Miracle-dz/UniFAR-FTUs huggingface dataset:
EVENT DRIVEN VIDEO SUMMARIZATION FOR SPORTS

Broadcasters generate a huge amount of sports videos due to massive viewership all over the world. Analysis and consumption of this huge repository urges the broadcasters to apply video summarization to extract exciting segments of a full-duration video and reap storage and transmission benefits. Summarization of sports videos is a challenging task due to variations in camera orientation, illumination conditions and game structure, background noise in audio stream, editing effects introduced by the broadcasters, etc. This thesis work focuses on developing reliable methods for shot classification, replay detection, key-events detection, and used them for video summarization. More specifically, shot boundary detection and classification is proposed to partition the sports videos into small segments that are convenient to process further for summarization. Shot classification is used to bridge the semantic gap between the low-level features and high-level events. A decision tree based architecture is designed to classify the sports videos into various shots. Rule-based induction is applied to determine the most reliable rules that are least unpredictable for shot classification. Robustness of the proposed method has been evaluated for camera variations, game structure, sports genre, illumination conditions, broadcaster’s
Locality sensitive discriminant projection for feature extraction and face recognition

Abstract. As an effective feature extraction method, locality sensitive discriminant analysis (LSDA) utilizes the neighbor relationship of data to characterize the manifold structure of data and uses label information of data to adapt to classification tasks. However, the performance of LSDA is affected by outliers and the destruction of local structure. Aiming at solving the limitations of LSDA, a locality sensitive discriminant projection (LSDP) algorithm is proposed. LSDP minimizes the distance of intraclass neighbor samples to maintain local structure and minimizes the intraclass non-neighbor samples to increase the compactness of intraclass samples after projection. The problem of outliers is alleviated by increasing the compactness of intraclass samples in subspace. At the same time, we redefine the weights of interclass neighbor samples to maintain the neighbor relationship of different labels samples. Holding the local structure of interclass samples maintains the manifold structure of data. Experiments on face datasets demonstrate the effectiveness of the LSDP algorithm.
Deep Learning-Based Power Control for Non-Orthogonal Random Access

This letter presents deep learning (DL) based non-orthogonal random access (NORA) where multiple nodes utilizing the identical preamble simultaneously transmit data over the same time-frequency resources. Effective power control algorithms are essential for the NORA, however, only partial information of channels such as the timing advance (TA) is available. This poses challenges for existing algorithms requiring full channel knowledge. We propose unsupervised DL-based power control schemes which maximize the minimum rate based only on the TA information. Numerical results verify the effectiveness of the proposed DL-based NORA over conventional methods.
Solving navigation-based goal recognition design problems with action graphs

To proactively assist humans robots require the ability to recognise a human’s goal. Unfortunately, as plans can often start with the same actions, i.e. are initially non-distinctive, it is often impossible to determine which goal a human is aiming to achieve until they have neared their goal. By modifying the environment we aim to force humans into revealing their goal sooner without increasing the cost of the optimal plan to any of the possible goals. Previously proposed approaches, that apply classical planning techniques to this problem, are very computationally expensive. This paper presents an early version of our work, in which we propose transforming goal recognition design problems into Action Graphs. Experiments show that our approach is able to force humans into revealing their goal sooner in environments with various numbers of goals and of differing sizes in an average time of 1.42 seconds, whereas a current state-of-the-art approach often breaches a 10 minute timeout.
A Fair TDMA Scheduling Based on Weight of Flows

Nodes in wireless ad hoc networks transmit data to 1-hop neighbour nodes through broadcast channel. Conflicts occur when multiple nodes within the communication range send data at the same time or when one node receives data sent by multiple nodes at the same time. For broadcast scheduling problem (BSP), many researchers used TDMA (Time Division Multiple Access) scheme based on SVC (Sequential Vertex Colouring) in ad hoc networks to achieve conflict-free scheduling. However, SVC is related to the order of nodes. To improve the fairness of network, the slots of nodes are adjusted by changing the order of nodes. In this paper, we proposed a fair scheduling algorithm for TDMA, which takes into account the number of flows, the size of the flows and the bandwidth of each node. The simulation and analysis results show that this scheduling algorithm can enhance the fairness of the ad hoc compared with the traditional algorithm based on SVC.
1-bit compressed sensing based on reweighting approximate message passing

