Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling VR/AR and Tactile through 5G Network

After the introduction of fifth generation network, there are several prospects and encounters for the evolving technologies like Virtual Reality (VR) and Augmented Reality (AR). This paper emphases on the possibilities of 5G network and how 5G will integrate with Virtual Reality and Augmented Reality and Tactile Internet. Tactile Internet in AR/VR is intended to generate a model alteration from the content-oriented interactions (through internet) towards remote based connections by empowering synchronized broadcast of tactile data like vibration, touch, gestures, surface texture over wireless network with the regular audio and visual data traffics which are also transferred over wireless network. Finally, this research paper intends to provide an overall opinion on the use of Tactile Internet in AR/VR, and to recognize and examine the involved drawbacks and technical problems, and to provide the possible resolutions and to suggest upcoming research guidelines. Initially, opening with the future of Tactile Internet in AR/VR and recent developments in this field, this paper presents a comprehensive outline for Tactile Internet in AR/VR in the past the 5G Era including a Tactile Internet planning, the VR/AR planning, the core technical problems, the important application studies, how it can be possibly enable ground-breaking ways to use Augmented Reality and Virtual Reality. Later, we can see a complete evaluation of the present Tactile Internet progress by generally classifying them into two main instances; specifically, haptic feedback and portable AR/VR device using 5G network. Finally, learning about both Tactile Internet and Augmented Reality/Virtual Reality, we will learn how 5G can change the way Tactile Internet integrates and works with AR/VR that can make an impact in the ever-growing IoT world.
Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.
FCS-MPC Based Fault Tolerant Control for Permanent Magnet Synchronous Machines

In the present work, a fault-tolerant control strategy based on the predictive control by finite state model for permanent magnet synchronous machines is proposed. The behavior of the strategy is analyzed in a 4-wire topology. In this topology the neutral point of the machine is connected to the midpoint of a bank of direct current capacitors. The advantage of the strategy proposed in this document resides in the control scheme, which allows stable operation through a fault without the need to reconfigure either the inverter topology or the structure of the control algorithm. In this way, transients and the use of additional hardware components are avoided. The experimental results, obtained from a laboratory prototype, validate the practical viability of the proposed strategy.
Design of Novel Mobility and Obstacle-Aware Algorithm for Optimal MANET Routing

Existence obstacles in terrain area and the mobility of the nodes in the network are the vital constrains that degrade the performance in Mobile Adhoc Network (MANET). To diminish the effect of such obstacles and mobility on the performance of MANET, the protocol used for routing must be efficient enough to handle both the factors. In the present work, we have considered the issues of Mobility and obstacle aware multipath communication in MANET. For avoiding obstacles, DeCasteljau Algorithm with Bezier curve concept has been applied. To the minimize impact of mobility, a speed based mobility prediction concept is used. Here optimal routing path across the obstacle is selected depending on its mobility prediction indicator, link duration, network connectivity and path availability. The performance of the suggested protocol is evaluated with Network Simulator-2. Simulation results confirm that the suggested protocol minimizes average energy consumption, network overhead, average delay and improves data deliverance in MANET.
Neural Dense Captioning with Visual Attention

Image captioning is the task of how to represent image information more effectively and efficiently. It uses both Natural Language Processing and Computer Vision for generating the caption. In the existing research, many methods for caption generation were proposed, but the information in the description is not so related to the image and also most of them uses the global information of the image. In order to solve this problem, in this paper we have described three image captioning model namely ResNet, Glove and VGG16.These are the pretrained models which uses Convolutional Neural Network (CNN) and Deep Recurrent Neural Network (RNN) based on Long Short Term Memory (LSTM). CNN has one or more convolutional layers and are used for image classification and features extraction. Only the meaningful area’s features were extracted through CNN. For generating the caption LSTM uses over RNN because it has many layers so the predictions for generating the captions are more accurate in LSTM. The dataset use in our project is Flicker8k dataset. It contains total 8k images where 2k images are test dataset and 6k images are trained dataset. The Flicker8k dataset is used to demonstrate the proposed methodology using python as a language.
SVM based Supervised Machine Learning Framework for Glaucoma Classification using Retinal Fundus Images

Glaucoma is group of ocular conditions that damage the optical nerve. Glaucoma diagnosis in the early condition is beneficial for better vision. The available clinical instruments are nonautomated and work on the manual operating principle. In this article, we proposed an SVM with the supervised algorithm of a machine-learning framework for glaucoma classification. In this study, a 2-dimensional variational mode decomposition tool has been employed for fundus image extraction. Then, texture-based features such as Zernike moment, chip histogram, haralick features have been computed from the high-frequency modes. The Students t-test approach is applied for the chosen robust features. In the end, a multi-stage classifier (support vector machine) has been used for glaucoma prediction. The effectiveness of the deployed technique is tested using a publically available dataset. The developed automated system obtained the highest Acc of 89.45%.
Combining Context-Aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter

