Miracle-dz/UniFAR-FTUs huggingface dataset:
OmniGaze: A Display-covered Omnidirectional Camerafor Conveying Remote User's Presence

This paper introduces a telepresence system called OmniGaze, which displays a remote user's presence using a spherical display on an omnidirectional camera. In a telepresence system, gaze has a variety of important functions such as presenting the direction of attention and making simple eye gestures. An omnidirectional camera is an effective tool for gaze communication because it provides a wider range of view for remote users. OmniGaze is a telepresence system which leverages an omnidirectional camera. We designed a modular spherical LED matrix display that covers an omnidirectional camera and projects a spherical animation to visually convey remote user's gaze information. Telecommunication by gaze interaction differs from face-to-face communication in various aspects, so we conducted a user study to investigate practical applications of our system. This paper presents feedback and findings pertaining to the proposed gaze animation technique, advantages and disadvantages of asymmetric communication, and design implications. We also discuss applying our system to communication of humans and automated robots.
Estimating the Intrinsic Dimensionality of Hyperspectral Remote Sensing Imagery with Rare Features

Estimating the intrinsic dimensionality of hyper spectral remote sensing imagery is an essential step in processing this kind of data. A novel estimation algorithm is proposed, which can preserve both abundant and rare features in original data. First of all, the QR decomposition of an original data matrix is carried out in order to decrease computational complexity, and a sliding noise detection window is applied to noise reduction for improving the accuracy of dimensionality estimation. Furthermore, a manifold learning method is used to determine a limit of intrinsic dimensionality and finally, intrinsic dimensionality is estimated through the singular value decomposition and $l_{2,\infty }$-norm theory. The experimental results of simulated and real data are presented, which shown our proposed algorithm outperforms some classical algorithms.
When Quantized Massive MIMO Meets Large MIMO With Higher Order Modulation

In this letter, we compare the capacity of a massive multiple-input multiple-output (MIMO) system using a low-resolution analog-to-digital converter (ADC) and a linear detector against a conventional MIMO system with higher order modulation and near maximum likelihood (ML) detection. We show that in the low-signal-to-noise ratio (SNR) regime, the quantized massive MIMO system can outperform the conventional large MIMO system; however, for high SNR, the conventional MIMO system with a near ML detector can outperform the extreme 1-bit quantized massive MIMO system. An analytical framework that derives the achievable rate of a linear minimum mean-squared error (MMSE)-based detector in a massive MIMO configuration, with the assumptions that the front-end is limited to a low-resolution ADC and channel estimation is imperfect, is presented.
Multi-Frame Multi-Sensor Multi-Target Task Allocation Method Based on Improved Wolf Colony Algorithm

With the increasing number of targets, limited sensor resources cannot meet the requirements of reasonable management. Therefore, how to solve the problem effectively has been widely concerned by scholars. The traditional allocation method only considers single frame allocation, and ignores the relationship between the results of consecutive several frame. Thinking about the sequentiality for continuous time frames, a multi-frame multi-sensor multi-target task allocation method based on improved Wolf Colony Algorithm (WCA) is proposed. In the paper, the influence of the allocation results of the previous frames on at current frame is considered. The threat degree of the next frame of the target which has not been assigned sensor monitoring for several consecutive frames is updated based on the pervious allocation results in multi frames. The multi-sensor multi-target task allocation problem is modeled as a constrained optimization problem, which is solved by the Improved WCA. Simulation results show that the result of allocation of sensor resources is more reasonable, with increase slightly of the allocation time cost, compared with Improved WCA without the method.
The Android Malware Static Analysis: Techniques, Limitations, and Open Challenges

This paper aims to explain static analysis techniques in detail, and to highlight the weaknesses and challenges which face it. To this end, more than 80 static analysis-based framework have been studied, and in their light, the process of detecting malicious applications has been divided into four phases that were explained in a schematic manner. Also, the features that is used in static analysis were discussed in detail by dividing it into four categories namely, Manifest-based features, code-based features, semantic features and app’s metadata-based features. Also, the challenges facing methods based on static analysis were discussed in detail. Finally, a case study was conducted to test the strength of some known commercial antivirus and one of the stat-of-art academic static analysis frameworks against obfuscation techniques used by developers of malicious applications. The results showed a significant impact on the performance of the most tested antiviruses and frameworks, which is reflecting the urgent need for more accurately tools.
Traffic Characterization on Airport Surface Using Aircraft Ground Trajectories

