) interacts with Fe-(hydr)oxide nanoparticles, affecting the availability and geochemical cycle of other important oxyanions in nature. Here, we studied the carbonate–phosphate interaction in closed systems with freshly prepared ferrihydrite (Fh), using batch experiments that cover a wide range of pH values, ionic strength, and CO3 and PO4 concentrations. The surface speciation of CO3 has been assessed by interpreting the ion competition with the Charge Distribution (CD) model, using CD coefficients derived from MO/DTF optimized geometries. Adsorption of CO3 occurs predominately via formation of bidentate inner-sphere complexes, either (≡FeO)2CO or (≡FeO)2CO··Na+. The latter complex is electrostatically promoted at high pH and in the presence of adsorbed PO4. Additionally, a minor complex is present at high CO3 loadings. The CD model, solely parametrized by measuring the pH-dependent PO4 adsorption as a function of the CO3 concentration, successfully predicts the CO3 adsorption to Fh in single-ion systems. The adsorption affinity of CO3 to Fh is higher than to goethite, particularly at high pH and CO3 loadings due to the enhanced formation (≡FeO)2CO··Na+. The PO4 adsorption isotherm in 0.5 M NaHCO3 can be well described, being relevant for assessing the reactive surface area of the natural oxide fraction with soil extractions and CD modeling. Additionally, we have evaluated the enhanced Fh solubility due to Fe(III)-CO3 complex formation and resolved a new species (Fe(CO3)2(OH)23−(aq)), which is dominant in closed systems at high pH. The measured solubility of our Fh agrees with the size-dependent solubility predicted using the surface Gibbs free energy of Fh.
Posterior Calibration for Multi-Class Paralinguistic Classification

Computational paralinguistics is an area which contains diverse classification tasks. In many cases the class distribution of these tasks is highly imbalanced by nature, as the phenomena needed to detect in human speech do not occur uniformly. To ignore this imbalance, it is common to measure the efficiency of classification approaches via the Unweighted Average Recall (UAR) metric in this area. However, general classification methods such as Support-Vector Machines (SVM) and Deep Neural Networks (DNNs) were shown to focus on traditional classification accuracy, which might lead to a suboptimal performance for imbalanced datasets. In this study we show that by performing posterior calibration, this effect can be countered and the UAR scores obtained might be improved. Our approach led to relative error reduction values of 4% and 14% on the test set of two multi-class paralinguistic datasets that had imbalanced class distributions, outperforming the traditional downsampling.
A Word Embedding Model Learned from Political Tweets

Distributed word representations have recently contributed to significant improvements in many natural language processing (NLP) tasks. Distributional semantics have become amongst the important trends in machine learning (ML) applications. Word embeddings are distributed representations of words that learn semantic relationships from a large corpus of text. In the social context, the distributed representation of a word is likely to be different from general text word embeddings. This is relatively due to the unique lexical semantic features and morphological structure of social media text such as tweets, which implies different word vector representations. In this paper, we collect and present a political social dataset that consists of over four million English tweets. An artificial neural network (NN) is trained to learn word co-occurrence and generate word vectors from the political corpus of tweets. The model is 136MB and includes word representations for a vocabulary of over 86K unique words and phrases. The learned model shall contribute to the success of many ML and NLP applications in microblogging Social Network Analysis (OSN), such as semantic similarity and cluster analysis tasks.
Security Perspective in RAMI 4.0

Cloud Computing, Internet of Things (IoT) are the main technologies contributing to the adoption of the fourth revolution in manufacturing, Industry 4.0 also known as smart manufacturing or digital manufacturing. Smart manufacturing facilitates and accelerates the process of manufacturing with the connection of all the systems related to the manufacturing process starting with the Enterprise Resource Planning (ERP) systems, the Industrial Control Systems (ICSs) which control the production line and the Cyber Physical Systems (CPSs). Before the emerging of web applications, cloud applications and thin clients, ICSs and CPSs were already present but the protocols used for those systems were not designed for the Internet. In this paper, we tackle the security challenges that are accompanied by the emerging of this new technology, the mitigation techniques and the governance and compliance issues associated with it.
Jointly Design of Beamforming and AN in SWIPT-Enabled Secure Cognitive Radio Networks

This paper considers a power-splitting-based MISO secured radio network for simultaneous wireless information and power transfer (SWIPT), where a multiantenna secondary transmitter sends confidential information to multiple single-antenna secondary users (SUs) in the presence of multiple single-antenna primary users (PUs) and multiple energyharvesting receivers (ERs). To ensure the security of secondary networks, we use the artificial noise (AN) to mask the transmit beamforming. The optimization objective is to minimize the transmit power, subject to the secrecy rate constraint, the harvested energy constraint and the total transmit power. We transform the original non-convexproblem into a convex problem and propose an iterative algorithm based a successive convex approximation (SCA) method. Also, we analysis the convergence of the SCA-based iterative algorithm. Simulation results are presented to validate the performance of the proposed algorithm.
Detection of High-Risk Intoxicated Passengers in Video Surveillance

In this paper, we present a method that is able to detect abnormal behavior of intoxicated people in surveillance videos. We first describe typical behavior patterns of intoxicated people in videos and derive two visual features that distinguish them effectively. We define a motion efficiency as one feature to capture intoxicated motion and the aspect ratio of a bounding box of an object as the other to detect intoxicated postures. For the computation of the proposed visual features, the method detects and tracks individual pedestrians in videos and evaluates their motion trajectories and pose trajectories, respectively. The experimental results on the test dataset on railway platform show that the proposed method is able to detect drunken passengers effectively and robustly in a real environment.
Evaluating the physical and basic gymnastics skills assessment for talent identification in men’s artistic gymnastics proposed by the International Gymnastics Federation

This study aimed to determine the specific physical and basic gymnastics skills considered critical in gymnastics talent identification and selection as well as in promoting men’s artistic gymnastics performances. Fifty-one boys from a provincial gymnastics team (age 11.03 ± 0.95 years; height 1.33 ± 0.05 m; body mass 30.01 ± 5.53 kg; body mass index [BMI] 16.89 ± 3.93 kg/m²) regularly competing at national level voluntarily participated in this study. Anthropometric measures as well as the men’s artistic gymnastics physical test battery (i.e., International Gymnastics Federation [FIG] age group development programme) were used to assess the somatic and physical fitness profile of participants, respectively. The physical characteristics assessed were: muscle strength, flexibility, speed, endurance, and muscle power. Test outcomes were subjected to a principal components analysis to identify the most representative factors. The main findings revealed that power speed, isometric and explosive strength, strength endurance, and dynamic and static flexibility are the most determinant physical fitness aspects of the talent selection process in young male artistic gymnasts. These findings are of utmost importance for talent identification, selection, and development.