Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Status Predicts the Incidence of Gesture in Discourse: An Experimental Study

ABSTRACT The study aimed to disentangle the influence of information status and referential form on the distribution of gestures in sustained discourse. Previous research shows that new and less accessible rather than old and more accessible information, expressed by rich rather than lean referential forms, is more likely to be accompanied by gestures. However, earlier studies have drawn on correlational results. This study probes the relationship between information status and gesture production experimentally. Participants retold stories referring to discourse entities as normal (Control), using only lexical noun phrases (NOUN condition), or only pronouns (PRONOUN condition). The results from the experimental conditions showed that speakers tend to produce gestures with reintroduced rather than maintained referents regardless of referential form. The findings suggest a strong and direct relationship between information status and gesture production when referential forms are controlled for, lending further support to a view of speech and gesture as an integrated system.
A Floating-Body Transistor-Based Power Amplifier for Sub-6-GHz 5G Applications in SOI CMOS 130-nm Process

A floating-body transistor based two stages power amplifier (PA) with lateral NPN based bandgap voltage reference (BVR) is implemented in a 130 nm Silicon-On-Insulator (SOI) CMOS process for sub-6GHz 5G applications. Floating-body (FB) transistors instead of traditional body-contacted (BC) transistors have been adopted in driver stage and power stage amplifiers of the proposed PA. On-chip input and output transformer baluns (TB) are customized designed for this PA which can realize impedance matching. Split push-pull structure is adopted in both driver stage and power stage with inter-stage matching networks. Incorporating bonding wire multi-physics models into input and output matching and power gain temperature compensation, the proposed PA can achieve maximum 25.6 dB power gain, 22.2 dBm output power and 28% power added efficiency (PAE) at 1 dB compression point (OP-1dB). The 3 dB bandwidth of the proposed PA is from 4.3 GHz to 6.4 GHz and the core size is 0.59 mm2. When the proposed PA is used for IEEE 802.11ac application and measured at VHT80 MCS9 (80MHz, 256-QAM), it can achieve 21.4 dBm output power and 24.8% PAE at 5.5 GHz operating frequency with −36 dB error vector magnitude (EVM).
Cardiovascular Fitness and Associated Comorbidities in An Executive Health Program

Objectives: Low cardiorespiratory fitness, defined as a VO2 max below the 25th percentile for age and sex, is associated with greater body mass index (BMI), blood pressure, and plasma cholesterol values and is an independent risk factor for cardiovascular and metabolic diseases. Given that sedentarism has substantially increased in the U.S. population in the last 20 years and that office workers have the lowest cardiorespiratory fitness of the workforce, we aimed to assess the prevalence of low cardiorespiratory fitness in an corporate wellness program and determine its relationship with associated comorbidities.Methods: For this retrospective observational analysis demographics, height, weight, blood pressure at rest, plasma glucose, lipids, comorbidities, and VO2 max data was extracted from the medical records of 199 participants attending the Executive Health Program at M Health Fairview of the University of Minnesota. Participants were predominantly white, middle-aged men with near-optimal blood pressure values.Results: Participants with low cardiorespiratory fitness had a VO2 max [mean (range) of 28 (19.4 - 36.1) mL/kg/min], and was observed in 33% of all participants. Participants with low cardiorespiratory fitness were more likely to have higher BMI, dyslipidemia and hypertension than those in the excellent to superior category of cardiorespiratory fitness, VO2 max [mean (range) 45.6 (31.8 - 61.2) mL/kg/min]. Prevalence of obesity (17%) was lower than in the general U.S. population, and those who were obese were more likely to be of low cardiorespiratory fitness. Those with low cardiorespiratory fitness had a four fold relative risk of belonging to the group at high risk of cardiovascular and metabolic diseases when compared to those with a fair to superior cardiorespiratory fitness.Conclusions: Low cardiorespiratory fitness identified in a third of all participants, is a modifiable risk factor associated with risk for cardiovascular and metabolic disease, should be evaluated in executive health programs.
GoPro: a Low Complexity Task Allocation Algorithm for a Mobile Edge Computing System