Affect analysis has recently attracted a great deal of attention due to the rapid development of online social platforms (i.e., Twitter, Facebook). Affect analysis is a part of a broader area of affective computing that aims to detect and grasp human emotions or affects within a piece of writing. Context awareness is very relevant for identifying human emotions and affects behind a piece of text. Capturing the context of a piece of text is often perceived as a challenge. In addition to the own unique features of tweets (shortness, noisiness, short length, etc.), the Arabic language is characterized by its agglutination and morphological richness. In this paper, we address the problem of Arabic affect detection (multilabel emotion classification) by combining the transformer-based model for Arabic language understanding AraBERT and an attention-based LSTM-BiLSTM deep model. AraBERT generates the contextualized embedding, and the attention-based LSTM-BiLSTM determines the label-emotion of tweets by extracting both past and future contexts considering temporal information flow in both directions. Additionally, the attention mechanism is applied to the output of LSTM-BiLSTM to emphasize different words. Our proposed approach was evaluated using the reference dataset of SemEval-2018 Task 1 (Affect in Tweets). The comprehensive results show that the proposed approach outperforms eight current state-of-the-art and baseline methods, and it achieves significant accuracy (53.82%) compared to 1st place in SemEval2018-Task1: (Affect in Tweets) competition. In addition, our proposed model outperforms the best recently reported model in the literature, with an enhancement of 2.62% in accuracy.
Un modelo preliminar para la recomendación de ítems basada en resúmenes textuales

Los sistemas de recomendación constituyen hoy una de las principales áreas de investigación dentro de la Inteligencia Artificial. U na de las técnicas más u sadas p or e stos s istemas es el filtrado b asado e n c ontenido, q ue b asa s us recomendaciones en los ítems consumidos por un usuario y sus características. Describir estas características de manera automática puede ser todo un desafíío. En este trabajo se propone un enfoque basado en aprendizaje profundo y procesamiento de lenguaje natural para realizar recomendaciones personalizadas usando filtrado basado en contenido. Se analizaron diferentes modelos usando redes neuronales. Se detallan experimentos preliminares en el dominio de las pelíículas con resultados muy prometedores.
Systematic Literature Review of the Water-Energy- Food Nexus: An Overview of the Field

Water, energy and food are vital resources for human well-being, poverty reduction, and sustainable development. The study of each of the resources mentioned presents its own individual complexity and when considering the interrelationships between them, this becomes more complex. The interconnection of these resources is known as the Water-Energy-Food Nexus (WEF Nexus), and represents a framework that captures the interrelationships, synergies and trade-offs between the demand for water, energy and food in the context of threats, emerging constraints and development sustainable in particular regions or systems. Water, energy and food are limited but critical resources for human and natural systems and are being depleted at a faster rate than the planet can replenish. The WEF Nexus has become an essential approach to achieve the Sustainable Development Goals (SDGs) mainly SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation) and SDG 7 (Clean and Affordable Energy). This work presents an exhaustive and systematic review of the literature on the WEF Nexus in the 2008-2020 period, addressing the articles published in journals, conferences and technical reports where the case studies carried out, the proposed methodologies and the types of data are analyzed that are required in the different models. This will achieve a clear view of the current global level regarding the WEF Nexus. As a complementary contribution, a methodological proposal is presented for the beginning of the Nexus WEF study for countries with emerging economies with a multi-criteria approach, taking the Republic of Paraguay as a case study.
FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN), respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the FN minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.
In-Network Flow Classification With Knowledge Distillation

Recent research has incorporated machine learning with software-defined networking to support intelligent traffic engineering. However, most frameworks only enable machine learning in remote controllers, which introduce significant signaling overhead and data forwarding costs. In this work, we present a new architecture called in-network inference (INI) to realize local learning in Neural Compute Stick (NCS), a portable device that can be connected to a programmable switch via a USB port. While NCS can flexibly extend the computing power of a switch, its limited capacity however cannot afford real-time inference for enormous traffic demands. To develop a practical local learning architecture, we design a two-phase learning framework that combines local learning with knowledge distillation and remote learning to achieve lightweight but accurate traffic classification. We further design an inference model deployment and adaptation algorithm to utilize multiple NCS devices equipped with different switches to share the inference workload of a network. Our testbed experiments show that the two-phase learning framework reduces the inference rejection rate by 46.5% and maintains the inference accuracy of 98.10%. The trace-driven simulations verify that the proposed adaptive model placement scheme considers load balancing and, hence, better utilizes the computing resources of NCS to serve dynamic inference requests.
Can social media be beneficial for eating disorders?

