Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Efficient Energy Transmission on a SWIPT Interference Channel Under Linear/Nonlinear EH Models

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output interference channel (IC) system where the power-splitting (PS) receivers utilize linear/nonlinear energy-harvesting (EH) models. The functions that express the relationship between the input radio frequency power and the output direct current harvested energy of the EH circuits are usually assumed to be linear in recent SWIPT works, while the nonlinear function is suitable for practical data. In a SWIPT IC system, two important targets are sum harvested energy maximization and harvested energy efficiency maximization at the PS receiver, and they are investigated under linear/nonlinear EH models. Thus, some nonconvex resource allocation problems with the two targets and linear/nonlinear EH models are formulated to jointly optimize beamforming vectors at the transmitters and the PS ratios at the receivers. By exploiting a successive convex approximation method and a semidefinite relaxation (SDR) technique, iterative algorithms are proposed to obtain local optimal solutions. Interestingly, the optimal solution to the subproblem is proven to satisfy the rank-1 constraint of the SDR technique. Finally, numerical experiments illustrate effective performance from the proposed solutions, in comparison with some special schemes.
Choosing Between Smartphones, Tablets and Laptops: User Perspectives On Using Multiple Mobile Devices

This study describes a semi-structured interview conducted to evaluate how users with multiple mobile devices choose which device to use. The interviews were conducted at the Chapel Hill Public Library and University Mall with a total of 21 participants. Participants were asked to indicate how frequently they used a particular device, depending on the task to perform and the context. Four follow-up questions asked participants to describe how they originally intended to use their devices, how behavior has changed as they’ve started using them, if or when they multi-task and to summarize what each device is used for.
Belief Error and Non-Bayesian Social Learning: An Experimental Evidence

This paper experimentally studies whether individuals hold a first-order belief that others apply Bayes’ rule to incorporate private information into their beliefs, which is a fundamental assumption in many Bayesian and non-Bayesian social learning models. We design a novel experimental setting in which the first-order belief assumption implies that social information is equivalent to private information. Our main finding is that participants’ reported reservation prices of social information are significantly lower than those of private information, which provides evidence that casts doubt on the first-order belief assumption. We also build a novel belief error model in which participants form a random posterior belief with a Bayesian posterior belief kernel to explain the experimental findings. The structural estimation of the model suggests that participants’ sophisticated consideration of others’ belief error and their exaggeration of the error both contribute to the difference in reservation prices.
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System

Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles are proposed. A hybrid feature selection technique comprising three methods, i.e., particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensemble based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. The results regarding the UNSW-NB15 dataset also improve the ones achieved by several state-of-the-art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by the IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier.
Universal Dependencies 2.1

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
True random bit generation and faster Diffie-Hellman for systemic cryptography

Inthecurrentcontroversialcontextcausedbythe disclosure of classified details of several top-secret United States and British government mass surveillance programs to the press by former NSA contractor Edward Snowden, issues of data privacy, anonymity, unlinkability, forward secrecy and deniability have raised to public prominence. In this work we investigate how an alternate usage of state- of-the-art yet ubiquitous computing platforms might help sovereign, citizen and general public recovery of control over privacy. These goals are notoriously difficult to achieve on the Internet today due to the insufficient public-key infrastructure at the user level. Our approach leverages mod- ern multi-core processors and general-purpose computing on graphics processing units, both as a source of true ran- dom entropy pools and computational engines for very fast elliptic curve cryptography (ECC). Such autonomous, high- frequencyDiffie-Hellman-readyagentsresideinabreadthof devicesrangingfromsmartphonesandtablets,tolaptopsand high-end servers in datacenters. In contrast to the current cir- cumstance, this suggested infrastructure enables generalized symmetric exchanges with the Vernam cipher without com- promising ease-of-use nor requiring revolutionary changes in today's well-grounded ECC theory.
The Use of Visualization in Teaching and Learning Process for Developing Critical Thinking of Students

Many researches has been conducted for the need to increase critical thinking of students (in (different fields, (also many researches has been done on the importance and the role of critical thinking for students achievements. In this context this requires a critical approach. To achieve this should be used effective teaching methods that develop critical thinking and also facilitate and enhance the learning of students and their performance in general, making them able to solving problems in their fields. A visualization approach increase communication, increase critical thinking and provides analytical approach to various problems. Therefore, this research is aimed to investigate visualization for the purpose of examining its role in developing critical thinking. In order to achieve this it was made an experiment for the use of visualization and from this experimentation are extracted the results of the effect of using the visualization for the aspect of developing and increasing critical thinking. The results which are taken from this research highlight the positive effect that the use of visualization in teaching and learning process has in developing the critical thinking of students and their overall performance. The results also shows that the visualization motivates students to learn, making them more cooperative and developing their skills for critical approach. Keywords: visualization, critical thinking, teaching, learning, student performance
A computer interface for the assessment of the effect of reaction time on hand deviation (Tremor)