In an Internet of Things (IoT) based network, tasks arriving at individual nodes can be processed in-device or at a local Mobile Edge Computing (MEC) server. In this paper, we focus on the optimal resource allocation problem for tasks arriving in an MEC based IoT network. To address the inherent trade-off between the computation time and the power consumption, we aim to minimize the average power consumption subject to a constraint on the deadline violation probability. The problem is formulated as a Constrained Markov Decision Process (CMDP) problem. To address the high complexities of achieving optimality, we propose a low-complexity heuristic task scheduling scheme. Efficacy of our approach is demonstrated using simulations.
Datasets and Approaches of COVID-19 Misinformation Detection: A Survey

Social media has become a primary source of news, providing a fertile environment for spreading misinformation. Since the outbreak of the COVID-19 pandemic, misleading information related to COVID-19 has been spreading rapidly and widely on social media. Several conspiracy theories have emerged regarding the origin of the COVID-19, potential treatments, and vaccines posing a real threat to the public health of people. Fake news that promotes vaccine hesitancy might jeopardize achieving the levels of vaccination needed to reach herd immunity and end the pandemic. The need for automatic tools that detect COVID-19 related misinformation has encouraged researchers to propose several Machine learning (ML) and Deep Learning (DL). Many datasets have been released since the start of the pandemic, aiming to assess the performance of misinformation detection methods. This survey reviews the datasets that have been released to analyze the related to COVID-19 in general and COVID-19 misinformation detection in particular released in Arabic, English, and other languages. We also provide an overview of the different methods used to detect COVID-19 fake news. In this paper, the terms “misleading information”, “misinformation”, and “fake news” are used interchangeably.
Integrated surgery scheduling by constraint programming and meta-heuristics

ABSTRACT Complexity of surgery scheduling reduces surgical staff efficiency and patient satisfaction. Nurses and surgeons face extreme workload every day, whose schedules can be improved by correct scheduling of surgeries. This paper addresses integrated scheduling of operating rooms, nurses, and surgeons aiming to minimize makespan and maximize patient satisfaction and surgical teams’ affinity. A constraint programming model and a hybrid method of NSGA-II and multi-objective dragonfly algorithm (MODA) are developed. Results indicate that high-quality solutions for problem instances with up to 150 surgeries are obtained by the CP model in less than 500 seconds. Superiority of the proposed meta-heuristic method compared to NSGA-II, MODA, SPEA-2, and a hybrid of NSGA-II and SPEA-2 is also shown.
A Data-Driven Optimisation Model for Designing Islanded Microgrids

In practice, electrification of remote and islanded communities with no connection to the main grid is entangled with many techno-economic issues. These technical and more importantly economical challenges often justify the use of Micro-Grids (MGs) as self-sufficient electrical networks with a group of controllable/non-controllable consumers and producers in remote and islanded areas. However, the optimal design of sustainable MGs, even in small communities, is a complex optimisation problem due to the uncertain nature of load consumption and renewable production as well as the non-convex characteristics of network constraints. In this paper, we propose a model to design sustainable MGs using the notion of Distributionally Robust Optimisation (DRO) to handle the uncertainties arising from forecast data wherein the non-convex AC power flow equations are reformulated into convex constraints. Furthermore, a three-step approach is introduced to recast the tri-level DRO-based model into a tractable single-stage Mixed-Integer Linear Programming (MILP) problem. The proposed approach is tested on a modified Europrean CIGRE 18-bus test network and its performance is compared with the stochastic optimisation approach.
First-Order Convex Fitting and Its Application to Economics and Optimization

