Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifeatured method for detection and classification of epileptic seizure based on time frequency analysis of EEG signals

Epileptic seizure describes a recurrent abnormal but synchronized surge of electrical activity in the brain, which is the second common neurological disease. Signal processing methods try to model visual information into few parameters, which can be easily detected thus decision making becomes more accurate compared to the method based on visual observation of EEG. For seizure detection, the features can be categorized as univariate/bivariate and linear/nonlinear types. But, the use of composite feature set has been limitedly reported. Since, EEG is a non-stationary signal and distribution of its energy demonstrates the seizure activities, time-frequency distribution can perform better than the conventional frequency analysis methods. Effectiveness of time-frequency based feature extraction depends on the choice of a kernel and its processing time. Moreover, development of a multifeatured set capable of detecting and classifying epileptic seizure originated from different parts and state of the brain is still a challenging problem. Prior to feature extraction, pre-processing involving Hilbert transform is performed. For the preprocessed EEG signal, time-frequency distributions (TFDs) are obtained by using twelve Cohen Class kernels capable of reducing influence of cross-terms. The TFDs are examined to find out nonuniform modules corresponding to dominant bands, namely δ, θ, α, β and γ components and to form a feature set containing modular cumulative energy at percentile frequencies and modular entropy. This feature set when fed to each of the decision tree, KNN and ANN classifier, can produce greater detection accuracy independent of the kernels and offers lesser processing time. But, it is unable to classify epileptic seizure originated from different parts and state of the brain. Therefore, a high number of uniform modules are formed to compute a mutifeatured set containing modular energy and entropy which is found effective in detecting as well as classifying epileptic seizure originated from five different parts and state of the brain. Simulations are carried out using standard EEG dataset to evaluate the performance of the proposed method in terms of selectivity, sensitivity and accuracy. It is shown that the proposed method outperforms a state-of-the-art method with superior efficacy.
Communication and Self‐Presentation Behavior on Academic Social Networking Sites: An Exploratory Case Study on Profiles and Discussion Threads on ResearchGate

Several Academic Social Networking Sites (ASNSs) have been launched in the last few years and their number of members is growing. Researchers using ASNSs come from very divergent scientific backgrounds and academic levels, prompting one to ask the question, What kind of communication and self‐presentation behaviors occur within these structures? The qualitative study presented in this article analyzed the communication behavior of a selected sample on ResearchGate (RG). It investigates how researchers present themselves on their personal‐profile sites and how they interact with other researchers. Overall, the results show that mostly young male academics without previous connections to each other (for example, faculty colleges) use RG for their scholarly exchanges. In general, communication behavior is characterized by an objective, professional, unemotional choice of words and seldom the use of polite salutations or words of farewell. However, there seems to be a correlation between long discussion and an increased use of colloquial and emotional language. Based on our findings, we derived preliminary practical recommendations for communications on ASNSs to improve the relationships in online academic interactions, to foster inclusiveness of gender and culture, and to reduce insecurity in matters of communication, presentation, and the exchange of scientific data.
Environmental acoustic transformation and feature extraction for machine hearing

This paper explores the transformation of environmental sound waveform and feature set into a parametric type representation to be used in analysis, recognition, and identification for auditory analysis of machine hearing systems. Generally, the focus of the research and study in sound recognition is concentrated on the music and speech domains, on the other hand, there are limited in non-speech environmental recognition. We analyzed and evaluated the different current feature algorithms and methods explored for the acoustic recognition of environmental sounds, the Mel Filterbank Energies (FBEs) and Gammatone spectral coefficients (GSTC) and for classifying the sound signal the Convolutional Neural Network (CNN) was used. The result shows that GSTC performs well as a feature compared to FBEs, but FBEs tend to perform better when combined with other feature. This shows that a combination of features set is promising in obtaining a higher accuracy compared to a single feature in environmental sound classification, that is helpful in the development of the machine hearing systems.
How to Verify Conway’s Law for Open Source Projects

Conway’s Law states that in successful software projects, the organizational structure of programmer teams corresponds to the architecture of the developed system. It means that, ideally, each developer team works on its software module, and only on that module. We propose an approach to assess the difference between code structure and organizational structure. It is based on agglomerative clustering of modules and developers, followed by the search for best possible mapping between the groups. We applied the approach to a number of popular open source projects. The results show that these projects hardly obey Conway’s law, due to the scale-free nature of both types of deduced networks, i.e., of software modules and the developers.
Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy

A n optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction. These various velocity predictions are used as an input into an Optimal EMS derivation algorithm to derive an engine torque and engine speed control strategy that improves FE compared to current vehicle operation. Dynamic programming is used as the Optimal EMS because it provides a globally optimal control which is preferable for this investigatory study. The vehicle model used is real world validated and represents a 2017 Toyota Prius Prime operating in charge sustaining mode. The results show that actual vehicle velocity prediction from the prediction model achieves 85% of the FE improvement from an Optimal EMS derived using perfect 10 second prediction.
Contributing factors in pursuit of a Ph.D. in engineering: The case of lebanon

