Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder

Fall detection is a hot research issue in intelligent video surveillance. Falls can generate physical and psychological damage, especially for the elderly. Different from most conventional vision-based fall detection methods typically relying on hand-crafted features, fall detection methods based on deep learning techniques can automatically learn features and hence have got widespread concern recently. However, as deep networks are increasingly applied to fall detection, the problem of information loss in the deep networks can not be ignored, because this will ultimately affect the performance of fall detection. To solve the above problem, we propose a vision-based fall detection method using multi-task hourglass convolutional auto-encoder (HCAE). In this method, hourglass residual units (HRUs) are introduced into the encoder of the HCAE to extract multiscale features by expanding receptive fields of neurons. A multi-task mechanism is presented to enhance the feature representativeness of the network by completing an auxiliary task of frame reconstruction while realizing the main task of fall detection. Experimental results demonstrate that, the proposed method can effectively achieve accurate fall detection with the shallow-layer network, and outperforms several state-of-the-art methods.
A Study on Polyvinylidene Difluoride (PVDF) Anchoring Polymer Layer (APL) Solder Anisotropic Conductive Films (ACFs) for Fine-Pitch Flex-on-Flex (FOF) Interconnection

Fine-pitch flex-on-flex (FOF) assembly has been introduced for various electronic packaging applications because electronic products require higher functionality, flexibility, and I/O density. In this article, novel polyvinylidene difluoride (PVDF) anchoring polymer layer (APL) solder anisotropic conductive films (ACFs) were investigated for fine-pitch FOF applications. The capture rates of conductive particles and the insulation rates between neighboring electrodes have been significantly enhanced using PVDF APL solder ACFs as a result of suppressing the movement of conductive particles by the high tensile strength of the PVDF APL layer. For good solder wetting on electrodes, flux activators were added into the PVDF APL layer to remove the solder oxide layer. However, the tensile strength of the PVDF layer decreased as the flux activator content in the APL increased. The capture rates of conductive particles decreased as more flux activators were added because of the lower tensile strength of PVDF. Furthermore, optimized flux activator content in ACFs with 0.5 wt% in the APL and 2-wt% flux activators were also added in non- conductive films (NCFs) to obtain good solder wetting. Using the optimized APL solder ACFs, excellent contact resistances and insulation resistances were obtained before and after a pressure cooker test (PCT) and 85 °C/85% relative humidity (RH) reliability tests. As a conclusion, the PVDF APL solder ACF assembly can provide an excellent interconnect solution for fine-pitch FOF assemblies used for portable electronic product applications.
UXJs: Tracking and Analyzing Web Usage Information With a Javascript Oriented Approach

Knowing what the user does inside your web and how he does it is crucial nowadays to understand the strengths and inconveniences of your web’s design and architectural structure as well as about the usability of the site. Currently, there are several solutions that allow the tracking of the user behavior but these have some limitations due to the information they are able to capture and how they can present that information in a useful way for the web developer. Many of these platforms don’t capture information about the user activity in the websites, clicks, mouse movements, etc. Some solutions do capture some of this user activity, but they only process the information visually showing heatmaps. In this paper we present UXJs, a novel research approach for collecting automatically all possible information about the user activity in websites, showing this information quantitatively and allowing its automatic statistical analysis and the rapid understanding by web developers.
A Study of Intrusion Detection System using Machine Learning Classification Algorithm based on different feature selection approach

Network security is the most challenging task of the modern digital era. Due to the development in internet, the number of network attacks has also increased, this is prevented by access control, key manager, and intrusion detection system. Among these the most challenging task is intrusion detection system that ensures the network security. The current approach focuses on the important issues in intrusion detection system, which will identify the unwanted attacks and unauthorized access in the network. The comprehensive overview of the detailed survey is analyzed with the existing data set for identifying the unusual attacks that can understand the current issues in intrusion detection problems. The detailed investigation is reported for observing several issues on the intrusive performance by using the machine learning classification. Here machine learning classification algorithm is used for detecting the several category of attacks. Furthermore, this study evaluates the performance criteria based on the feature extraction and machine learning classification techniques algorithm. Finally, based on the results observed we recommend some important features by using machine learning classification in order to find out the efficient method for detecting the particular attack.
Electrically Tilted Broadband Antenna using Negative Refractive Index material