This paper studies a function fitting problem which we coin first-order convex fitting (FCF): given any two vector sequences x1, . , xT and p1, . , pT, when is it possible to efficiently construct a convex function f(x) that ``fits'' the two sequences in the first-order sense, i.e, the (sub)gradient of f(xi) equals precisely pi, for all i = 1, . , T? Despite a basic question of convex analysis, FCF has surprisingly been overlooked in the past literature. With an efficient constructive proof, we provide a clean answer to this question: FCF is possible if and only if the two sequences are permutation stable: p1 * x1 + . + pT * xT is greater than or equal to p1 * x’1 + . + pT * x’T where x’1, . , x’T is any permutation of x1, . , xT. We demonstrate the usefulness of FCF in two applications. First, we study how it can be used as an empirical risk minimization procedure to learn the original convex function. We provide efficient PAC-learnability bounds for special classes of convex functions learned via FCF, and demonstrate its application to multiple economic problems where only function gradients (as opposed to function values) can be observed. Second, we empirically show how it can be used as a surrogate to significantly accelerate the minimization of the original convex function.
Forensic Identification Science Evidence Since Daubert: Part II—Judicial Reasoning in Decisions to Exclude Forensic Identification Evidence on Grounds of Reliability

Abstract: Many studies regarding the legal status of forensic science have relied on the U.S. Supreme Court’s mandate in Daubert v. Merrell Dow Pharmaceuticals Inc., and its progeny in order to make subsequent recommendations or rebuttals. This paper focuses on a more pragmatic approach to analyzing forensic science’s immediate deficiencies by considering a qualitative analysis of actual judicial reasoning where forensic identification evidence has been excluded on reliability grounds since the Daubert precedent. Reliance on general acceptance is becoming insufficient as proof of the admissibility of forensic evidence. The citation of unfounded statistics, error rates and certainties, a failure to document the analytical process or follow standardized procedures, and the existence of observe bias represent some of the concerns that have lead to the exclusion or limitation of forensic identification evidence. Analysis of these reasons may serve to refocus forensic practitioners’ testimony, resources, and research toward rectifying shortfalls in these areas.
Niche Sociality: Approaching Adversity in Everyday Life

How should sociologists understand the everyday lives of those living in adversity, coping with the experience of structural violence? In this article, focusing on the urban experience, we suggest a perspective on ‘everyday life’ that can encompass corporeal, mental, relational and social dimensions, which we term ‘niche sociality’. First, we use Gibson’s niches and affordances to enrich the post-representationalist understanding of human beings as embodied/cultural/environmentally embedded organisms. Second, we enrich Gibson’s niches and affordances with theories for ‘small-scale’ sociality drawn from social practice theory and interaction ritual chains. Third, we illustrate the productivity of these ideas throughout the article, by grounding our conceptual work in empirical examples that analyse the everyday lives and mental life of migrant workers in Shanghai. Niche sociality, we argue, is a way of framing the experience of the everyday, a perspective that could – perhaps should – provoke novel ecosocial studies of adversity.
Assessment of healthcare professionals' knowledge and attitudes regarding blood safety culture in hospitals

Safety culture encompasses the knowledge, procedures and practices that are shared by the actors of an organization regarding the control of risks related to its activities. In the hospital environment, the introduction of a safety culture in the care services is still not very evident. Generally, it is focused on the patient, even though health professionals are also constantly exposed to various risks. Indeed, the infectious risk secondary to blood exposure accidents constitutes a serious threat to the health of healthcare professionals. This requires improvements in the knowledge and attitudes of the safety culture by all operational and management staff in hospitals. The aim of this work is to study the knowledge and attitudes of health professionals regarding the safety culture towards blood exposure accidents, using a questionnaire that will allow to evaluate the perception of safety in a Moroccan hospital.
Engineering a 3D In Vitro Model of Human Gingival Tissue Equivalent with Genipin/Cytochalasin D

