Miracle-dz/UniFAR-FTUs huggingface dataset:
Arabic Sign Language Recognition System Using 2D Hands and Body Skeleton Data

This paper presents a novel Arabic Sign Language (ArSL) recognition system, using selected 2D hands and body key points from successive video frames. The system recognizes the recorded video signs, for both signer dependent and signer independent modes, using the concatenation of a 3D CNN skeleton network and a 2D point convolution network. To accomplish this, we built a new ArSL video-based sign database. We will present the detailed methodology of recording the new dataset, which comprises 80 static and dynamic signs that were repeated five times by 40 signers. The signs include Arabic alphabet, numbers, and some daily use signs. To facilitate building an online sign recognition system, we introduce the inverse efficiency score to find a sufficient optimal number of successive frames for the recognition decision, in order to cope with a near real-time automatic ArSL system, where tradeoff between accuracy and speed is crucial to avoid delayed sign classification. For the dependent mode, best results were obtained for dynamic signs with an accuracy of 98.39%, and 88.89% for the static signs, and for the independent mode, we obtained for the dynamic signs an accuracy of 96.69%, and 86.34% for the static signs. When both the static and dynamic signs were mixed and the system trained with all the signs, accuracies of 89.62% and 88.09% were obtained in the signer dependent and signer independent modes respectively.
Triamcinolone acetonide alters expressions of matrix metalloproteinase-3 gene in primary human chondrocytes

Intra-articular glucocorticoid (GC) injection such as triamcinolone acetonide is an ultimate pharmaceutical treatment for knee osteoarthritis (OA) patients who do not respond well to other pharmaceutical treatments. Effects of GCs on homeostasis of cartilage’s extracellular matrix (ECM) are not conclusive. In this study, we investigated whether TA caused destruction to ECM by altering expression of genes involved in digestion of collagens and proteoglycans. Chondrocytes isolated from cartilages of 10 knee OA patients were treated with TA at 1 and 5 mg/ml. Reverse-transcription real-time polymerase chain reaction was used for evaluating expressions of genes involving ECM degradation ( matrix metalloproteinase ( MMP )- 1, MMP-3, MMP-13, ADAM with thrombospondin motives ( ADAMTS ) -5 and tissue inhibitor of matrix metalloproteinase ( TIMP ) -3 ). Statistical differences in relative-quantification expression levels between TA-treated chondrocytes and non-treated controls were found in MMP-3 (6.59 ± 5.2-fold for 1 mg/ml TA ( p = 0.022) and 5.43 ± 3.46-fold for 5 mg/ml TA ( p < 0.01)). Levels of MMP-1 , MMP-13 , ADAMTS-5 , and TIMP-3 were not significantly changed. TA-increased expressions of ­ MMP-3 in chondrocytes might assist further degradation of cartilage and accelerate OA progression. Results from this study may assist physicians in TA treatment planning for OA patients.
A Multi-Strategy Combination Framework for Android Malware Detection Based on Various Features

With the increasing popularity of smartphones, the mobile security issues have become serious, and more and more malware has been found. Android applications are often used to handle sensitive information, thus they have become the main targets of malware attacks. In order to efficiently detect Android malware, in this paper, we present a multi-strategy combination framework. We use five types of static features to characterize Android applications from multiple aspects. To improve the classification accuracy and reduce the overfitting of the framework, we use three filter-based feature selection methods to identify the most informative top-k features. Then we input the applications represented by the feature subsets into five classification algorithms to build classifiers. Finally, we predict the classification results by hard voting or soft voting. We have performed many experiments in a well-marked dataset consisting of 41,155 samples. The experimental results show that our approach can achieve over 98% in accuracy, precision, recall and F-score. Compared with other existing methods, our approach has the best malware detection rate of 98.75%.
Quality of Metadata in Open Data Portals