A negative refractive index material loaded wideband patch resonator is presented for the wireless applications. The negative refraction has been achieved by creating a semi-circular and linear defect in the antenna ground plane. The antenna demonstrates an electrical tilt. This presents the ability to keep the communication module mechanically vertical. The engineered metallic strip excites the resonance mode. The structure of the metallic strip is a modification of thin wire and split-ring resonator (SRR) which are responsible for negative values of permeability and permittivity respectively. The finite truncated ground plane significantly helps to improve the antenna bandwidth. The antenna has an electrical length of $0.65\lambda \times 0.65\lambda$ where $\lambda$ is the wavelength at lowest resonance. The resonating frequencies of antenna are 2.46, 3.5, and 5.5 GigaHertz, respectively. The antenna has impedance bandwidth of 8.94%, 14.57% and 8.72% for the presented center frequencies respectively. The antenna prototype was fabricated where measured and simulated results show good correlation.
Estimation of Impulse Noise Parameters in Power Line Communication

In this paper, an algorithm that estimates the parameters of impulse noise (IN) is proposed in power line communication. The Peak to Average Power Ratio (PAPR) of the Orthogonal Frequency Division Multiplexing (OFDM) symbol sent by the transmitter is reduced by the selective mapping (SLM) technology. In addition, the peak value of each OFDM symbol is sent with a separate control channel. At receiver, the frequency characteristics of IN is calculated using the received peak value and the OFDM symbol. The noise used for simulation comes from actual measurement. Results from simulations show that the proposed algorithm can estimate the IN well under different IN environments, and the obtained parameters are very valuable for mitigating IN in power line communication.
A Quality-Aware Reverse Auction-Based Incentive Model for Mobile Crowdsourcing

Mobile crowdsourcing is a promising paradigm where a group of people performs an allocated task requested by a person or an organization. In this mechanism, the task requester can provide the requirements of the contributor with the description of the task. The coordinator calls a reverse auction to select the perfect contributor to complete the task. We propose a reverse auction based incentive mechanism which encourages the contributor to participate in crowdsourcing. We also incentivize the dropout users from the auction with some virtual rewards. This mechanism is computationally efficient, truthful, individually rational and profitable. We also evaluate the performance of the proposed mechanism by conducting several experiments.
Development of a 50 kW Wireless Power Transfer System

A high-power modular wireless power transfer system has been developed intended for use in larger vehicles. This paper presents the design methodology and evolution of the system, including test results. The system utilises SiC switching devices and an 85 kHz operating frequency. 50 kW of wireless power transfer has been achieved over a 200 mm gap at 89% efficiency, showing good promise for the system.
Speed Control of Permanent Magnet Synchronous Motor Drives Based on Model Predictive Control

A control technique model for predicting the speed of permanent magnet synchronous motor is proposed in this paper in order to cut costs and fortify the stability and effectivity of motor drive, and some simple models of PMSM are employed in the MPC structure. In view of the cross-coupling electromotive force between the direct axis and the quadratic axis in the voltage equation, the dynamic model of PMSM, it is proposed to add a feedforward decoupler to achieve decoupling. The performance of the comparison between MPC with feedforward decoupling and without feedforward decoupling has been carried out, and the feasibility of the proposed scheme is verified by scientific and rigorous simulation experiments. The results proved that the PMSM with the speed and current MPC controller with feedforward decoupling has superior transient response. In addition, more accurate tracking performance has come true.
Identification of Critical Airports from the Perspective of Delay and Disruption Propagation in Air Travel Networks

It is well-known that delays and disruptions that originate at certain airports end up spreading spread on the network to other airports, sometimes straining a large fraction of the system. Hence data-driven modeling of this phenomenon is important from the standpoint of planning and strategy. In this work, utilizing the publicly available data from the Federal Aviation Administration (FAA), we employ the well-known influence maximization paradigm, to mine for the top airports in the US airport network, from the perspective of delay spread. In the process, we develop a diffusion simulator and implement a greedy algorithm to aid our task. We then explore the temporal trends by considering one winter month and one summer month in our analysis. Finally, we characterize the clusters in the US airport network and investigate the patterns of delay spread when airports in each of the clusters are affected.
A Review on Basic Methodology of Twitter Base Prediction System

Twitter is a free and most popular social networking service that allows registered and authorised user to transfer their opinion called short messages also known as tweets. Twitter users can broadcast their opinion in the form of tweets and track other users' opinion in the form of tweets from multiple platforms and from any devices. There are lots of user tweets and reviews are available on Twitter. Twitter has become one of the best ways of getting customers' opinions like political tweets, movie ratings and making predictions about the results of Products and location. The Sentiment analysis of the general people's opinion is made in the Twitter data, which can recognize the opinion of people in general about the social event that is published on Twitter. This study presents current scenarios such as product reviews, political tweets, film classifications, etc., which are published on Twitter as an example of sentiment analysis. In this paper we will discuss or describe methodology, pre-processing, feature extraction, different types of classification techniques etc. And also compare different techniques with each other.
Exploring Function Call Graph Vectorization and File Statistical Features in Malicious PE File Classification