1-bit compressed sensing (1-bit CS) examines the efficient acquisition of sparse signals via linear measurement systems followed by a 1-bit quantizer. In this paper, we discuss 1-bit CS reconstruction in the scenario that the sparsity level of the signal is unknown. We introduce reweighting approximate message passing (AMP) into the 1-bit CS problem and propose the binary iterative reweighting AMP algorithm (AMP-BRW). This algorithm performs binary reweighting AMP in the iterative process, which conforms to the binary manner of the 1-bit CS measurements and inherits the advantages of AMP. Simulation results show that AMP-BRW can realize 1-bit CS reconstruction without the prior knowledge of the sparse level of the signal. Moreover, AMP-BRW can achieve higher reconstruction performance and higher convergence performance than the original binary iterative reweighted algorithm.
Vital Signs Monitoring based on a Developed Accelerometer Sensor for Sporty Purposes

This paper proposes developing a wireless sensor for heartbeat and respiration rate monitoring for the sportsman. The developed wireless sensor, includes an accelerometer sensor, a processor and a transceiver which operates in Industrial, Scientific and Medical (ISM) band and the frequency of 2.54 to connect with a central node for transmitting the measured data. In order to evaluate the accuracy of the presented sensor, the different experiments are carried out on several volunteers and the resultant data are compared to a gold standard for respiration rate and also heartbeat as a reference. As the outcomes illustrate, the root means square error (RMSE) and the standard deviation of the error (SD) are calculated less than 2.8 and 1.43 beats per minutes as well as 1.01 and 0.85 breaths per minutes for the heartbeat and respiration rate, respectively. Therefore, the sensor can account a good alternative for cardiorespiratory system monitoring for sporty application, due to low-cost, portable and also availability.
Energy Efficient Data Forwarding Scheme in Fog-Based Ubiquitous System With Deadline Constraints

Ubiquitous Computing (UbiComp) is a computational paradigm that enhances the use of computing devices by making them available to the user anywhere and anytime. From the energy perspective, it is often very important to compute the entire UbiComp task within a specific deadline with minimum energy. The literature on determining the energy consumption of the system for computing the task does not consider periodic tasks and different sampling rate of the sensors, which eliminates the deadline constraints in the analysis. Since the period of the tasks is not fixed, the estimated delay without considering the fixed period is lower than the actual value. In this paper, we assume that an Edge, Fog, and Cloud layers based UbiComp system computes the periodic task within the specific deadline. We derive the expressions of total delay and energy consumption of the UbiComp system. Using the derived expressions, we estimate fractions of the task that are computed at each layer to reduce the energy consumption such that the task is computed within a specific deadline. Our numerical and prototype results demonstrate the impact of the data size, network topologies, deadline, and characteristics of the sensors on the energy consumption, delay, and accuracy of the system.
Securing 3rd Party App Integration in Docker-based Cloud Software Ecosystems

—Open software ecosystems are beneﬁcial for customers; they beneﬁt from 3 rd party services and applications, e.g., analysis of data using apps, developed and deployed by other companies or open-source communities. One signiﬁcant advantage of this approach is that other customers may beneﬁt from these newly developed applications as well. Especially software ecosystems utilizing container technologies are prone to certain risks. Docker, in particular, is more vulnerable to attacks than hypervisor based virtualisation as it directly operates on the host system. Docker is a popular representative of containerisation technology, which offers a lightweight architecture in order to facilitate the set-up and creation of such software ecosystems. Popular Infrastructure as a Service cloud service providers, like Amazon Web Services or Microsoft Azure, jump on the containerisation bandwagon and provide interfaces for provisioning and managing containers. Companies can beneﬁt from that change of technology and create software ecosystems more efﬁciently. In this paper, we present a new concept for signiﬁcant security improvements for cloud-based software ecosystems using Docker for 3 rd party app integration. Based on the security features of Docker, we describe a secure integration of applications in the cloud environment. Our approach considers the whole software lifecycle and includes sandbox testing of potentially dangerous 3 rd party apps before these became available to customers.
It matters to me if you are human - Examining categorical perception in human and nonhuman agents

