Miracle-dz/UniFAR-FTUs huggingface dataset:
A Face Expression Recognition Using CNN & LBP

Visual interaction is an effective means of communication for human beings as social beings. Even a simple change in facial expression signifies happiness, sorrow, surprise and anxiety. The facial expressions of every person should vary in various contexts such as lighting, posture and even background. All these factors still remain an issue while recognizing facial expressions. This paper hopes to bring out a fair comparison between two of the most commonly used face expression recognition [FER] techniques and to shed some light on their precision. The methods being used here are Local binary patterns [LBP] and Convolution neural networks [CNN]. The LBP is meant as a method only for the purpose of extracting features so the Support vector machine [SVM] classifier is being utilized for classifying the extracted features from LBP. The dataset used for the purpose of testing and training in this paper are CK+, JAFFE and YALE FACE. About 70% is being utilized for training and the rest 30% is used in the testing. CNN gives better precision than any other dataset with 97.32% of recognition rate on CK+ dataset and also shows 31.82% of the YALE FACE dataset that is the least accurate we have.
Leveraging Phase Transition of Topics for Event Detection in Social Media

With the advancement of technology, many processes in our world have been reformulated, updated, and digitized. Therefore, interpersonal relationships have also been following this trend so that social networks have become increasingly present in our lives. Given this context, social network users create and share a large amount of data, from content about their daily lives, funny facts, as well as information about traffic, weather, and various subjects. The problem of event detection in social media, such as Twitter, is related to the identification of the first story on a topic of interest. In this work, we propose a novel approach based on the observation that tweets are subjected to a continuous phase transition when an event takes place, i.e., its underlying dynamic changes. Our proposal consists of a formal characterization of the phase transition that occurs when an event takes place, and the use of this characterization to devise a new method to detect events in Twitter, based on calculating the entropy of the keywords extracted from the content of tweets (regardless of the language used). We evaluated the performance of our approach using seven data sets, and we outperformed nine different techniques present in the literature. Unlike the work found in the literature, we present a theoretical rationale about the existence of phase transitions. For this, we characterize a model, already existing in the literature, of phase transitions described by differential equations, where we find correspondence between the model used in the study and the real data. The experimental results show that our proposal significantly improves the learning performance for the metrics used.
Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric Features

An important contribution to computer vision applications has been made by recognizing human emotion. Although it is very significant, this work considers the security of autistic people while in meltdown crisis by introducing a new system to warn caregivers through facial expressions detection. A precautionary approach has been taken to deal with meltdown crisis. Certainly, the indications of Meltdown are linked to abnormal facial expressions related to compound emotions. Actually, researchers thought long ago that Human Facial Expressions (HFE) are not able to express more than the seven basics emotions. HFE have been considered by psychologists as very complicated one, which can indicate two or even more emotions known as compound or mixed ones. A few studies have been done concerning Compound Emotion (CE). As well as, many difficult tasks to detect Compound Emotion Recognition (CER). In this paper, we empirically assess a group of deep spatio-temporal geometric features of micro-expressions of autistic children during a meltdown crisis. To achieve this goal, we make a comparison of the CER performance and diverse collections of micro-expressions features to select the features which best differentiates autistic children CE in meltdown crisis from normal state, and the best classifier performance. We record autistic children videos in normal and meltdown crisis using Kinect camera in serious circumstances. The experimental evaluation shows that the deep spatio-temporal geometric features and Recurrent Neural Network RNN with 3 hidden layer using Information Gain Feature Selection methods provide best performance (85.8%).
A Proposed Implementation of Raptor Code using a Microcontroller

This paper, discuss raptor code implementation using a microcontroller. The work considers raptor code constructed from serial concatenation between two codes; these two codes are error-correcting. The first code is the low-density parity-check code LDPC code, and the second is the Luby transform code LT code. This work focus on compatibility concept of serially concatenated codes. The compatibility between two codes considers the matching of the code rate between two serially concatenated codes. The work discusses multiple cases of code rates with different modulation schemes. The simulation of each case of serial concatenation investigated and analyses using MATLAB. The simulation results show that raptor code provides excellent performance of Bit error rate BER vs SNR through AWGN channel. This work also introduces the raptor code implementation using a microcontroller. The implementation includes both raptor code system transmitter and receiver. The implementation uses a bit flipping decoding algorithm in the system receiver. The raptor code implementation tested in both transmitter and receiver using random input data; the decoding process provides output precisely the same as the input message without errors.
Characterization of Intracellular Trafficking of Streptococcus agalactiae

