Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ensemble Approach for Handling the Novel Instances in Dynamic data

Many algorithms have already been designed to control concept drift problem in dynamic data. In this paper, authors propose the “Weighted Novel Instances Detection”. This approach handles the novel instances with diversified ensemble learning. It uses various concepts such as diversified online ensembles, instance weighting and detection of novel instances. The final prediction of an instance is the class predicted by an ensemble. Experimental results prove that it efficiently classifies recent examples.
An Efficient Authentication and Secure Vehicle-to-Vehicle Communications in an IoV

The Worldwide road traffic problems, accident rates, transportation issues are increasing despite the improvements in Intelligent Transportation Systems (ITS). Internet of Vehicles (IoV) is a promising paradigm to the better and safe future of the automotive field. This complex, dynamic mobile network system allows data sharing between vehicles, its surrounding portable devices, road-side units, infrastructures, sensors, cloud, etc. However, the IoV provides several benefits and opportunities in different applications, it introduces challenges in security. In this paper, we propose an efficient authentication system for secure Vehicle-to- Vehicle (V2V) communications in an IoV scenario. The security analysis reveals that our scheme can defend against strong attacks. Our experimental outcomes on a simulated environment (on a desktop computer and Raspberry Pi) reveal that our proposed scheme can work with less computation time while ensuring integrity of the message in a strong adversarial scenario.
Lexicon-free stemming for Kazakh language information retrieval

In paper are considered existing algorithms for automatically isolating the bases for a number of natural languages and possible ways of synthesizing a normal form of a word for the Kazakh language. In paper are described the complete system of endings of the Kazakh language. The paper proposes a new approach to constructing a lexicon-free stemming algorithm for the Kazakh language on the basis of a complete set of endings of the Kazakh language. The stemming algorithm will be used for Kazakh language information retrieval to finding specific words in the documents by stemming base of word.
Traffic Analysis Based on Bluetooth Passive Scanning

During the last decade, Bluetooth has become a widespread feature in the automobile industry, meaning that its signal activity can be correlated with road traffic. Using this technology as a means for assessing traffic is very cost-effective and has a low impact on the infrastructure. Nevertheless, unlike other techniques, it does not provide direct sensing of vehicles only. Statistical analysis methods need to be applied to the collected data. In this paper, we first propose a machine learning method for traffic flow estimation using Bluetooth sensors. We also propose a method for estimating the mean travel speed between two sensors. The performance of the proposed methods is evaluated through eight weeks of experimentation. Finally, we envision a potential solution for building real-time Origin- Destination matrices.
Power Factor Control Algorithm for Matrix Rectifier for Battery Charging Systems of Electric Vehicles

This paper proposes a novel input power factor control method, to provide unity input power factor for three-phase matrix rectifiers. The proposed algorithm introduces, at the input terminal of the matrix rectifier, an imaginary input capacitor generated by modifying a reference vector of the space vector modulation method. The proposed method yields a unity power factor operation despite the presence of the LC filter of the three-phase matrix rectifier. In addition, the proposed algorithm can be easily incorporated with conventional space vector modulation algorithms for the three-phase matrix rectifiers. Thus, the proposed method can control input power factor without employing proportional integral controllers used in conventional power factor control methods for the matrix rectifiers, leading to simple control structure and straightforward tuning process for power factor control.
Off-line Handwritten Hindi Consonants Recognition System using Zemike Moments and Genetic Algorithm

Developing an efficient character recognition system is supposed to be a very challenging research problem. In the present work, an offline handwritten Hindi character recognition technique is proposed using Zernike moments as the descriptor of character image with a feature selection algorithm. For feature selection, use of the Genetic algorithm is proposed to reduce the length of the feature vector. The core idea of the paper is to first generate the significant Zernike complex moments and then to select the most relevant moments using the Genetic algorithm which are in turn used to classify the individual characters. The significance of low-order as well as high-order Zernike moments is also studied in recognizing the first ten consonants of Hindi script. Two resilient backpropagation classifiers are trained one for the feature vector without selection and another one for feature vector obtained after selection. The average character recognition accuracies obtained are 90% and 94.3%, respectively.
A framework for social signal processing and analysis：from social sensing networks to computational dialectical analytics

