Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Generic Modelling of Viewer Interest Using Facial Expression and Heart Rate Features

Automatic detection of viewer interest while watching video contents can enable multimedia applications, such as online video streaming, to recommend contents in real time. However, there is yet a generic model for detecting viewer interest that is independent of subject and content while using non-invasive sensors in near-natural settings. This paper is the first attempt at solving this issue by investigating the feasibility of a generic model for detecting viewer interest based on facial expression and heart rate features. The proposed model adopts deep learning features, which are trained and tested using multi-subjects’ data across different video stimuli domains. The experimental results show that the generic model can reach a similar accuracy to a domain-specific model.
Self Driving Robot using Neural Network

Nowadays development of autonomous vehicles gained a lot of interest of most of researchers. Autonomous vehicle has ability to sense surrounding environment and navigate without any human intervention. The potential benefits of autonomous vehicle include reduction in infrastructure cost, increased safety with significant reduction in traffic collisions. This paper introduces the autonomous robot which is a scaled down version of actual self-driving vehicle and designed with the help of neural network. The main focus is on building autonomous robot and train it on a designed track with the help of neural network so that it can run autonomously without a controller or driver on that specific track. The robot will stream the video to Laptop which will then take decisions and send the data to raspberry pi which will then control the robot using motor driver. This motor driver will move the robot in required directions. Neural Network is used to train the model by first driving the robot on the specially designed track by labeling the images with the directions to be taken. After the model is trained it can make accurate predictions by processing the images on computer. This approach is better than conventional method which is done by extracting specific feature from images.
Joint Fair Resource Allocation of D2D Communication Underlaying Downlink Cellular System With Imperfect CSI

Device-to-device (D2D) communication as a feasible technique can improve the system efficiency without fixed infrastructures. In this paper, we focus on the fair resource allocation of D2D communication underlaying cellular system with channel uncertainty and propose a joint resource allocation policy about the resource block assignment and power allocation among the D2D and cellular users (CUs). We formulate a nominal optimization problem to improve the sum rate of the D2D system and guarantee the quality of service of the CUs. Moreover, taking the uncertainty of the channel state information into consideration, we employ the robust optimization theory to define the uncertainty and construct a robust optimization problem with uncertain parameters in both objective function and constraints. To facilitate the solution, we segment the robust optimization problem into two subproblems. Furthermore, we utilize the chance constraint approach to cut down the cost of the robustness and achieve a tradeoff between the robustness and the optimality. An insight into the influence of the protection function to the achievable D2D sum rate is obtained by sensitivity analysis. In addition, the effect of the robustness on the D2D sum rate with different channel uncertainty is presented in the simulation, and a rate tradeoff between the D2D users and CUs is achieved by the proposed policy.
Predicting enterprise cyber incidents using social network analysis on the darkweb hacker forums

With rise in security breaches over the past few years, there has been an increasing need to mine insights from social media platforms to raise alerts of possible attacks in an attempt to defend conflict during competition. We use information from the darkweb forums by leveraging the reply network structure of user interactions with the goal of predicting enterprise cyber attacks. We use a suite of social network features on top of supervised learning models and validate them on a binary classification problem that attempts to predict whether there would be an attack on any given day for an organization. We conclude from our experiments using information from 53 forums in the darkweb over a span of 12 months to predict real world organization cyber attacks of 2 different security events that analyzing the path structure between groups of users is better than just studying network centralities like Pagerank or relying on the user posting statistics in the forums.
Would Current Ad-Hoc Routing Protocols be Adequate for the Internet of Vehicles? A Comparative Study

In recent years we have seen a great proliferation of smart vehicles, ranging from cars to little drones (both terrestrial and aerial), all endowed with sensors and communication capabilities. It is hence easy to foresee a future with even more smart and connected vehicles moving around, occupying space and creating an Internet of Vehicles (IoV). In this IoV, a multitude of nodes (both static and mobile) will generate a continuous multihop flow of local information to support local smart environment applications. Therefore, one interesting environment for the IoV would be in the form of 3-D mobile ad-hoc networks (MANETs). Unfortunately, MANET routing protocols have generally been designed and analyzed keeping in mind a 2-D scenario; there is no guarantee on how they would support a 3-D topology of the IoV. To this end, we have considered routing protocols deemed as the state-of-the-art for classic MANETs and tested them over 3-D topologies to evaluate their assets and technical challenges.
The Essence of Time: Taking a Look at Learning Sessions in MOOCs

