Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.
A new kernel-based possibilistic intuitionistic fuzzy c-means clustering

Fuzzy c-means and its derivatives such as possibilistic c-means and possibilistic fuzzy c-means are the most widely used clustering algorithms in the literature. Though efficient, these clustering algorithms do not achieve high cluster quality on real-world datasets, which are not linearly separable. Kernel-based clustering algorithms employ nonlinear similarity measures to define the inter-point similarities. As a result, they are able to identify clusters of arbitrary shapes and densities. Comparative analysis over standard datasets has established the superiority of kernel methods over its corresponding standard algorithms. In this paper, we propose a kernel-based Atanassov's possibilistic intuitionistic fuzzy clustering for data clustering and image segmentation. The paper explores the performance of the proposed methodology with respect to various internal and external indices for various real datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as kernel-based algorithms. Experimental results on noisy image datasets also show the competence of the proposed approach.
Using temporal logics for specifying weak memory consistency models

The formal verification of multithreaded programs is not just more difficult due to the concurrent behaviours, but also due to the used underlying weak memory consistency models. Weak memory models arise from techniques like store buffering that were introduced to increase the performance. However, all of these techniques weaken the memory consistency, and may result in unintuitive behaviours where processors may disagree on the order in which write operations occurred. Requirements for verification are therefore unambiguous and complete specifications of such memory consistency models. In the past, specifications based on different formalisms have been presented which often lacked of comparability and the direct usability for model checking. In this paper, we therefore introduce the use of temporal logic to describe the behaviour of memory systems. In particular, we use linear temporal logic (LTL) to define the weak memory models. Thereby, we can easily check the properties of a multithreaded program against several different consistency models and determine the weakest consistency guarantees required to fulfil the given specification.
Iterated greedy insertion approaches for the flexible job shop scheduling problem with transportation times constraint

This paper proposes two greedy heuristics based on an iterated insertion technique to solve the flexible job shop scheduling problem with transportation time's constraint. The approaches treat the two sub-problems of: the assignment of machines to operations and the sequencing of the operations on the assigned machines. The idea is to start with a greedy construction method, then, apply an iterative destroy and recreate algorithm, to minimise the maximal completion time of machines (makespan). The heuristics were tested on a benchmark that considers the transportation times between the machines and based on a real flexible job shop 'AIP-PRIMECA cell of Valenciennes University'. The results of the proposed heuristics are compared with the results of a mixed integer linear program (MILP) and the potential fields (PF) approach. The results obtained are very promising. [Submitted 2 May 2017; Accepted 26 January 2018]
Correlation-guided multi-object tracking with correlation feature transfer

Here, the authors propose a correlation-guided Monte Carlo Markov chain (MCMC) solver to promote the efficiency for tracking multiple objects under recursive Bayesian filtering framework. Instead of randomly proposing the target location according to certain distribution, the authors' method guides the MCMC solver to sample among locations that the targets are more likely to appear. The high possible locations for each target are obtained using its corresponding response map by evaluating the correlation between the target appearance and its online model. Furthermore, the proposed tracking framework is natural to transfer the rich domain-specific offline correlation features into the target online model. With the calculation in the Fourier domain and the reversible jump strategy for MCMC, the correlation-guided method is able to track variable multiple objects with high efficiency. At the same time, the correlation feature transfer strategy is capable of improving tracking precision with easy offline training. The proposed method is evaluated by both the synthetic and real scenario videos. The experimental results demonstrate the effectiveness of the proposed method and its superior performance against its counterparts.
A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy.
Developing new robust motion templates of martial art techniques using R-GDL approach: a case study of SSCM