Although three-dimensional (3D) co-culture of gingival keratinocytes and fibroblasts-populated collagen gel can mimic 3D structure of in vivo tissue, the uncontrolled contraction of collagen gel restricts its application in clinical and experimental practices. We here established a stable 3D gingival tissue equivalent (GTE) using hTERT-immortalized gingival fibroblasts (hGFBs)-populated collagen gel directly crosslinked with genipin/cytochalasin D and seeding hTERT-immortalized gingival keratinocytes (TIGKs) on the upper surface for a 2-week air–liquid interface co-culture. MTT assay was used to measure the cell viability of GTEs. GTE size was monitored following culture period, and the contraction was analyzed. Immunohistochemical assay was used to analyze GTE structure. qRT-PCR was conducted to examine the mRNA expression of keratinocyte-specific genes. Fifty µM genipin (G50) or combination (G + C) of G50 and 100 nM cytochalasin D significantly inhibited GTE contraction. Additionally, a higher cell viability appeared in GTEs crosslinked with G50 or G + C. GTEs crosslinked with genipin/cytochalasin D showed a distinct multilayered stratified epithelium that expressed keratinocyte-specific genes similar to native gingiva. Collagen directly crosslinked with G50 or G + C significantly reduced GTE contraction without damaging the epithelium. In summary, the TIGKs and hGFBs can successfully form organotypic multilayered cultures, which can be a valuable tool in the research regarding periodontal disease as well as oral mucosa disease. We conclude that genipin is a promising crosslinker with the ability to reduce collagen contraction while maintaining normal cell function in collagen-based oral tissue engineering.
Genomic dynamics of brown trout populations released to a novel environment

Abstract Population translocations occur for a variety of reasons, from displacement due to climate change to human‐induced transfers. Such actions have adverse effects on genetic variation and understanding their microevolutionary consequences requires monitoring. Here, we return to an experimental release of brown trout (Salmo trutta) in order to monitor the genomic effects of population translocations. In 1979, fish from each of two genetically (F ST = 0.16) and ecologically separate populations were simultaneously released, at one point in time, to a lake system previously void of brown trout. Here, whole‐genome sequencing of pooled DNA (Pool‐seq) is used to characterize diversity within and divergence between the introduced populations and fish inhabiting two lakes downstream of the release sites, sampled 30 years later (c. 5 generations). Present results suggest that while extensive hybridization has occurred, the two introduced populations are unequally represented in the lakes downstream of the release sites. One population, which is ecologically resident in its original habitat, mainly contributes to the lake closest to the release site. The other population, migratory in its natal habitat, is genetically more represented in the lake further downstream. Genomic regions putatively under directional selection in the new habitat are identified, where allele frequencies in both established populations are more similar to the introduced population stemming from a resident population than the migratory one. Results suggest that the microevolutionary consequences of population translocations, for example, hybridization and adaptation, can be rapid and that Pool‐seq can be used as an initial tool to monitor genome‐wide effects.
D-vlog: Multimodal Vlog Dataset for Depression Detection

Detecting depression based on non-verbal behaviors has received great attention. However, most prior work on detecting depression mainly focused on detecting depressed individuals in laboratory settings, which are difficult to be generalized in practice. In addition, little attention has been paid to analyzing the non-verbal behaviors of depressed individuals in the wild. Therefore, in this paper, we present a multimodal depression dataset, D-Vlog, which consists of 961 vlogs (i.e., around 160 hours) collected from YouTube, which can be utilized in developing depression detection models based on the non-verbal behavior of individuals in real-world scenario. We develop a multimodal deep learning model that uses acoustic and visual features extracted from collected data to detect depression. Our proposed model employs the cross-attention mechanism to effectively capture the relationship across acoustic and visual features, and generates useful multimodal representations for depression detection. The extensive experimental results demonstrate that the proposed model significantly outperforms other baseline models. We believe our dataset and the proposed model are useful for analyzing and detecting depressed individuals based on non-verbal behavior.
DDoS Intrusion Detection Model for IoT Networks using Backpropagation Neural Network

