Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Emotion Recognition of Students using Convolutional Neural Network

Nowadays, deep learning techniques know a big success in various fields including computer vision. Indeed, a convolutional neural networks (CNN) model can be trained to analyze images and identify face emotion. In this paper, we create a system that recognizes students’ emotions from their faces. Our system consists of three phases: face detection using Haar Cascades, normalization and emotion recognition using CNN on FER 2013 database with seven types of expressions. Obtained results show that face emotion recognition is feasible in education, consequently, it can help teachers to modify their presentation according to the students’ emotions.
Robotic Feeder for Disabled People (RFDP)

Bio-Robotics is an innovative field that helps in improving the quality of life of people with special needs. People who suffer from hands related inability or immobility encounter many problems in their life. Eating, a basic necessity is a troublesome task that negatively impacts these people. Their inability to easily feed themselves may lead to a feeling of frustration, and embarrassment from the people who feed them. In order to combat the aforementioned problem, RFDP (Robotic Feeder for Disabled people) system is proposed; it gives disabled people the sense of freedom at the dinner table. RFDP consists of an arm of five degree of freedoms. The system is centered at the middle of a table, surrounded by four different types of food, each inside a plate. It is programmed to take the order from the user by using blink detection technology; the user selects the appropriate food plate and moves the arm towards it by just blinking the eye. The design incorporates three main components. The first is the arm structure that is composed of links, and joints. The second is the eye blink detection system. The third is the connection between the user and the arm. The user controls the arm by blinking the eye via camera, and the robot moves to the selected bowl, dip into the food, and bring the spoon-full to the user’s mouth.
Bearing Fault Diagnosis Based On Reinforcement Learning And Kurtosis

Vibration signal of rolling element bearing is usually much complicated due to the presence of random slipping of rolling element and a lot of noise. Therefore, it is often difficult to extract fault features from the collected signal. In classic bearing fault diagnosis, it is necessary to use a bandpass filter to process the original vibration signal, and use demodulation techniques to obtain fault features. The kurtosis of vibration signal is sensitive to fault information, which makes it a good indicator. In this paper, a reinforcement learning system with kurtosis as an index is constructed to train a bandpass filter to adjust its upper and lower cutoff frequencies. Thereby, the bandpass filter can select the frequency band with the largest signal to noise ratio. Then, demodulation of the filtered signal is performed so as to diagnose the fault of rolling bearings. In order to verify the effectiveness of the proposed method, vibration signal collected from rolling element bearing with an inner race fault is studied. The comparison with fast kurtogram method indicated that the proposed reinforcement learning approach has great potential for diagnosing bearing faults.
Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders

Extensive technology scaling has not only increased the complexity of Integrated Circuit (IC) fabrication but also multiplied the challenges in the Design For Manufacturability (DFM) space. Among these challenges, detection of design weak-points, popularly known as ‘Lithographic Hotspots’, has attracted substantial attention. Hotspots are certain patterns which exhibit a higher probability of causing defects due to complex design-process interactions. Identifying such patterns and fixing them in the design stage itself is imperative towards ensuring high yield. In the early days of hotspot detection, Pattern Matching (PM) based methods were proposed. While effective in identifying previously known patterns, these methods failed to identify Never-Seen-Before (NSB) hotspots. To address this drawback, Machine Learning (ML) based solutions were introduced. Over the last decade, we have witnessed a plethora of ML-based hotspot detection methods being developed, each slightly outperforming its predecessors in accuracy and false-alarm rates. In this paper, we critically analyze the ML-based hotspot detection literature and we highlight common misconceptions which are found therein. We also pinpoint the underlying reasons that have led to these misconceptions by dissecting the ICCAD-2012 benchmark dataset, which has largely guided the evolution of this area, and revealing its limitations. Furthermore, we propose an enhanced version of this benchmark dataset, which we deem more appropriate for accurately assessing hotspot detection methods. Finally, we offer our suggestions to improve the effectiveness of ML-based Hotspot Detection methods and demonstrate about 5X reduction in false-alarms in comparison to the state-of-the-art.
Open-Source Incubation Ecosystem for Digital Microfluidics — Status and Roadmap: Invited Paper

