Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization-Based Truth Discovery Method with Claim Relation

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.
Deep Q-Learning and Particle Swarm Optimization for Bot Detection in Online Social Networks

Social bot is a software program which preforms malicious activities, such as distributing spam content, phishing URLs and creating fake accounts. Moreover, social bots dynamically change their behavior by manipulating their trust value and pretend like a legitimate user in order to avoid detection. Hence, detecting social bots more accurately is an important task. In traditional Q-learning methods, Q-values have to be computed for all learning actions. As a result, convergence of Q-learning will be slow. In this paper, a particle swarm optimization (PSO) method is adopted to improve Q-learning in order to obtain an optimal sequence of learning actions. We define position of particle as a sequence of learning actions with respect to Q-values and the velocity of particle as state transition probability values for choosing a particular action. Further, a Deep $\boldsymbol{Q}$-Learning algorithm based on Particle Swarm Optimization (DQL-PSO) has been proposed for social bot detection by considering updation strategy of PSO and $\boldsymbol{Q}$-value function. The experimental results illustrate the efficacy of the proposed algorithm by considering the real-world Twitter dataset.
Network Simulation and Vulnerability Assessment Tool for an Enterprise Network

The system proposed in this paper is the development of a methodology for a Network Vulnerability Assessment (NVA) tool capable of scanning and reporting vulnerabilities over the intranet and network simulator for enterprise networks which is capable of analyzing network behavior using enhanced GNS3. The proposed NVA tool implements network function in a controller which has the potential to extend vulnerability scanning for any devices. We had tested the system using a test bed with the features like scanning various vulnerabilities based on the type of networking device from the controller/vulnerability scanner in the same network. We have automated telnet/SSH connection using python script, which can help in automation and integrate network programming features from a controller. This version of the tool can be used to identify the possible vulnerabilities in any enterprise network and the methodology can be extended to incorporate better-secured networks.
A GUI for Wideband Spectrum Sensing using Compressive Sampling Approaches

Cognitive Radio is a prominent solution for effective spectral resource utilization. The rapidly growing device to device (D2D) communications and the next generation networks urge the cognitive radio networks to facilitate wideband spectrum sensing in order to assure newer spectral opportunities. As Nyquist sampling rates are formidable owing to complexity and cost of the ADCs, compressive sampling approaches are becoming increasingly popular. One such approach exploited in this paper is the Modulated Wideband Converter (MWC) to recover the spectral support. On the multiple measurement vector (MMV) framework provided by the MWC, threshold based Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) algorithms are employed for support recovery. We develop a Graphical User Interface (GUI) that assists a beginner to simulate the RF front-end of a MWC and thereby enables the user to explore support recovery as a function of Signal to Noise Ratio (SNR), number of measurement vectors and threshold. The GUI enables the user to explore spectrum sensing in DVB-T, 3G and 4G bands and recovers the support using OMP or SBL approach. The results show that the performance of SBL is better than that of OMP at a lower SNR values.
Indoor Air Quality Monitoring on AWS Using MQTT Protocol

In Modern world, air pollution is becoming a major concern whose effects if not reduced may lead to major health problems. Indoor air quality has been ignored for a long period of time. Since we spend time indoors major, it is important to monitor the air we breathe. Taking appropriate measures in a timely manner relies on the measurement and analysis of the parameters of the air, which demands the need for the development of real time air quality monitoring. Air quality monitoring module is proposed to deal with this problem. In this Internet of things project, indoor air quality is measured by using various sensors. Data is pushed and stored on cloud and for better visualization of data, graphs of real time data are plotted. User is alerted if the indoor air quality is poor.
Texture-Based Malware Family Classification

Malware is one of the major threats on internet whose count is increasing rapidly every year in millions. Most of the time similar malware files are modified for creation of new variants and most of the existing technique are obfuscated. So, malware visualization using image helps to overcome this problem. Motivated by the visual similarities of malware, this paper presents the malware family classification using some texture-based features. HOG (Histogram of Oriented Gradient), LBP (Local Binary Pattern) and GIST features are used as feature descriptor. We have performed our experiment on 6370 samples of 15 different families. We have observed the highest accuracy of 98.73% using HOG descriptors by SVM classifier. This method shows better results than existing malware classification approaches.
Intra-Inter Feature Ranking based Feature Selection Method for Bearing Fault Classification

The intelligent condition monitoring system has been accepted by the modern industry. As a result, recently, fault diagnosis methods are developed by integrating signal processing and artificial intelligence techniques. The various signal processing methods are utilized to create a bearing feature space. As an effect, existing feature space is ample and therefore, in this paper, feature selection routine for bearing fault diagnosis is proposed to identify the dominant feature from existing feature space. The suggested feature selection routine uses an ensemble of inter-intra feature ranking method to determine the fault representative, and then the ensemble classifier has trained to learn the defect patterns. The proposed method testified using two vibration datasets, and decent results are achieved. Also, the effect of ensemble feature ranking and intra feature ranking method has observed in comparison to the proposed feature selection routine.
Simulation of Biologic Synapse Through Organic-Inorganic Hybrid Memristors Using Novel Ti-Based Maleic Acid/TiO2 Ultrathin Films