Martial art (MA) is considered a conserved heritage primarily for promoting certain level of identities and cultures. With the technology advancement, motion capture has been widely used in MA to capture and evaluate human performance. However, methods to create motion templates (templates) of MA techniques from scratch are rarely exposed because there is no complete evaluation system framework suggested for MA. This paper presents in detaile how to create the MA templates using framework of extrinsic feedback-based evaluation system generally and R-GDL specifically. To create more robust templates, GDL has been used in validation tests. SSCM becomes the case study in this research. The main contribution in this paper is introduction of new datasets of templates for SSCM techniques. Evaluation of trainees' performance will be done using the datasets for future works. The datasets are believed to promise MAs' authenticity and originality and to preserve this valuable cultural heritage.
A Unified Feature Selection Model for High Dimensional Clinical Data Using Mutated Binary Particle Swarm Optimization and Genetic Algorithm

This article contends that feature selection is an important pre-processing step in case the data set is huge in size with many features. Once there are many features, then the probability of existence of noisy features is high which might bring down the efficiency of classifiers created out of that. Since the clinical data sets naturally having very large number of features, the necessity of reducing the features is imminent to get good classifier accuracy. Nowadays, there has been an increase in the use of evolutionary algorithms in optimization in feature selection methods due to the high success rate. A hybrid algorithm which uses a modified binary particle swarm optimization called mutated binary particle swarm optimization and binary genetic algorithm is proposed in this article which enhanced the exploration and exploitation capability and it has been a verified with proposed parameter called trade off factor through which the proposed method is compared with other methods and the result shows the improved efficiency of the proposed method over other methods.
Proposing a new mathematical model and a meta-heuristic algorithm for scheduling and allocating automated guided vehicle

One of the substantial things, which has been perceived more than ever by captains of industry in recent years, is the necessity of earning competitive value. As a result, mechanisation and enhancing the level of automation of the process has become one of the most fascinating issues to researchers. In this regard one of the most appealing systems to industries is flexible manufacturing systems which contribute to achievement of higher levels of automation in business environments. Since transportation plays an essential role in flexible production systems, automated guided vehicles (AVGs) have been utilised to carry material in these systems in order to maintain the flexibility, and increase the efficiency of production and distribution throughout the system. In this paper, a mathematical model for scheduling and allocating AVGs in the manufacturing process of a specific project is proposed and in the end, a heuristic algorithm is proposed and used to solve the model problem.
Depth-based end-to-end deep network for human action recognition

Recognition of human actions from videos can be improved if depth information is available. Depth information certainly helps in segregating foreground motion from the background. Single image depth estimation (SIDE) is a commonly used method for the analysis of weather degraded images. In this study, the idea of SIDE is extended to human action recognition (HAR) on datasets where depth information is not available. Several depth-based HAR algorithms are available but all of them are using the depth information given with the dataset. Some other methods are using depth motion map which refers to the depth of motion in a temporal direction. Here, a new depth-based end-to-end deep network is proposed for HAR in which the frame-wise depth is estimated and this estimated depth is used for processing instead of RGB frame. As colour information is not required for estimating motion, a single channel depth map is used for estimating motion in the video. It makes the system computationally efficient. The proposed method is tested and verified on three benchmark datasets namely JHMDB, HMDB51 and UCF101. The proposed method outperforms the existing state-of-the-art methods for HAR on all the three tested datasets.
Real-time signal processing of photoplethysmographic signals to estimate the on-demand and continuous heart rate by spectral analysis

In healthcare applications, heart rate is one of the vital signs which give the health informatics of a person. Nowadays, the evolution of PPG sensors help to develop low cost heart rate monitoring devices with minimal hardware resources compared to ECG. In this paper, an efficient algorithm is proposed to find the heart rate using frequency spectrum analysis on PPG signals. Using the proposed algorithm, the heart rate is calculated with a pass percentage of 99.2, mean absolute percentage error (MAPE) of 1.59%, mean absolute error (MAE) of 1.20 BPM and reference closeness factor (RCF) of 0.989. The first reliable heart rate output comes in 6.5th second, which is the minimum possible time. The algorithm operates with a speed of 2 MIPS and with a memory of 18 KB. So the proposed method can be integrated to any low cost real-time embedded platforms to accurately measure the heart rate.
FPGA-Based Symmetric Re-Encryption Scheme to Secure Data Processing for Cloud-Integrated Internet of Things