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Needham Model Based Instructional Multimedia Material for Teaching Digital Logic Gates

The purpose of this study was to design and develop an instructional multimedia for digital logics gate topic in Electrical and Electronic subject. This topic was chosen as the content-wise of this instructional media due to the teachers’ recommendation. Teachers stated that students are facing difficulty to identify types, symbols, functions and completing the truth tables for logic gate. The failure of students to master this basic concept led them facing difficulty to solve problems involving combinational logic gate, which required students' skills to build equations, draw logic circuits and complete the truth table in order to get the circuit output. This instructional media was developed using constructivist theory approach, namely Needham Model. This model consists of five phases: orientation, generation of idea, restructuring of idea, application of idea, and reflection. Meanwhile, Three Product Development Life Cycle (PDLC) Phases (Design, Develop, and Review) was used as the research methodology for this study. This instructional media was developed using Microsoft Office Power Point 2007, Adobe Photoshop CS3, Photoscape and Edius 4. This study was divided into three main phases, namely (I) Need Analysis Phase, (II) Development of Instructional Media Phase, and (III) Instructional Media Evaluation Phase. At the Phase I, the user requirements for the instructional multimedia was identified for the purpose to design. Based on the findings at Phase I, the instructional media was developed at Phase II. At Phase III, the developed instructional multimedia was evaluated through the experts’ validation and review from the aspect of pedagogy, engineering content-wise, and the multimedia elements. The findings showed that experts’ feedback and comments for the instructional multimedia was positive. As a conclusion, teachers were encourages to use instructional multimedia based on Needham Model to enhance student performance in logic gate topic.
Web Cache Simulation

Web caching is the storage of Web objects near the user to allow fast access, thus improving the user experience of the Web surfer. Examples of some Web objects are Web pages (the HTML itself), images in Web pages, etc. We assume that system has only one server who serves the clients web requests. The arrival of requests is uncertain and cannot be predicted. Simulation is the most commonly usedmethod to explore new proposals due to both itsflexibility and the relatively reduced time taken to obtain performance results. This paper presents a simulation system to simulate web proxy cache systems. Experimental Results show that the maximum requests’s waiting time is noted to be between 0 and 62 seconds. Therefore, the requests waiting for 2 minutes is bearable and does not require an additional server.Similarly, the server’s service time for most number of requests is between 3 and 4 minutes.
A Hybrid Model Predictive Multilayer Control Strategy for Modular Multilevel Converters

This paper presents a novel hybrid model predictive multilayer control (HMPMC) strategy for modular multilevel converters (M2LCs). The proposed hybrid strategy uses a modified model predictive direct slope control (MPDSC) concept to regulate the load current while concurrently minimizing the switching frequency, circulating current, and capacitor voltage variations through a new MPMC concept. The multilayer philosophy uses dedicated control layers to independently evaluate its control variables. As the main advantage, the hybrid strategy enables operation of M2LC at low switching frequencies. Moreover, this paper proposes a hierarchical screening algorithm, which estimates the minimum number of switching states required to be evaluated for each control variable, reducing the computational burden. An execution order for each control layer, in which the variables are assigned to control layers as appropriate, is also incorporated to further reduce the computational burden. Instead of using conventional weighting factors, this paper uses tuning factors for each layer, which is based on the number of switching states of the lowest cost to make the tuning process efficient and adaptable to any operating conditions. To demonstrate the validity, a prototype single-phase, five-level, 380-VA M2LC is designed, built, and controlled through the proposed strategies. The performance of prototype is investigated under both the steady-state and transient conditions, and the experimental results are presented in comparison with simulations and benchmarked against both the conventional finite control set model predictive control (FCS-MPC) and an improved MPC strategy. The results clearly indicate that the proposed strategy enables efficient operation of the converter at both significantly lower switching frequency and reduced computational burden, which is preferred for high-power applications.
3DCarRecog: Car Recognition Using 3D Bounding Box.

We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our unified framework learns a 2D global texture and a 3D-bounding-box based feature representation in a mutually correlated and reinforced way. These two kinds of feature representation are combined by a novel fusion network, which predicts the vehicle's category. The 2D global feature is extracted using an off-the-shelf detection network, where the estimated 2D bounding box assists in finding the region of interest (RoI). With the assistance of the RoI, the 3D bounding box and its corresponding features are generated in a geometrically correct way using a novel 3D perspective Network (3DPN). The 3DPN consists of a convolutional neural network (CNN), a vanishing point loss, and RoI perspective layers. The CNN regresses the 3D bounding box under the guidance of the proposed vanishing point loss, which provides a perspective geometry constraint. Thanks to the proposed RoI perspective layer, the variation caused by viewpoint changes is corrected via the estimated geometry, enhancing feature representation. We present qualitative and quantitative results for our approach on the vehicle classification and verification tasks in the BoxCars dataset. The results demonstrate that, by learning how to extract features from the 3D bounding box, we can achieve comparable or superior performance to methods that only use 2D information.
<inline-formula> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula>-Dropout: A Unified Dropout

