Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Domain Sentiment Analysis on Social Media Interactions using Senti-Lexicon based Hybrid Features

Analyzing the sentiment information from the social media interactions is a rapidly growing research area. Several studies in the literature focus on modeling the sentiment information using linguistics, generic word counts and even the contextual information, including the presence of punctuations, elongated words, emoticons, etc. In this paper, we experiment on the effectiveness of lexicon information in combination with other information, for the effective analysis of sentiment in social interactions. The objective of this study is to experimentally verify how senti-lexicons can take part in the process of modeling the sentiment information even in cross-domain sentiment analysis. In general, this paper explores the effectiveness of several feature vectors including the generic Bag of Word (BoW), linguistic (N-Gram and Part-of-Speech (POS)) and the lexicon features (number of positive and negative words). Other than the traditional features we generate hybrid features by combining the lexicon features with the BoW and linguistic features. We conduct the experiments on sentiment classification using supervised models like Linear SVC (L-SVC), Multi-Layer Perceptron (MLP), Multinomial Naïve Bayes (MNB) and Decision Tree (DT). The experiments are conducted on three different types of sentiment document datasets - the Amazon food review dataset, student opinion tweet dataset, and the Large Movie Review Dataset v1.0. We also verify the efficacy of these features in cross-domain sentiment analysis. Experiments show that hybridizing the BoW, linguistic N-Gram and POS method with lexicon features improves the accuracy of sentiment classification even for cross-domain sentiment analysis.
A Review on Iris Liveness Detection Techniques

Iris recognition systems have been widely deployed for authentication in many sensitive security areas for their accuracy and consistency. However, as the iris technology evolves, ways to attack it evolve too. Fake iris samples could be used to spoof the iris recognition system. As a result, Iris liveness detection methods have been developed. These methods read the users physiological signs of life to verify if the iris pattern acquired for identification is fake or real. In this paper, a review for the previous work done in iris liveness detection is presented.
Adaptive NonLinear Equalizer for SISO Wireless Channels.

Channel equalization is an essential component of wireless receiver used in SISO (Single Input and Single Output) communication systems to mitigate the effect of inter symbol interference (ISI) and other channel impairments. Adaptive signal processing models with weight adjustment mechanisms can be utilized to design equalizer for wireless environments. In this paper nonlinear adaptive channel equalizer for SISO communication channels has been designed with the help of variable step size Least Mean Square/Fourth (VSSLMS/F) algorithm with a suitable control parameter. 4-QAM modulation is used to generate pass band input data sequence which is transmitted through the nonlinear channel. Output sequence is estimated by using equalized output with help of decision device. The performance of the proposed equalizer has been evaluated on basis of bit error rate (BER) with help of comparison results using MATLAB simulations in presence of additive channel noise.
OntoQSAR: an Ontology for Interpreting Chemical and Biological Data in Quantitative Structure-Activity Relationship Studies

Recent developments in the fields of medicinal chemistry and computer science have made possible to perform rational drug design, helping reduce the cost and time required to discover new therapeutic agents. Particularly, quantitative structure-activity relationship (QSAR) studies have been widely employed to correlate molecular structure to biological activity. Such analyses take into account a set of chemical compounds encoded by numerical descriptors and biological data for generating mathematical models capable to predict biological activity values for structurally similar unknown compounds. Although a huge amount of chemical and biological data has become publicly available, there is an increasing need for integrating and sharing the data from various repositories in order to derive a reusable domain knowledge base. Attempting to overcome such limitation, this article proposes an ontology for QSAR studies, named OntoQSAR, which describes the major concepts in these analyses, like methods used to obtain chemical descriptors and biological properties of chemical compounds. Since the compounds of interest in these studies must be subjected to the same methods for obtaining the necessary numerical descriptors, the developed ontology will allow that information to be reused in other related researches.
Towards Practical Computer Vision in Teaching and Learning of Image Processing Theories

This research to practice full paper presents a methodology that leads students towards innovative solutions to complex computer vision real problems. The cornerstone of teaching-learning process of image processing theories is based on strong interdisciplinary concepts (e.g. calculus, logic, data structures, perception, graphs, etc). However, just this theoretical background is not sufficient to reach the right practical understanding of how each type of technique interferes in an image considering real problems. To address this issue, we proposed to focus on the application of the image processing theories in a real industrial problem, through a computer vision pipeline. To do so, we used the concept of non-zero-sum game where both players win (e.g. students and industry). To evaluate the effectiveness of our methodology, students answered a questionnaire about their satisfaction. Based on these answers we map how much it helps the undergraduate students to understand the implications of different image processing concepts in a real environment. We also analyzed the satisfaction rate and the students’ scores to obtain a well defined analysis. Our methodology presents a strong level of satisfaction according to the students, reaching up to 91% of acceptance rate.
Electrode Channel Selection for Emotion Recognition based on EEG Signal