In this letter, biologic synaptic functions of organic-inorganic hybrid memristors using novel Ti-based maleic acid (Ti-MA)/TiO2 ultrathin films have been demonstrated. The memristive functional layer consists of 4-nm Ti-MA and 4-nm TiO2, which was fabricated at 160° C by molecular layer deposition (MLD)/atomic layer deposition (ALD), respectively. A typical bipolar resistive switching characteristic has been confirmed in the TaN/Ti-MA/TiO2/Pt bilayer memristors with the space charge limited current model. Using different electrical pulse modules, several important biologic synaptic functions such as nonlinear transmission characteristics, short-/long-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity have been achieved. The organic-inorganic hybrid memristors of ultrathin bilayer Ti-MA/TiO2 derived by low temperature MLD/ALD are attractive for neuromorphic simulation and flexible neuroscience applications.
Application of Compressed Sensing using a Reed Solomon (RS) code based Deterministic Measurement Matrix

Compressed Sensing (CS) is an emerging technique in the field of acquiring and compressing signals as this technique allows for sampling a signal which is sparse in some domain with a rate well below the limit prescribed by the conventional Shannon-Nyquist sampling theorem. As a result, this new sensing paradigm is applicable to many fields like medical imaging, Data streaming, UWB-based communication systems, wireless sensor networks etc. A sensing matrix is one of the principal components in the architecture of compressed sensing. Traditionally, random sensing matrices have been used for CS but these matrices prove difficult for practical implementation and hence the development of deterministic sensing matrix have gathered recent momentum. In this paper, CS is applied to gray scale images using the deterministic sensing matrix based on Reed-Solomon (RS) code with asymptotically optimal coherence. The performance is compared with random measurement matrix for different reconstruction algorithms like Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP). The performance metrics considered for comparison of the original and reconstructed images are Structural Similarity Index (SSIM), PSNR, SNR and run time. Also, the role of the level of signal sparsity in CS is analyzed using simulation results.
Parallel Heuristic Community Detection Method Based on Node Similarity

Community structure discovery can help us better understand the capabilities and functions of the network. However, many existing methods have failed to identify nodes in communities accurately. In this paper, we proposed a heuristic community detection method based on node similarities that are computed by assigning different edge weight influence factors based on different neighbor types of nodes. Concretely, by arbitrarily choosing a pair of nodes, we firstly found out the common neighbor nodes of the node pair and their corresponding neighbor nodes. Then, different edge weight influence factors are assigned according to the impact of different types of neighbor nodes on node similarity. Finally, the similarities between a pair of nodes are calculated by the proportion of various edge weight influence factors related to the node pair. Along the direction, a hash table based data storage and retrieval strategy with a lower conflict rate is introduced to hash the edge information into a ternary bucket structure that can be merged according to the same starting node. This operation can reduce the time complexity of the data query to a constant level, and realize the parallel computing of node similarity. When obtaining similarity of node pair, we merged nodes into communities by a heuristic hierarchical clustering. And, the resulting community structure is detected until all node similarities are calculated. With the help of the comparison tests of different methods based on the benchmark networks that have ground-truth communities, the proposed method for community detection provides better performance in both identification accuracy and time efficiency.
Laser Opto-Electronic Airspace Monitoring System in the Visible and Infrared Ranges

The article considers creating Laser Opto-Electronic Airspace Monitoring System in the visible and infrared ranges and its practical applications in airspace security, trajectory measurements and guidance systems. There are review of information-analytical technology and mathematical algorithms, software and technical characteristics of the real-time working prototype fully deployment and functional system. The main features of this system is using both visual and infrared thermal ranges, high precision servo drives and rotating platform, high precision of measurements, adaptive real time video processing algorithms with using several methods at the same time.
Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition

Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer-assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep-learning-based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep-learning-based image-level classification, lesion region detection, and pixel-accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions.
Dynamics of Fast-Switching Electrodynamic Actuator for Fuel Injection in Internal Combustion Engines

This paper presents a dynamic model of an electrodynamic (voice-coil) actuator that has fast-switching operation. The actuator is intended for fuel injection in internal combustion engines. The full general model of the actuator that includes electric, magnetic, thermal and mechanic phenomena had been discussed. The magnetic subsystem was qualitatively considered, and its best configuration had been suggested. The dynamics of actuator was simulated by a Simulink/Matlab model. The influence of stroke limiters (stoppers) on dynamic behavior had been studied. The modelling results showed the possibility of the obtaining of the switching speed that is necessary for the fuel injection.
Classification of Changes in API Evolution

Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs). Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle. These changes are of different types, caused by different needs and are affecting consumers in different ways. In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs. The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution. Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them. In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).
Elimination of redundant data to enhance wireless sensor network performance using Multi level data aggregation technique

