Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Clustering of Shared Images on Social Networks for Digital Forensics

Clustering the images shared through social network (SN) platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The sensor pattern noise (SPN) caused by the camera sensor imperfections during the manufacturing process can be extracted from the images and used to fingerprint the smartphones. The process of content compression performed by the SNs causes loss of image details and weakens the SPN, making the clustering task even more challenging. In this paper, we present a hybrid algorithm capable of clustering the images captured and shared through SNs without prior knowledge about the types and number of the acquisition smartphones. The hybrid method exploits batch partitioning, image resizing, hierarchical and graph-based clustering approaches to cluster the images. Using Markov clustering, the hierarchical clustering is conducted in such a way that the representative clusters with a higher probability of belonging to the same camera are selected for merging, which accelerates the clustering. For merging the clusters, the adaptive threshold updated iteratively through the hybrid clustering is used, which results in more precise clusters even for images from the same model of smartphones. The results on the VISION dataset, including both native and shared images, prove the effectiveness and efficiency of the hybrid method in comparison with the state-of-the-art SPN-based image clustering algorithms.
Age of information of two-way data exchanging systems with power-splitting

We consider a two-way data exchanging system consisting of an access point and a smart device. The access point has a constant power supply and the smart device does not. The access point simultaneously transmits information and energy to the smart device over block fading channels, with fixed powers Pt and Pt, respectively. The smart device receives data and energy at the same time, and stores the harvested energy in an energy buffer. Upon collecting enough energy, the smart device performs one block of transmission immediately. We investigate the timeliness and the efficiency of the system in terms of age of information (AoI) and data rate, respectively. We also investigate the trade-off between downlink communications and uplink communications by optimizing the weighted-sum average AoI and weighted-sum data rate. Moreover, we present the optimal power-splitting ratio p and the optimal weighting coefficient w explicitly or via efficient algorithm.
Direct Torque and Suspension Force Control for Bearingless Induction Motors Based on Active Disturbance Rejection Control Scheme

The bearingless induction motor (BIM) system has the characteristics of non-linear, strongly coupled, and highly accurate. To achieve the stable and accurate control of BIM, the methods of direct torque control (DTC) and direct suspension force control (DSFC) based on space vector modulation (SVM) are introduced first. However, the dynamic performance and uncertain anti-jamming robustness of the system need to be further researched. Based on the BIM control system, the active disturbance rejection control (ADRC) technology is selected in this paper. The total disturbance of the system is estimated in real time by using the extended state observer (ESO). According to the estimated value of the ESO, the disturbance can be compensated by the ADRC in time to eliminate the steady-state tracking error to achieve good anti-interference performance. The ADRC-based direct torque and suspension force control system for the BIM is designed and simulated by the MATLAB software. The simulation and experiment results show that the ADRC technology can effectively suppress the impact of load disturbance, reduce the overshoot, and have strong robustness.
Experimental Performance Comparison of Emerging Low Power Wide Area Networking (LPWAN) Technologies for IoT

In this work we present experimental results on the evaluation of two commercial integrated circuits for IoT connectivity, using a systematic approach. One devoted to LoRa and the other to IEEE 802.15. 4g, which is the physical layer adopted by the WI-SUN Alliance. The goal behind this evaluation is to present results to support those who will make use of LoRa or IEEE802.15. g/Wi-SUN or other type of connectivity to compare technologies in a fair fashion. The results show that there are differences between datasheet values and the measures collected during the experiments. There are a number of reasons for this divergence, such as the setup, transmitted packet length, equipment calibration and test specifications. This shows the importance of a systematical approach when comparing technologies.
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits

Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometer-based RFID sensor embodiment—ID-Sensor—for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient’s upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.
Distributed Transmit Power Optimization for Device-to-Device Communications Underlying Cellular Networks