During the last decade, numerous governmental, educational or cultural institutions have launched Open Data initiatives that have facilitated the access to large volumes of datasets on the web. The main way to disseminate this availability of data has been the deployment of Open Data catalogs exposing metadata of these datasets, which are easily indexed by web search engines. Open Source platforms have facilitated enormously the labor of institutions involved in Open Data initiatives, making the setup of Open Data portals almost a trivial task. However, few approaches have analyzed how precisely metadata describes the associated datasets. Taking into account the existing approaches for analyzing the quality of metadata in the Open Data context and other related domains, this work contributes to the state of the art by extending an ISO 19157 based method for checking the quality of geographic metadata to the context of Open Data metadata. Focusing on metadata models compliant with the Data Catalog Vocabulary proposed by W3C, the proposed extended method has been applied for the evaluation of the Open Data catalog of the Spanish Government. The results have been also compared with those obtained by the Metadata Quality Assessment methodology proposed at the European Data Portal.
Precise Angle Estimation by Jointly Using Spatial/Temporal Change of Channel State Information and Its Application in Pedestrian Positioning

Previously, we have proposed a pedestrian positioning method that uses vehicles near a pedestrian as anchors, and combines vehicle-to-vehicle (V2V) communication signals and GPS signals for high precision positioning. In this method, pedestrian-vehicle distance and angle of arrival (AoA) are estimated from the channel state information (CSI) of vehicular signals and used in position computation, but the precision of angle estimation is limited due to the small number of antennas that can be mounted on a mobile device. To solve this problem, in this paper, we propose a new method to estimate AoA precisely by jointly using spatial change of CSI at multiple antennas and temporal change of CSI per antenna (caused by pedestrian/vehicle movement), and on this basis improve pedestrian positioning precision. Specifically, a two-dimensional antenna array is constructed from CSI acquired from multiple antennas/timings, and the MUSIC method with spatial smoothing is applied to estimate AoA. 3D ray tracing simulations with real 3D map confirm that the proposed method can effectively improve the precision of angle estimation and reduce positioning error when only a small number of antennas are available at a pedestrian device, and the performance increases with the number of antennas.
GSSA: A Resource Allocation Scheme Customized for 3D NAND SSDs

The high density of 3D NAND-based SSDs comes with longer write latencies due to the increasing program complexity. To address this write performance degradation issue, NAND flash manufacturers implement a 3D NAND-specific full-sequence program (FSP) operation. The FSP can program multiple-bit information into a cell simultaneously with the same latency as the baseline program operation, thereby dramatically boosting the write performance. However, directly adopting the (large granularity) FSP operation in SSD firmware can result in a lifetime degradation problem, where small writes are amplified to large granularities with a significant fraction of empty data. This problem cannot completely be mitigated by the DRAM buffer in the SSDs since the “sync” commands from the host prevent the DRAM buffer from accumulating enough written data. To solve this FSP-induced performance/lifetime dilemma, in this work, we propose and evaluate GSSA (Generalized and Specialized Scramble Allocation), a novel written-data allocation scheme in SSD firmware, which considers both various 3D NAND program operations and the internal 3D NAND flash architecture. By adopting GSSA, SSDs can enjoy the performance benefits brought by the FSP without excessively consuming the lifetime. Our experimental evaluations reveal that GSSA can achieve the throughput and the spent-lifetime of the best-performance and best-lifetime single granularity schemes, respectively.
3-D Full-Band Monte Carlo Simulation of Hot-Electron Energy Distributions in Gate-All-Around Si Nanowire MOSFETs

The energy distributions of electrons in gate-all-around (GAA) Si MOSFETs are analyzed using full-band 3-D Monte Carlo (MC) simulations. Excellent agreement is obtained with experimental current–voltage characteristics. For these 24-nm gate length devices, the electron distribution features a smeared energy peak with an extended tail. This extension of the tail results primarily from the Coulomb scattering within the channel. A fraction of electrons that enter the drain retains their energy, resulting in an out-of-equilibrium distribution in the drain region. The simulated density and average energy of the hot electrons correlate well with experimentally observed device degradation. We propose that the interaction of high-energy electrons with hydrogen-passivated phosphorus dopant complexes within the drain may provide an additional pathway for interface-trap formation in these devices.
Ascend: a Scalable and Unified Architecture for Ubiquitous Deep Neural Network Computing : Industry Track Paper