A new scheme using field programmable gate arrays (FPGAs) to secure Internet of Things (IoT) data processing in public clouds against various attacks (including attacks from insiders) is proposed. The proposed scheme supports various business models involving multiple parties and allow the data owner to give temporary access to IoT data to specific clients at a public market place (the cloud). The scheme achieves perfect forward secrecy, provides FPGA authentication, a secure way to establish a symmetric session key between the on-cloud FPGA, the IoT device and the client, and allows user’s configuration integrity check while running in the cloud FPGA. A symmetric proxy re-encryption (PRE) scheme is used to support the publish/subscribe mode of operation of IoT. A complete prototype has been implemented to show the feasibility of the proposed scheme. Formal verification of the proposed protocol verified that it does not have any vulnerabilities. Experimental results showed that an FPGA implementation of the proposed PRE was 6 ${\times }$ faster than the SW implementation in transforming a ciphertext of size 1 GB.
Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect

Mobile crowdsensing (MCS) offers a promising paradigm for large-scale sensing with the rapid growth of mobile smart devices. Compared with traditional sensing methods, MCS is more effective and efficient in energy and cost. Task allocation is a key problem in MCS, which has a significant impact on the performance. It is challenging to design a generic solution to the task allocation problem because MCS applications typically consider distinct targets under specific constraints. However, there are many common interests such as data quality, budget, and energy consumption. In this paper, we analyze and formulate the task allocation problem from two perspectives, respectively. First, we focus on data quality and propose a genetic algorithm (GA) to maximize data quality. Then, we take the profit of workers into account and propose a detective algorithm (DA) to improve the profit. In the GA-based solution, only the platform is able to decide the task assignment. However, in the DA-based solution, the workers are allowed to determine and submit their task sets to the platform, which just needs to make a selection from these task sets. In addition, we consider the clustering effect of tasks and the influence caused by different geographic distributions of tasks. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm and there is a tradeoff between the data quality and the profit of workers.
Ranking and Risk Factor Scheme for Malicious applications detection and Classifications

Being an open source operating system, android mobiles are attacked by hundreds of malware every year. Moreover, malware are using many veiled techniques that makes it difficult to detect them. Android official markets and the Google Play Store are also not left untouched by malware. This article presents the Ranking and Risk Factor Scheme (RRFS), a hybrid intrusion detection technique for Android devices for the detection of malicious android applications. Ranking and risk factor schemes perform an analysis of Android permissions requested and system calls invoked features by ranking these features with some criteria and calculating the risk factor of each application for the detection and classification of malicious applications of 81 malware families. In the results, the ranking and risk factor scheme outperforms several related approaches and has the detection and classification performance of 99.2% and 88.7%, respectively and proved fast, an energy-efficient technique for resource constraint mobile device
Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

In this paper, we consider an Internet of Electric Vehicles (IoEV) powered by heterogeneous charging facilities in the transportation network. In particular, we take into account the state-of-the-art vehicle-to-grid (V2G) charging and renewable power generation technologies implemented in the charging stations, such that the charging stations differ from each other in their energy capacities, electricity prices, and service types (i.e., with or without V2G capability). In this case, each electric vehicle (EV) user needs to decide which path to take (i.e., the routing problem) and where and how much to charge/discharge its battery at the charging stations in the chosen path (i.e., the charging scheduling problem) such that its journey can be accomplished with the minimum monetary cost and time delay. From the system operator’s perspective, we formulate a joint routing and charging scheduling optimization problem for an IoEV network, and show that the problem is NP-hard in general. To tackle the NP-hardness, we propose an approximate algorithm that can achieve affordable computational complexity in large-size IoEV networks. The proposed algorithm allows the routing and charging solution to be calculated in a distributed manner by the system operator and EV users, which can effectively reduce the computational complexity at the system operator and protect the EV users’ privacy and autonomy. Besides, a proximal method is introduced to improve the convergence rate of the proposed algorithm. Extensive simulations using real world data show that the proposed distributed algorithm can achieve near-optimal performance with relatively low computational complexity in different system set-ups.
Concurrency Bug Avoiding Based on Optimized Software Transactional Memory