Over the last few years, the malware propagation on PC platforms, especially on Windows OS has been even severe. For the purpose of resisting a large scale of malware variants, machine learning (ML) classifiers for malicious Portable Executable (PE) files have been proposed to achieve automated classification. Recently, function call graph (FCG) vectorization (FCGV) representation was explored as the input feature to achieve higher ML classification accuracy, but FCGV representation loses some critical features of PE files due to the hash technique. This paper aims to further improve the classification accuracy of FCGV-based ML model by applying both graph and non-graph features. We propose an FCGV-SF based Random Forest classification model, which applies both FCGV features (graph features) and statistical features (SF, non-graph features) extracted from disassembled PE files. Six types of effective non-graph features are chosen for our integrated vector, namely, metadata, symbol, operation code, register, section and data definition. We evaluate our model on a dataset provided by Microsoft hosted at Kaggle, and the experimental results indicate that the classification accuracy increases from 0.9851 to 0.9957 compared with the existing model based on FCGV only.
On second order consensus protocols allowing joint-agent interactions

In this note, we propose a second order consensus protocol allowing joint-agent interactions. Our analysis is addressed to networks where agents are grouped in interaction triplets. A triplet is a set of three distinct agents, where the interaction dynamics is such that one member is sensitive to the influence brought by the other two only when it receives simultaneous and consistent opinions from them. This allows the network to reach a second order consensus configuration more robustly, even in the presence of exogenous disturbances on the dynamics of one or more agents. The piecewise linear function used to model the robust interaction within a triplet leads to the energy of the interaction being conserved along certain directions, as if some agents were connected by virtual springs. In conclusion, a convergence analysis is performed by means of the recursive detection of such conservation laws.
An Unmanned Aerial Vehicle Pose Estimation System Guided by Desired Shot

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid.
I/O Scheduling for Limited-Size Burst-Buffers Deployed High Performance Computing

Burst-Buffers is a high throughput, small size intermediate storage system integrated between computing nodes and permanent storage system to mitigate the I/O bottleneck problem in modern High Performance Computing (HPC) platforms. This system, however, is unable to effectively handle variable-intensity I/O bursts resulted by unpredictable concurrent accesses to the shared Parallel File System (PFS). In this paper, we introduce a probabilistic I/O scheduling method that takes into account of the burst-buffer load state and instantaneous I/O load distribution of the system based on the probabilistic model of applications to relieve the I/O congestion when I/O load exceeds the PFS bandwidth caused by dynamic application interference. The proposed scheduling method for limited-size Burst-Buffers deployed HPC platforms makes online decision of probabilistic selection of concurrent I/O requests for going through (to PFS), buffering (to Burst-Buffers) or declination in accordance to both the available I/O bandwidth and the current buffer state in order to maximize system efficiency or minimize application dilation. Extensive experiment results on actual characteristic synthetic data show that our method handles the I/O congestion effectively.
A Hybrid Indoor Positioning System Using a Linear Weighted Policy Learner and Iterative PDR

Electronic indoor positioning systems deal with the combination of sensors, actuators, and computational algorithms for precisely locating subjects, delivering navigation directives, and keeping track of particular objects. The main factors considered for the construction and evaluation of these systems are the localization accuracy and the time spent to calculate and deliver this information. The challenge in developing successful positioning systems is to find a tolerable relationship between those factors. In this proposal, after a careful analyses of related works, we associated different methodologies and technologies to construct a hybrid positioning model that uses a mapping algorithm called Linear Weighted Policy Learner, a navigation model called iterative Pedestrian Dead Reckoning (which uses the Kalman filter to deliver real-time location), and an obstacle detection algorithm that combines sounds and stereo vision sensorial capabilities. The adopted choices were based on the published state-of-the-art, and comparisons of the obtained results showed that our system is accurate and fast enough to be very competitive with the current stage of the technology.
Development a planar neuro-musculoskeletal arm model in post-stroke patients

