Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Software Quality Assurance Based on Software Quality Standards and Technology Management

This paper focuses on the relationship between software quality standards and software quality assurance. According to the software engineering international standard ISO/IEC 25000 series localization process in China, how to ensure the software quality by software standard and technology management is explored. It is pointed out that software quality needs to be guaranteed by software quality standard, software quality assessment and software test process management mechanism.
Compressive Online Video Background-Foreground Separation Using Multiple Prior Information and Optical Flow

In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-l1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background–foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.
Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks

Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.
Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers

In recent years, the number of people studying English as a second language (ESL) has surpassed the number of native speakers. Recent work have demonstrated the success of providing personalized content based on reading difficulty, such as information retrieval and summarization. However, almost all prior studies of reading difficulty are designed for native speakers, rather than non-native readers. In this study, we investigate various features for ESL readers, by conducting a linear regression to estimate the reading level of English language sources. This estimation is based not only on the complexity of lexical and syntactic features, but also several novel concepts, including the age of word and grammar acquisition from several sources, word sense from WordNet, and the implicit relation between sentences. By employing Bayesian Information Criterion (BIC) to select the optimal model, we find that the combination of the number of words, the age of word acquisition and the height of the parsing tree generate better results than alternative competing models. Thus, our results show that proposed second language reading difficulty estimation outperforms other first language reading difficulty estimations.
High-content imaging assay to evaluate Toxoplasma gondii infection and proliferation: A multiparametric assay to screen new compounds

Toxoplasma gondii is an intracellular protozoan parasite widely distributed in animals and humans. Infection of host cells and parasite proliferation are essential steps in Toxoplasma pathology. The objective of this study was to develop and validate a novel automatic High Content Imaging (HCI) assay to study T. gondii infection and proliferation. We tested various fluorescent markers and strategies of image analysis to obtain an automated method providing results comparable to those from gold standard infection and proliferation assays. No significant difference was observed between the results obtained from the HCI assay and the standard assays (manual fluorescence microscopy and incorporation of [3H]-uracil). We developed here a robust and time-saving assay. This automated technology was then used to screen a library of compounds belonging to four classes of either natural compounds or synthetic derivatives. Inhibition of parasite proliferation and host cell toxicity were measured in the same assay and led to the identification of one hit, a thiosemicarbazone that allows important inhibition of Toxoplasma proliferation while being relatively safe for the host cells.
An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors

In this article, we present a public human activity dataset called ‘HAD-AW’. It consists of four types of 3D sensory signals: acceleration, angular velocity, rotation displacement, and gravity for 31 activities of daily living ADL measured by a wearable smart watch. It is created as a benchmark for algorithms comparison. We succinctly survey some existing datasets and compare them to ‘HAD-AW’. The goal is to make the dataset usable and extendible by others. We introduce a framework of ADL recognition by making various pre-processing steps based on statistical and physical features which we call AMED. These features are then classified using an LSTM recurrent network. The proposed approach is compared to a random-forest algorithm. Finally, our experiments show that the joint use of all four sensors has achieved the best prediction accuracy reaching 95.3% for all activities. It also achieves savings from 88% to 98% in the training and testing time; compared to the random forest classifier. To show the effectiveness of the proposed method, it is evaluated on other four public datasets: CMU-MMAC, USC-HAD, REALDISP, and Gomaa datasets.
A Survey on Socially Aware Device-to-Device Communications

Device-to-device (D2D) communication is continuously evolving and engulfing a wide diversity of proximate services and applications, which is considered as one of the key technologies in the 5G ecosystem. It is argued that direct communications paradigm advantages may not be entirely captured without embracing the concepts of the social-domain. Following the explosion of social networks and smart mobile devices, social-interactions among mobile users enable the promotion of D2D co-operations. In this paper, we first present a systematic overview of the socially unaware technical challenges and the prospective applications to consolidate a background on D2D communications. Next, all the social features utilized in tackling and improving the technical issues and the applications are briefed, respectively. Then, we provide a comprehensive survey on socially aware D2D communications, where various features of social-domain are leveraged, such as ties, community, trust, and selfishness to tackle the D2D technical issues and improve the performance of prospective D2D applications. We categorize state-of-the-art socially aware D2D communications into two main sections. The first section considers technical issues, while the second section pertains to their applications to provide insightful information and motivate more in-depth studies in this area. For each aforementioned section, we overview technical aspects and classify the core contributions based on approaches used and the social features utilized. In the discussion section, we summarize the socially aware D2D communications and present lessons learned. Finally, we highlight the open research challenges in this field and predict future research trends.
Multiple Collision Avoidance between Human Limbs and Robot Links Algorithm in Collaborative Tasks