Subjects were asked to draw a line of fixed length, through a prescribed horizontal track using the light pen which is a computer interface (similar in action to the mouse), which is connected to the personal computer and programmed to capture the deviations of the hand movement and measure its latency. The ratio of the horizontal deviations to the vertical deviations is termed as skew ratio. The latency (reaction time) was measured and skew ratio was calculated for each subject. Males and females subjects were examined. A comparative study of the effect of reaction time (latency) on skew ratio of the horizontal hand movement for controls and others suffering from mental illness have been conducted. Controls mean reaction time was equal to 2.15s and 5.28s for males and females, respectively; while they were equal to 7.32s and 7.33s for male and female patients, respectively. Reaction time was lower for controls compared to that of patients; which ascertain its dependence on state of health and gender. Controls mean skew ratio was equal to 36.5 for males and equal to 27.6 for females, while this ratio was equal to 4.9 for abnormal males and was equal to 3.8 for abnormal females respectively. A high value of skew ratio and a small value of reaction time are indicators of sound hand movement and hence good health while low values are indicators of poor hand movement and hence poor health. When gender was considered the skew ratio for normal males to normal females was found to be as high as 1.32:1, while that for abnormal males to females was found to be as high as 1.29: 1 which ascertain its dependence on gender. No relationship was noticed between Skew ratio and Reaction time.
Virtual Reality: Where Have We Been? Where Are We Now? and Where Are We Going?

The rising popularity of virtual reality has seen a recent push in applications, such as, social media, educational tools, medical simulations, entertainment and training systems. With virtual realitythe ability to engage users for specific purposes, provides opportunities to entertain, develop cognitive abilities and technical skills outside of the standard mediums (e.g., the television or theclassroom) thereby optimizing exposure with realistic (live) opportunities. However, before these applications of virtual reality become more widespread, there are a number of open questions andissues that must be addressed including limitations, challenges, relationships between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this article, we begin with a brief overview of virtual reality methods, followed by a discussion of virtual reality and its applications (both historically, currently and in the future). We review virtual reality trends both from the early artistic days through to current day state of the art technological advancements. We explore emerging and futuristic breakthroughs - and their applications in virtual reality - showing how virtual reality will go way beyond anything we could envision. Infact, after reading this article, we hope the reader will agree, that virtual reality, is possibly one of the most powerful mediums of our time. While the earliest mechanistic virtual reality prototypes (e.g., Sensorama) allowed us to view stereoscopic 3D images accompanied by stereosound, smells, as well as wind effect - set the foundation and direction for future pioneers - there have been spearheaded developments which have continually pushed the concept of virtual reality to new domains. As virtual reality evolves, many new and yet-to-be-imagined applications will arise, but we must have understanding and patience as we wait for science, research andtechnology to mature and improve. The article ends with a short overview of future directions based upon recent breakthroughs in research and what this will mean for virtual reality in the coming years.
Truly Distributed Optimal Synchronization

In this thesis we consider the distributed linear quadratic optimal control problem for single integrator multi-agent systems. We show that optimal control gains depend on the graph structure of the system and hence the optimal control can only be computed globally. To overcome this problem we design a control protocol that minimizes local cost functions while still achieving consensus. Finally, we provide a simulation to compare the newly designed control protocol to that of the global optimum.
Adaptive controller design for nonlinear uncertain systems using multiple model based two level adaptation technique

Adaptive control technique is a popular and successful control strategy for controlling nonlinear uncertain systems. However, using adaptive control schemes in parametrically uncertain environments often lead to poor transient response and sluggish steady state response. Use of multiple estimation models has been found to be promising in addressing these issues. This thesis proposes an adaptive control method for nonlinear uncertain systems using multiple models based two level adaptation (MMTLA). At the first level, multiple models are used and a single model at the second level is proposed by combining these first level models for controlling different classes of nonlinear uncertain systems. The proposed control method is applied to nonlinear single-input single-output (SISO) systems with linear and nonlinear parameterizations, nonlinear multiple-input multiple-output (MIMO) coupled systems and nonlinear MIMO model following control systems. For all the considered systems, state transformation and feedback linearization method have been used to algebraically transform nonlinear system dynamics to linear ones. The unknown system parameters are assumed to be bounded within a set of compact parameter space. Multiple estimation models are distributed evenly in this region of uncertainty and their unknown parameters are tuned. The tuning laws for estimator parameters have been obtained using Lyapunov stability criterion. Stability analysis using Lyapunov’s criterion has been carried out to assess the close loop stability and tracking error convergence of the overall system. The transient and steady state performances using the proposed scheme are evaluated by conducting simulation studies, which confirm superior performance of the proposed control technique over some existing adaptive control methods. The common problems with adaptive control like oscillatory transient response, poor parameter convergence and sluggish response are found to be improved considerably by using the proposed multiple model based two level adaptive controller. In addition to simulation studies, the proposed controller is tested experimentally on real time by applying it to a laboratory set-up of twin rotor MIMO system (TRMS). Experimental studies are performed
A Survey on Digital Image Steganography

