Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Facial Expression Recognition System Using Artificial Neural Network Classification of Gabor Feature Based Facial Expression Information

Facial expressions contribute highly in conveying the feelings of a person. An emotion recognition system through facial expression recognition is proposed in this paper. Preprocessed input images are segmented into four facial expression regions by following the proposed highly effective image segmentation method. 2D Gabor filter with different frequencies and orientations are used to extract features from the segmented parts. Reduction of the dimension of the extracted features is done using downsampling and Principal Component Analysis (PCA). Classification of the features is done using the artificial neural network (multilayer perceptrons with backpropagation). To evaluate the performance of the proposed method three widely used facial expression datasets (JAFFE, CK+, RaFD) are used. Performance on these datasets by the proposed method is compared with the performance on these datasets by other methods to indicate that state-of-the-art performance is achieved by the proposed method.
Auto-Encoder Based Nonlinear Dimensionality Reduction of ECG data and Classification of Cardiac Arrhythmia Groups Using Deep Neural Network

In this paper, we propose a neural network based dimensionality reduction approach to classify the four different categories of cardiac arrhythmias such as 'Normal', 'Bradycardia', 'Tachycardia' and 'Block' using MIT-BIH Arrhythmia database. We designed a nonlinear auto-encoder with three hidden layers and applied it to a dataset of ECG signal. It was found that our method is able to compress the data with less reconstruction error than that of linear transformation such as Principal component analysis. Using a deep neural network, we get the best accuracy (92.1%) for classifying the cardiac arrhythmia groups into four categories compared to Ensemble of Binary Support Vector Machine Decision Tree and Multilayer Perceptron (MLP) feed-forward Neural Network with back-propagation. The classifier performance is evaluated using Positive predictive Value (Precision), False discovery Rate, True Positive Rate (Recall) and False Negative Rate. This method has a great importance for researcher to predict the potential cardiac disease before it is too late.
On-Chip Static Phase Difference Measurement Circuit With Gain and Offset Calibration

A gain and offset-calibrated 3-state phase detector is proposed to accurately measure static phase difference. Forcing the phase detector to operate in two different modes provides the full-scalegain since the sum of the two results is <inline-formula> <tex-math notation="LaTeX">${2}{\pi }$ </tex-math></inline-formula>. Flipping the inputs and averaging the results cancels the offset. Proposed circuit enhancements realize a robust reset and a gain, independent of waveform rise/fall times. A <inline-formula> <tex-math notation="LaTeX">$0.13-\boldsymbol {\mu }\text{m}$ </tex-math></inline-formula> CMOS prototype of the proposed enhanced phase detector has errors ≤ 0.5° for 2.4-GHz quadrature inputs. It consumes 3.6mA from 1.2V and occupies 0.0416 mm<sup>2</sup>. The proposed circuit can be used for on-chip measurement and calibration of multi-phase clock generators in radios and clocking circuits.
On Empirical Evaluation of Deep Architectures for Indonesian POS Tagging Problem

Models with deep architectures have been state-of-the-arts technologies in many natural language problems such as text classification, name entity recognition, language models, and Part-of-Speech (POS) tagging. Usually, the models are trained with large number of data to produce satisfactory results. In Indonesian POS tagging problems, we must deal with small number of data. In this paper, we evaluate models with deep architectures for Indonesian POS tagging problems to find the best structures for Indonesian POS tagging. Models with various number of hidden layers are investigated. We also investigate the effect of adding a regularization method such as dropout on the performance. The experimental results show that the model with 2 hidden layers shows to have better accuracy than the models with deeper structures.
Detection of Flow Based Anomaly in OpenFlow Controller: Machine Learning Approach in Software Defined Networking

Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential in shaping the future of networking by separating control plane from data plane. OpenFlow is the first and most widely used protocol that makes this separation possible in the first place. As a newly emerged technology, SDN has its inherent security threats that can be eliminated or at least mitigated by securing the OpenFlow controller that manages flow control in SDN. SDN provides us a chance to strengthen our network security by decoupling its control plane and data plane. At this level, there also exists some certain risk, which is associated with this technology. In this research, a flow based anomaly detection method in OpenFlow controller have been approached by using machine-learning algorithms in SDN architecture. In order to improve the classifier performance, some feature selection methods namely Info Gain, Gain Ratio, CFS Subset Evaluator, Symmetric Uncertainty, and Chi-square test have been applied as a processing of dataset. A full dataset of 41 features and a reduced dataset has experimented. A dataset with feature selection ensures the highest accuracy of nearly 82% with Random Forest classifier using Gain Ratio feature selection Evaluator. Experimental results ratify that machine-learning approach with feature selection method indices strong potential for the detection of flow based anomaly in OpenFlow controller.
A 0.53-THz Subharmonic Injection-Locked Phased Array With 63- $\mu$ W Radiated Power in 40-nm CMOS