Students in MOOCs may dropout because of poor instructional design quality of courses, poor learning experiences, and failing to regulate their learning times wisely. Given that there are few studies that covered examining students timing in MOOcs in terms of sessions, the current study primarily focuses on investigating the differences in sessions between students who completed MOOCs and earned a “passed” grade, and students who did not complete sufficient assessments to pass the course. Results show that session duration matters and longer or shorter ones have an impact on students' retention in MOOCs.
Impact of modular organization on dynamical richness in cortical networks

Balance of functional integrability and spatial segregation mediates dynamical richness in modular cortical networks. As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.
A Dynamic Tracking Algorithm Based SAR ADC in Bio-Related Applications

This paper presents a novel dynamic tracking algorithm to boost the energy quality of successive approximation register analog-to-digital converter (ADC) in bio-related applications. By introducing two tracking windows that are adjacent to prediction interval, the proposed algorithm can track down the input signal’s variation range, automatically adjust the subrange interval, and then update prediction code. The 12-bit ADC is designed in a 0.13- $\mu \text{m}$ CMOS process with a 0.6-V supply. Given a 41.5-Hz sinusoid signal, the proposed ADC exhibits 11.77 effective number of bit and 85.4-dB spur-free dynamic range at 10-kHz sample rate with a power consumption of 960 nW. Given an electrocardiogram (ECG) signal with baseline drift and vibrations, the ADC achieves 94.42% prediction accuracy. Also, dynamic tracking method provides an approach of ECG feature extraction in time domain.
Comparison of platelet-rich plasma vs hyaluronic acid injections in patients with knee osteoarthritis

Background: Knee osteoarthritis (KOA) is a progressive joint disease involving intraarticular and periarticular structures. In recent years, there has been increasing interest in the use of autologous growth factors, such as intraarticular injections of platelet-rich plasma (PRP), to treat KOA. It is necessary to update the research and reevaluate the efficacy and safety of PRP to provide up-to-date evidence for KOA management. Therefore, we provide a protocol for a systematic review of PRP for KOA. Methods: The aim of this study was to retrieve papers on the topic of PRP treatment for KOA in electronic databases including PubMed, Embase, and the Cochrane Library. The search will include studies that were published from the time the databases were established until April 2018. The entire process will include study selection, data extraction, risk of bias assessment, and meta-analyses. Results: The literature will provide a high-quality analysis of the current evidence supporting PRP for KOA based on various comprehensive assessments including the Western Ontario and McMaster Universities Osteoarthritis Index, visual analog scale scores, International Knee Documentation Committee scores, Lequesne index scores, and adverse events. Conclusion: This proposed systematic review will provide up-to-date evidence to assess the effect of PRP treatment for patients with KOA. PROSPERO registration number: CRD42018108825.
Partial On-Off Keying - A Simple Means to Further Improve IoT Performance

Power consumption is expected to be a key performance indicator for ultra-low power IoT devices. A disruptive step towards achieving years of battery life is the introduction of wake-up radios (WURs). The use of a WUR allows the main radio to be turned off, and only woken up when a wake-up packet is received. A commonly used modulation for the wakeup packet is on-off keying (OOK) which can be demodulated using a simple envelope detector. In this paper we introduce partial-OOK (P-OOK), a modification of OOK that can be used to improve the sensitivity performance of a WUR without increasing the complexity.
A Power-Efficient and High-Throughput Hardware Design for 3D-HEVC Disparity Estimation

This paper presents a hardware design for the 3D-High Efficiency Video Coding (3D-HEVC) Disparity Estimation (DE) based on the Unidirectional Disparity Search (UDS) algorithm. The architecture was developed to process the four square-shaped Prediction Unit (PU) sizes rather than using all 24 possible PU sizes. These two design approaches caused a coding efficiency degradation of only 0.554% in the BD-rate when compared to the traditional approach whereas allowed a significant reduction in the required calculations and also in the memory accesses, contributing to generate a fast and energy-efficient design. The architecture has a high level of parallelism to reach the desired high throughput and explored clock gating to reduce the power dissipation. As the best of the author's knowledge, this is the first work on literature presenting a hardware design for the 3D-HEVC DE. The architecture was ASIC synthesized and can reach up to 1.457 GHz. This result allows a processing rate of five views of UHD 2160p videos at 52 frames per second. When compared with related works targeting the HEVC Motion Estimation, the developed architecture uses a smaller area, requires a smaller memory, reaches a higher throughput and dissipates at least 92% less power.
Immune Correlates of Protection Induced by Virus-Like Particles Containing 2009 H1N1 Pandemic Influenza HA, NA or M1 Proteins