In this paper a real-time collision avoidance approach useful for human-robot interaction was presented and tested. This approach is based on a collision avoidance algorithm and a distance calculation algorithm. The collision avoidance algorithm was developed to avoid possible collisions between the upper part of the human body and the links of the robot. The positions of 25 characteristic points of the body of the human operator are acquired by two vision sensors. The distances between the robot and the operator are evaluated with the proposed distance calculation algorithm. These algorithms permit a real-time control of a collaborative robot. The collision avoidance approach was tested with two kinds of experiments. The results of the tests are
A Novel Line-Start Permanent Magnet Synchronous Motor With 6/8 Pole Changing Stator Winding

Line-start permanent magnet synchronous motors (LSPMSMs) have become superior to induction motors in general industrial applications due to their high efficiency, improved power factor, and enhanced torque density. However, the LSPMSM suffers the poor starting torque owing to the existence of the braking and pulsating torque at start-up, which seriously plagues its development. Therefore, this paper presents a novel LSPMSM with 6/8 pole changing stator windings, which can fundamentally improve the starting capability. At the beginning of the starting, the 6-pole stator windings are energized, whereas the rotor permanent magnets are 8 poles, so the braking torque and pulsating torque can be highly decreased. When the motor reaches a certain speed, the stator winding changes to 8 poles to pull into synchronization and stable operation. The design of the 6/8 pole changing winding is presented based on the slot-number phase diagram method. Then, the starting process of the conventional and the proposed pole changing LSPMSMs are analyzed and compared. Through the calculation of impedance parameters and the analysis of magnetic fields at the start-up, the torque components are calculated utilizing the analytical calculation method. Finally, the experiments are carried out to verify the validity and superiority of the proposed design and method.
Thermal Analysis and Modelling of Power Consumption for FPGAs

In recent process technologies, leakage power dissipation is increasing at a very rapid rate as compared to dynamic power. The factors contributing high leakage power are small chip area, die's temperature etc. Very limited works have been published in literature to estimate the leakage power of FPGAs. So, this paper represents a regression based power estimation model for Spartan 3E FPGAs for early power estimation. Several designs are coded in Verilog and Xilinx Xpower Analyser is used to compute various parameters like junction and ambient temperature, total power, and effective resistance. Using curve fitting and regression analysis on these parameters some parameterized equations are created and converted into a MATLAB script. The analysis reveals that there is a percentage error of 2.36%, 2.74% and 2.84% between Matlab script and the Xilinx Power estimator at different frequencies.
Intelligent Image Processing for Design of Support Systems

This paper reports on tools useful for the design of intelligent support systems. It describes the preliminary results of a project relating to the implementation of an intelligent image processing structure, primarily based on artificial neural networks, for the development of diverse support systems. Based on a sample application, this paper highlights a development structure in the OpenCV environment, which can be utilized for intelligent image processing and the design of support systems.
Dropout with Tabu Strategy for Regularizing Deep Neural Networks

Dropout has been proven to be an effective technique for regularizing and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability of p during the training stage of DNN to avoid overfitting. The working mechanism of dropout can be interpreted as approximately and exponentially combining many different neural network architectures efficiently, leading to a powerful ensemble. In this work, we propose a novel diversification strategy for dropout, which aims at generating more different neural network architectures in less numbers of iterations. The dropped units in the last forward propagation will be marked. Then the selected units for dropping in the current forward propagation will be retained if they have been marked in the last forward propagation, i.e., we only mark the units from the last forward propagation. We call this new regularization scheme Tabu dropout, whose significance lies in that it does not have extra parameters compared with the standard dropout strategy and is computationally efficient as well. Experiments conducted on four public datasets show that Tabu dropout improves the performance of the standard dropout, yielding better generalization capability.
Top-N Recommendation using Bi-Level Collaborative Filtering