This paper presents a 0.53-THz subharmonic injection-locked <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> phased array based on an injection-locked oscillator (ILO) chain. Thanks to the ILO chain, the phase errors in the phased array can be compensated without introducing power variation. This technique enables accurate beam steering with a 60° scan range in the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane at 0.53 THz. A six-stage triple-push oscillator is proposed to generate the signal at 0.53 THz. Compared with a conventional triple-push oscillator, it reduces layout constraints, improves signal balance, and enhances output power by at least 3 dB. The phased array is implemented in 40-nm CMOS. With an injection power of 0 dBm, the injection-locked phased array generates an output frequency from 529 to 534 GHz. At 531.5 GHz, the proposed phased array achieves −12-dBm radiated power and 0.24‰ dc-to-terahertz (THz) efficiency without the use of silicon lens, quartz superstrate, or substrate thinning. The chip consumes 260-mW dc power and occupies an area of 2.5 mm<sup>2</sup>.
Enhancing Gender Balance in Engineering Education and Practice

Despite the rapid development brought about by the exponential expansion of engineering in technological scope and societal impact that are quite noticeable, the level of representation of women in this unique profession is still very low. Research has shown that few women study engineering at the tertiary institution or university-level with less than 25% of total enrollment, pursuing an engineering career after graduation. Thus, the underutilised pool of talent that women represent may actually limit access to a larger pool of future engineers, entrepreneurs, innovators, etc., and hence a limiting access to a more diverse talent pool. This situation has resulted in impaired creativity and innovation, which is the essence of engineering. This paper therefore reviews the issue of gender balance in engineering education and practice, with the main objective of highlighting the likely causes of the disparity and how it can be alleviated, in order to facilitate and enhance gender balance in engineering education, practice and the profession at large. Closing the gender gap in engineering enrollment and practice will require a lot of re-appraisal of existing strategies on the part of stakeholders. Delimiting policies, primordial cultures, curricula, etc., may have to be changed and career paths made more welcoming in order to enhance women enrollment and participation in engineering.
Virtual Gaming

Brain-Computer Interface (BCI) is a progressing area that has been adding this whole new dimension of capability to HCI. BCI has created a unique communication channel, mainly for the ones who're suffering from ADHD (Attention Deficit Hyperactivity Disorder). The ADHD patient finds it difficult to pay attention and control their impulsive behaviors. As a result, they become restless, being overactive and act without thinking. Traditional treatments like medication or pharmacotherapy by psycostimulants have been proved inadequate to decrease hyperactivity level. In consideration of these issues, we proposed a solution called Virtual Gaming. Proposed solution comprises of a video game named “Mind Race” and the EEG (electroencephalogram) headset, which safely measures brainwave signals and monitors the concentration and attention levels of users as they interact with the system in order to play the game. The proposed solution aims to provide entertainment-therapy to the patients of ADHD to increase their attention, focus and concentration.
Designing Website E-Learning Based on Integration of Technology Enhance Learning and Human Computer Interaction

Proof of integration between Technology Enhanced Learning and Human-Computer Interaction requires a significant evaluation, Collaboration method was developed in the form of an e-learning website. The evaluation is done with WebQual 4.0 and modeled in several variables. Based on the evaluation obtained e-learning website sasmoko.com can be said enough quality significantly at $\alpha$ <0.05 and the most decisive improvement of e-learning website sasmoko.com is on the usability.
dnyNSA: A Novel Real-Value Based Negative Selection Algorithm

Real-value based negative selection algorithm (NSA) is an important detector generation algorithm of Artificial Immune System. Traditional real-value based negative selection algorithm generates immune detectors randomly and does not consider the sample distribution, therefore too many candidate detectors overlap in the feature space resulting in excessive redundancy. This leads to a low efficiency of detector generation and decreases the performance of NSA. To aim at this problem, the paper proposes a novel real-value negative selection algorithm based on Delaunay triangulation (dnyNSA). Firstly, dnyNSA uses Delaunay triangulation method to analyze the topological structure of training dataset and produces a set of simplicial cells (triangle in 2-dimensional space and tetrahedron in 3-dimensional space, etc.). Then, based on the simplicial cell circum-circle(-sphere) property, dnyNSA generates detectors in more reasonable position and size. Therefore, it only needs a small number of detectors to cover the same feature space, avoiding the generation of redundant detectors in traditional random mode, which greatly improves the efficiency of NSA. Theoretical analysis showed the time complexity of dnyNSA is in logarithmic level. The experimental results showed that, compared with the RNSA and V-Detector, dnyNSA can raise the detector generation efficiency over ten times while maintain similar detection performance.
Performance Analysis of Peak to Average Power Ratio (PAPR) Reduction Techniques in OFDM System for Different Modulation Schemes