Deep neural networks (DNNs) have been successfully applied to a great variety of applications, ranging from small IoT devices to large scale services in a data center. In order to improve the efficiency of processing these DNN models, dedicated hardware accelerators are required for all these scenarios. Theoretically, there exists an optimized acceleration architecture for each application. However, considering the cost of chip design and corresponding tool-chain development, researchers need to trade off between efficiency and generality. In this work, we demonstrate that it is practical to use a unified architecture, called Ascend, to support those applications, ranging from IoT devices to data-center services. We provide a lot of design details to explain that the success of Ascend relies on contributions from different levels. First, heterogeneous computing units are employed to support various DNN models. And the datapath is adapted according to the requirement of computing and data access. Second, when scaling the Ascend architecture from a single core to a cluster containing thousands of cores, it involves design efforts, such as memory hierarchy and system level integration. Third, a multi-tier compiler, which provides flexible choices for developers, is the last critical piece. Experimental results show that using accelerators based on the Ascend architecture can achieve comparable or even better performance in different applications. In addition, various chips based on the Ascend architecture have been successfully commercialized. More than 100 million chips have been used in real products.
A Comparison between NSGA-II and NSGA-III to Solve Multi-Objective Sectorization Problems based on Statistical Parameter Tuning

This paper compares the non-dominated sorting genetic algorithm (NSGA-II) and NSGA-III to solve multiobjective sectorization problems (MO-SPs). We focus on the effects of the parameters of the algorithms on their performance and we use statistical experimental design to find more effective parameters. For this purpose, the analysis of variance (ANOVA), Taguchi design and response surface method (RSM) are used. The criterion of the comparison is the number of obtained nondominated solutions by the algorithms. The aim of the problem is to divide a region that contains distribution centres (DCs) and customers into smaller and balanced regions in terms of demands and distances, for which we generate benchmarks. The results show that the performance of algorithms improves with appropriate parameter definition. With the parameters defined based on the experiments, NSGA-III outperforms NSGA-II.
Fiber Loop Ring-Down Magnetic Field Sensor Using Frequency-Shifted Interferometry and D-Shaped Fiber Coated With Magnetic Fluid

In this letter, we demonstrate a low-cost fiber loop ring-down (FLRD) magnetic field sensor using frequency-shifted interferometry. A D-shaped fiber coated with magnetic fluid was used as the sensor head for enhancing the sensitivity and improving the stability. With this scheme, the magnetic field was sensed in the space domain by measuring the ring-down distance, and its measurement accuracy was improved by introducing a temperature controller. The experimental results showed that a sensitivity of 2.53 × 10−3 (km−1ċG−1) was achieved, and a stability of 0.146% was obtained, which is better than those of the current time-domain FLRD sensors based on evanescent field effect.
Stock Price Prediction With ARIMA and Deep Learning Models

Financial markets are vital to the capitalist economies and often volatile and hard to predict. This work compares the performance of different time–series models in predicting close price movement for 30 listed stocks from the Dow Johns Industrial Average (DIJA). The mechanisms of autoregressive moving average (ARIMA), artificial neural network (ANN), recurrent neural network (RNN), and long short–term memory (LSTM) are briefly explained in the essay. Comparison results suggest that LSTM and sequence to sequence (Seq2Seq) model with attention estimates fit the price movement pattern well with relatively low latency. Built upon LSTM, Seq2Seq models exhibit good performance in forecasting. The vanilla Seq2Seq model is compared with Seq2Seq with attention in forecasting price several days in the future. Seq2Seq with attention outperforms other models and is capable of generating sequential predictions.
Demonstration of the Electronic Cutoff Field in Millimeter-Wave Extended Interaction Oscillators