Humanlike but not perfectly human agents frequently evoke feelings of eeriness, a phenomenon termed the Uncanny Valley (UV). The Categorical Perception Hypothesis proposes that effects associated with the UV are due to uncertainty as to whether to categorize agents falling into the valley as “human” or “nonhuman”. However, since UV studies have traditionally looked at agents of varying human-likeness, it remains unclear whether UV-related effects are due to categorical uncertainty in general or are specifically evoked by categorizations that require decisions regarding an agent’s human-likeness. Here, we used mouse tracking to determine whether agent spectra with (i.e., robot-human) and without (i.e., robot-animal and robot-stuffed animal) a human endpoint cause phenomena related to categorical perception to comparable extents. Specifically, we compared human and nonhuman agent spectra with respect to existence and location of a category boundary (H1-1 and H2-1), as well as the magnitude of cognitive conflict around the boundary (H1-2 and H2-2). The results show that human and nonhuman spectra exhibit category boundaries (H1-1) at which cognitive conflict is higher than for less ambiguous parts of the spectra (H1-2). However, in human agent spectra cognitive conflict maxima were more pronounced than for nonhuman agent spectra (H2-1) and category boundaries were shifted towards the human endpoint of the spectrum (H2-2). Overall, these results suggest a quantitatively, though not qualitatively, different categorization process for spectra containing human endpoints. Possible reasons and the impact for virtual and robotic agent design are discussed.
Efficiency evaluation of shortest path algorithms

—While the ever growing computational capabilities of devices that are used for man-machine interaction are taken for granted, the need to ﬁnd their most optimum use is as important as ever. This issue is particularly relevant when considering solutions where the determination of the shortest path between given points (nodes) is one of the basic operations. In more complex executions of the shortest paths, sets of paths with the shortest distance between a single initial (source) point and all other destination points, as well as between all pairs of points, are to be found. For each of these approaches, individual algorithms with speciﬁc features have been worked out over the past decades. With that in mind, the present article seeks to explore this problem and is structured in such a way as to describe some of the selected algorithms solving the shortest path problem, and to analyse the efﬁciency of these algorithms during their operation in directed graphs of different type. The study shows that the efﬁciency varies among algorithms under investigation and allows to suggest which one ought to be used to solve a speciﬁc variant of the shortest path problem.
FMProjector: A Formal Verification Framework for an Operating System Complying with a Standard Interface

Formal verification techniques facilitate the verification of functional correctness of software. The verification, however, is rarely applicable to large-scale software, such as operating systems, because of the state explosion problem. International standards or certifications, such as IEC-61508 or DO-178, highly recommend formal verification of such software according to the level of safety. The paper introduces a formal verification framework, FMProjector, for operating systems complying with a standard interface. The framework includes horizontal and vertical approaches for systematic analysis of the software based on traceability from the standard interface to the source code. The paper also introduces a case study for the application of FMProjector to Qplus-AIR complying with ARINC-653 which is a standard interface for avionics real-time operating system.
Application of Time-Weighted User Behavior Analysis Collaborative Filtering Algorithm in Short Video

In the big data environment, the information overload is serious. The traditional search engine does not satisfy the user’s demand for information. The user wants to obtain the user’s acquired information conveniently and quickly, and the information producer wants to transmit the information to the target user. The intermediary that acts as both is the recommendation system. Recommendation system is an important way to deal with massive information. This technology brings great convenience to viewers in the field of short video. It is very difficult to extract features from missing short video data and from content information of short video. About this question. This paper proposes a time-weighted user behavior analysis collaborative filtering algorithm. This paper starts with the behavior of short video users. In order to compensate for date sparseness, the explicit and implicit behaviors of users are deeply analyzed and a user-video scoring model is established. The user’s interest in video will change with time, so we use time weighting to adjust the time variable. Finally, the K most similar neighbors are extracted and recommended to the user in order from high to low. Experiments show that the proposed algorithm alleviates sparsity of date and improves the accuracy of recommendation.
Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems

The primary focus of the dissertation is to develop Distributionally Robust Optimization (DRO) models and related solution approaches for decision making in energy and healthcare service systems with uncertainties, which often involves nonlinear constraints and discrete decision variables. Without assuming specific distributions, DRO techniques solve for solutions against the worst-case distribution of system uncertainties. In the DRO framework, we consider both risk-neutral (e.g., expectation) and risk-averse (e.g., chance constraint and Conditional Value-at-Risk (CVaR)) measures. The aim is twofold: i) developing efficient solution algorithms for DRO models with integer and/or binary variables, sometimes nonlinear structures and ii) revealing managerial insights of DRO models for specific applications. We mainly focus on DRO models of power system operations, appointment scheduling, and resource allocation in healthcare. Specifically, we first study stochastic Optimal Power Flow (OPF), where (uncertain) renewable integration and load control are implemented to balance supply and (uncertain) demand in power grids. We propose a ChanceConstrained OPF (CC-OPF) model and investigate its DRO variant which is reformulated as a Semidefinite Programming (SDP) problem. We compare the DRO model with two benchmark models, in the IEEE 9-bus, 39-bus, and 118-bus systems with different flow congestion levels. The DRO approach yields higher a probability of satisfying the chance constraints and shorter solution time. It also better utilizes reserves at both generators and loads when the system has congested flows. Then we consider appointment scheduling under random service durations with given
Controlling oscillations in high-order schemes using neural networks