With the increasing demand for high power efficiency, robustness to multipath delay spread and high transmission speed, OFDM has become one of the most promising techniques for next generation wireless communication systems. However, high peak to average power ratio is a major limitation of OFDM system which makes the overall system less effective. Various methods are proposed by the researchers to reduce high PAPR. In this paper, we review and analyze clipping and filtering, exponential companding (EC), selected mapping (SLM) and partial transmit sequence (PTS) techniques and make a comparison among them to identify the best solution for reducing PAPR. Moreover, we change different modulation schemes such as QPSK, BPSK and 16 QAM to observe the effect of modulation on the PAPR reduction techniques.
Stabilization of switched systems with input delay and saturation: a parametric multiple Lyapunov equations approach

This paper investigates the stabilization problem of switched linear systems with input delay and saturation. By the parametric multiple Lyapunov equations (PMLEs) approach, the parametric low-gain feedback controller and a hysteresis switching law are designed to guarantee the switched systems to be semi-global asymptotically stable. It is possible by adjusting the low-gain parameter to stabilize the system without exceeding the saturation limit. Moreover, the low-gain feedback controller is explicitly constructed. An illustrative example is concerned to demonstrate the effectiveness of the obtained results.
A Hybrid Deep Learning Model to Predict Business Closure from Reviews and User Attributes Using Sentiment Aligned Topic Model

Business closure is a very good indicator for success or failure of a business. This will help investors and banks as to whether to invest or lend to a particular business for future growth and benefits. Traditional machine learning techniques require extensive manual feature engineering and still do not perform satisfactorily due to significant class imbalance problem and little difference in the attributes for open and closed businesses. We have used historical data besides taking care of the class imbalance problem. Transfer learning also has been used to tackle the issue of having small categorical datasets. A hybrid deep learning model has been proposed to predict whether a business would be shut down within a specific period of time. Sentiment Aligned Topic Model (SATM) is used to extract aspect-wise sentiment scores from user reviews. Our results show a marked improvement over traditional machine learning techniques. It also shows how the aspect-wise sentiment scores corresponding to each business, computed using SATM, help to give better results.
Enhancing the Quality of Phrase-Table in Statistical Machine Translation for Less-Common and Low-Resource Languages

The phrase-table plays an important role in traditional phrase-based statistical machine translation (SMT) system. During translation, a phrase-based SMT system relies heavily on phrase-table to generate outputs. In this paper, we propose two methods for enhancing the quality of phrase-table. The first method is to recompute phrase-table weights by using vector representations similarity. The remaining method is to enrich the phrase-table by integrating new phrase-pairs from an extended dictionary and projections of word vector presentations on the target-language space. Our methods produce an attainment of up to 0.21 and 0.44 BLEU scores on in-domain and cross-domain (Asian Language Treebank - ALT) English - Vietnamese datasets respectively.
On the Accuracy of Inter-Vehicular Range Measurements Using GNSS Observables in a Cooperative Framework

Many vehicular applications such as navigation, collision avoidance, and location-based services envisioned under intelligent transportation system (ITS) framework require position information with certain accuracy and reliability. Global navigation satellite system (GNSS) are widely deployed for absolute and relative positioning in a wide range of ITS applications due to their global coverage but these systems have limited accuracy and availability to meet the requirement of many safety-critical applications. This limitation has led to the development of cooperative positioning (CP) methods in a vehicular ad-hoc network. CP methods are based on the assumption that the inter-vehicular ranges are available through some radio ranging method, such as received signal strength, time of arrival, or time difference of arrival. However, the feasibility and accuracy of these radio ranging methods is limited due to the constraints of communication medium and rapidly changing vehicular environment. In this paper, we propose a generalized version of a theoretical framework for the measurement and analysis of inter-vehicular ranges by exchanging GNSS observables between vehicles. More specifically, we investigate the utilization of GNSS observables in the following four categories for range measurement: 1) absolute positions of the vehicles provided by GNSS receivers; 2) raw code pseudoranges; 3) single difference of the raw code pseudoranges; and 4) double difference of the raw code pseudoranges. The developed theoretical framework compares the accuracy of inter-vehicular range measurement using these four observables. This framework is further validated by actual field trials in different mobile environments using two vehicles equipped with GNSS receivers.
A Ka Band FMCW Transceiver Front-End With 2-GHz Bandwidth in 65-nm CMOS

