Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic time-unbounded reachability analysis of hybrid systems

Hybrid automata combine finite automata and dynamical systems, and model the interaction of digital with physical systems. Formal analysis that can guarantee the safety of all behaviors or rigorously witness failures, while unsolvable in general, has been tackled algorithmically using, e.g., abstraction, bounded model-checking, assisted theorem proving. Nevertheless, very few methods have addressed the time-unbounded reachability analysis of hybrid automata and, for current sound and automatic tools, scalability remains critical. We develop methods for the polyhedral abstraction of hybrid automata, which construct coarse overapproximations and tightens them incrementally, in a CEGAR fashion. We use template polyhedra, i.e., polyhedra whose facets are normal to a given set of directions. While, previously, directions were given by the user, we introduce (1) the first method for computing template directions from spurious counterexamples, so as to generalize and eliminate them. The method applies naturally to convex hybrid automata, i.e., hybrid automata with (possibly non-linear) convex constraints on derivatives only, while for linear ODE requires further abstraction. Specifically, we introduce (2) the conic abstractions, which, partitioning the state space into appropriate (possibly non-uniform) cones, divide curvy trajectories into relatively straight sections, suitable for polyhedral abstractions. Finally, we introduce (3) space-time interpolation, which, combining interval arithmetic and template refinement, computes appropriate (possibly non-uniform) time partitioning and template directions along spurious trajectories, so as to eliminate them. We obtain sound and automatic methods for the reachability analysis over dense and unbounded time of convex hybrid automata and hybrid automata with linear ODE. We build prototype tools and compare—favorably—our methods against the respective state-of-the-art tools, on several benchmarks.
University Dropout Prediction through Educational Data Mining Techniques: A Systematic Review

The dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools.
Using machine learning techniques to identify rare cyber‐attacks on the UNSW‐NB15 dataset

This paper uses a hybrid feature selection process and classification techniques to classify cyber‐attacks in the UNSW‐NB15 dataset. A combination of k‐means clustering, and a correlation‐based feature selection, were used to come up with an optimum subset of features and then two classification techniques, one probabilistic, Naïve Bayes (NB), and a second, based on decision trees (J48), were employed. Our results show that this hybrid feature selection method in combination with the NB model was able to improve the classification accuracy of most attacks, especially the rare attacks. The false alarm rates were lower for most of the attacks, and particularly the rare attacks, with this combination of feature selection and the NB model. The J48 decision tree model, however, did not perform any better with the feature selection, but its classification rate for all attack families was already very high, with or without feature selection.
Enhancing Camera-Based Multimodal Indoor Localization With Device-Free Movement Measurement Using WiFi

Indoor localization is of great significance to a wide range of applications in the era of mobile computing. The maturity of the computer vision techniques and the ubiquity of embedded sensors in commercial off-the-shelf (COTS) smartphones shed the light on the submeter localization services for indoor environment. The state-of-the-art indoor localization works suffer from high-cost deployment and inaccurate results due to the coarse readings from internal measurement units (IMUs) sensors in the smartphones. In this article, we mainly innovate in introducing the WiFi-sensing technology to extract the distance information in a low-cost and device-free manner. Along with the computer vision technology, we model and implement an accurate and easy-to-deploy system for indoor localization. This system enhances indoor localization with multimodal sensing via two images, IMU sensors reading and CSI of WiFi signal. Specifically, we first model and design camera-based, sensor and WiFi-assisted indoor localization and propose several algorithms in this model. We then implement a prototype with smartphones and commercial WiFi devices and evaluate it in several distinct indoor environments. The experimental results show that 92-percentile error is within 0.2 m for indoor targets which sheds light on submeter indoor localization.
Designing the half-masks of filter respirators for workers of mining enterprises