The continuous increase in air traffic is pressuring airport operators to augment their capacity while still complying with safety regulations. To achieve this goal, operators need a thorough knowledge of how the airport surface is put to use (i.e., how aircraft are routed) under various conditions. We propose an approach to characterizing traffic in an airport from radar-collected aircraft ground trajectories. The approach does not require prior knowledge of the airport's topology (since it reconstructs it from the available data) and uses network-constrained trajectory clustering to produce groups of similar trajectories that represent frequently traveled routes in the airport. Our main findings from experimental results on real data from the Paris-Charles-de-Gaulle airport (France) indicate that the retrieved routes are highly linked to exploitation constraints (maneuver undertaken by the aircraft, runway, and configuration) and that the approach can help uncover the existence of less traveled alternative routes that can be useful for rerouting purposes.
Feature article: A reconfigurable SDR architecture for parallel satellite reception

Small satellites have moved from being a niche sector for research to special missions, such as disaster monitoring, Earth observation, remote sensing, communications, and navigation [1]. This is evident in Spaceworks' projection (Figure 1) based on announced and future plans of developers and programs indicating around 2,000 small satellites to be launched in the next four years [2]. Also, the trend of small satellite constellations is continuously increasing due to [3], [4] their simplicity and low mass, which makes them the ideal candidates for Low Earth Orbit (LEO) constellations [5]. As shown in Figure 2, the categorization of future multisatellite missions is based on the number of satellites and the type of mission. Universities, government, and commercial institutions developing CubeSats and smallsats for the purposes of performing space-based research still rely heavily on amateur communication technology and frequency allocations. The problem is compounded by the shortage of available frequencies in very high frequency (VHF), ultra high frequency (UHF), and S-bands due to rapid increase in the number of CubeSats orbiting the Earth [6]. This has created strain for licensing and coordinating organisations such as the Federal Communication Commission and the International Amateur Radio Union [7], [8].
Violence Detection in Indoor Surveillance Cameras Using Motion Trajectory and Differential Histogram of Optical Flow

Intelligent surveillance systems and automatic detection of abnormal behaviors have become a major problem in recent years due to increased security concerns. Violence behaviors have a vast diversity so that distinction between them is the most challenging problem in video-surveillance systems. In recent works, introducing unique and discriminative feature for representing violence behaviors is needed strongly. In this paper, a novel violence detection method has been proposed which is based on combination of motion trajectory and spatio-temporal features. A dense sampling has been carried out on spatiotemporal volumes along target's path to extract Differential Histogram of Optical Flow (DHOF) and standard deviation of motion trajectory features. These novel features were employed to train a Support Vector Machine (SVM) to classify video volumes into two normal and violence categories. Experimental results demonstrate that the proposed method outperforms other state-of-the-art violence detection methods and achieves 91 % accuracy for detection result.
Deep Construction of an Affective Latent Space via Multimodal Enactment

We draw on a simulationist approach to the analysis of facially displayed emotions, e.g., in the course of a face-to-face interaction between an expresser and an observer. At the heart of such perspective lies the enactment of the perceived emotion in the observer. We propose a novel probabilistic framework based on a deep latent representation of a continuous affect space, which can be exploited for both the estimation and the enactment of affective states in a multimodal space (visible facial expressions and physiological signals). The rationale behind the approach lies in the large body of evidence from affective neuroscience showing that when we observe emotional facial expressions, we react with congruent facial mimicry. Further, in more complex situations, affect understanding is likely to rely on a comprehensive representation grounding the reconstruction of the state of the body associated with the displayed emotion. We show that our approach can address such problems in a unified and principled perspective, thus avoiding ad hoc heuristics while minimizing learning efforts.
Hankel Matrix Rank as Indicator of Ghost in Bearing-Only Tracking

Usually, bearing angle measurements are employed in triangulation methods to display the position of targets. However, in multiradar and multitarget scenarios, triangulation approaches bring out ghosts that operate like real targets. This paper proposes a target/ghost classifier that relies on the fact that the trajectory of a ghost is actually a function of trajectories of at least two targets and therefore, the complexity of a ghost trajectory is “greater” than the complexity of targets’ trajectories.
Gerenciamento do ciclo de vida de dados de sensores: da produção ao consumo