High-order numerical solvers for conservation laws suffer from Gibbs phenomenon close to discontinuities, leading to spurious oscillations and a detrimental effect on the solution accuracy. A possible strategy to reduce their amplitude aims to add a suitable amount of artificial viscosity. Several models are available in the literature, which rely on the identification of a shock sensor, adding dissipation when the solution regularity is lost. The dependence on problem-specific parameters limits their performances. To solve this issue, in this thesis we propose a new technique based on artificial neural networks. In particular, we focus on the construction of multilayer perceptrons. Emphasis is given to the training phase, which is carried out using a robust dataset created using the classical models with optimal parameters. The online evaluation is then integrated in the numerical solver of the partial differential equation. Even though most of the effort is put in one-dimensional problems, an extension to a two-dimensional scenario is provided. Several numerical results are presented to demonstrate the capabilities of the network-based technique. Initially, we focus on one-dimensional scalar problems, where Burgers equation represents our first benchmark test. Then, we move to less simple cases, characterized by non-convex flux functions or involving multiple equations (compressible Euler equations). The same strategy is followed for multi-dimensional problems. In most of the cases, the proposed model is able to guarantee high accuracy in presence of smooth solutions and to capture discontinuities (shock and contact waves). In general, the results are comparable to (or better than) the classical models with properly tuned parameters. A final performance analysis is carried out.
Influence of the topology on the dynamics of a complex network of HIV/AIDS epidemic models

In this paper, we propose an original complex network model for an epidemic problem in an heterogeneous geographical area. The complex network is constructed by coupling nonidentical instances of a HIV/AIDS epidemiological model for which a disease-free equilibrium and an endemic equilibrium can coexist. After proving the existence of a positively invariant region for the solutions of the complex network problem, we investigate the effect of the coupling on the dynamics of the network, and establish the existence of a unique disease-free equilibrium for the whole network, which is globally asymptotically stable. We prove the existence of an optimal topology that minimizes the level of infected individuals, and apply the theoretical results to the case of the Cape Verde archipelago.
Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.
Fashion popularity analysis based on online social network via deep learning

In this paper, we provide an idea about how to utilize the deep neural network with large scale social network data to judge the quality of fashion images. Specifically, our aim is to build a deep neural network based model which is able to predict the popularity of fashion-related images. Convolutional Neural Network (CNN) and Multi-layer Perceptron (MLP) are the two major tools to construct the model architecture, in which the CNN is responsible for analyzing images and the MLP is responsible for analyzing other types of social network meta data. Based on this general idea, various tentative model structures are proposed, implemented, and compared in this research. To perform experiments, we constructed a fashion-related dataset which contains over 1 million records from the online social network. Though no real word prediction task has been tried yet, according to the result of dataset-based tests, our models demonstrate good abilities on predicting the popularity of fashion from the online social network using the Xception CNN. However, we also find a very interesting phenomenon, which intuitively indicates there may be limited correlation between popularity and visual design of a fashion due to the power and influence of the online social network.
An efficient ReRAM-based inference accelerator for convolutional neural networks via activation reuse

In this paper, a novel resistive random access memory (ReRAM) based accelerator is proposed for convolution neural network (CNN) inference accelerations. In ReRAM-based CNN computation, weight parameters can be pre-programmed in ReRAM crossbar arrays, and activations are generated by processing the multiplication-and-accumulation (MAC) operations in the ReRAM crossbar arrays. However, priorworkscannotreuseactivationsincomputation,inwhichtheactivation dominates the data movements and raises signiﬁcant energy cost. To deal with this dilemma, a tiling-based dataﬂow is proposed to enable activation reuse among adjacent ReRAM crossbar arrays to reduce the activation movements. We then develop a ReRAM-based CNN accelerator that can well suit the dataﬂow to reduce the cost of ReRAM access. Evaluation resultsshowthattheproposeddesignachieves1.8 × energysavingand2.8 × bandwidth saving compared with a state-of-the-art PipeLayer accelerator.