For extended interaction structure devices (EIDs), the determinants of field flatness are analyzed. It is found that the flatness of field distribution is determined by a cutoff condition existing in EIDs, which is based on the concepts of both resonant cavity and wave propagation. Such an analytical conclusion is verified by a novel scheme for the measurement of field distribution in an extended interaction oscillator (EIO) based on the perturbation technique. The sinusoidal-like axial field components with different flatness are obtained by utilizing the measured frequency shifts. Based on the cutoff condition, three field distribution patterns are established, including middle-concentrated, flat-distributed, and side-concentrated types. Particle-in-cell (PIC) simulations of a ${W}$ -band EIO show that three field distribution patterns have the advantages of low surface-loss power, high power-capacity, and low oscillating-threshold, respectively.
Phase Transition Material-Assisted Low-Power SRAM Design

The threshold switching properties of the phase transition material (PTM) can be exploited to realize a heterogeneous static random access memory (SRAM) bitcell, which can obviate the need for assist techniques. This unique PTM-SRAM bitcell is designed by placing the PTM in series with the gate of pull-down nMOS transistors. The large insulating state resistance of the PTM device blocks the propagation of read voltage rise during a read operation and noise during retention operation, thereby enhancing read/retention stability. On the flip side, the write-time and write-ability are impacted, which can be improved by tuning the access transistor strength. Overall, the PTM-SRAM bitcell achieves active-<inline-formula> <tex-math notation="LaTeX">$V_{\text {MIN}}$ </tex-math></inline-formula> equivalent to baseline SRAM <inline-formula> <tex-math notation="LaTeX">$V_{\text {MIN}}$ </tex-math></inline-formula> aided by both read- and write-assist techniques. Furthermore, the read access time of the PTM-SRAM bitcell does not degrade with reducing read <inline-formula> <tex-math notation="LaTeX">$V_{\text {MIN}}$ </tex-math></inline-formula> in contrast to other read-assist techniques. For an isoactive-<inline-formula> <tex-math notation="LaTeX">$V_{\text {MIN}}$ </tex-math></inline-formula> of 0.52 V, the PTM-SRAM has 20% lower read access time, 35% lower read power, 16% higher write time, and 55% lower write power compared to the baseline SRAM aided by assist techniques. Also, the dynamic retention stability of the PTM-SRAM improves by <inline-formula> <tex-math notation="LaTeX">$11.36\times $ </tex-math></inline-formula> compared to baseline SRAM. Detailed analysis highlighting the sensitivity of PTM parameters on PTM-SRAM performance metrics is also presented.
Online Parameter Estimation for Permanent Magnet Synchronous Machines: An Overview

Online parameter estimation of permanent magnet synchronous machines is critical for improving their control performance and operational reliability. This paper provides an overview of the recent achievements of online parameter estimation of PMSMs with examples. The critical issues in parameter estimation are firstly analysed, especially the rank-deficient issue and inverter nonlinearities. Then, the state-of-the-art online parameter estimation modelling techniques are reviewed and assessed. Finally, some typical applications and examples are outlined, e.g. estimation of mechanical parameters, improvement of sensored and sensorless control performance, thermal condition monitoring, and fault diagnosis, together with future research trends.
Measuring the Impact of Beamwidth on the Correlation Distance of 60 GHz Indoor and Outdoor Channels

Due to narrow beamwidth and channel sparseness, millimeter-wave receivers will detect much less multipath than their microwave counterparts, fundamentally changing the small-scale fading properties. By corollary, the de facto Rayleigh-Rice model, which assumes a rich multipath environment interpreted by the Clarke-Jakes omnidirectional ring of scatterers, does not provide an accurate description of this fading nor of the correlation distance that it predicts. Rather, a model interpreted by a directional ring of scatterers, recently proposed in seminal work by Va et al., theoretically demonstrated a strong dependence of correlation distance on beamwidth. To support Va's model through actual measurement, we conducted an exhaustive measurement campaign in five different environments – three indoor and two outdoor – with our 60 GHz 3D double-directional channel sounder, compiling over 36,000 channel captures. By exploiting the super-resolution capabilities of the channel sounder, we were the first, to our knowledge, to measure correlation distance as a function of continuous beamwidth. We showed that for narrow beamwidth, correlation was maintained for much longer distances than predicted by the Rayleigh-Rice model, validating Va's model. As the beamwidth approached omnidirectionality, with increasing number of multipath detected, the behavior indeed approached the Rayleigh-Rice model.
News Text Classification and Recommendation Technology Based on Wide & Deep-Bert Model