Emotions are an integral part of mankind. The advancement in the field of brain computing interfacing has made it possible to model human emotions based on EEG data. EEG acquisition modules use the International standard 10-20 electrode placement system which can have as many as 256 channels. Thus, there is a need to identify which regions of the brain are more responsive to a particular emotion so that only the corresponding channels can be used. This paper undertakes a comparison between the responses of different electrode channels to 6 different emotion eliciting stimuli.
Wideband Design of Dielectric Resonator Antenna using Defected Ground Structure

A wideband design of dielectric resonator antenna (DRA) has been proposed by imposing a defected ground structure (DGS). A non-radiating ground plane slot functions as a DGS in order to change the boundary condition of the aperture-fed cylindrical DRA. The new radiating modes have been successfully excited along with the dominant HEM11δ mode of the cylindrical DRA. The matching bandwidth of the proposed antenna has been widened up to 27% (3.58 GHz to 4.7 GHz) due to the coexistence of the newly generated radiating modes. The broadside gain of the antenna is obtained between 4-7 dBi over the operating frequency band.
Discovering of the unobservable behaviour of an Interpreted Petri Net model

This paper focuses on the problem of discovering a Petri Net model from long event sequences generated by a discrete event system. Precisely, it is assumed that the relations between input and output events (i.e. the observable behaviour of the system) are already modelled by a set of Interpreted Petri Net fragments while the behaviour of the internal state evolutions (i.e. the unobservable behaviour) must be discovered. An approach inspired to net synthesis is proposed. It relies on an optimization-based procedure for the identification of the unobservable net structure and marking.
Broadband Antenna Design With Integrated CB-CPW and Parasitic Patch Structure for WLAN, RFID, Wimax, and 5G Applications

This paper presents a novel CB-CPW wideband antenna design. HFSS software simulations reveal that the proposed design has a semicircle ground structure, transmission line, parasitic ground, CPW, relatively low return loss, broadband effect of 3.3377 GHz (4.8337 GHz–8.1714 GHz), double beam capability at 5.2 GHz, and four-beam capability at 5.5 GHz (5.8 GHz). It is compact in size at only 40 mm<inline-formula> <tex-math notation="LaTeX">$\times 40$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> mm. A prototype of the proposed antenna was fabricated and measured to validate the simulated design; the results show a bandwidth of 3.38 GHz (3.95 GHz–7.33 GHz), relative bandwidth of 74%, and maximum gain of 3.87 dBi in the range of 5 GHz to 6 GHz; the prototype also achieves two and four-beam performance. Its working frequency covers WLAN (5.15 GHz–5.25 GHz and 5.725 GHz–5.825 GHz), radio frequency identification RFID (5.8 GHz), and global microwave internet access WiMAX (5.25 GHz–5.85 GHz) frequency bands.
Constrained Mean-Field-Type Games: Stationary Case

In this paper, we study a class of constrained mean-field-type games with several decision-makers interacting in a stationary environment. The payoff functionals depend not only on the strategy profile but also on the first moment. This novel static game approach leads to risk-aware solution concepts. We introduce several solution concepts: mean-field-type best-response strategies, mean-field-type generalized Nash equilibria, mean-field-type variational equilibria. We provide a structure of payoffs with a decomposition that includes variance-aware cost. We discuss some learning algorithms to reach mean-field-type best Nash equilibria under constraints. The methodology is extended to include migration costs and migration incentives for each decision-maker and each choice component.
A 32-bit Energy Efficient Exact Dadda Multiplier

In this paper, a 32-bit exact Dadda Multiplier is realized using 4:2 compressor. A Dadda Multiplier is similar to Wallace Multiplier. Both of the multipliers are used to reduce the partial products but Dadda Multiplier is faster than other multipliers and require less gates than Wallace Multiplier and a low power consumption. The compressors used has it’s own accuracy level in both the exact and approximate mode with variable delay and less power consumption. Synopsys Design Compiler using SCL 180nm CMOS technology was used to evaluate the efficiency of the compressors in 32-bit Dadda Multiplier and compared with other 4, 8 and 16-bit Dadda multipliers. The results depicts around 48% decrease in power consumption with an area of 18955. 74μm2.
Intelligent Fault Diagnosis of Bearing Based on Stacked Denoising Autoencoders