Electrowetting-on-dielectric (EWOD) is a mechanism that allows physical handling of liquids with only electrical signals, such as digitizing a liquid into tiny droplets and manipulating them on a chip, thus enabling “digital microfluidics”. As an elegantly simple platform free of pumps or valves, EWOD digital microfluidics has been attracting high research interest in the past two decades and has recently been transitioned to a few commercial products in displays and biochemistry. However, the number of labs utilizing this technology is still small, due to the difficulty in translating design intent to manufactured devices. Accordingly, in this paper, we propose a cloud-based open-source EWOD cybermanufacturing ecosystem that enables an automatic translation from user requirements to manufactured digital microfluidics. The incubation cyber ecosystem is to be accessible to a wide range of end users, allowing researchers, entrepreneurs, students, and hobbyists alike to focus on their own ideas and applications without having to master the subtleties of EWOD engineering and manufacturing. This can be thought of as an “operating system” for EWOD community, similar to Windows and macOS for people with no computer hardware background. The proposed cyber ecosystem aims to ease the process of designing and eliminate the burden of fabricating EWOD chips so that the user pool is increased and more applications found for EWOD digital microfluidics.
Substrate Integrated Waveguide Antenna with Metasurface for Cube Satellites

In this paper, the substrate integrated waveguide (SIW) slot array antenna using metasurface for cube satellite operating in the C-band was simulated. The antenna is sandwiched structure arranging a $2 \times 5$ periodic square conductor patch on the substrate integrated waveguide. As the surface wave excited in the SIW slot array propagates on the metasurface, additional resonance occurred and the impedance matching was better than the conventional antenna. The overall size of the final antenna was ${\mathrm {6.75cm\times 2.35cm\times 0.37 cm}} (1.07\lambda_{0}\times 0.37\lambda_{0}\times 0.06\lambda_{0}$ at 4.74 GHz). The simulated results have a bandwidth of 4.73-4.87 GHz (3.38%) satisfying the condition of |S11|<-10dB. The maximum realization gain is 6.58 dB at 4.76 GHz.
Detecting Undefined Behaviors in CUDA C

With the increasing deployment of GPUs in many domains, it is important to enhance the dependability of GPU-based systems especially in mission critical environments. We believe that undefined behaviors present a major challenge for the CUDA platform, because its programming language and compiler are derived from traditional C/C++ languages. In this paper, we first present a suite of benchmarks for CUDA, which contains 77 categories of undefined behaviors corresponding to the core language in C11 standard. It is developed to evaluate how current CUDA compilers deal with undefined behaviors. Then, we present the design and implementation of a system that can detect and locate vulnerable code that may result in undefined behaviors in CUDA programs. Our system is composed of a program converter and a static analyzer; the former transforms CUDA programs into a portable and semantic-reserved C/C++ programs, and the latter performs analysis on the transformed programs to statically identify undefined behaviors. Using CUDA SDK and a set of real CUDA applications, we conduct extensive experiments to investigate the effectiveness of our system on finding undefined behaviors in CUDA C programs.
Motifs in Big Networks: Methods and Applications

Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed.
Extracting Specific Voice from Mixed Audio Source

We propose auditory diminished reality by a deep neural network (DNN) extracting a single speech signal from a mixture of sounds containing other speakers and background noise. To realize the proposed DNN, we introduce a new dataset comprised of multi-speakers and environment noises. We conduct evaluations for measuring the source separation quality of the DNN. Additionally, we compare the separation quality of models learned with different amounts of training data. As a result, we found there is no significant difference in the separation quality between 10 and 30 minutes of the target speaker's speech length for training data.
User-Independent Classification of Emotions in a Mixed Arousal-Valence Model

In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the "peak-end rule". Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.
Replay Attack Detection Using Linear Prediction Analysis-Based Relative Phase Features

Recent studies have reported the success of linear prediction analysis (LPA)-related features, which are extracted as a short-term spectral feature for replay attack detection due to the advantage of the imperfection in the LPA-based signal produced by recording and playback devices. However, exploiting LPA-based signals is focused on only magnitude-based features and ignores phase-based features. In this paper, we propose two novel LPA-based relative phase features, namely, linear prediction residual-based relative phase (LPR-RP) and linear prediction analysis estimated speech-based relative phase (LPAES-RP). The key idea of both LPR-RP and LPAES-RP is to extract the phase information based on LPA-based signals. In the LPR-RP feature, we modify the relative phase (RP) feature extraction using a linear prediction residual (LPR) derived from the difference between the original/raw speech and LPA estimated speech signal (LPAES) instead of the original/raw speech signal. LPES-RP feature exploits the LPAES signal to replace the original/raw speech signal. Because the trace of the recording and playback device artifacts is the primary evidence for detecting the replayed signal, the advantages of the imperfection of LPR and LPAES are expected to provide efficient phase information for the replay attack detection task. In addition, using the individual LPR-RP/LPAES-RP feature, our proposed features are combined with two standard features, mel-frequency cepstral coefficients (MFCC), constant Q transform cepstral coefficients (CQCC) and the original RP feature, at score level to further improve the detection decision. The performance of the proposed LPR-RP/LPAES-RP feature and combination are evaluated using the ASVspoof 2017 version 2 database. On the evaluation subset, our proposed LPR-RP and LPAES-RP feature achieves a promising improvement over baseline features (MFCC/CQCC). Moreover, the combined systems of LPR-RP, RP, and CQCC obtains an equal error rate of 9.26%.
Data Zeroing Based on Correlation and Linear Interpolation of the Blade Tip-Timing Data