In this paper, we propose four transmit power control strategies for the underlay device-to-device (D2D) communications, in which the spectral efficiency (SE) of the D2D communications is maximized while the amount of interference caused to a base station (BS) is kept less than a predefined threshold. To this end, we first propose a centralized power control strategy based on instantaneous and global channel state information (CSI) by formulating a convex optimization problem. Then, three distributed power control strategies are taken into account in which each D2D pair adjusts its transmit power in a distributed manner based on interference price and its local CSI, which significantly reduces the signaling overhead. In the distributed strategies, the interference price can be determined based on 1) the instantaneous local CSI; 2) the statistics of the local CSI (average power); and 3) the number of the D2D pairs without any CSI knowledge. Through extensive computer simulations, we show that the performances of the proposed strategies optimally adjust the transmit power of the D2D communications. Especially, we find that the distributed power control strategies can achieve almost the same SE with the centralized strategy with much lower signaling and control overhead.
Secure State Estimation under Measurement Attacks using Virtual State in Distributed Observer System

In this paper, a distributed secure state estimation problem that is state estimation in distributed observer system in the presence of attacks is mainly discussed. We introduce the notion of graph-based distributed observer representation, virtual state, and a subgroup to derive a condition for the secure state estimation. By the graph-based distributed observer representation, we can consider the system’s observability and communication pass connectivity as graph connectivity at once. With the notion of virtual state and the subgroup, we derive the state estimation condition which is the inequality relationship between the number of the allowable attacked observer and the graph connectivity as a theorem. Finally, a simple numerical example verifies the theorem.
Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation

Neural machine translation (NMT) heavily relies on parallel bilingual corpora for training. Since large-scale, high-quality parallel corpora are usually costly to collect, it is appealing to exploit monolingual corpora to improve NMT. Inspired by the law of total probability, which connects the probability of a given target-side monolingual sentence to the conditional probability of translating from a source sentence to the target one, we propose to explicitly exploit this connection and help the training procedure of NMT models using monolingual data. The key technical challenge of this approach is that there are exponentially many source sentences for a target monolingual sentence while computing the sum of the conditional probability given each possible source sentence. We address this challenge by leveraging the reverse translation model (target-to-source translation model) to sample several mostly likely source-side sentences and avoid enumerating all possible candidate source sentences. Then we propose two different methods to leverage the law of total probability, including marginal distribution regularization and likelihood maximization of monolingual corpora. Experiment results on English$\rightarrow$French and German$\rightarrow$English tasks demonstrate that our methods achieve significant improvement over several strong baselines.
WETRP: Weight Based Energy & Temperature Aware Routing Protocol for Wireless Body Sensor Networks

The technological advancements in wireless communication and miniaturization of sensor nodes have resulted in the development of Wireless Medical Sensor Networks (WMSNs) which can be effectively used for remote patient monitoring. Remote patient monitoring is one such application of wireless sensor networks which is becoming increasingly prevalent in healthcare. The healthcare applications of the WMSNs are delay-sensitive and require timely delivery of patient-critical data. However, the frequent exchange of critical data packets results in higher delays, collisions, packet drop, and re-transmissions. Consequently, it brings a detrimental impact on the performance of the WMSNs. In addition, the implanted biomedical sensor nodes produce electromagnetic radiations, pose a serious threat of damaging sensitive tissues in the human body. Protecting tissue damage requires thermal-aware routing protocols. However, most of the thermal-aware routing protocols developed for the WBSNs primarily focused on minimizing temperature, while overlooking the energy conservation goal and optimization of route selection. In this paper, we propose a weighted, QoS-based, energy and temperature-aware routing protocol, referred to as (WETRP), for the WMSNs that utilizes a composite routing metric by keeping in view temperature, remaining node energy, and link-delay estimation during route selection decisions. The simulation results presented in the paper demonstrates the efficacy of the proposed scheme in terms of preventing temperature rise, dealing with hotspot nodes, and maximizing network’s lifetime.
Automatic Identification of Anthropological Face Landmarks for Emotion Detection

We propose a facial landmark system constructed on anthropological principles that is intended to substitute the (costly) use of Action Units (AU) in characterizing facial dynamics, for the purpose of automatic emotion detection. We demonstrate the suitability of the method by implementing a lightweight automatic Anthropological Face Landmarks (AFL) identification algorithm and compare the task of emotional valence classification using the proposed system to explicitly using AU labels. Despite the comparison being unfair to our method (AUs are labeled by experts, while the landmarks are detected automatically), we show that even in a Proof-of-Concept setting, our system follows closely the performance of detecting emotions with Action Units.
Exploring the Adoption of Virtual and Augmented Reality in Enhancing Interactive Learning in Tanzania