The purpose of paper is to develop an algorithm for designing the half-masks of air-purifying respirators and assessment of their safety factor of insulation. To create a 3D model of a head, two-parameter NIOSH tables (National Institute for Occupational Safety and Health) were used. The model drawings were made with the use of Solid works software package. Differential geometrical method based on applying the moving Frénet trihedral was used to design half-masks. Two designs of the respirator half-mask were developed based on the averaged length and width of face (Modification 1), and length of face and lips (Modification 2). As a result of modeling test, the area and perimeter of the mask contact line have been defined. The safety factor of half-mask insulation was calculated theoretically. It has been established that the safety factor of Modification 1 half-mask is 2.5 times higher.
An electric power trading framework for smart residential community in smart cities

This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.
CSRN 2901-WSCG 2019 proceedings, Part I

Light field data records the amount of light at multiple points in space, captured e.g. by an array of cameras or by a light-field camera that uses microlenses. Since the storage and transmission requirements for such data are tremendous, compression techniques for light fields are gaining momentum in recent years. Although plenty of efficient compression formats do exist for still and moving images, only a little research on the impact of these methods on light field imagery is performed. In this paper, we evaluate the impact of state-of-the-art image and video compression methods on quality of images rendered from light field data. The methods include recent video compression standards, especially AV1 and XVC finalised in 2018. To fully exploit the potential of common image compression methods on four-dimensional light field imagery, we have extended these methods into three and four dimensions. In this paper, we show that the four-dimensional light field data can be compressed much more than independent still images while maintaining the same visual quality of a perceived picture. We gradually compare the compression performance of all image and video compression methods, and eventually answer the question, "What is the best compression method for light field data? ".
Face Recognition Via Segmentation Processing Based on the Fusion of Residual

Sparse representation is an efficient image representation method, the face recognition algorithm based on sparse representation is widely used in the recognition and classification of face images because it has the advantages of being insensitive to feature extraction and easy to construction algorithm model. However, the original algorithm does not effectively discriminate non-face images. At the same time, the algorithm is based on the whole face, ignoring the positive influence of local features on the whole recognition process. Aiming at the problems existing in the classical sparse representation method, we propose the face recognition algorithm via segmentation processing based on the fusion of residual. First, the test image is identified by the improved 2 (cid:2) -norm value detection method, then the test image which is judged to be legal is processed into non-superimposed blocks and finally to be classified. This method reduces the negative impact of the noise-containing sub-blocks on the whole face recognition process and enhances the positive influence of the feature significant sub-blocks. In addition, under the premise of ensuring the recognition rate and controlling the calculation cost, the optimal segmentation strategy is proposed. The comparison experiments were carried out in the standard Extended Yale B face database and AR face database, the experiment results show that the proposed algorithm achieves better recognition effect.
Dual Stream Spatio-Temporal Motion Fusion With Self-Attention For Action Recognition

Human action recognition in diverse and realistic environments is a challenging task. Automatic classification of action and gestures has a significant impact on human-robot interaction and human-machine interaction technologies. Due to the prevalence of complex real-world problems, it is non-trivial to produce a rich representation of actions and to produce an effective categorical distribution of large action classes. Deep convolutional neural networks have obtained great success in this area. Many researchers have proposed deep neural architectures for action recognition while considering the spatial and temporal aspects of the action. This research proposes a dual stream spatiotemporal fusion architecture for human action classification. The spatial and temporal data is fused using an attention mechanism. We investigate two fusion techniques and show that the proposed architecture achieves accurate results with much fewer parameters as compared to the traditional deep neural networks. We achieved 99.1 % absolute accuracy on the UCF-101 test set.
A statistical approach for improvement of Best Worst Method (BWM)

This paper endeavors to measure the consistency of a decision-making tool, popularly known as Best Worst Method (BWM), which is one of the latest developments in multiple-criteria decision analysis (MCDA). BWM is finding a vast arra of applications in the literature. Several investigators have extended this tool. BWM measures the weight of decision-making criterion and is recognized as a subjective decision tool. The first step in this method is to find the best and worst criterion, while we suppose several experts are asked to present their evaluation over set of criteria. The aim is to measure how these judgments are consistent and reliable. So, we statistically (using χ2 distribution) add a pre-evaluation to experts’ opinion and analyze whether the agreement of experts’ opinions is satisfactory and group opinion is established. This action improves the quality of the decision-making process by incorporating the reliability evaluation of experts’ idea. This extension for BWM helps decision makers in facilitating and getting results that are more consistent for criteria evaluation. We present examples in sustainable construction and architecture project.
A Prediction Approach of Hot Electric Power Researches for Heterogeneous Time Spans