The blade tip-timing has become the most promising technique in the field of rotating blade vibration monitoring with its advantages of non-contacting. However the signal can be disturbed by many factors, especially the noise and drift of the blade vibration displacement curve caused by the centrifugal force changed with rotating speed. The main difficulty to data zeroing is to prevent the peak amplitude from being attenuated or eliminated. In this paper, a method was developed using blade vibration displacement to identify the areas of resonance by calculating the correlation of the data over a number of assembly revolutions from the multi-probe. The blade vibration simulator is carried out to study the relationship between the number of probes and the window width in the correlation. Applying this method into the experimental data, and verify the superiority of the correlation method.
Performance Analysis of CSS based Low-power Underwater Communication Systems by Lake Experimental Results

The difficulty of continuously supplying battery power in underwater communication greatly threatens the stability of the underwater communication network. The noise-robust feature of the CSS (Chirp Spread Spectrum) method makes low-power communication possible in the underwater. This paper investigates the performance comparison of Orthogonal Frequency Division Multiplexing (OFDM) method, which is commonly used for underwater communication, with CSS method. As a result, the performance gain of about 17dB based on AWGN BER was confirmed at the approximate data rate, and the performance gain of the CSS method was further confirmed by the lake experiment.
Analysis of Smart Citizens: A Fuzzy Based Approach

Advancement in the technology is drawing an positive and prosperous impact on human life. Thus, the development of smart city applications and services enhances the way of life style in the contemporary cities. The citizens are the real stakeholders of smart city and to maintain the life standard of citizens is a key challenge. Citizens-centric and less technology oriented approaches have become proliferating interest in smart cities. Therefore, there is need of an effective approach to explore the standard of individuals in smart cities. Consequestnly this research article investigates the standard of citizens by providing a fuzzy based smart system. The proposed smart system is an efficient tool to analyze the standard of citizens based upon different parameters such as, safety, health, education, quality of houses, and so on. The proposed system analysis the standard of citizens with high accuracy and confirms that the selected parameters have immense impact on standard of life in smart cities.
Formation Control of Multi-UAV with Collision Avoidance using Artificial Potential Field

To solve the problem of obstacle collision and inner collision during the formation flight, we propose an artificial potential field (APF) approach and employ it to the formation controller, which is based on the passivity-based control. Furthermore, we apply the concept of centroid to set consensus mission velocity, which ensures that each unmanned aerial vehicle (UAV) is navigated to the target set. Finally the proposed methodologies are proved to be effective in Matlab simulation.
Parasocial Relationship Analysis on Digital Celebrities Follower’s Purchase Intention

The relationship of interaction between celebrities and consumers in social media is increasing and from the parasocial relations that are formed can encourage consumers to increase the intention to purchase the products endorsed by celebrities. This study aims to study the effect of the parasocial relationship between digital celebrities and their followers on shopping intentions and eWOM users’ intentions in Indonesia. This study uses a quantitative approach. The questionnaire is used as an instrument for data collection. Respondents who were included in this study are 135 social media users in Indonesia who participated in at least one Indonesian digital celebrity account. Data is processed using PLS-SEM using SmartPLS 3.0 software. The results of this study concluded that the stronger the parasocial relationship, the stronger the purchase intention and eWOM intention of digital celebrities’ followers.
Misinformation-Aware Social Media: A Software Engineering Perspective