ABSTRACT Influenza virus-like particle (VLPs) vaccines are a promising alternative to conventional egg-based vaccines. Evaluation of vaccine efficacy induced by HA-M1 VLPs, NA-M1 VLPs or M1 VLPs against virus challenge infection would provide important insight into vaccine design strategy. In this study, we generated VLPs containing hemagglutinin (HA), neuraminidase (NA) or M1 proteins derived from the A/California/04/09. Mice were immunized intramuscularly with HA-M1, NA-M1 or M1 VLPs and protective immunity was evaluated by assessing lung virus loads against low (5LD50) or high (100LD50) lethal dose of homologous virus challenges. High levels of virus-specific serum IgG antibody responses were induced in mice after HA-M1 VLPs immunization, whereas low or no IgG antibody responses were detected from immunization with NA-M1 VLPs or M1 VLPs, independently. Mice that were immunized with HA-M1 VLPs showed below the limit of detection on lung virus loads against low dose (5LD50) of challenge and significant reduction against high dose (100LD50) of challenge infection. Mice that were immunized with NA-M1 or M1 VLPs also displayed reduced lung viral loads compared to naïve control. In vitro cultures of cells from mouse spleen and bone marrow revealed that HA-M1 VLPs and NA-M1 VLPs induced higher levels of antibody-secreting cell (ASC) responses compared to naïve control, whereas M1 VLPs showed no ASC responses. HA-M1, NA-M1 or M1 VLPs immunization demonstrated varying degree of protection with respect to body weight changes and survival rates, which are consistent with the levels of antibody responses in sera and ASC responses from spleen and bone marrow.
Identifying and Structuring Challenges in Large-Scale Agile Development Based on a Structured Literature Review

Over the last two decades, agile methods have transformed and brought unique changes to software development practice by strongly emphasizing team collaboration, customer involvement, and change tolerance. The success of agile methods for small, co-located teams has inspired organizations to increasingly apply agile practices to large-scale efforts. Since these methods are originally designed for small teams, unprecedented challenges occur when introducing them at larger scale, such as inter-team coordination and communication, dependencies with other organizational units or general resistances to changes. Compared to the rich body of agile software development literature describing typical challenges, recurring challenges of stakeholders and initiatives in large-scale agile development has not yet been studied through secondary studies sufficiently. With this paper, we aim to fill this gap by presenting a structured literature review on challenges in large-scale agile development. We identified 79 challenges grouped into eleven categories.
Step Length Estimation Using UWB Technology: A Preliminary Evaluation

The information about the pedestrians' step length is useful for a great variety of applications. The technologies that are commonly selected for estimating the step length offer very high accuracies, such as millimeter-level accuracy in the case of optical systems or centimeter-level with ultrasound technology. However, their use is usually restricted to dedicated small indoor laboratories, which makes it difficult to collect realistic data belonging to day-to-day activities. For this reason, despite their high precision, these systems are not usually used in the field of inertial pedestrian navigation, in which it is common to have to train step length models. UWB technology offers only decimeter-level ranging accuracy but provides larger coverage areas. Therefore, it could be considered as a candidate technology for step length estimation purposes in more larger testing scenarios, which can be useful for the training of pedestrian navigation systems. In this paper, it is presented a preliminary evaluation of the accuracy of UWB technology for step length estimation purposes. Four different methods are implemented and tested: one based on the variations of the ranges to a fixed point, another based on the inter-feet ranges and two more methods based on the relative positions between footsteps. The method based on the inter-feet ranges obtained an average step length error of 9.9 cm $(\pm\mathbf{7.1}$ cm) and a median relative error of 15%, while one of methods based on the feet positions got an average error of 11.9 cm $(\pm\mathbf{6.3}$ cm), but only a median error of 5.9 cm if Line-Of-Sight (LOS) scenarios are only considered. These preliminary results confirm that errors of a few centimeters can be achieved. Now, further research is needed in order to evaluate if the combination of the accuracy and coverage provided by UWB is really useful for training pedestrian navigation systems.
Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network