The availability of real-time and reliable social signals is a necessity for implementing closed-loop and feedback based dynamic social management. However, the concepts and methods for social signals are far behind those for physical signal processing and analysis, and need to be developed in a systematic approach. As social and new media and social networking reach to all corners of societies rapidly, the problem of social signal processing and analysis becomes significant and the demand for effective solutions is great and urgent. This paper investigates issues related to the modeling and management of social systems, characterization of social signals and channels, social sensors and construction of social sensing networks, artificial societies, computational experiments, and parallel execution (the ACP approach) for reasoning and synthesis in computational social systems, computational dialectics, and computational analytics. Our objective is to establish a framework and methodology for acquisition, processing, analysis, analytics, and application of social signals.
Real Time Web-based Data Monitoring and Manipulation System to Improve Translational Research Quality

The use of the internet technology and web browser capabilities of the internet has provided researchers/scientists with many advantages, which includes but not limited to ease of access, platform independence of computer systems, relatively low cost of web access etc. Hence online collaboration like social networks and information/data exchange among individuals and organizations can now be done seamlessly. In practice, many investigators rely heavily on different data modalities for studying and analyzing their research/study and also for producing quality reports. The lack of coherency and inconsistencies in data sets can dramatically reduce the quality of research data. Thus to prevent loss of data quality and value and provide the needed functionality of data, we have proposed a novel approach as an ad-hoc component for data monitoring and manipulation called RTWebDMM (Real-Time Web-based Data Monitoring and Manipulation) system to improve the quality of translational research data. The RTWebDMM is proposed as an auditor, monitor, and explorer for improving the way in which investigators access and interact with the data sets in real-time using a web browser. The performance of the proposed approach was evaluated with different data sets from various studies. It is demonstrated that the approach yields very promising results for data quality improvement while leveraging on a web-enabled environment.
Brain-Computer Interface: Challenges and Research Perspectives

Nowadays, the interest in the Brain-Computer Interfacing (BCI) domain is continuously growing, only judging by the number of BCI related papers published or presented in neuro-engineering or neuroscience journals, conferences or workshops. In all these studies, brain activity is considered as a simple modality of providing a system or a device with knowledge from human interactions. Although this simple transfer of information represents a significant development since BCI also integrates brain signals with data from sources like speech, eye gaze, gestures, facial expressions, body postures or physiological measures like heart rate, blood pressure or electrodermal activity. This fusion creates a context-aware and a context-dependent interpretation of the brain signals but represents in the same time a challenge for the neuroscience researchers that must process and represent all this data for clinical or non-clinical applications. BCI has become an addictive and powerful research domain offering a wide range of applications from cognitive load in educational tasks and gaming to automotive and assistive technology. This paper aims to outline current trends in brain-computer interface development and research from the perspective of the authors' experience and scientific literature.
Discovering Motifs in DNA Sequences: A Candidate Motifs Based Approach

Motif finding is a classical combinatorial problem in the domain of bioinformatics. Motifs are the small set of immunity gene present in the DNA sequences as a binding site and turn on whenever the organism gets infected. Hence, to identify these motifs for transcription factors is of great biological importance. Recently, this field of bio-informatics has grown significantly and many algorithms have been proposed to solve this problem. However, high complexity is the most challenging aspect of this problem which still grabs the attention of many researchers. This paper presents a proficient algorithm that extracts binding sites in a set of DNA sequences for transcription factors using some operations on the DNA sequences. The motif we work on is of known length, un-gapped and non-mutated. The proposed algorithm does some preprocessing and formulates an adjacency list for finding such sites. Although any two randomly selected sequences can be used for preprocessing, we have used first two sequences as a base for constructing the adjacency lists which is later used for fast detection of common l-mers from both of them. These l-mers are considered as candidate motifs and then checked for its existence in all of the remaining DNA sequences using a sliding window approach. The proposed algorithm CMMF is experimentally validated on millions of DNA sequences. Additionally, the formulation of motif finding algorithm is also applicable to related problems in the field of data mining, pattern detection, etc.
Fuzzy Control Design for Quasi-Z-Source Three Phase Inverter