Misinformation is extensively and speedily spreading on social media platforms. This leads to sever negative impact on users of social media and the quality of online created content. Fortunately, there is a growing interest among researchers to fight misinformation on social media by the production of algorithms that can detect low quality information. However, none of these studies focus on how to promote a healthier behaviour among social media users to minimize the act of spreading misinforming. In this paper, the author advocates that gamification can be adopted for the aim of enhancing users’ behaviour towards misinformation and increasing their critical digital literacy. An empirical study was conducted to investigate users’ perceptions with regards to the use of several gamification elements on social media to combat misinformation spread. The results indicated that users’ preferences and perceptions vary and highlights the need for systematic and novel approaches to incorporate gamification into the design process of social media to combat misinformation spread. Based on the results, the author devised a conceptual framework that can serve as a guide for software engineers to design a gamified misinformation-aware social media.
A new adaptive sampling approach for Genetic Programming

Genetic Programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or distributed implementations. In this paper, we focus on dynamic sampling algorithms that mostly give to GP learner a new sample each generation. In so doing, individuals do not have enough time to extract the hidden knowledge. We propose adaptive sampling which is half-way between static and dynamic methods. It is a flexible approach applicable to any dynamic sampling. We implemented some variants based on controlling re-sampling frequency that we experimented to solve KDD intrusion detection problem with GP. The experimental study demonstrates how it preserves the power of dynamic sampling with possible improvements in learning time and quality for some sampling algorithms. This work opens many new relevant extension paths.
Method for Non-intrusive Electric Current Estimation with Concentrated Magnetic Field

Contactless current monitoring can play a vital role in electric power distribution side for applications such as power theft control. Operating current can be monitored by contactless magnetic field sensors around the lines. Magnetic flux concentrators (MFCs) are placed to concentrate the magnetic field generated by the current carrying conductor and amplify the magnetic field signals to the magnetic sensors in close vicinity. In this study, three sensors and three MFCs are placed to measure the magnetic field produced by three-phase HV side of the distribution system. Sensors are placed at a distance of 1.2 m from the conductors outside the induction zone. But magnetic fields are weak at this distance. If there is a small change in current through conductors, the resultant magnetic field will also be changed small and at a distance where magnetic fields are already weak, a small change in a magnetic field is difficult for the sensors to be detected. To amplify the weak magnetic field Rectangular shaped Mu metal material base MFCs are designed while the concentration effect can vary by changing the length of MFCs.
SpotDSQ: A 2D-Gel Image Analysis Tool for Protein Spot Detection, Segmentation and Quantification

The field of proteomics offers powerful methods for studying and analyzing protein expression levels in cells. The Two-Dimensional Gel Electrophoresis technique is a well-established proteomics technique focusing on protein separation and identification that provides digital images containing thousands of protein spots. 2D-gel images are then segmented into spots and background in order to quantify the expression levels of proteins located on a single gel or of differentially expressed proteins between samples from a series of 2D-gels. Spot detection and segmentation are complex and arduous tasks due to the inherent characteristics of 2D-gel images. Several software packages and experimental methods are available for 2D-gel image analysis, achieving different levels of success, each one having its respective advantages and drawbacks. A common characteristic is their dependency on user intervention in order to achieve optimal results, a process that leads to subjective and usually non-reproducible results. In this work, the authors present SpotDSQ, a software tool for 2D-gel electrophoresis image analysis that incorporates novel algorithms for accurate spot detection, segmentation and quantification. SpotDSQ provides high quality automatic protein spot detection, segmentation and quantification by employing novel algorithms that outperform state-of-the-art alternatives. Local automated multi-thresholding along with a modified version of the grow-cut segmentation algorithm is utilized in order to detect areas containing spots as well as the spot centers. The segmentation process is then guided by the information gathered during the detection step, utilizing the detected spot centers as seeds for a region growing approach that separates spot areas, while morphological operators are then utilized in order to accurately detect the protein spots boundaries. SpotDSQ offers an easy-to-use graphical user interface that requires no special training to operate. Results are exported as images and text data in order to facilitate further analysis. The performance of SpotDSQ was evaluated on real as well as synthetic 2D-gel images using well established statistical measures. Spot detection performance was evaluated by means of precision, sensitivity, and the F-measure. Volumetric overlap, volumetric error and volumetric overlap error were utilized for evaluating the segmentation performance. The high F-measure (94.8%) value, the low volumetric overlap error (8.3%), and the accurate spot boundaries achieved by SpotDSQ indicate its effectiveness compared to alternative methods. Experimental results show that SpotDSQ outperforms state-of-the-art software packages as well as methods proposed in the literature, achieving high accuracy and reduced errors. The advantages of SpotDSQ indicate that it has the potential to be a powerful and reliable tool for 2D-gel image analysis in biomedical laboratories.