Human identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of deep learning algorithms, studies that are based on the human gait using convolutional neural networks (CNNs) and long short-term memory (LSTM) have achieved promising performance for human identification. However, CNN and LSTM-based methods have the limitation of having higher loss of temporal and spatial information, respectively. In our approach, we use shallow CNN stacked with LSTM and deep CNN followed by score fusion to capture more spatial and temporal features. In addition, there have been a few studies regarding gait-based human identification based on the front and back view images of humans captured in low-illumination environments. This makes it difficult to extract conventional features, such as skeleton joints, cycle, cadence, and the lengths of walking strides. To overcome these problems, we designed our method considering the front and back view images captured in both high- and low-illumination environments. The experimental results obtained using a self-collected database and the open database of the institute of automation Chinese academy of sciences gait dataset C show that the proposed method outperforms previous methods.
Semantic Interoperability for Demand-Response programs: RESPOND project’s use case

It is well known that peak demand nowadays has a negative impact on energy grid capital, operational cost and environmental aspects. Specially due to the carbon-intense generation plants that are deployed to address these peak demands. Demand side management activities such as peak demand curtailment or load reallocation, have a huge potential to match energy demand with energy supply side as well as for a better management of their disparity. This is particularly true for residential buildings which are still a largely untapped sector. Since renewable energy sources (e.g. solar and wind power) are increasingly penetrating the energy production side, their dependence on the weather and climatic conditions largely influences their management and exploitation. In order to tackle these issues, Demand Response (DR) programs are introduced into the smart grids so that reliable and economical operation of power systems are ensured. The RESPOND project aims to deploy an interoperable energy automation, monitoring and control solution that will deliver demand response at a dwelling, building and district level getting advantage of the user engagement to achieve a cooperative demand response behavior. In this paper, focus is placed on the data integration and semantic interoperability issues identified in this project. In order to deal with data heterogeneity and its consequent integration and interoperability issues, the RESPOND project envisions the use of Semantic Technologies and a Canonical Data Model.
E-Learning Adoption in Rural-Based Higher Education Institutions in South Africa

In South African tertiary institutions, adoption rate for e-learning, is higher in urban-based higher education institutions and lower in rural-based higher education institutions. Even though with the supposed capacity that e-learning provides, the bigger part of the e-learning approach in rural-based higher education institutions do not achieve their potential. This study focuses on identifying the perception and usage of e-learning which impact the adoption at rural based, higher education institutions. A conceptual framework was proposed using the UTAUT (Unified Theory of Acceptance Model) to identify the usage, perception and location challenges of e-learning. This study uses case-University of Venda towards a quantitative approach for data collection and analysis. The results indicated that students have a positive perception towards technology, however the main factor which influences e-learning adoption is the lack of institutional support from management and educators which is one of the underlying issues of e-learning adoption in rural-based HEIs.
SNOW: Detecting Shopping Groups Using WiFi

Detecting shopping groups is gaining popularity as it enables various applications ranging from marketing to advertising. Existing methods exploit WiFi probe requests to detect shopping groups by identifying co-located customers. However, the probe request is prone to suffer from device heterogeneity which might pose a severe data sparseness problem. More importantly, we find that a certain amount of shopping groups would separate sometimes which makes traditional methods unreliable. In this paper, we propose a shopping group detection system using WiFi (SNOW). Instead of collecting probe requests, SNOW utilizes the WiFi data from smartphones associated with the deployed access points (APs). We could thus obtain data from different devices and even ensure a data granularity of seconds using Arping. Besides, we exploit an effective heuristic extracted from two observations of shopping group dynamics to improve the detection performance. First, the probability of group separation differs in diverse areas. Second, the proportion of group participation and individual engagement differs in different activities of the mall. Therefore, APs under which shopping groups appear more frequently and barely separate should contribute more in measuring customer similarity. Lastly, we represent the measured similarity into a matrix format and apply matrix factorization with a sparsity constraint to derive grouping results directly. According to our experiments in a large shopping mall, SNOW improves the detection performance of baseline approaches by 13.2% on average.