A fault diagnosis method based on stacked denosing autoencoder network is proposed to apply deep learning to equipment fault diagnosis. A deep-layer network model is established, pre-training is performed in a layer-by-layer greedy coding mode to achieve adaptive extraction and mining of high-dimensional deep fault features, and then a back-propagation algorithm is used to supervise fine-tune the model. The method integrates the two steps of feature extraction and state classification, and gets rid of the dependence of traditional machine learning methods on artificially extracted sample features, and effectively overcomes the problems of gradient disappearance and local extremum. Through the rolling bearing data experiment, the method’s ability to identify and generalize faults was verified.
Self-efficacy Promoting Interventions for Breastfeeding Outcomes: An Integrative Review of Research Conducted in Thailand

Abstract: Low breastfeeding rates have continued to generate public health concerns in Thailand, and a number of nursing intervention programs have been implemented among Thai mothers to enhance the breastfeeding outcomes like practice and duration. Enhancement of self-efficacy in breastfeeding is a substantial method used in the interventions because of the evidence supporting the influence of self-efficacy on breastfeeding behavior and duration. However, the state of evidence relevant to the effects of self-efficacy promoting interventions on breastfeeding outcomes has not yet been examined. The purpose of this integrative review was to determine the methodological and substantive features of the studies of self-efficacy promoting interventions for breastfeeding outcomes published between 2005 and 2015, and listed on Thai databases and CINAHL. Methodological quality was assessed using the Scottish Intercollegiate Guideline Network Methodological Checklist. The review included 10 eligible studies with acceptable quality. No study with randomized control trial design was found. The reviewed outcomes of the interventions including breastfeeding self-efficacy were found in 7 studies, breastfeeding behavior in 7 studies, and number of mothers giving exclusive breastfeeding in 4 studies. The review indicated the positive effects of self-efficacy promoting interventions on the breastfeeding outcomes. However, clinical heterogeneity was found from diverse sample characteristics, different usual nursing practice across the hospitals, varied dose of the self-efficacy promoting interventions given, and different time of outcomes measurement. Due to insufficient rigorous designs and clinical heterogeneity of the studies reviewed, well-designed clinical trials are needed.
Formation and Evolution Control of Pipeline Inspection Robot Queue

This paper is aimed at the pipeline inspection robot queue system, and establishes the system's behavior library, relay communication mode and control structure. Aiming at the formation and evolution of the pipeline inspection robot queue, the specific process and method of formation and evolution of pipeline inspection robot queue are presented; and the communication model of pipeline inspection robot queue system is established. The simulation results show that the formation and evolution method of the pipeline inspection robot queue has good applicability to the actual pipeline detection project, and provides a new idea for the multi-robot cooperation in the gas pipeline inspection.
Numerical modeling of hyperfoam behavior of periodontal ligament in mechanical loading

This paper investigates the biomechanical response of the tooth and the surrounding tissues under a special type of the orthodontic loading. A 3D model of the upper left incisor of the human tooth is constructed. The periodontal ligament (PDL) is considered as a hyperfoam material and the other tissues are studied as elastic materials. The material properties of the PDL are determined by fitting the experimental stress-strain data of a lamb specimen to the hyperfoam constitutive equations. The goal of this study is to analysis the stress distribution under two loading conditions where the force magnitudes are set to 1N and 2N and they are applied in the mesial-distal direction on three different regions of the tooth crown. Results showed that by applying force to the tooth, the stress concentration is observed mostly on the mesial and distal sides. The maximum value of the compressive stress by the loads of 1N and 2N on the topmost point is 0.219MPa and 0.878Mpa, respectively. The tensile stress is detected on the mesial side where its minimum value by the loads of 1N and 2N on the topmost point is -0.069MPa and 0.307MPa, respectively. According to the results, the PDL absorbs the stress and prevents the large deformation of the alveolar bone. Results showed that the behavior of the tooth during the orthodontic loading reduces the possible injury to the tooth and bone tissues.
Finite Time Semistability and Consensus in Networks with Communication Uncertainty