The challenge of inadequate teaching and learning facilities impedes the development of the education system in Tanzania. This instigates the need to combine education and technology for provision of education. This paper assessed the adoption of Virtual and Augmented Reality as a tool to improve interactive learning. The research assessed the awareness and perceived usefulness of virtual and augmented realities in education. The research utilized a quantitative methodology whereby an online survey was used to collect data. Results indicate 69.7% of the respondents were aware of virtual and augmented reality. Moreover, 87.4% of the respondents agreed that virtual and augmented reality can be adopted to improve interactive learning. Interestingly, Science, Engineering, and Medical fields were proposed for adoption. These technologies influence students’ interest and motivation to learn through visualizations and interactivity. However, further research is recommended on cost-effective gadgets for virtual reality technology and suitable content for augmented reality technology.
Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Automatic image annotation is an effective and straightforward way to facilitate many applications in computer vision. However, manually annotating images is a computation-expensive and labor-intensive task. To address these problems, this paper proposes a novel approach by using a cograph regularized collective nonnegative matrix factorization method to annotate images, which is referred to as CG-CNMF; CG-CNMF maximizes the annotation consistency for each image and minimizes the semantic gap for good annotation performance. To reduce the computation cost, this method formulates the annotation problem as a recommending issue and uses nonnegative matrix factorization (NMF) to recover the image-to-label relation for the testing images. Moreover, to find the most similar latent image features and latent label features during the matrix factorization, it exploits the image-to-image relation and label-to-label relation by utilizing the visual content information of images and the semantic cooccurrence information of labels, respectively. To reduce the semantic gap between the image visual content and semantic concepts, both the semantic features and convolutional neural networks (CNNs)-based visual features are considered. Moreover, to address the label-imbalance and incomplete-label problems, the visual-based label cooccurrence information is also considered. In this way, visually similar images are highly correlated with the true semantics of the test images. The experimental results for three multilabel image datasets demonstrate the effectiveness and the efficiency of the proposed method.
High Cognitive Load Assessment in Drivers Through Wireless Electroencephalography and the Validation of a Modified N-Back Task

This paper explores the influence of high cognitive load on vehicle driver's electroencephalography (EEG) signals collected from two channels (Fp1, Fp2) using a wireless consumer-grade system. Although EEG has been used in driving-related research to assess cognitive load, only a few studies focused on high load, and they used research-grade systems. Recent advancements allow for less intrusive and more affordable systems. As an exploration, we tested the feasibility of one such system to differentiate among three levels of cognitive taskload in a simulator study. Thirty-seven participants completed a baseline drive with no secondary task and two drives with a modified version of the n-back task (1-back and 2-back). The modification removed the verbal response required during task presentation to prevent EEG-signal degradation, with the 2-back task expected to impose higher load than that by the 1-back task. Another objective of this study is to validate that this modified task increased the cognitive load in the expected manner. The modified task led to significant trends from baseline to 1-back, and from 1-back to 2-back in participants’ heart rate, galvanic skin response, respiration, horizontal gaze position variability, and pupil diameter, all in line with the previous driving-related studies on cognitive load. Furthermore, the EEG system was observed to be sensitive to the modified task, with the power of alpha band decreasing significantly with increasing n-back levels (baseline versus 1-back: 0.092 Bels on Fp1, 0.179 on Fp2; 1-back versus 2-back: 0.209 on Fp1, 0.147 on Fp2). Thus, a consumer-grade EEG system has the potential to capture high levels of cognitive load experienced by drivers.
AC/DC Converter for DC Traction Power Supply System with High-Speed Train Operation

Purpose of the work is improved approaches to ensure the required quality parameters of voltage in the traction network based on modern technologies and equipment. The pulsating mode of the power consumption in the railroad power supply network is the cause of the occurrence of voltage pulsations in the contact network. The use of active rectifiers in the electric power supply system of the railroad is proposed. The control system is developed by the converter, which allows to stabilize the output voltage in the DC link and also provides the unit power factor consumed by the converter and the THD consumption current at the level of 8%. A mathematical model of the converter was developed and a study of its main modes of operation was performed.
Stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks

In this paper, we propose a Deep Neural Network model based on WiFi-fingerprinting to improve the accuracy of zone location in a multi-building, multi-floor indoor environment. The proposed model is presented as a Stacked AutoEncoder (SAE) to allow efficient reduction of the feature space in order to achieve robust and precise classification. The multi-label classification is used to simplify and reduce the complexity of the learning classification task during the training phase. To achieve a hierarchical classification, we applied an argmax function on the multi-label output to convert the multi-label classification into multi-class classification ones to estimate the building, the floor and the zone identifier. Experimental results show that the proposed model achieves an accuracy of 100% for building, 99.66% for floor and 83.47% for zone location with a test time that does not exceed 10.21s.
Research data management in social sciences and humanities: A survey at the University of Lille (France)

The paper presents results from a campus-wide survey at the University of Lille (France) on research data management in social sciences and humanities. The survey received 270 responses, equivalent to 15% of the whole sample of scientists, scholars, PhD students, administrative and technical staff (research management, technical support services); all disciplines were represented. The responses show a wide variety of practice and usage. The results are discussed regarding job status and disciplines and compared to other surveys. Four groups can be distinguished, i.e. pioneers (20-25%), motivated (25-30%), unaware (30%) and reluctant (5-10%). Finally , the next steps to improve the research data management on the campus are presented.
Fault-Tolerant Spike Routing Algorithm and Architecture for Three Dimensional NoC-Based Neuromorphic Systems

Neuromorphic computing systems are an emerging field that takes its inspiration from the biological neural architectures and computations inside the mammalian nervous system. The spiking neural networks (SNNs) mimic real biological neural networks by conveying information through the communication of short pulses between neurons. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also necessary. On the other hand, the combination of Two-dimensional Networks-on-Chip (2D-NoC) and Three-dimensional Integrated Circuits (3D-ICs) can provide a scalable interconnection fabric in large-scale parallel SNN systems. Although the SNNs have some intrinsic fault-tolerance properties, they are still susceptible to a significant amount of faults; especially, when we talk about integrating the large-scale SNN models in hardware. Consequently, the need for efficient solutions capable of avoiding any malfunctions or inaccuracies, as well as early fault-tolerance assessment, is becoming increasingly necessary for the design of future large-scale reliable neuromorphic systems. This paper first presents an analytical model to assess the effect of faulty connections on the performance of a 3D-NoC-based spiking neural network under different neural network topologies. Second, we present a fault-tolerant shortest-path k-means-based multicast routing algorithm (FTSP-KMCR) and architecture for spike routing in 3D-NoC of spiking neurons (3DFT-SNN). Evaluation results show that the proposed SP-KMCR algorithm reduces the average latency by 12.2% when compared to the previously proposed algorithm. In addition, the proposed fault-tolerant methodology enables the system to sustain correct traffic communication with a fault rate up to 20%, while only suffering 16.23% longer latency and 5.49% extra area cost when compared to the baseline architecture.
L1 Related-Issues of Turkish Speaking Learners of English in Use of Possessives

The major purpose of this study is to find out the extent of the influence of first language (Turkish) in use of possessives in second language (L2, English) and whether it is possible to overcome the first language (L1, Turkish) transfer effects in use of possessives through explicit instruction. The participants were 44 students at the beginner level of proficiency being enrolled in a language preparatory program at two private universities in Turkey. To enhance the credibility of the research, data were collected from three different types of sources namely; a translation activity, a multiple-choice activity and a picture description activity. The findings revealed that Turkish, as a mother tongue, had both positive and negative effect on learning English possessive structures both due to the similarities and differences between the two languages.
A BP-like Distributed Algorithm for Weighted Average Consensus

A novel distributed algorithm designed for weighted average consensus is presented. This algorithm is developed based on the well-known Belief Propagation (BP) algorithm for statistical learning. For a connected network without loops, the algorithm will converge in finite number of iteration and produces a correct average consensus value for every node in the network. For a network with loops, we convert the weighted average problem into an optimization problem with a relaxation factor whose solution approaches to the weighted average with appropriate relaxation factor. A modified algorithm is also proposed for loopy networks and consensus will be reached asymptotically with low computation complexity and fast convergence rate.