A fully integrated Ka band frequency modulation continuous wave single-chip radar transceiver front-end is proposed in 65-nm CMOS technology. By utilizing VCO with multi-biasing varactor banks, the critical bandwidth and linearity have been improved. To address the challenges of losses and complexities in local oscillator (LO) distribution chain, a three-way transformer-based power divider is proposed and implemented to split the input differential signals to three outputs, in three orthogonal directions. The poly phase filter is also used to obtain the in-phase and quadrature LO signals. To minimize the reflections between the poly phase filter and the following stages, a 1:2 transformer is inserted to provide sufficient matching. For the receiver part, differential cascode amplifier with neutralization capacitors is implemented in the low noise amplifier to obtain gain and stability. By utilizing double balanced passive mixers, noise performances of I/Q paths are improved at low intermediate frequency. The transceiver demonstrates 4.4-dBm output power, 2-GHz bandwidth with 7.5-cm range resolution, and 5.9–7.5 dB double side band noise figure at 4 MHz. The whole transceiver front-end consumes 132 mW from 1.2 V and 2.16 mm2 die size.
Study of Application Interface based on User’s Perception Using Kansei Approach

Users’ psychological emotion plays important role in designing an interface for web based information system. This research attempted to adopt Kansei Engineering Type I (KEPack) as a method o analyze kinds of emotional factors related to user interface of web based information system in case of children control information system. Adjective words were used as Kansei Words to show users’ implicit requirements based on psychological factors. Eighteen words were selected as Kansei Words to measure the impact of users’ emotional factors through the appearance of candidate interface for this information system, and ten samples of mobile information system were selected as specimens considered suitable for designing interface of system. Data questionnaires were collected from thirty respondents, and processed by Kansei Analysis using multivariate statistical analysis such as Factor Analysis. This research found that there were four important emotional factors such as funny, unique, cheerful and informative that have to be considered as main psychological factors in designing the user interface for this kind of information system.
Modified Generalized Random Grids-Based Progressive Secret Sharing with Lossless Ability for Binary Image

This paper proposes a new technique on progressive secret sharing which extends the generalized random grids to generate a set of shared images. The proposed method has lossless ability in the recovery process of secret image. This approach exploits the simplicity of bitwise-based computation. As reported in the experimental section, the proposed method yields better results compared to the former existing scheme under visual investigation and objective assessment on the recovered secret image. Yet, it can be regarded as good candidate for applying the progressive secret sharing with lossless reconstruction constraint.
Distributed Finite-time Lag-consensus for Second-order Nonlinear Multi-agent Systems with Disturbances

The finite-time lag-consensus problems of second-order multi-agent networks which have inherent nonlinear dynamics and bounded external disturbances are studied in this paper. Lag consensus here means the followers can follow with the leader‘s trajectory with a time delay so as to avoid congestion. For the leader-following case, we construct a novel consensus control protocol with the relative information of velocity and position, and it is proved that all agents can reach finite-time lag consensus by Lyapunov theory when the communication is directed. Furthermore, the finite convergence time is explicitly estimated. Finally, a simulation example is presented to show the validity of theoretical results.
Computing Tasks Distribution in Fog Computing: Coalition Game Model

Files and medias are not the only existing content. Computing tasks output content that can be described, stored, cached and offloaded. In this paper, we investigated the problems of edge computing and offloading in fog computing networks. Edge devices have always been limited in computing capabilities. The problem of latency occurs frequently when having huge amount of data to be processed. In Considering a given number of fogs serving multiple users, this paper investigates the problems of distributing computational tasks among the fogs. We propose a coalition game based solution to motivate the fogs to work together and cooperate, which in turn processes huge amounts of data within a short delay. Simulation results shows that the proposed model offers better latency and improved load balancing of the fogs resources.
The magic of projection : augmentation and immersion in media art

Sophie Ernst’s doctoral thesis is an artist’s contribution to media art theory. It focusses on the role of projection as material for sculpture. Her research addresses the question in what manner are projections applied in contemporary art and what image traditions does this relate to. She considers projections to be either immersive, like a cinematic experience, or augmentative, in the sense of a mixed reality. Immersions, the dominant mode in projection art and large parts of the theoretical discourse, presuppose a willing suspension of disbelief. Augmentation, on the other hand, can be seen as ‘magical’. It is a technique in art to ‘make strange’ by creating a distance that can be either pleasant or unsettling. Ernst argues that augmenting projections are persuasive, not because they are materially ‘real’, but rather since they make visible what we could imagine as real.
Human Body Odor Based Authentication Using Machine Learning

The wearable devices become very popular in the people’s daily lives. Because the wearable device is close to human body and the human biometric is unique for individuals, more and more wearable devices use the biometric parameters such as fingerprint, face, voice, iris, and hand geometry to perform authentication. But the above authentication methods require human intervention to enter authentication information scanning human body. This paper proposes a new biometric authentication method using human body odor, which makes the authentication more convenient and effective. We show that the human odor based authentication is accurate since every human body odor is determined by the major histocompatibility complex genes through some measures using gas sensors and machine learning techniques. In our analysis, we use three machine learning methods: K-means, Principal Component Analysis, and Neural Network, and show that the authentication accuracy using human body odor is very high as a result of experiments.