In today's digital landscape, Internet of Things (IoT) networking has grown dramatically broad. The major feature of IoT network devices is their ability to connect to the internet and interact with it through data collecting and exchanging. Distributed Denial of Service (DDoS) is one form of cyber-attacks in which the hackers penetrate a single connection and then multiple machines are operating together to attack one target. The direct connectivity of IoT devices to the internet makes DDoS attacks worse and more dangerous. The more businesses adapted IoT networks to streamline the operations, the more allowing of DDoS intrusions at small and large scales to take place. Therefore, the intrusion detection module in the IoT networks is not optional in today’s business environment. To achieve this objective, in this paper, an intelligent intrusion detection model is proposed to detect DDoS attacks in IoT networks. The intelligent model is a backpropagation neural network-based framework. The results are analyzed using different performance measures. The proposed model proves a detection rate of 99.46% and detection accuracy of 95.76% using the up-to-date benchmark CICDDoS2019 dataset. Furthermore, the proposed model has been compared with the most recent DDoS intrusion detection schemes and competitive performance is achieved. Keywords—DDoS; backpropagation neural network; IoT network; intrusion detection; CICDDoS2019
A Deep Learning Classification Approach using Feature Fusion Model for Heart Disease Diagnosis

Early Diagnosis has a very critical role in medical data processing and automated system. In medical diagnosis, automation is focused in different area of applications, in which heart disease diagnosis is a prominent domain. An early detection of heart disease can save many lives or criticality issues in diagnosing patients. In the process of heart disease diagnosis spatial and frequency domain features are used in making decision by the automation system. The processing features are observed to time variant or invariant in nature and the criticality of the observing feature varies with the diagnosis need. Wherein, the current automation system utilizes the features extracted in a large count to attain a higher accuracy, the processing overhead, and delay are considerable. Different regression approaches were developed in recent past to minimize the processing feature overhead the features are optimized based on gain performance or distance factors. The characteristic variation of feature and the significance of the feature vector are not addressed. This paper outlines a method of feature selection for heart disease diagnosis, based on weighted method of feature vector in consideration of feature significance and probability of estimate. A new optimizing function for feature selection is proposed as a dual function of probability factor and feature weight value. Simulation results illustrate the improvement of accuracy and speed of computation using proposed method compared to other existing methods. Keywords—Deep learning approach; heart disease diagnosis; feature fusion model; ECG analysis; weighted clustering; F-Score
Controlled Thin Polydimethylsiloxane Membrane with Small and Large Micropores for Enhanced Attachment and Detachment of the Cell Sheet

Polydimethylsiloxane (PDMS) membranes can allow the precise control of well-defined micropore generation. A PDMS solution was mixed with a Rushton impeller to generate a large number of microbubbles. The mixed solution was spin-coated on silicon wafer to control the membrane thickness. The microbubbles caused the generation of a large number of small and large micropores in the PDMS membranes with decreased membrane thickness. The morphology of the thinner porous PDMS membrane induced higher values of roughness, Young’s modulus, contact angle, and air permeability. At day 7, the viability of cells on the porous PDMS membranes fabricated at the spin-coating speed of 5000 rpm was the highest (more than 98%) due to their internal networking structure and surface properties. These characteristics closely correlated with the increased formation of actin stress fibers and migration of keratinocyte cells, resulting in enhanced physical connection of actin stress fibers of neighboring cells throughout the discontinuous adherent junctions. The intact detachment of a cell sheet attached to a porous PDMS membrane was demonstrated. Therefore, PDMS has a great potential for enhancing the formation of cell sheets in regenerative medicine.
Analysis of People Flow Image Detection System Based on Computer Vision Sensor

In order to meet the needs of accurately grasping the situation of people in the mall at all times, the author proposes an analysis method based on computer vision for people flow image detection system. This method combines the HOG feature with the SVM classifier, detects pedestrians through dual cameras, and builds an experimental research platform for dual-camera joint detection of pedestrians. The result shows that the error rate of human flow detected by the author’s method is the lowest of 0% and the highest of 6.25%. Conclusion. This method has a good effect on the statistics of the number of people in the shopping mall and can reduce the workload of the monitoring personnel in the shopping mall.