With the rapid development of Internet technology, news information on various social platforms is growing wildly, generating large amounts of data. Since short text information such as news headlines, short messages, and newsletters has a small number of words and limited content, it is often difficult to extract effective information. The sparse feature information causes difficulties in text classification. If the news system cannot efficiently and accurately realize news classification and users preference recommendation, it will inevitably affect the experience and frequency of platform users. This paper mainly studies the application of deep learning in the field of text classification and users' personal recommendation. It uses multiple English text data sets to learn text features. Based on the Wide&Deep model, combined with the improved BERT pretraining model, the Wide&Deep-BERT model is designed. In addition, the corresponding news text classification and recommendation technology process is proposed, and the Tensorflow deep learning framework is used to experimentally verify the technology, which proves the effectiveness and practicability of the design technology.
Feature Selection for Real-Time NLOS Identification and Mitigation for Body-Mounted UWB Transceivers

Ultra-wideband (UWB) is a popular technology for indoor positioning systems (IPSs) due to its robust signaling in harsh environments, through-wall propagation, and high-resolution ranging. However, the propagation of wireless signals in indoor environments is affected by nonline-of-sight (NLOS) conditions, which can lead to positively biased distance estimates. Several research works were carried out in this area, but the NLOS effect caused by the human body shadowing on the ranging performance is not properly addressed in the literature. In this paper, a commercial UWB transceiver is used to assess the impact of the human body shadowing on the ranging accuracy. A set of real-time features is combined with different machine learning algorithms for NLOS identification and mitigation. With a subset of four features, a 0.97 F1-score was obtained for NLOS identification. For NLOS mitigation, with a subset of three features, the residual error follows a Gaussian distribution, has a mean error close to zero, and an STD of 0.67 m.
Distributed Task Offloading and Resource Allocation in Vehicular Edge Computing

With the powerful storage and computation capability, vehicular edge computing (VEC) is considered as a promising paradigm to enhance the safety and service quality of vehicles in intelligent transportation systems (ITS). In this paper, we formulate a joint road side units (RSUs) selection and resource allocation problem, which minimizes the total task offloading delay subject to the bandwidth and computation resources constraints in VEC system. Considering the formulated problem is a mixed-integer nonlinear programming (MINLP) problem, the original problem is reformulated as a convex problem. Due to the high complexity of the problem, we decompose it into a distributed manner. By utilizing the alternating direction method of multipliers (ADMM), a joint RSUs selection and resource allocation (JRSRA) algorithm is proposed with low complexity. Simulation results shows that the proposed JRSRA algorithm can reduce the total task offloading delay compared with other benchmark methods.
Safe and Efficient Robot Manipulation: Task-Oriented Environment Modeling and Object Pose Estimation

Mobile manipulation is essential for robots to accomplish everyday household chores such as set the table. In order to successfully perform manipulation tasks, a 3-D representation of the environment and the pose of the target object are needed. In this article, we propose a systematic solution for safe and efficient robot manipulation. Innovatively, a task-oriented environment modeling strategy is presented for collision-free navigation and motion planning, which integrates the grid-based 2-D map and local real-time octree-based 3-D representation. Furthermore, we introduce a task-oriented object pose estimation approach based on the fiducial marker and ontology technology. In particular, a property-driven target object inference and pose estimation algorithm is designed, which allows the mobile robot to implement the task-oriented object manipulation. The proposed solution is evaluated through real-world experiments, where memory usage, computation time, collision checking, and object pose estimation are extensively investigated. Also, application scenarios are presented to validate the effectiveness and efficiency of our proposal.