Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Machine Learning Techniques for Emotion Recognition from Peripheral Physiological Signals

Recent developments in wearable technology have led to increased research interest in using peripheral physiological signals for emotion recognition. The non-invasive nature of peripheral physiological signal measurement via wearables enables ecologically valid long-term monitoring. These peripheral signal measurements can be used in real-time in many ways including health and emotion classification. This paper investigates the utility of peripheral physiological signals for emotion recognition using the publicly available DEAP database. Using this database (which contains electroencephalogram (EEG) signals and peripheral signals), this paper compares eight machine learning models in the classification of valence and arousal emotion dimensions. These were applied to the peripheral physiological signals only. These models operate on three groupings of the peripheral data: (i) the raw peripheral physiological signals; (ii) individual feature sets extracted from each peripheral signal; and (iii) a fusion data set made of the combined features from the individual peripheral signals. The results indicate that support vector machine, linear discriminant analysis and logistic regression give the best recognition results on all three data groups considered. The feature fusion data set, which is made up by fusing all the features from the peripheral signals, gives the best recognition accuracy on both valence and arousal dimensions. In addition, subject dependency for emotion classification from peripheral signals is examined and significant individual variability is observed. The recognition rate varies between each participant from 10% to 87.5%.
Exploiting Manipulated Small Extracellular Vesicles to Subvert Immunosuppression at the Tumor Microenvironment through Mannose Receptor/CD206 Targeting

Immunosuppression at tumor microenvironment (TME) is one of the major obstacles to be overcome for an effective therapeutic intervention against solid tumors. Tumor-associated macrophages (TAMs) comprise a sub-population that plays multiple pro-tumoral roles in tumor development including general immunosuppression, which can be identified in terms of high expression of mannose receptor (MR or CD206). Immunosuppressive TAMs, like other macrophage sub-populations, display functional plasticity that allows them to be re-programmed to inflammatory macrophages. In order to mitigate immunosuppression at the TME, several efforts are ongoing to effectively re-educate pro-tumoral TAMs. Extracellular vesicles (EVs), released by both normal and tumor cells types, are emerging as key mediators of the cell to cell communication and have been shown to have a role in the modulation of immune responses in the TME. Recent studies demonstrated the enrichment of high mannose glycans on the surface of small EVs (sEVs), a subtype of EVs of endosomal origin of 30–150 nm in diameter. This characteristic renders sEVs an ideal tool for the delivery of therapeutic molecules into MR/CD206-expressing TAMs. In this review, we report the most recent literature data highlighting the critical role of TAMs in tumor development, as well as the experimental evidences that has emerged from the biochemical characterization of sEV membranes. In addition, we propose an original way to target immunosuppressive TAMs at the TME by endogenously engineered sEVs for a new therapeutic approach against solid tumors.
An Efficient Privacy-Preserving Multi-Keyword Query Scheme in Location Based Services

With the proliferation of location-aware mobile devices and the prevalence of wireless communications, location-based services (LBS) have attracted much particular attention in recent years. For flexibility and cost savings, the LBS provider outsources the LBS data to the cloud in order to serve the increasing number of mobile users. To guarantee users’ privacy and data confidentiality, some excellent works have been proposed which focus on secure query over the location server. However, these existing works have two limitations. On the one hand, they cannot preserve users’ location and query content privacy simultaneously. On the other hand, they fail to support multi-keyword queries. In this article, aiming at a multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the data users. To improve the efficiency of our proposed scheme, we utilize the linear quad-tree technique to build a grid system to represent the location information in the query condition as well as a searchable index. To protect the location privacy, we combine decimal Morton code and public-key cryptography techniques to build a searchable index or to generate a trapdoor. To enable the cloud server to perform a secure multi-keyword query, we systematically construct a privacy-preserving query scheme with bilinear pairing-based cryptography. In particular, our proposed scheme is scalable and very suitable for multi-user environments due to the flexible user registration and revocation mechanisms. Furthermore, a detailed security analysis shows that the proposed scheme can ensure the confidentiality of LBS data, and protect the location and query content privacy. Extensive experiments are conducted on a real LBS dataset, and the simulation results confirm the security and efficiency of our scheme.
O^2PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection

Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, they still commonly present low accuracies and demand considerable time and effort from professionals. Therefore, a computer-aided diagnosis (CAD) system capable of providing early detection becomes hugely desirable. In the last decade, machine learning-based techniques have been of paramount importance in this context, since they are capable of extracting essential information from data and reasoning about it. However, such approaches still suffer from imbalanced data, specifically on medical issues, where the number of healthy people samples is, in general, considerably higher than the number of patients. Therefore this paper proposes the O2PF, a data oversampling method based on the unsupervised Optimum-Path Forest Algorithm. Experiments conducted over the full oversampling scenario state the robustness of the model, which is compared against three well-established oversampling methods considering three breast cancer and three general-purpose tasks for medical issues datasets.
High Efficiency Scheme For Permutation Index Differential Coherent Chaos-based Communication System

In this paper, a developed version of Permuated Index-Differential Chaos Shift Keying (PI-DCSK) is presented and initially tested in AWGN channel environment. Information bearing signal is generated by mapping first set of information bits to the reference signal while the second set is mapped to the permuated-time reversed version of same reference signal. Both information bearing signals are added together on the same time slot and sent after the reference signal. At the receiver, each incoming signal undergoes two operations; permutation and time reversal followed by permutation to detect the two different sets of information. System performance is evaluated analytically to predict the Bit Error Rate (BER) using computer simulations. Results show that system performance is notably enhanced with respect to standard chaos based systems. Moreover, the analytical form which are developed to predict the BER is validated by simulation. Result demonstrate that theoretical performance in AWGN is very closely matching with the simulation result particularly at high SNR.
Correlations between Serum Interleukins-2,-4 Levels and Some Biochemical Parameters in Iraqi Patients with Osteoporosis

Background: Osteoporosis is a frequent disease that is manifested by reduced in mineral density and raised in fracture risk. Recent studies have indicated that osteoporosis is caused by composite connections among local and systemic regulators of bone cell function. Objective: The purpose of this study was to investigate the relationship between interleukin-2, interleukin-4, and some biochemical markers in Iraqi patients with osteoporosis. Patients and Methods: Forty five osteoporotic patients were incorporated in this study (30 women and 15 men). Serum fasting glucose, lipid profile, alkaline phosphatase activity, calcium, magnesium, interleukin-2, and interleukin-4 were measured in osteoporotic patients and compared them with the control group. Results: There was a significant increase in serum fasting glucose, lipid profile except high density lipoprotein cholesterol, and serum alkaline phosphatase activity in osteoporotic patients as compared to the control, (P=0.001). Also, there was a significant increase in interleukin-2 and interleukin-4 in osteoporotic patients as compared to the control, (P= 0.01). While there was a decrease in serum calcium and magnesium levels in osteoporotic patients as compared to the control. Also, there was a significant positive correlation between serum alkaline phosphatase activity with interleukin-2 and interleukin-4 levels in osteoporotic patients, (P= 0.01). Conclusion: Elevated levels of interleukin-2 and interleukin-4 along with some biochemical markers like; serum alkaline phosphatase in osteoporotic patients might include significant functions in the pathogenesis of osteoporosis.
Relationship between heart rate and metabolism in the hyrax (Procavia Capensis) and guineapig (Cavia porcellus)

The relationship between heart rate and metabolism was investigated in the hyrax Procavia capensis and the guineapig Cavia porcellus. In both animals a positive linear relationship was found described by Vo2(ml 02(g.h)-1)=0,003157 (HR) + 0,3678 for the hyrax and Vo2(ml 02(g.h)-1)=0,00757 (HR) -0,4914 in the guineapig. The response to lowered ambient temperature differs between the species, the guineapig increases metabolic rate and maintains body temperature while the hyrax allows body temperature to drop while the increase in metabolic rate is relatively small. It is postulated that this response by the hyrax is an energy conserving strategy.
Clock Synchronization with Adaptive Weight Factor for Mobile Networks