Introduction Eating Disorders are a frequent pathology, particularly among teenagers, a group characterized by its vulnerability and body dissatisfaction. Social networks (SN) can be a gateway to ED, mainly with Pro-Ana and Pro-Mia resources. Despite the aforementioned, SN can also be helpful for professionals, either as a tool of approach to vulnerable groups or as a way of interaction in patients already diagnosed. Objectives To study the relationship between ED and SN, using the open access evidence available in Pubmed over the last 5 years. Methods A single-phase computerised search was carried out in Pubmed. The search terms were: (“Anorexia Nervosa”[Mesh] OR “Bulimia Nervosa”[Mesh] OR “Feeding and Eating Disorders”[Mesh] OR “Eating Disorders”[Tiab] OR “Eating Disorder”[Tiab] OR “Disorder, Eating”[Tiab] OR “Disorders, Eating”[Tiab] OR “Anorexia”[Tiab] OR “Bulimia”[Tiab]) AND (“blogging”[Mesh] OR “social media”[Mesh]). The filters applied were: “free Full Text” and publications for the last 5 years. Results 36.84% studied SN as a positive tool for ED. 47.37% revealed negative influence, only 44.44% focused on Pro-Ana and Pro-Mia. 15.79% provided both positive and negative arguments. The most studied SN were Twitter and Facebook. Conclusions Despite the known negative effect that SN can have on ED, they can also be used as a supportive recovery framework. They can be used to identify dangerous behaviours and intervene or as a prevention tool. Disclosure No significant relationships.
When years of many different diagnosis may turn into one – a case of munchausen syndrome

Introduction The term Munchausen syndrome was first used in 1951 after Baron von Münchhausen, a German nobleman known for his exaggerated storytelling. DSM-5 refers to it as factitious disorder imposed on self: “falsification of physical or psychological signs or symptoms, or induction of injury or disease, (…) in the absence of obvious external rewards”. Objectives To report a case of Munchausen syndrome and highlight the impact on its physical and psychiatric approaches. Methods Description of a clinical case based on medical records and a brief review on Munchausen syndrome. Results A 57-year-old female, with no previous psychiatric history, was evaluated by Psychiatry for complaints of depression with suicidal ideation. She reported family conflicts and a list of medical conditions and surgical interventions. According to the patient she was waiting for a cardiac transplant and said she had type 1 diabetes, myasthenia gravis, hepatic steatoses, dyslipidemia, hyperuricemia, mitral valve prolapse and was submitted to a thymectomy and cervical herniated disc surgery. She was on many different prescription pills. Even though she had blocked the access to her clinical records in other institutions, at our hospital she had multiple admissions to the emergency room, numerous follow-up appointments of different specialties and several allergies documented. She displayed many incoherencies throughout the interview, had a circumstantial speech and exuberant appearance. Conclusions Munchausen syndrome remains a challenging diagnosis to physicians. This condition is not only associated with significant morbidity and mortality, but also with unnecessary tests and procedures, iatrogenesis, prolonged hospitalizations and increased health costs. Disclosure No significant relationships.
Efficacy and safety results from the first pivotal phase 3 randomized controlled trial of mdma-assisted psychotherapy for treatment of severe chronic PTSD

Introduction Posttraumatic stress disorder is a prevalent mental health condition with substantial impact on daily functioning that lacks sufficient treatment options. Previous research has led to the designation of 3,4-methylenedioxymethamphetamine (MDMA) as a Breakthrough Therapy for treatment of post-traumatic stress disorder (PTSD) when administered as an adjunct to psychotherapy. Objectives Here we report the findings of the first randomized, double-blind, Phase 3 trial assessing the efficacy and safety of 3 sessions with a flexible dose of MDMA or placebo administered under direct observation to participants with severe PTSD (n = 100) as an adjunct to inner-directed psychotherapy. Methods Change in PTSD symptoms (CAPS-5) and functional impairment (SDS) were assessed by a central, blinded Independent Rater Pool at baseline and following each treatment session. Adverse events (AEs), concomitant medications, suicidal ideation and behavior were tracked throughout the study. Vital signs were measured during experimental sessions. The primary endpoint was 18 weeks post-randomization. Results Change in CAPS-5 and SDS, placebo-subtracted Cohen’s d effect size, and a responder analysis will be presented. There were three serious AEs of suicidal ideation or behavior reported. MDMA was well tolerated, with some treatment emergent AEs occurring at greater frequency for the MDMA group during and after experimental sessions. Conclusions If MDMA-assisted psychotherapy significantly attenuates PTSD symptomatology and associated functional impairment, these results will form the basis for marketing authorization applications worldwide, including among participants with dissociative subtype of PTSD, depression, history of alcohol and substance use disorders, and adverse childhood experiences. Disclosure No significant relationships.
Roses are Red and Violets are Blue - Everywhere? Cultural Differences and Universals in Color Preference and Choice Among Consumers and Marketing Managers

This paper describes research examining the similarities and differences in the effects of color across cultures. Consistent with the literature, Study 1a suggested that cultural differences in hue choice do emerge when there are clear, salient social norms prescribing appropriate color usage, which differ across cultures. However, in the absence of such norms, hue choices reflected people's general hue preferences, which were similar across the two cultures examined. Study 1b examined hue preferences in a wide variety of cultures, and further documented both their similarity and that blue was the most preferred hue. Study 1c provided support for a link between the feelings (e.g., relaxation, excitement) that the hue elicits and preferences, with the preference for blue being linked to the feelings of relaxation that it evokes across cultures. Study 2 showed that actual color choices made in a variety of marketing situations, by managers in ad agencies from Hong Kong and Canada, reflected consumer preference for blue hues. Overall, the conceptual framework and the findings suggest that most aspects of color preference are likely to be culturally universal and applicable across cultural groups and marketing settings. Managerial implications are discussed regarding standardizing versus adapting color usage across cultures.