Software transactional memory is an effective mechanism to avoid concurrency bugs in multithreaded programs. However, two problems hinder the adoption of such traditional systems in the wild world: high human cost for equipping programs with transaction functionality and low compatibility with I/O calls and conditional variables. This paper presents Convoider to solve these problems. By intercepting interthread operations and designating code among them as transactions in each thread, Convoider automatically transactionalizes target programs without any source code modification and recompiling. By saving/restoring stack frames and CPU registers on beginning/aborting a transaction, Convoider makes execution flow revocable. By turning threads into processes, leveraging virtual memory protection and customizing memory allocation/deallocation, Convoider makes memory manipulations revocable. By maintaining virtual file systems and redirecting I/O operations onto them, Convoider makes I/O effects revocable. By converting lock/unlock operations to no-ops, customizing signal/wait operations on condition variables, and committing memory changes transactionally, Convoider makes deadlocks, data races, and atomicity violations impossible. Experimental results show that Convoider succeeds in transparently transactionalizing twelve real-world applications with averagely incurring only 28% runtime overhead and perfectly avoid 94% of thirty-one concurrency bugs used in our experiments. This study can help efficiently transactionalize legacy multithreaded applications and effectively improve the runtime reliability of them.
Hybrid Precoding Design for Adaptive Subconnected Structures in Millimeter-Wave MIMO Systems

Hybrid precoding design for an adaptive subconnected structure offers a compromise between hardware complexity and spectral efficiency in massive multiple-input multiple-output (MIMO) systems at millimeter-wave (mmWave) frequencies. This paper focuses on the impacts of the partial antenna connections on the achievable sum-rate (ASR) performance for the uniform linear array and uniform square planar array configurations in mmWave networks. The subarray architectures consist of the overlapped, interlaced, and dynamically selective subset schemes in mmWave MIMO systems. Notably, on the basis of the ASR maximization, the Gram–Schmidt (GS)-based antenna selection (AS) algorithm is used to acquire an appropriate antenna subset and magnificently simplifies the radio frequency chain–antenna mapping operation in the sequel. Finally, simulation results demonstrate that an adaptive subarray architecture in conjunction with the GS-based AS search strategy is able to accomplish the ASR performance approaching the fully connected architecture with an alleviated demand on both computation and hardware complexities.
A Hybrid Estimation and Identification Method for Online Calculation of Voltage-Dependent Load Parameters

This paper presents a hybrid estimation and identification (HEI) method for identifying the load model parameters of voltage-dependent loads along with estimating the system states. The parameters of voltage-dependent load models are identified using multiple snapshots consecutively gathered through supervisory control and data acquisition system. The proposed HEI method imposes no additional cost to the installed metering devices or communication network. Mathematical aspects of the proposed HEI method are analytically proved and a four-step approach is presented for implementing it. Comprehensive numerical experiments and statistical analysis are presented to demonstrate the effectiveness of the proposed method for both load model identification and state estimation.
Using type and temporal semantic enrichment to boost content discoverability and multilingualism in the Greek cultural aggregator SearchCulture.gr

Most aggregators face challenges regarding searchability, discoverability and visual presentation of their content due to metadata heterogeneity across the collections. Particularly for cultural and historical material, keyword-based searching is far from sufficient. Structured item types and temporal information are key metadata for the discoverability of cultural heritage content. We developed an innovative metadata enrichment and homogenisation scheme for types and temporal information that is both effective and user-friendly and we embedded it in the ingestion workflow of SearchCulture.gr, the Greek cultural heritage aggregator developed by the National Documentation Centre (EKT). Two key components of the enrichment scheme are Semantics.gr, a platform for publishing vocabularies that contains a mapping tool for massive semantic enrichment, and a parametric tool for chronological normalisation. We enriched and homogenised the aggregated content with respect to types and temporal information which subsequently allowed us to develop advanced multilingual search and browsing features, including hierarchical navigation on types and historical periods, searching and faceting on types, time spans and historical periods, a tag cloud of types and an interactive timeline/histogram.