This paper deals with high mobility mobile ad-hoc networks (MANETs) whose nodes need to be synchronized. Unlike existing solutions, which either use reference clock or broadcast synchronization messages, our algorithm achieves synchronization by sending zero-bit pulses over the network and is thus usable in the first steps of establishing MANETs. We develop a dynamic adaptive selection technique for choosing an exponential smoothing factor and show the impact of using a fixed value of this parameter on the performance of the algorithm. In addition to that, we demonstrate the strength of our algorithm to withstand the network topological changes during execution. We evaluate the algorithm through extensive simulation in vehicular ad-hoc networks (VANETs). The simulation results show significant performance improvement in terms of reducing the clock offset and drift over the previous works.
DuCaGAN: Unified Dual Capsule Generative Adversarial Network for Unsupervised Image-to-Image Translation

With the appearance of Generative Adversarial Network (GAN), image-to-image translation based on a new unified framework has attracted growing interests. As a new technique, it can generate synthesizing images for various requirements in both computer vision and image processing. However, the cycle consistent structure adopted in some common models, such as cycle generative adversarial network (CycleGAN), is usually unable to learn more abundant image features. In this work, we developed a novel model based on GAN, named as dual capsule generative adversarial network (DuCaGAN), by utilizing the distinctive characteristic of view angle invariance and rotation equivariance in capsule network. Firstly, two capsule networks were introduced into the traditional CycleGAN model as discriminators to form our proposed model with six agents. To improve the feature capturing performance, we modified the full objective by combining the margin loss and the original adversarial loss. Furthermore, the Routing Algorithm in the capsule network was optimized by changing its compression function. Finally, experimental results on conventional visual tasks with paired and unpaired datasets demonstrated the superiority and effectiveness of the proposed approach compared to both deep convolutional generative adversarial network (DCGAN) and CycleGAN methods. More importantly, the proposed DuCaGAN was applied for the first time to augment the surface defect data from the real industrial field, and exhibited better performance than those methods available.
Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey

Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.
Aspect based Fuzzy Logic Sentiment Analysis on Social Media Big Data

Social Media is good information medium for disclosing information of products and services of E-Commerce Business. This information is provided by customers themselves on their purchased product. Aspect based sentiment analyzes of feature/specification quoted by the customers. Such specifications/reviews are available in Twitter, Flipkart websites. This research work considered reviews of features/specifications on Twitter and Flipkart websites. Hence this work focused on analyzing the problems of customer on buying quality products. This work is automated to extracts the semantic based aspects or features and their opinion for the task of analyzing comments. Fuzzy logic and NLP are employed in this work with consider all categories of comments to measure their given weightage by customer tweets. For experimentation real time Twitter data and Flipkart data has been used. Experimental result and performance analysis report proved that aspect based sentiment analysis on social data on this work is performing well. In this work hidden information about the product specification has been retrieved and produced concise information to customers perceives about the product specification and eases the customers to take decision on the purchase quality product. And also assist the business organization to take decision on the product specification and improve their business according to their customer’s opinion and expectation.
Effectiveness of a Problem-Solving, Story-Bridge Mental Health Literacy Programme in Improving Ghanaian Community Leaders’ Attitudes towards People with Mental Illness: A Cluster Randomised Controlled Trial

Abstract In Ghana, people with mental disorders commonly experience negative attitudes and discrimination because of deep-rooted public stigma. The aim of the study was to assess the effectiveness of a mental health literacy programme in improving community leaders’ attitudes toward people with mental disorders. A cluster randomised controlled trial, comprising an intervention and control group, participated in a 3-hour problem-solving, Story-bridge mental health literacy programme. Data were collected at baseline and 12-week follow-up. The intervention group performed better in most outcome measures at follow-up compared to the control group. There were statistically significant differences between the two groups, in perceived stigma, community mental health ideology (CMHI), and benevolence outcome measures over the two time-points. Overall, the findings suggest that the programme was somewhat effective in improving community leaders’ attitudes and who might, subsequently, foster supportive, non-judgemental and empathetic attitudes toward individuals with mental disorders in their communities. There is scope for community psychiatric nurses and other primary health care workers to work with community leaders to increase public awareness of, and favourable attitudes toward, people with mental health problems in the community.
Mapping genome variation of SARS-CoV-2 worldwide highlights the impact of COVID-19 super-spreaders