Dispositivos sensores estao se tornando bastante disseminados e veem sendo aplicados em diversos dominios, principalmente em pesquisa cientifica. Entretanto, o aumento n numero e variedade desses sensores introduz problemas de gerenciamento dos dados gerados, tais como a disponibilizacao de dados de sensores em diferentes taxas ou resolucoes temporais e espaciais. Este trabalho trata de quatro aspectos do gerenciamento de dados de sensores para aplicacoes cientificas: (i) prover acesso homogeneo a dispositivos sensores heterogeneos e aos dados produzidos por eles; (ii) gerenciar a composicao de operacoes aplicadas a dados de sensores; (iii) oferecer funcionalidades de pre-processamento de dados que sejam flexiveis e possam ser executadas antes da publicacao dos dados; e, (iv) propagar e criar anotacoes validas (metadados) associadas aos dados durante todo seu ciclo de vida. A solucao proposta para o aspecto (i) envolve o encapsulamento uniforme de dados e software, atraves da extensao de uma tecnologia de componentes chamada Componentes de Conteudo Digital (DCCs), oferecendo tambem a associacao de anotacoes a esse conteudo. Tendo esses componentes como base, a solucao proposta para (ii) e baseada no uso de workflows cientificos para coordenar a combinacao de DCCs de dados e software. A solucao proposta para (iii) considera a invocacao de workflows armazenados no provedor de dados e a submissao de novas especificacoes de workflows para armazenamento e/ou execucao. Alem disso, a solucao usa as anotacoes dos DCCs para enriquecer consultas e suas respostas. Finalmente, um mecanismo de propagacao de anotacoes e proposto como solucao para (iv). As contribuicoes desta tese sao apresentadas em um framework para gerenciamento de dados de sensores, considerando aspectos de acesso, pre-processamento, publicacao e anotacoes de dados Abstract
Identification of Faulty TSV with a Built-In Self-Test Mechanism

Three-dimensional Integrated Circuit (3D IC) based on through silicon via (TSV) has brought a drastic change in the IC technology. Since TSVs connect different layers of 3D stacks, their proper functioning is an essential prerequisite for the system operation. So, testing of TSV is essential for 3D IC. In this paper a cost-effective Built-in Self-Test (BIST) mechanism is proposed for the post-bond test of TSVs in 3D ICs. The test method aims at identifying single and multiple defective TSVs using low test time with minimum hardware. The time cycle needed for testing is calculated and is compared with previous proposed methods. The simulation result shows that the proposed BIST circuit is beneficial over prior BIST technology in terms of test time cycle and hardware requirement.
A Race Simulation for Strategy Decisions in Circuit Motorsports

In motorsports, every racing participant pursues the goal to finish the race in the shortest time possible. There are several ways to influence the race with race strategy decisions, e.g. the timing of pit stops and the choice of tires. In order to be able to evaluate the race strategy before and to quickly adjust it during a race, a tool is required that simulates an entire race in a short time. For this purpose, the paper presents the methodology of a race simulation for circuit motorsports. It simulates races in dependency of various race strategy inputs and is based on a lap-wise discretization. It includes the effects of tire degradation, fuel mass loss, pit stops and overtaking maneuvers. The simulation parameters are chosen in such a way that they can be determined based on publicly accessible lap time data. The Formula 1 2017 Abu Dhabi Grand Prix is analyzed as an exemplary result. An application area is the support of race engineers in present motorsports. Future work aims towards the automation of strategy decisions in motorsports with autonomous racecars.
Multi-Objective Economic Dispatch and Unit Commitment Problems (EDUCP) Considering Valve Point Effect Using MOPSO Algorithm

This paper presents a Multi-objective Economic Dispatch and Unit Commitment Problems (EDUCP) Considering valve point effect Using heuristic algorithm (MOPSO). The proposed method can be applied to solve dynamic power dispatch problem (with specific period such as 24 h) and to solve unit commitment problem. The proposed method also can be combined with other metaheuristic or classical methods to create more powerful optimization technique. The test systems with up to 10 generating units have been used to show the performance of MOPSO. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from ECE, MDE, HSP and HBPSO.
[Regular Paper] Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning

Emotion recognition is a increasingly popular topic because of its potential applications in the field of affective learning. It allows the development of systems able to adapt themselves to the users' emotional state to improve the learner's experience and learning. In this paper, we introduce a new biomedical multi-sensor platform for realtime acquisition of physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate and Blood Oxygen Saturation. We describe experimental scenarios for the induction of emotions relevant in a context of affective learning (happiness, frustration, boredom) to build a set of emotionrelated data. We carry out a basic classification study by computing hand-crafted features on the time and frequency domains of signals, and training a Support-Vector-Machine (SVM) classifier to demonstrate the feasibility of our approach.
5-HT1A Receptor Function Makes Wound Healing a Happier Process