Inverters are used widely nowadays because of the increasing availability of many of renewable energy sources. Not only energy sources like solar panels but also storage banks like batteries provide DC voltage. Traditional DC-AC inverters have main limitation; its output voltage value is limited to DC-rail voltage and cannot exceed it. To overcome this limitation and to avoid the usage of transformer for boosting; in this paper brief Z-source network technique in inverters is explained. This technique can work buck/boost and inverting in one stage without need to boost DC, invert DC and finally boost AC with transformer. Quasi-Z-Source network was developed, to operate better technique than Z-Source inverter by reducing component rating, quantity and voltage stress and simplifying control strategies. Fuzzy Logic control is used as a controller for the inverter. This paper uses Mamdani method with 3 VDC, IDC and Vc1 input and 2 outputs M and Do. The designed inverter in this paper could keep the output voltage 208v AC rms 60 Hz with wide range of variable DC input (200-400) v DC.
Design Issues in Distributed Software

With the advent of fast advancing microprocessor technology, software designers and users in various fields tend to use distributed software to solve realworld problems. The researchers already worked on the issues regarding the distributed systems. Now, in this work, we identify and address the design issues underlying distributed software. Before this, we give the motivation behind the development of distributed software and describe the objectives that should be addressed when dealt with the issues of distributed software. Further, we carefully analyze and locate constraints related to identified issues, that become associated challenges in the context of the objectives governing the distribution idea.
Stage-wise Development of a Remote Controlled Robotic Arm

This paper represents the stage-wise development of trainable remotely controlled robotic arm based on servo motors. The robot is expected to perform primary pick and place operations and repeat the recorded actions with error corrections. The arm has four degrees of freedom. The robotic arm is mounted on a base that allows it to traverse between different coordinates. Arduino Mega 2560 is used for controlling functionalities of the robot. The robot was developed in four stages. In the first stage, the robot was controlled with the help of potentiometer. In the second stage of development, remote functionality was added. This functionality was achieved using Bluetooth module. The third stage of development included usage of the real-time remote control over the internet. The robot was controlled using an android application for the stages mentioned above. In the fourth stage, a Unity-3D model was integrated to realize the real-time visual simulation of the robot.
Survey of Algorithms based on Dynamic Graph Mining

Now a days people find social networks as an effective means of communication and interaction. Therefore, it is beneficial to analyse the information from these social networks to get the idea of dynamics of human interaction. The basic aspect to understand in behavioral analysis of human interactions is to extract the idea of how and when the communication takes place or unfold. Graph mining is the efficient way to analyse such data and get the required information. As the nature of data involved is dynamic in nature i.e changes wrt time, therefore to mine this type of dynamic data is a topic of interest these days in the form of dynamic graph mining, where time considerations are explicitly incorporated in a set of graph. Dynamic graph is a set of consecutive timestep graphs which can be analysed based on different patterns like frequent, periodic, trend motif, clique etc. In this paper we represent overview of different algorithm used to mine and analyse the evolving patterns in dynamic graph.
A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.
Research on Parallel Operation Control of Modules Based on Common DC Bus

Electric vehicles are the new energy vehicles with the least exhaust emissions. Therefore, the development of electric vehicles has been valued by many countries. The charging station is the energy supply center for electric vehicles. Whether the charging station is built well determines the degree of popularization of electric vehicles. This paper studied the parallel operation control of modules which are based on the common DC bus in charging station. In the vehicle-to-grid (V2G) mode, when the output sides of the rectifier modules are connected in parallel, all DC/DC charging modules will supply power to the DC bus. Whether the modules can achieve current-sharing is a key factor that affects the reliable operation of the system. The I/U droop control is utilized to achieve current-sharing of all the parallel modules. Consequently, the complicated and less reliable high-speed communication can be eliminated, which improved the system reliability greatly. Simulation in MATLAB verified the feasibility of the strategy.
Hyperspectral targetdetection by using superpixels and signature based methods