The human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the major pandemic of the twenty-first century. We analyzed more than 4700 SARS-CoV-2 genomes and associated metadata retrieved from public repositories. SARS-CoV-2 sequences have a high sequence identity (>99.9%), which drops to >96% when compared to bat coronavirus genome. We built a mutation-annotated reference SARS-CoV-2 phylogeny with two main macro-haplogroups, A and B, both of Asian origin, and more than 160 sub-branches representing virus strains of variable geographical origins worldwide, revealing a rather uniform mutation occurrence along branches that could have implications for diagnostics and the design of future vaccines. Identification of the root of SARS-CoV-2 genomes is not without problems, owing to conflicting interpretations derived from either using the bat coronavirus genomes as an outgroup or relying on the sampling chronology of the SARS-CoV-2 genomes and TMRCA estimates; however, the overall scenario favors haplogroup A as the ancestral node. Phylogenetic analysis indicates a TMRCA for SARS-CoV-2 genomes dating to November 12, 2019, thus matching epidemiological records. Sub-haplogroup A2 most likely originated in Europe from an Asian ancestor and gave rise to subclade A2a, which represents the major non-Asian outbreak, especially in Africa and Europe. Multiple founder effect episodes, most likely associated with super-spreader hosts, might explain COVID-19 pandemic to a large extent.
New perspectives on trophic guilds of arthropodivorous bats in North and Central America

Trophic guilds are useful concepts for advancing our knowledge of trophic structure of communities, dynamics of species interactions, redundancy in ecosystem services, resilience to disturbances, response to climate change, conservation strategies, etc. For insectivorous bats, current literature suggests 8 trophic-related guilds. These include 3 guilds based on the openness of foraging areas, 3 based on the style of feeding, and 2 recently proposed subguilds among gleaners. Some gleaners are “passive,” using densely cluttered vegetation in which echolocation is ineffective, and others are “actively” gleaning, using echolocation to procure prey. None of these guilds is based on the actual diets of bats. We analyzed 33 reports of diet composition representing 51 species of arthropod-feeding bats inhabiting North and Central America. We wanted to determine if the classical guild structure was concordant with the actual diets of bats and to compare guild structure in the Nearctic with that in the Neotropics. Discriminant function and principle component analyses generated 5 groups of genera based on the proportion of various arthropod taxa (mainly orders) in their diets. These groups were very different from classical guilds and showed almost no overlap among bat genera between the 2 continental regions. A similar analysis based on prey flying ability and hardness of their exoskeletons suggested 4 guilds that were more consistent with classical guild concepts, had higher rates of unambiguous guild assignment, and also showed major continental differences. Our results suggest a new arrangement of 4 guilds for arthropod-feeding bats in North and Central America that are based primarily on 2 features of their prey. New molecular techniques should allow us to build on this arrangement by significantly improving the taxonomic level of prey identification.
Self-Efficacy, Self-Regulated Learning, and Motivation as Factors Influencing Academic Achievement Among Paramedical Students: A Correlation Study.

INTRODUCTION Academic achievement is the most important indicator of the success of students in educational activities, but its nature is complex and multifactorial. The purpose of the present study was to determine self-efficacy, self-regulated learning, and motivation as potential factors influencing academic achievement among paramedical students. METHODS In this correlation study, 400 undergraduate students were selected through a stratified random sampling method. The criterion for academic achievement was the student's grade point average. The data were analyzed using multiple linear regression equation, ANOVA, and independent t-tests. RESULTS The mean score of the self-efficacy, self-regulated learning, and motivation was 62.18±9.3, 76.31±11.3, and 81.83±8.58, respectively. Self-regulated learning, self-efficacy, and motivation together accounted for 19.6% of the variance in academic achievement (p<0.001). Self-regulated learning had a direct relationship with academic achievement (p<0.001) and was the better predictor of academic achievement (b=0.337). A significant relationship between self-regulated learning and academic achievement was seen in operating room nursing, radiology sciences, anesthesiology (p<0.001), and laboratory sciences (p<0.01) students. CONCLUSIONS Self-regulated learning was the better predictor of academic achievement. Self-regulated learning seems to encourage students to believe in their abilities and to be more motivated and self-learning in both classroom and practical settings.