In this paper, we develop a conservation-based framework for addressing finite time semistability and almost sure consensus problems for nonlinear stochastic multiagent dynamical systems with fixed communication topologies. Specifically, we present a distributed nonlinear controller architecture for multiagent coordination over networks with state-dependent stochastic communication uncertainty. The proposed controller architecture involves the exchange of generalized charge or energy state information between agents guaranteeing that the closed-loop dynamical network is stochastically finite time semistable to an equipartitioned equilibrium representing a state of almost sure consensus consistent with basic thermodynamic principles.
Design, Implementation and Control of a Multilevel Cascade Inverter for AC Drive Application with Improved Low Speed Operation

AC electric drives today are widely used in small and large industries to start up and control single-phase and three-phase AC motors. During the start-up period, these drives should limit the current magnitude in addition to maintaining torque. It must also be able to control the speed of the motor in a steady state with the electrical and mechanical parameters that meet the standards at the same time. In this paper, some of the standards for adjustable speed AC drives are first reviewed. Then the design and implementation process of a low voltage/ medium power multilevel inverter is briefly explained. Some electrical/ mechanical constrains that an AC motor drive should satisfy are extracted from the standard. Then a hybrid control algorithm is proposed for low speed and high speed operation which is simple and efficient. Simulation and experimental results on a 400 V three phase 5 hp induction motor is presented to evaluate the design.
Discounted Mirror Descent Dynamics in Concave Games

In this paper, we investigate the convergence of mirror descent-type dynamics in concave continuous-kernel games whereby the game map is characterized by monotonicity properties. We propose two discounted variants of the mirror descent dynamics and we show that these new dynamics can converge asymptotically in concave games with monotone (negative) pseudo-gradient. We provide simulation results of these game dynamics in representative games.
An Efficiency-Improved Clustering Algorithm Based on KNN Under Ultra-Dense Network

Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it’s only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.
Authenticated Communication in Crises: Toward an Infrastructureless Trust Model for Challenged Networks

Natural as well as human-caused disasters and catastrophes easily lead to chaos. Effective crisis communication and informing the public about the ongoing situation can reduce chaos and maintain social resiliency. Communication, however, relies on a working physical infrastructure, which usually is broken in the incident area. Whereas first response teams and authorities benefit from special communication equipment, civilians do not and experience longer periods of being disconnected from the outside world. Even if messages come through occasionally, the communication is too intermittent to allow for channel-based trust models. TLS, for example, requires access to trusted third parties to authenticate data. In this position paper, we argue that end-to-end communication conflicts with disaster scenarios. We propose an approach that leverages information-centric networking (ICN) to authenticate risk and crisis communication in loosely connected communication systems. Our proposal makes use of spatiotemporal decoupling of data from their producers based on ICN for optimal message propagation in fragmented networks, while introducing a trust bootstrapping phase to enable off-line authentication. The data-oriented security model of ICN is used to tailor a trust model specifically for scenarios during which access to trusted third parties or data owners is not given and messages are relayed through untrusted parties.
An Alternate Arrangement of Active and Repeater Coils for Quasi-Constant Power Wireless EV Charging

As a means of transportation, electric vehicles (EVs) are gradually replacing oil-fueled vehicles and widely seen in the field of vision. To facilitate the charging of electric vehicles, static charging stations and highway dynamic wireless power transfer (WPT) systems are being popularized and promoted in many countries. Aiming at the problem of multiple inverters and high costs in the dynamic WPT systems for EVs, this paper proposes a scheme of alternately arranging the active coils and the repeater coils at the transmitter sides, which has the advantage of lower economic costs and reduces the number of the power converters by half. Dynamic quasi-constant power wireless EV charging can be achieved by adjusting the input voltage of the active coil. A mathematical model including three different operating modes during the dynamic process is developed to analyze the output power and system efficiency, and design the system parameters. The simulation results of the 1 kW system verify the validity of the proposed wireless EV charging scheme.
Application of Conditional Variational Auto-Encoder to Magnetic Circuit Design with Magnetic Field Computation

In the design optimization of electric machine, we ordinarily derive the objective physical quantities, e.g., the shape of the investigated model as design variables, by using numerical method such as the finite element method with the analysis conditions. In recent years, the representation learning using Deep Learning much attracts attention because it can acquire the features of data as a distributed representation and reproduce corresponding data. In this paper, utilizing machine learning technology, we propose an application of Conditional Variational Auto-Encoder (CVAE) to reproduce the more adequate shape of magnetic materials, i.e., design variables, corresponding to the intended magnetic energy, i.e., objective function values.