Computational models of the central nervous system after a stroke helps to reveal the physiological mechanisms that may have strong impacts on the neuro-motor rehabilitation approaches. This paper studies the stroke subject’s motor control mechanism in reaching movements by extending the previous study by incorporating the kinematics of motion as well as neural disconnection between the muscles and the CNS to further develop a planar patient specific neuro-musculoskeletal model of arm. The developed model was calibrated to eight post-stroke individuals by altering the Muscle Significance Factors (MFS) using numerical optimization to match the simulated motions with those measured experimentally. Eventually, the reaches generated by our model replicated the features observed in reaches by individual stroke survivors with a promising level of accuracy. Our results show that trajectory planning after stroke still argues for optimal control of movement in which internal models can play a significant role.
Automatic Classification of Computational Thinking Skills in Elementary School Math Questions

This research full paper presents the design of an automatic classifier for Computational Thinking skills in math questions, based on Machine Learning and Natural Language Processing techniques. We trained and evaluated our model using a dataset of real-world math questions. We obtained encouraging results that indicate the proposed approach may ease the process of assessing the level of alignment between math questions and Computational Thinking skills, which may help improve the problem-solving ability in students of elementary school. These results stimulate the use of automated environments for the elaboration, classification, and resolution of math questions in conformity with Computational Thinking skills.
NLP methods for automatic candidate’s CV segmentation

The problem of CV (or resume) segmentation and automatic extraction becomes increasingly relevant nowadays as long as it could simplify candidate selection process. The paper proposes a new method of automatic CV segmentation and parsing. The described algorithm is based on Natural Language Processing and Machine Learning methods. The proposed procedure allows to extract information related to the candidates’ work experience and education from their CVs which come in pdf or docx format. In particular, CV segmentation into 3 blocks (Basic Information, Education and Work Experience) is performed.
Distributed Constraint-Coupled Optimization over Random Time-Varying Graphs via Primal Decomposition and Block Subgradient Approaches

In this paper, we consider a network of processors that want to cooperatively solve a large-scale, convex optimization problem. Each processor has knowledge of a local cost function that depends only on a local variable. The goal is to minimize the sum of the local costs, while making the variables satisfy both local constraints and a global coupling constraint. We propose a simple, fully distributed algorithm, that works in a random, time-varying communication model, where at each iteration multiple edges are randomly drawn from an underlying graph. The algorithm is interpreted as a primal decomposition scheme applied to an equivalent problem reformulation. Almost sure convergence to the optimal cost of the original problem is proven by resorting to approaches from block subgradient methods. Specifically, the communication structure is mapped to a block structure, where the blocks correspond to the graph edges and are randomly selected at each iteration. Moreover, an almost sure asymptotic primal recovery property, with no averaging mechanisms, is shown. A numerical example corroborates the theoretical analysis.
Early Detection of Brain Tumor by Using K-Means Clustering, Convolutional Neural Network and Support Vector Machine without any Imaging Test

Among the list of dreadful diseases, 'Brain Tumor and Brain Cancer' is one of them. According to survival rate, after the detection of cancerous Brain or Central Nervous System (CNS) tumor, 34% of men and 36% of women survive at least 5 years. Therefore, 'Early Detection of tumor' can be a savior for so many lives and artificial intelligence will make the way very accurate and appropriate. A new non-invasive approach to detect brain tumors in early-stage, cost-efficiently with the lowest risk factor is-'Liquid Biopsy'. It is a technique for analyzing non-solid biological tissue, i.e. - blood, plasma, cerebrospinal fluid (CSF). As human inspection maybe error full and inaccurate, in this paper, we have used 'k-means clustering' in 'Image Processing' as visual perception of artificial intelligence for detection of 'CTC' and we have used the technique 'Convolutional Neural Network' (CNN) for ctDNA detection and 'Support Vector Machine'(SVM) algorithm for classification of ctDNA and normal cell-free DNA (cfDNA). 'ctDNA' could provide clues for growth factor of cancerous cells, type of tumors, location, etc. Therefore, our work provides a new way to early detection and a new prospect for early cure.
Training of Generative Adversarial Networks with Hybrid Evolutionary Optimization Technique

This paper proposes a new method for training Generative Adversarial Networks(GANs), named as Hybrid Evolutionary Optimization. The proposed method trains GAN networks by evolving generator set to reduce Fréchet Inception Distance. To overcome the problems such as non-convergence and mode collapse which are associated with conventional GANs, this paper uses an evolutionary algorithm to train in the initial iterations to stabilize the weights and followed by conventional optimization for remaining iterations. The efficiency of proposed method is evaluated with MNIST and celebA dataset. The results show that the proposed training algorithm converges faster than conventionally trained GANs. Moreover, the training with CPU and GPU is compared and analyzed.