Recommendation systems have been developed to provide personalized items to user based on his/her preferences. User-based collaborative filtering has been the most successful recommendation system, providing the most reasonable level of accuracy. However, with the continuous rise in number of users over the Internet, the algorithm suffers from the scalability problem. To cope up with this issue, item-based collaborative filtering (CF) system has been developed, which is scalable, however, not as accurate as user-based recommendation algorithms. In item-based CF similarity is computed among the entire set of items every time, which is not much reasonable as the entire set of items contains many items which user have no interest upon. In this paper, we have proposed a methodology for providing top- $N$ recommendation, which is basically an improved version of item-based CF. The proposed methodology consists of mainly two parts. In the first part, we reduce the number of items to work upon by sorting out items that is more likely to be fit in user's preference. Secondly, we compute the similarity among the user rated items and candidate recommendable item, generated in the first part. Finally, the proposed methodology has been implemented with movielense data set. Reported results in this paper shows that the proposed methodology improves the recommendation accuracy with less computational time.
Artificial Neuron using MoS2/Graphene Threshold Switching Memristors

With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex CMOS circuitry in the past. Recently, metal-insulator-transition materials have been used to realize artificial neurons [1]–[3]. Although memristors have been used to realize synaptic behavior, not much work has been done in demonstrating neuronal response with these devices. A threshold switching memristor with Ag/SiO2/ Au was used recently to demonstrate the integration-and-fire behavior of a neuron [4] while a neuristor was realized using Mott memristors [5]. In this work, we use the volatility of threshold switching MoS2/Graphene (Gr) 2D/2D heterojunction system to realize the integration-and-fire response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MOS2, enabling large scale realization of these devices. These low power devices can emulate the most vital properties of a neuron, such as a threshold driven spiking and a refractory period. These results show that the developed artificial neuron can playa crucial role in neuromorphic computing.
Two-Cell Spheroid Angiogenesis Assay System Using Both Endothelial Colony Forming Cells and Mesenchymal Stem Cells

Most angiogenesis assays are performed using endothelial cells. However, blood vessels are composed of two cell types: endothelial cells and pericytes. Thus, co-culture of two vascular cells should be employed to evaluate angiogenic properties. Here, we developed an in vitro 3-dimensional angiogenesis assay system using spheroids formed by two human vascular precursors: endothelial colony forming cells (ECFCs) and mesenchymal stem cells (MSCs). ECFCs, MSCs, or ECFCs+MSCs were cultured to form spheroids. Sprout formation from each spheroid was observed for 24 h by real-time cell recorder. Sprout number and length were higher in ECFC+MSC spheroids than ECFC-only spheroids. No sprouts were observed in MSC-only spheroids. Sprout formation by ECFC spheroids was increased by treatment with vascular endothelial growth factor (VEGF) or combination of VEGF and fibroblast growth factor-2 (FGF-2). Interestingly, there was no further increase in sprout formation by ECFC+MSC spheroids in response to VEGF or VEGF+FGF-2, suggesting that MSCs stimulate sprout formation by ECFCs. Immuno-fluorescent labeling technique revealed that MSCs surrounded ECFC-mediated sprout structures. We tested vatalanib, VEGF inhibitor, using ECFC and ECFC+MSC spheroids. Vatalanib significantly inhibited sprout formation in both spheroids. Of note, the IC50 of vatalanib in ECFC+MSC spheroids at 24 h was 4.0 ± 0.40 μM, which are more correlated with the data of previous animal studies when compared with ECFC spheroids (0.2 ± 0.03 μM). These results suggest that ECFC+MSC spheroids generate physiologically relevant sprout structures composed of two types of vascular cells, and will be an effective pre-clinical in vitro assay model to evaluate pro- or anti-angiogenic property.
Phylogenetic Analysis for Ransomware Detection and Classification into Families

: The widespread of ransomware experienced in the last years has been caused also by the ability of attackers to introduce changes and mutations that make the malware hard to identify from antimalware software. In this paper we propose a two-phase method based on machine learning on API-level analysis aimed (i) to effectively detect ransomware despite the applied techniques for obfuscation and introduced variations, (ii) to provide a tool for security analysts to track phylogenetic relationships exploiting the binary tree obtained by the classi-ﬁcation analysis. We preliminary experimented the proposed method on real-world ransomware applications belonging to three widespread families (i.e., petya, badrabbit and wannacry), obtaining encouraging results in ransomware detection and family identiﬁcation. A discussion about the ransomware-related phylogenetic relationships is also provided.
Gating Protein Transport in Solid State Nanopores by Single Molecule Recognition