OF THE THESIS Characterization of Intracellular Trafficking of Streptococcus agalactiae by Yvette W. Del Rosario Master of Science in Microbiology San Diego State University, 2014 Streptococcus agalactiae (Group B Streptococcus, GBS) is an important human pathogen because of its ability to cross the blood brain barrier (BBB) to cause meningitis in neonates. Previous studies have demonstrated that GBS can invade and transcytose through the cells that constitute the BBB to enter the brain. However, the exact mechanisms of intracellular trafficking have not been characterized. Previous electron micrographs have shown that when GBS invades brain endothelial cells, they reside in membrane-bound vesicles. Therefore, I hypothesized that GBS may be trafficked through the endocytic pathway, which can serve as an important host defense mechanism against intracellular pathogens. In order to examine if GBS is shuttled through this pathway, I infected human brain microvascular endothelial cells (hBMEC) with GFP-expressing GBS and stained for early and late endosomal markers, Rab5 and Rab7 respectively. GFP-GBS was shown to colocalize with early and late endosomes, as well as acidic vesicles. I have also shown that an important GBS response regulator CiaR, which promotes bacterial intracellular survival, acts to limit bacterial trafficking to acidic compartments and that one of the CiaR-regulated genes, SAN_2180, contributed to intracellular survival. Since approximately 50% of GBS was colocalized with acidic compartments, I hypothesized that selective autophagy may be induced in hBMEC to respond to GBS infection. An important indicator of autophagy is the microtubule-associate protein light chain 3 (LC3). LC3 exists in 2 forms: LC3-I, an inactive cytosolic form, and LC3-II, the active lipidated form that associates with the formation of the autophagosome; therefore, an increase in LC3-II conversion is indicative of autophagy activation. During GBS infection, I have observed an increase in LC3-II levels, indicating induction of autophagy. I also demonstrated that autophagy is a critical host defense mechanism against intracellular GBS, and that specific bacterial factors such as the βhemolysin/cytolysin may modulate this response. Further studies will focus on examining other host trafficking pathways that are not destined to fuse with lysosomes and further elucidate the various mechanisms associated with autophagy induction.
Cybernetic Diagnosis Approach for Publication Productivity of Malaysian Research University

It is essential for academic staffs to keep track of their publication records. Publication is seen to be the main resource for evaluation in the academic world. Besides, the publication criteria are mainly being evaluated for promotion and to access research grants from the institution or the government entities. The basis of this research is to unveil personal, behavioral and environmental factors using social cognitive theory (SCT) and by implementing the Viable System Model (VSM) as the diagnostic tool using cybernetic concept which is more viable to changes in the organization, which relate to key performance indicators (KPI) delivery on scholarly publications (SP). Thus, this study is aimed at investigating factors associated with publication productivity among the research university academic staffs by integrating VSM and SCT. This study applies qualitative method triangulated with quantitative method. The findings show that the most influential factors for the SP of Universiti Teknologi Malaysia (UTM) are age, gender, experience, rank, teaching load, collaboration, funding, resource, discipline and skill. The model was evaluated for the SP KPI monitoring process, which further can be used by public and private universities to improve the performance of their institution’s publication.
AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection

Deep Learning has been widely applied to problems in detecting various network attacks. However, no cases on network security have shown applications of various deep learning algorithms in real-time services beyond experimental conditions. Moreover, owing to the integration of high-performance computing, it is necessary to apply systems that can handle large-scale traffic. Given the rapid evolution of web-attacks, we implemented and applied our Artificial Intelligence-based Intrusion Detection System (AI-IDS). We propose an optimal convolutional neural network and long short-term memory network (CNN-LSTM) model, normalized UTF-8 character encoding for Spatial Feature Learning (SFL) to adequately extract the characteristics of real-time HTTP traffic without encryption, calculating entropy, and compression. We demonstrated its excellence through repeated experiments on two public datasets (CSIC-2010, CICIDS2017) and fixed real-time data. By training payloads that analyzed true or false positives with a labeling tool, AI-IDS distinguishes sophisticated attacks, such as unknown patterns, encoded or obfuscated attacks from benign traffic. It is a flexible and scalable system that is implemented based on Docker images, separating user-defined functions by independent images. It also helps to write and improve Snort rules for signature-based IDS based on newly identified patterns. As the model calculates the malicious probability by continuous training, it could accurately analyze unknown web-attacks.
Interconnection of Northern Canadian Communities to the Manitoba Hydro Power System: Evaluation of AC and DC Alternatives