Recently with the rapid growth of electric power literatures, it is hard to artificially track and process hot electric scientific researches. In the past most, professionals use simple statistics to get high-frequency words, which is time-consuming and ignores the similarity between words. Moreover, different researchers have different requirements for prediction time span. In the paper we propose a prediction system for hot electric scientific researches and gives its implementation. It is based on our previous work and we improve it to suit indefinite prediction period. The proposed embedded RNN prediction model is flexible for heterogeneous time spans and can return prediction results rapidly and accurately. Our extensive experiments demonstrated that our approach has acceptable precision ratio as well as training time in comparison to SVM, RNN and linear regression algorithms. It also performs better when the embedded layers are multiple.
Performance driven facial animation with blendshapes

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Lempel-Ziv-Markov Chain Algorithm Modeling using Models of Computation and ForSyDe

The data link is considered a critical function of modern aircraft, responsible for exchanging information to the ground and communicating to other aircraft. Nowadays, the increasing amount of exchanged data and information brings the need for network usage optimization. In this sense, data compression is considered a key approach to make data packages size smaller. Regarding the fact that avionics systems are safety-critical, it is fundamental not losing data nor performance during the compression procedures. In this context, manufacturers and regulatory agencies usually follow DO-178C guidance. Targeting model-based embedded design guidelines, DO-178C includes a supplement document, named DO-331. In this paper, we describe a widely used data compression algorithm, the Lempel-Ziv-Markov Chain algorithm (LZMA). Regarding formal model-based design, we argue that the synchronous dataﬂow model of computation captures the algorithm behavior more directly. The Formal System Design (ForSyDe) methodology is used to model the LZMA.
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation

The goal of this article is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, we propose an algorithm inspired by the knowledge distillation, denoted the neural couplings algorithm (NCA). The NCA yields a matrix that expresses the mapping of the mixture to the target source magnitude information. Using the NCA, we examine the mapping functions of three fundamental DAE-based models in music source separation; one with single-layer encoder and decoder, one with multi-layer encoder and single-layer decoder, and one using skip-filtering connections (SF) with a single-layer encoding and decoding. We first train these models with realistic data to estimate the singing voice magnitude spectra from the corresponding mixture. We then use the optimized models and test spectral data as input to the NCA. Our experimental findings show that approaches based on the DAE model learn scalar filtering operators, exhibiting a predominant diagonal structure in their corresponding mapping functions, limiting the exploitation of inter-frequency structure of music data. In contrast, skip-filtering connections are shown to assist the DAE model in learning filtering operators that exploit richer inter-frequency structures.
Algorithms for influence maximization and seed minimization

Graph is a basic mathematical tool that models information about identities as well as their complex relationships from various real world problems. It has been found important applications in analysis on social networks, route planning, telecommunication, etc. In recent years, the complexity and scale of real world graphs have increased dramatically. In particular, international social networks can comprise of hundreds of millions of users and up to billions of relationships. Thus, even algorithms with decent time or space complexities meet challenges dealing with large-scale networks for the queries like influence maximization and seed minimization on social networks. In this thesis, we investigate two problems on large scale networks for aforementioned applications, i.e., influence maximization and seed minimization. Given G a social network, M a probabilistic propagation model, and a small number k > 0, the influence maximization problem aims to find the largest expectation of the number of influenced nodes that k nodes can trigger under this pre-defined model M . This problem is derived from viral marketing, where a company gives away free samples to a small number of influential individuals in order to create a cascade of adoption via word-of-mouth effect. This study proposes a two-phase approach Influence Maximization via Martingales (IMM) that meets both practical efficiency and theoretical guarantees. In particular, IMM returns an (1− 1/e− ε)-approximate solution with at least 1− n−l probability in an O((k+l)(n+m) log n/ε) running time. IMM is further extended to fit in triggering model and time-continuous model. We experimentally evaluate IMM with the state-of-the-art benchmarks under several diffusion models and parameter settings, using large networks with up to 1.4 billion edges. The experimental results show that our approach consistently outperforms the states of the art in terms of efficiency.
Ultra-Low Leakage, Energy-Efficient Digital Integrated Circuit and System Design