Control of molecular translocation through nanoscale apertures is of great interest for DNA sequencing, biomolecular filters, and new platforms for single molecule analysis. However, methods for controlling the permeability of nanopores are very limited. Here, we show how nanopores functionalized with poly(ethylene glycol) brushes, which fully prevent protein translocation, can be reversibly gated to an “open” state by binding of single IgG antibodies that disrupt the macromolecular barrier. On the basis of surface plasmon resonance data we propose a two-state model describing the antibody–polymer interaction kinetics. Reversibly (weakly) bound antibodies decrease the protein exclusion height while irreversibly (strongly) bound antibodies do not. Our results are further supported by fluorescence readout from pore arrays and high-speed atomic force microscopy on single pores. This type of dynamic barrier control on the nanoscale provides new possibilities for biomolecular separation and analysis.
Load Balancing for Virtual Resources Management in Data Center

Cloud computing is an come out tendency in the field of Information Technology and load balancing repairs the dispersal of workloads across various computing resources. Load balancing purpose to improve resources manipulation, minimize action time, maximize throughput, and avoid overburden of any single resources. By using of virtualization technologies in the datacenter, the cloud allows organizing assets management. Since the hotspots (i.e., overloaded machines) can reduce the performance of these tasks, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). To address this issue, we propose a Tranche Markov Prediction load balancing scheme. The VM migration algorithm in the scheme aims to minimize the migration cost for load balancing considering the network topology and improves throughput, memory utilization, migration time, execution time and, the worst performance the system, performance of physical machines, frequency, decrease performance degradation and energy consumption could experience from the hotspots. We investigated the distribution of VM demands in a large-scale, and evaluate the impact of different distributions of workloads on the performance of load balancing.
Controlling Posture of Jumping Articulated Robot for Stable Landing

We propose a new control framework of a jumping articulated robot for a stable landing. We derive dynamics of a hybrid system which consists of a flight phase and a stance phase by connecting them through an inelastic impact model of Formalsky. We assume a flight phase is a nonholonomic Chaplygin system and a stance phase is a fully-actuated system. Based on this dynamics, we propose new time-varying control with considerations for features of jumping such as joint angle limit, short duration of flight. It can make a robot get the desired angle within a specific range at the moment of landing. In addition, we find an optimal control to return a robot to an upright pose based on gain tuning. Simulations using a 4-link robot are also performed to show this visually. The motion from new control framework performs in the limit of joints other and requires less torque than conventional controls without a given trajectory.
PCR-Pro: 3D Sparse and Different Scale Point Clouds Registration and Robust Estimation of Information Matrix for Pose Graph SLAM

For both indoor and outdoor environments, we propose an efficient and novel method for different scales and sparse 3D point clouds registration that cannot be handled by the current popular ICP approaches. Our algorithm efficiently detects the scale difference between point clouds and uses the keyframes to estimate the relative pose for calculating the scale difference. The algorithm applies a filter and computes the final transformation which coverages to a global minimum. The good estimation of transform and scale helps in the calculation of the covariance matrix using a closed form solution efficiently. This covariance between point clouds helps in the estimation of information matrix for pose-graph SLAM.
License Plate Detection of Myanmar Vehicle Images from Dissimilar Angle Conditions

It has been studied that there is no established LPR (License Plate Recognition) to detect and identify the license plates from dissimilar angles. The aim of the paper is to detect the dissimilar angles of the license plate with the non-fixed LPR system. Therefore, the horizontal and vertical dilation, skew angle detection and automatic bounding box have been proposed to detect the license plate. The proposed method has been applied to the four different types of Myanmar license plates, e.g., private cars, taxi, tour buses and religion cars. One car each is taken into four different types of angles on the dissimilar conditions. Experimental result indicated that this method can detect the disparate types of license plates with a high accuracy, i.e., the proposed approach achieved a favorable outcome rate of 97% at 100 license plates.