Skin wound healing is a multistage phenomenon that is regulated by cell–cell interplay and various factors. Endogenous serotonin is an important neurotransmitter and cytokine. Its interaction with the serotonin 1A receptor (5-HTR1A) delivers downstream cellular effects. The role of serotonin (5-hydroxytryptamine, 5-HT) and the 5-HT1A receptor has been established in the regeneration of tissues such as the liver and spinal motor neurons, prompting the investigation of the role of 5-HT1A receptor in skin healing. This study assessed the role of 5-HT1A receptor in excisional wound healing by employing an excisional punch biopsy model on 5-Ht1a receptor knockout mice. Post-harvest analysis revealed 5-Ht1a receptor knockout mice showed impaired skin healing, accompanied by a greater number of F4/80 macrophages, which prolongs the inflammatory phase of wound healing. To further unravel this phenomenon, we employed the 5-HT1A receptor agonist [(R)-(+)-8-Hydroxy-DPAT hydrobromide] as a topical cream treatment in an excisional punch biopsy model. The 5-HT1A receptor agonist treated group showed a smaller wound area, scar size, and improved neovascularization, which contributed to improve healing outcomes as compared to the control. Collectively, these findings revealed that serotonin and 5-HT1A receptor play an important role during the healing process. These findings may open new lines of investigation for the potential treatment alternatives to improve skin healing with minimal scarring.
A Pocket-Friendly and Sustainable Iontophoresis Apparatus for Palmoplantar Hyperhidrosis: Advancement over a Previously Described Homemade Design

Introduction and Objectives: Tap water iontophoresis is a promising therapy for palmoplantar hyperhidrosis. Non-affordability and nonavailability of the marketed device restrict its usage for many patients hailing from villages or small cities. Materials and Methods: In an article named “A simple user-made iontophoresis device for palmoplantar hyperhidrosis” published previously in this journal, a simple inexpensive homemade device that runs on 12 V direct current battery was described. We made a small modification by using a 220–12 V alternate current to direct current semiconductor diode–based transformer for the current supply. Results: The added innovation made the device lightweight, cheaper, and usable with domestic electric supply at home/clinic. Sustained supply of current at same voltage without dip and non-requirement to change/replace battery are added advantages. Cost of the whole assembly is ₹310. Working and clinical efficacy of our device were comparable to the commercially available apparatus as reported by 13 patients who received thrice a week of 15min sessions with our device. Mild electric pricking sensation was felt by all patients. Four of thirteen reported that pricking sensation was a bit more with our device as compared to the marketed device. No calibration for intensity is available in our device. Conclusion: This simple to assemble and inexpensive device makes iontophoresis applicable in many needy patients with similar efficacy and few limitations compared to expensive marketed devices.
Study of Puncturing Techniques for Polar Codes in 5G Cellular and IoT Networks

This paper presents a puncturing technique based on the channel polarization index for the design of rate-compatible polar codes in the fifth generation (5G) of wireless systems. The proposed strategy consists of two steps: we first generate the codeword message; and then we reduce the length of the codeword based on the channel polarization index where channels with the smallest channel polarization indices are punctured. We consider the proposed punctured polar codes with the successive cancellation (SC) decoder and the Cyclic Redundancy Check (CRC) aided SC list (CA-SCL) decoder and punctured bits known to both the encoder and the decoder. The Polar Spectra (PS) are then used to performance analysis the puncturing technique. Simulations for 5G scenarios show that the proposed polar codes perform comparable to Low-Density Parity-Check (LDPC) codes.
An Integrated Framework With Feature Selection for Dropout Prediction in Massive Open Online Courses

Massive open online courses (MOOCs) have flourished in recent years, which is conducive to the redistribution of high-quality educational resources globally. However, the high dropout rate in the course of operation has seriously affected its development. Therefore, in order to improve the degree of completion, it is an effective way to study how to effectively predict the dropout in MOOCs and intervene in advance. Traditional methods rely on manually extracted features, which is difficult to guarantee the final prediction effect. In order to solve this problem, this paper proposes an integrated framework with feature selection (FSPred) to predict the dropout in MOOCs, which includes feature generation, feature selection, and dropout prediction. Specifically, FSPred applies a fine-grained feature generation method in days to generate features and then uses an ensemble feature selection method to select valid features and feed them into a logistic regression model for prediction. Extensive experiments on a public data set have shown that FSPred can achieve the comparable results with other dropout prediction methods in terms of precision, recall, F1 score, and AUC score. Finally, through the analysis of the features of the final selection, the suggestions for the construction of the MOOCs are put forward.
Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.
Learning to Classify Blockchain Peers According to Their Behavior Sequences

Blockchain technologies have the potential to establish novel financial service infrastructures and reshape numerous fields. A blockchain is essentially a distributed ledger maintained by a set of peers (i.e., trading nodes) that do not fully trust each other. A key challenge that blockchain faces is to precisely classify the blockchain peers into categories with respect to their behavior patterns, which will not only enable deeper insights into the blockchain network but also facilitate more effective maintenance of the various peers (in private chains). In this paper, we introduce and formulate the problem of behavior pattern classification in blockchain networks and propose a novel deep-learning-based method, termed PeerClassifier, to address the problem. To the best of our knowledge, we are the first to formally define the problem of peer behavior classification in blockchain networks. Moreover, we conduct extensive experiments to evaluate our proposed approach. Experimental results demonstrate that PeerClassifier is significantly more effective than the existing conventional methods.