Spectral signature based methods which form the mainstream in hyperspectral target detection can be classified mainly in three categories as the methods using background modeling, subspace projection based methods, and hybrid methods merging linear unmixing with background estimation. A common characteristic of all these methods is to classify each pixel of the hyperspectral image as a target or background while ignoring the spatial relations between neighbor pixels. Integration of contextual information defined over neighboring relations can, however, suppress the noise on individual pixels and yield better detection. The proposed methodology in this paper adapts the usage of superpixels defined over neighboring relations to the mentioned three classes of target detection algorithms. In particular, ACE, DTDCA and HUD algorithms are selected for each class for the proposed methodology. The experiments reveal that using superpixels for target detection improves the detection performances compared to the baseline methods using only pixels.
Adaptive Perturb & Observe MPPT for PV System with Experimental Validation

Maximum power point tracking (MPPT) plays a vital role in photovoltaic (PV) system to utilize PV energy efficiently. The maximum power point (MPP) of PV system varies with atmospheric conditions and to track it, an efficient tracking system is needed. There are different MPPT algorithms available to track the maximum power. Among different MPPT techniques P&O MPPT is mostly used because of its plain structure and it is easy to implement. But conventional P&O method is slow in nature and suffers from steady state oscillations. To avoid this, this paper emphasizes on an adaptive P&O MPPT which can able to track the MPP rapidly with less steady state oscillation as compared with conventional P&O MPPT algorithm. The efficacy of the proposed method is verified considering both simulation and experimental results.
Performance of Dual-Hop DF Relay with SSK Modulation and SWIPT over Rayleigh Fading

Performance of space shift keying (SSK) modulation in cooperative decode-and-forward (DF) Relaying scheme using simultaneous wireless information and power transfer (SWIPT) is analyzed over the Rayleigh fading channel. SWIPT eliminates the need for frequent battery replacement at the relay in cooperative communications whereas SSK is a low complexity modulation technique to enhance data rate and minimize inter-channel interference, inter antenna synchronization requirement and the number of radio frequency chains. Closed-form and Upper bound expression for average bit error probability (ABEP) are derived with multiple DF relays with a direct link. Numerical and computer simulation results are presented with the discussion. Results demonstrate system performance improvement due to the combined effect of energy harvesting with SSK modulation.
Blending Situation Awareness with Machine Learning to Identify Children’s Speech Disorders

Situation Awareness (SA) involves the correct interpretation of scenarios, allowing a system to respond to the observed environment and providing decision-making support in several domains. Speech therapy is a domain where SA may provide benefits; however, there are few proposals that address reasoning about situations to improve therapeutic tasks. An early identification of speech sound disorders allows the diagnosis and treatment of various pathologies. So, in this paper, we present a novel situation-aware approach using machine learning for detecting patterns on sound frequencies, aiming to classify the correctness in the pronunciation of words spoken by children aged 3 to 8 years. The approach was evaluated through a speech corpus containing approximately 27,000 audio files, collected from pronunciation assessments performed by Speech-Language Pathologists with more than 1,300 children. Our results showed an average accuracy over 92% for classifying speech disorders. The classification results were used to elaborate a projection strategy based on the identified situation.
A Preliminary Security Assessment of 5G V2X

Research on intelligent transport systems (ITS) for improved traffic safety and efficiency has reached a high level of maturity and first applications will hit the market in 2019. Since 2004, the wireless standard 802.11p has been developed specifically for ITS services. Since then new telecommunication standards have been devised, and the new 5G telecommunication standard is nearing completion. Due to its technological advantages such as higher speeds and reliability, it is being considered to be used for ITS services. The new radio technology ``New Radio (NR)'', which is being developed as part of 5G, can complement or replace 802.11p in V2X applications. While there has been some work to compare 802.11p and 5G New Radio in terms of performance and applicability for safety-critical use cases, little work has been done to investigate the implications for security. In this paper, we provide an overview of the security requirements of known ETSI ITS use cases, and based on those use cases we compare and assess the security implications of replacing 802.11p with cellular V2X. We find that due to the use of millimeter waves, beamforming and massive MIMO, there will be an implicit improvement for confidentiality and privacy, and it may also be possible to shorten authentication procedures in certain cases. When a fully network-assisted C-V2X mode is chosen, it is also possible to outsource several of the ITS security requirements to the cellular network.