The WSN nodes are self configured to sense parameter like moisture, temperature, pressure, humidity, chemical content in an environment, The sensed data need to be handled efficiently to improve the network performance, therefore there is a need for data handling techniques or methods. Data handling methods like aggregation functions helps in removing redundant data which in turn reduces the traffic, congestion, and packet loss. Many data aggregation techniques aim at minimizing the network energy consumption which is suitable for small network [7]. Transmitting redundant data to the sink node brings down lifetime of the sensor nodes and also wastes of network resources. Therefore in this paper authors proposed an approach multi level data aggregation technique (MLDAT) which preprocess the sensor data to filter the raw data at different levels in the network to produce useful information or data for all size of network. The proposed approach also works for priority of the data which is requested by sending query to the target nodes. The sensor node from Texas instruments MSP430 have been considered in simulation as aggregator node for data aggregation. As a result the aggregator nodes unicast only the useful data towards sink node which reduces the latency, packet retransmission, effective utilization of the available bandwidth.
Localization Techniques Based on Ray-Tracing for Transportation Systems: The Case of Adolfo Suarez Madrid-Barajas Airport

The goal of this paper is to apply novel localization techniques to increase the airport operational security. These techniques would be ideal in areas where the satellite coverage or radar resolution is poor and have a clear impact on the people and goods located at these airports. We propose a novel fingerprinting localization technique using the relative ray delays due to ray-tracing effects as fingerprints information instead of traditional power levels measurements. Combined with other advanced algorithms, more localization precision could be obtained with these techniques, resulting in improved airport operational security in critical zones near the terminal building, such as maneuvering area and stands.
A Top-Down Performance Analysis Methodology for Workflows: Tracking Performance Issues from Overview to Individual Operations

Scientific workflows are well established in parallel computing. A workflow represents a conceptual description of work items and their dependencies. Researchers can use workflows to abstract away implementation details or resources to focus on the high-level behavior of their work items. Due to these abstractions and the complexity of scientific workflows, finding performance bottlenecks along with their root causes can quickly become involving. This work presents a top-down methodology for performance analysis of workflows to support users in this challenging task. Our work provides summarized performance metrics covering different workflow perspectives, from general overview to individual jobs and their job steps. These summaries allow to identify inefficiencies and determine the responsible job steps. In addition, we record detailed performance data about job steps, enabling a fine-grained analysis of the associated execution to exactly pinpoint performance issues. The introduced methodology provides a powerful tool for comprehensive performance analysis of complex workflows.
Effect of Synthesis Procedure on the Size of Ag/Ag2S Core-Shell Nanoparticles for Memristive Brain-Like Devices

For several decades, “von Neumann” computers have been downsized while their performances have been improved. However, the downsizing has reached its limit, imposed by the tunneling effect in the transistor, which also prevents energy consumption. To overcome such limitations, neuromorphic devices have been proposed, which exhibit high performances with significantly low power consumption. In this study, we realize a neuromorphic device based on Ag/Ag2S nanoparticles, which have a switching behavior and allow the easy fabrication of a random network for reservoir computing. We start by fabricating particles with a desired particle size distribution. The amount of allylmercaptan (AM), which is a source of sulfur, was varied in the synthesis stage, and the obtained particles sizes were compared. By changing the concentration of AM, the sizes of Ag/Ag2S core-shell nanoparticles were successfully controlled, which allow us to achieve reservoir computing by aggregation of the particles.
A Service-Oriented Architecture for Generating Sound Process Descriptions

Business process descriptions are useful documents that are becoming increasingly important for identifying and documenting business processes. They are particularly beneficial during discovery when information about the process is gathered in interviews or by observation. Such business process descriptions are written as natural language text, which makes them intrinsically ambiguous. For this reason, it is the major challenge to formulate them in a precise and correct way right from the start. Therefore, this paper presents a service oriented architecture that analyzes a process description written in natural language to generate a sound process description. Being sound means that a description is structured, unambiguous, reveals possible quality and soundness problems related to BPMN 2.0, and contains clear identifiers for all known process elements in the original text. More specifically, we develop specific analysis and transformation techniques that are integrated by our proposed architecture. For validation purposes, we have implemented a prototype of this architecture. Our evaluation demonstrates that our techniques to generate sound process descriptions cover an average of 95% of the information extracted from its original process description while maintaining quality properties. Finally, our architecture can be enhanced with additional services that contribute to the creation and management of processes descriptions in organizations.
A Data Mining Based Approach to Predict Autism Spectrum Disorder Considering Behavioral Attributes

Autism Spectrum Disorder (ASD) is a disease connected to the growth of brain. It affects a person's way of communication and behavior. It impacts a person's way of understanding and social attachments. Moreover, people with ASD experience various types of symptoms, for examples, difficulty while interacting with others, repetitive behaviors, and face difficulty to function properly in other areas of day-to-day life. These symptoms generally occur in early childhood. Most of the people are not well aware of the syndrome, therefore, they do not know whether a person is suffering from the disorder or not. As a result, instead of helping which very often leads the victim to become isolated from society. However, the prediction of the disorder at an early stage should help to get a quick recovery. In this paper, the Random Forest classifier was used for the prediction of ASD based on behavioral attributes. We are satisfied with the results with the overall accuracy of 0.96%. The dataset used for prediction had 10 behavioral attributes and 10 more individual attributes.