Ultra-Low Leakage, Energy-Efficient Digital Integrated Circuit and System Design João Pedro da Silva Cerqueira The advances of the complementary metal-oxide-semiconductor (CMOS) technology manufacturing and design over the years have enabled a diverse range of applications across the power consumption, performance, and area (PPA) spectra. Many of the recent and prospective applications rely on the availability of energy-autonomous, miniaturized systems, i.e., ultra-low power (ULP) VLSI systems, which are generally characterized by extreme resource limitations. Some examples of applications are wireless sensing platforms, body-area sensor networks (BASN), biomedical and implantable devices, wearables, hearables, and monitors. Within the context of such applications, the key requirements are long lifetime and miniaturized size (sub/millimeter-scale). In order to enable both requirements, energy-efficiency is the key metric. It allows for extended battery lifetime and operation with the energy that can be harvested from the environment, and it limits the size (volume) of the energy sources utilized to power these systems. Ultra-low voltage (ULV) operation is a key technique in which the VDD of circuits is reduced from nominal to near or below the threshold voltage of the transistor. It is a powerful knob that has been largely exploited by designers in order to achieve ultralow power consumption and high energy-efficiency in CMOS. Existing ULP VLSI systems typically operate at a lower supply voltage thereby reducing their energy consumption by one to two orders of magnitude in order to enable the aforementioned applications. While supply voltage scaling is a promising measure for achieving low power and reducing energy consumption, it brings up several challenges. One critical issue is the leakage energy dissipated by the devices, which is magnified in portion to the total energy consumption at ULV. The reason is that, as VDD scales from nominal to near-threshold and sub-threshold, transistors become increasingly slower and they accumulate more leakage (i.e., static) power over longer cycle times. This energy waste accounts for a significant portion of the system’s total energy consumption, offsets the gains provided by voltage scaling, defines the minimum energy per operation, and poses a practical limit for the system’s energy-efficiency. This thesis presents selected research works on ultra-low leakage, energy-efficient digital integrated circuit design. More specifically, it describes novel and key techniques for minimizing the energy waste of idle/underutilized and always-on hardware. The main goal of such techniques is to push the envelope of energy-efficiency in energy-autonomous, miniaturized VLSI systems. Such techniques are applied to key building blocks of emerging mobile and embedded computing devices resulting in state-of-the-art energy-efficiencies.
Learning-Based Resource Management in Device-to-Device Communications With Energy Harvesting Requirements

In this paper, we propose a resource management method based on deep learning, which controls both the transmit power and the power splitting ratio to maximize the sum rate with low computational complexity in D2D networks with energy harvesting requirements. The introduction of the energy harvesting requirements to D2D networks makes it hard to design an effective resource management solution since the treatment of interference signals should be completely different from the conventional resource management focusing only on the rate maximization. To deal with drawbacks of the conventional deep learning-based approach, we propose a new training algorithm suitable for our resource management problem. Numerical simulations show that the proposed learning-based method outperforms the benchmark methods, which are derived from some relevant works, in most situations and achieves performances comparable to an exhaustive search in terms of the sum rate and energy outage probability. Although the conventional optimization-based method is derived to achieve the asymptotic optimal performance for a large network, the proposed deep learning method is shown to achieve almost the same performance with much lower computational complexity. Furthermore, simulation results offer new insights to the impact of the energy harvesting requirements on the behaviour of the optimal resource management.