The communities in the Northern region of Canada have been using local power generation methods based on fossil fuels for a considerable time. These communities have land access only during the winter months, which poses problems in transportation of fuel. Power demands of these regions are increasing and to cater the increasing demands, it is essential to connect these communities to the main grid. One option will be to extend the existing ac power system to these regions. Another option is to use an HVDC Lite option based on a multiport VSC system. This paper studies the technical feasibility of both the ac and dc options using computer simulations. Merits and disadvantages of the two alternatives are assessed and outlined based upon simulation-based study results.
Empirical Study on the Relationship Between Subjective Well-being of Employees and Social Performance in Social Enterprises

Social enterprises are emerging enterprises that adopt commercial methods to solve social problems. However, previous studies have not studied the impact of employee characteristics on organizational performance. Under the background of China, this paper use 52.social enterprises and 270 employees as samples to verify the relationship between emotional commitment and subjective well-being of employees in social enterprises and social performance of enterprises by using multilevel structural equation model (MSEM). The study found that: life satisfaction and emotional balance, have a significant role in promoting affective commitment, and also have a significant role in promoting social performance of enterprises; affective commitment plays a mediating role between life satisfaction, emotional balance and social performance of enterprises.
Storage as a Service (STaaS) Security Challenges and Solutions in Cloud Computing Environment: An Evaluation Review

Cloud Computing is an emerging technology which aims to provide efficient access to remote and distributed resources with the help of cloud services providers such as Amazon Googles Application, Microsoft Azure. Storage as a Service (STaaS) is among the most important and popular kind of services being investigated in the Cloud computing community to serve both private users and business enterprise with remote and scalable data storage that can be charged per customer usage. Providing safety, privacy and availability for the data that is hosted in cloud are the major concerns for users and businesses. This survey presents the data storage security concerns that arise due to the nature of cloud computing like multi-tenancy and virtualization, in addition to the details of the recent solutions presented in the literature to overcome the security issues in cloud storage. Cloud computing, Data Security, Storage as a Service
Modeling a nanometer FD-SOI transistor with a basic all-region MOSFET model

The suitability of a basic, long channel, compact, bulk transistor model coupled with look-up-tables (LUTs) for application to a 28 nm FD-SOI technology is evaluated through simulations. The parameters comprising the LUTs are extracted as a function of the channel length and back-plane voltage, with very simple standard procedures intended for long channel transistors. The resulting model proved to be a simple, but very accurate way to describe the gm/I $D$ curve in the moderate and weak inversion regions, with a straightforward analytical expression, even for minimum length transistors. This approach coupled with a LUT approach for the ID/ gds ratio, provides the main small signal model for design. It was also confirmed that reasonably accurate modeling of the intrinsic capacitances require a more complete modeling of the device.
Alternatives for Running Linux Applications in Windows

The Linux operating system is often used alongside Windows in computer labs in universities for research, laboratories and general purpose use. It is therefore important to investigate the characteristics of some of the possibilities available for students to run Windows and Linux applications, if possible at the same time, on the same computer. This paper compares the use of Windows Subsystem Linux, Docker, Cygwin and Oracle VM VirtualBox as solutions for running Linux guest applications in a Windows 10 host environment. The tests use simple C code that targets process creation, application performance and inter-process communication performance. The conclusions show the advantages and disadvantages of using each solution for running Linux applications in a Windows host.
Time Dependent Threshold Voltage Variability due to Random Telegraph Noise