: Internet brings us not only the convenience of communication but also some security risks, such as intercepting information and stealing information. Therefore, some important information needs to be hidden during communication. Steganography is the most common information hiding technology. This paper provides a literature review on digital image steganography. The existing steganography algorithms are classified into traditional algorithms and deep learning-based algorithms. Moreover, their advantages and weaknesses are pointed out. Finally, further research directions are discussed.
Feasibility and use of psychophysiological responses based on cognitive load theory

The purpose of this study was to identify the feasibility of using the pupil dilation and heart rate variability (HRV), to diagnose learners’ changing cognitive load in a video learning setting. The pupil dilation and HRV of 23 participants were recorded during their learning progress, and the learning materials, including differences in task complexity, were presented to the groups of distinct prior knowledge. The results showed that the high prior knowledge group had lower pupil size than the low prior knowledge group, but the difference was not statistically significant. However, the ratio of low frequency to high frequency power (LF/HF ratio) was higher in the high prior knowledge group than in the low prior knowledge group throughout the entire learning process, and the two groups showed different arousal patterns. Overall, learners’ changing cognitive load could be measured and visualised based on psychophysiological responses, the types of cognitive load could be assumed by using the two indices in parallel, and this study raised practical issues to the use of pupil size as an indicator of cognitive load in video learning.
Phase-Locking Value Based Graph Convolutional Neural Networks for Emotion Recognition

Recognition of discriminative neural signatures and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Electroencephalogram (EEG) is a spatial discrete signal. In this paper, in order to extract the spatio-temporal characteristics and the inherent information implied by functional connections, a multichannel EEG emotion recognition method based on phase-locking value (PLV) graph convolutional neural networks (P-GCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is using PLV-based brain network to model multi-channel EEG features as graph signals and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed P-GCNN method uses the PLV connectivity of EEG signals to determine the mode of emotional-related functional connectivity, which is used to represent the intrinsic relationship between EEG channels in different emotional states. On this basis, the neural network is trained to extract effective EEG emotional features. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DEAP dataset. The experimental results demonstrate that novel framework can improve the classification accuracy on both datasets, but not so effective on DEAP as on SEED, in which with 84.35% classification accuracy for SEED, and the average accuracies of 73.31%, 77.03% and 79.20% are, respectively, obtained for valence, arousal, and dominance classifications on the DEAP database.
Cooperative Interference Mitigation in Wireless Cellular Networks

Cooperative Interference Mitigation in Wireless Cellular Networks Seyed Arvin Ayoughi Doctor of Philosophy The Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto 2019 In this thesis, we explore potentials of cooperative communication at receivers’ side in the downlink of wireless cellular networks for interference mitigation and signal enhancement. We investigate two regimes of latency and reliability of communication: one for providing high long-term average user rates for supporting a quality of service that is suitable for human visual and auditory perception, and the other for providing an ultrareliable low-latency machine-type wireless communication for streaming information in control applications. We consider three different models of cooperative communication. First, we study deploying a multi-antenna relay node that provides a nearby cell-edge user with extra dimensions over an out-of-band relaying link. We model this scenario by a Gaussian multiple-input multiple-output (MIMO) relay channel with correlated noise across relay and destination antennas, and analyze the capacity of this channel. This type of relay deployment is most effective when the number of receive antennas is small and the number of relay antennas is large enough. Second, we study deploying a multi-antenna half-duplex amplify-and-forward relay node that simultaneously provides multiple cell-edge users with extra dimensions. We show that the optimized relaying significantly improves the long-term average rates of cell-edge users, even after accounting for the extra bandwidth required for half-duplex relaying, provided that the relay is equipped with sufficiently many antennas. Third, we study cooperation among receivers for combating fading and mitigating interference for ultrareliable low-latency wireless communication. We consider multiple interfering broadcasts from controllers to their corresponding actuators. The recently-proposed Occupy CoW protocol efficiently exploits the spatial diversity of distributed receivers for combating deep fading. It consists of two consecutive phases: the broadcast phase and the cooperation
Methodology of Fault Diagnosis in Ductile Iron Melting Process

Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including datadriven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.
The more the better? The impact of number of stages of likelihood alarm systems on human performance

Responses to alarms involve decisions under uncertainty. Operators do not know if an alarm is more likely to be a hit or a false alarm. Likelihood alarm systems (LAS) help reduce this uncertainty by providing information about the certainty of their output. Unlike traditional binary alarm systems, they have three or more stages: each one represents a different degree of likelihood that a critical event is really present. Consequently, the more stages, the more specific is the information provided by the alarm system to reduce uncertainty. A laboratory experiment with 48 participants was conducted to investigate the effect of specificity of information of LAS on performances and responding behaviour. Specifically, a three-stage, four-stage, and five-stage LAS were compared using a multi-task environment. Results show higher percentages of correct decisions in the alarm task when participants used the fourand five-stage LAS than the three-stage LAS but no significant differences were found between the four-and five-stage LAS. Interesting differences in response patterns were also observed. This study suggests that four stages is the best degree of specificity for optimal performance.