Dropout is an effective regularization method for deep learning tasks. Several variants of dropout based on sampling with different distributions have been proposed individually and have shown good generalization performance on various learning tasks. Among these variants, the canonical Bernoulli dropout is a discrete method, while the uniform dropout and the Gaussian dropout are continuous dropout methods. When facing a new learning task, one must make a decision on which method is more suitable, which is somehow unnatural and inconvenient. In this paper, we attempt to change the selection problem to a parameter tuning problem by proposing a general form of dropout, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-dropout, to unify the discrete dropout with continuous dropout. We show that by adjusting the shape parameter <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, the <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-dropout can yield the Bernoulli dropout, uniform dropout, and approximate Gaussian dropout. Furthermore, it can obtain continuous regularization strength, which paves the way for self-adaptive dropout regularization. As a first attempt, we propose a self-adaptive <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-dropout, in which the parameter <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> is tuned automatically following a pre-designed strategy. The <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-dropout is tested extensively on the MNIST, CIFAR-10, SVHN, NORB, and ILSVRC-12 datasets to investigate its superior performance. The results show that the <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-dropout can conduct finer control of its regularization strength, therefore obtaining better performance.
ASR-based dictation practice for second language pronunciation improvement

In pronunciation learning, there is a need for resources and tools that help students monitor their speech or provide feedback on errors. While researchers have seen ASR-based technologies as potential tools, little attention has been paid to dictation programs, which have been criticized for low levels of recognition, but offer advantages such as accessibility and flexibility. This study examines two groups of learners in a pronunciation workshop: CONV, which had fully face-to-face instruction, and HYBRID, which had half of the instruction face-to-face and half using the computer, practicing production using a dictation program, Windows Speech Recognition. Results show that both groups improved from pre- to post-test and that there were no statistically significant differences between the two groups. Results indicate that dictation programs may be useful as a complement to face-to-face pronunciation teaching, especially if in-class time for pronunciation teaching is limited.
Regression Kink Design: Theory and Practice

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this paper, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (e.g. Imbens et al. (2012) and Calonico et al. (2014)) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.
Hypervelocity stars in the Gaia era

Context. Young massive stars in the halo are assumed to be runaway stars from the Galactic disk. Possible ejection scenarios are binary supernova ejections (BSE) or dynamical ejections from star clusters (DE). Hypervelocity stars (HVSs) are extreme runaway stars that are potentially unbound from the Galaxy. Powerful acceleration mechanisms such as the tidal disruption of a binary system by a supermassive black hole (SMBH) are required to produce them. Therefore, HVSs are believed to originate in the Galactic center (GC), the only place known to host an SMBH. Aims. The second Gaia data release (DR2) offers the opportunity of studying HVSs in an unprecedented manner. We revisit some of the most interesting high-velocity stars, that is, 15 stars (11 candidate HVSs and 4 radial velocity outliers) for which proper motions with the Hubble Space Telescope. were obtained in the pre-Gaia era, to unravel their origin. Methods. By carrying out kinematic analyses based on revised spectrophotometric distances and proper motions from Gaia DR2, kinematic properties were obtained that help constrain the spatial origins of these stars. Results. Stars that were previously considered (un)bound remain (un)bound in Galactic potentials favored by Gaia DR2 astrometry. For nine stars (five candidate HVSs plus all four radial velocity outliers), the GC can be ruled out as spatial origin at least at 2σ confidence level, suggesting that a large portion of the known HVSs are disk runaway stars launched close to or beyond Galactic escape velocities. The fastest star in the sample, HVS 3, is confirmed to originate in the Large Magellanic Cloud. Conclusions. Because the ejection velocities of five of our non-GC stars are close to or above the upper limits predicted for BSE and DE, another powerful dynamical ejection mechanism (e.g., involving massive perturbers such as intermediate-mass black holes) is likely to operate in addition to the three classical scenarios mentioned above.
The Impact of Teaching Practice on Professional Self-Esteem: A Covariance Analysis.

This study examined the professional self-perceptions and self-esteem of Hong Kong student teachers, investigating the impact of teaching practice. Participants were student teachers in their first or third year of full-time, 3-year courses at 1 of the 4 colleges of education in Hong Kong. A total of 424 student teachers completed a questionnaire before and after student teaching. The questionnaire measured self-esteem as a teacher, focusing on self-esteem, teacher-student relationships, teacher efficacy, teacher commitment, humanism, adequacy of teaching abilities, and classroom techniques. Data analysis indicated that student teachers' personal and professional self-esteem differed from each other as they were subject to different influences. The development of their professional self-esteem depended on the manner in which they perceived their teaching efficacy, teacher student relationships, and commitment to teaching. Student teachers' humanistic beliefs and perceptions of the adequacy of teaching skills overwhelmingly influenced the organization of self-structure. Except for their self-perceived adequacy of teaching abilities, the impact of the other teaching practice experiences on the development of self-esteem was fairly limited. (Contains 70 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. The Impact of Teaching Practice on Professional Self-Esteem: A Covariance Analysis Dr. Ka Wah Yeung The Hong Kong Institute of Education Professor David Watkins The University of Hong Kong