With the downscaling of MOSFETs to nanometer dimensions, performance variability is produced by factors other than variations of physical dimensions and doping profiles, which are there since device fabrication and remain static over lifetime. Besides these time-zero variability factors, factors that lead to performance variability from one instant in time to the other start playing a significant role. Random Telegraph Noise (RTN) is among these relevant time-dependent variability sources. In this work we advance the state of the art on modeling of the time-dependent random variability induced by RTN, by providing an analytical model for the threshold voltage jitter σΔVT produced by RTN. Besides analytical modeling, Monte Carlo simulations are run to validate the model and illustrate the applicability of the model. The area scaling of σΔVT is detailed and discussed, supporting designers in transistor sizing towards a more reliable design.
Social Degeneration through Social Media: A Study of the Adverse Impact of ‘Memes’

In this day and age of omnipresent electronic social media, ‘memes' have become a widespread means of communicating ideas, opinions and even news. Such memes are widespread and often produced by groups and organizations on social media. However, the credibility and impact of such memes is under-investigated in the literature. The intention of this paper is to evaluate such memes to study its impact on society. This paper reviews the literature on the ill effects of social media and particularly the use of memes in it. The paper, then, based on literature and logic posits that such memes can often be detrimental to society in a number of ways; which it identifies as ‘effects'. This paper attempts to explain in detail how memes can adversely impact our society through the abovementioned effects and the future of the society going forward with such memes. The primary contribution of this paper that it investigates a dark aspect of a popular tool used widely in social media for communication and explains how this tool can be seriously detrimental for the development of society. In a future paper, the intention is to test the hypotheses using empirical data using a survey of individuals using social media from different sections of the society in terms of age, gender, nationality and occupations, to find evidence of support for the claims posited in this paper. It is also intended to find mechanisms to monitor and censor such memes to avoid the adverse impact they may have on the society.
Effort Estimation Problems in Software Maintenance – A Survey

The software industry has been evolving over the years. Software maintenance is as crucial as software development. Software maintenance cost has gained a significant important both for practitioners and researchers. Software maintenance cost is very crucial to estimate the productivity of the software, reliability, adaptability, and control. This research investigation is a study that is based on the literature of investigation conducted previously in the field of software maintenance. We have found studies that reveal the effort estimation problems related and have a direct impact on software maintenance. We have also identified problems that have a direct impact on software maintenance. This study will act as a guideline for researchers to counter these problems. This study will act as a platform to classify new direction and initiate full-scale research.
A Survey of Current Datasets for Code-Switching Research

Code switching is a prevalent phenomenon in the multilingual community and social media interaction. In the past ten years, we have witnessed an explosion of code switched data in the social media that brings together languages from low resourced languages to high resourced languages in the same text, sometimes written in a non-native script. This increases the demand for processing code-switched data to assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc. The available corpora for code switching research played a major role in advancing this area of research. In this paper, we propose a set of quality metrics to evaluate the dataset and categorize them accordingly.
Soft Magnetic Tactile Skin for Continuous Force and Location Estimation Using Neural Networks

Soft tactile skins can provide an in-depth understanding of contact location and force through a soft and deformable interface. However, widespread implementation of soft robotic sensing skins remains limited due to non-scalable fabrication techniques, lack of customization, and complex integration requirements. In this work, we demonstrate magnetic composites fabricated with two different matrix materials, a silicone elastomer and urethane foam, that can be used as continuous tactile surfaces for single-point contact localization. Building upon previous work, we increased the sensing area from a 15 mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> grid to a 40 mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> continuous surface. Additionally, new preprocessing methods for the raw magnetic field data, in conjunction with the use of a neural network, enables rapid location and force estimation in free space. We report an average localization of 1 mm<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> for the silicone surface and 2 mm<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> for the urethane foam. Our approach to soft sensing skins addresses the need for tactile soft surfaces that are simple to fabricate and integrate, customizable in shape and material, and usable in both soft and hybrid robotic systems.
An Enhanced Digital Image Processing based Dehazing Techniques for Haze Removal

The proposed research paper explore Haze Removal a specific aspect of digital image processing. Visual images are often adversely affected by haze arising out of natural atmospheric phenomena such as moisture, aerosols, dust etc. or human action-smoke from fires, industrial emissions etc. Apart from these atmospheric factors, image quality could also be impacted by the positioning of the camera. Degradation of image quality can seriously affect critical decision making andleadeven to serious accidents. Our focus is to examine, characterize and quantify the difference of a clear digital image from a hazeaffected one and also to improve haze-affected images. We consider color images as well as grey scale ones. We apply the Dark Channel prior method in conjunction with approxdcp algorithm of Imreduce.