Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Bond in Dense WLANs With Random Traffic Demands

The Access Points (APs) in a Wireless Local Area Network (WLAN) must be assigned one or more channels to meet traffic demands from users. To date, prior works on channel assignment assume APs have a fixed traffic demand, meaning they do not consider or adapt to spatio-temporal changes in traffic demands. To this end, we leverage Deep Reinforcement Learning (DRL), where we equip APs with a DRL-based channel assignment solution, to maximize the average number of slots in which an AP has sufficient bandwidth to meet user demands. Our APs learn from their historical traffic loads and assign themselves partially overlapping channels with minimal interference. Simulation results show that our DRL solution leads to APs satisfying 60% more user demands as compared to fixed and greedy channel bonding algorithms.
A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Interpreting a speech signal is quite challenging because it consists of different frequencies and features that vary according to emotions. Although different algorithms are being developed in the speech emotion recognition (SER) domain, the success rates vary according to the spoken languages, emotions, and databases. In this study, a new lightweight effective SER method has been developed that has low computational complexity. This method, called 1BTPDN, is applied on RAVDESS, EMO-DB, SAVEE, and EMOVO databases. First, low-pass filter coefficients are obtained by applying a one-dimensional discrete wavelet transform on the raw audio data. The features are extracted by applying textural analysis methods, a one-dimensional local binary pattern, and a one-dimensional local ternary pattern to each filter. Using neighborhood component analysis, the most dominant 1024 features are selected from 7680 features while the other features are discarded. These 1024 features are selected as the input of the classifier which is a third-degree polynomial kernel-based support vector machine. The success rates of the 1BTPDN reached 95.16%, 89.16%, 76.67%, and 74.31% in the RAVDESS, EMO-DB, SAVEE, and EMOVO databases, respectively. The recognition rates are higher compared to many textural, acoustic, and deep learning state-of-the-art SER methods.
Effect of IGFBP2 Overexpression on the Expression of Fatty Acid Synthesis Genes in Primary Cultured Chicken Hepatocytes

The effects of insulin-like growth factor binding protein 2 (IGFBP2) on the expression of fatty acid synthesis regulators and triglyceride production were investigated in primary cultured chicken hepatocytes. The full-length chicken IGFBP2 coding region was synthesized by overlap extension PCR and cloned into the pcDNA3.1 vector. An in situ digestion method was used to prepare the chicken hepatocytes. Primary chicken hepatocytes were maintained in monolayer culture. Real-time PCR was used to detect changes in the expression of IGFBP2, PPARG, IGF1, IGF1R, APOAI, and LFABP, after the overexpression of IGFBP2 in chicken hepatocytes. Triglyceride production and glucose content were also evaluated using triglyceride and glucose analysis methods. The expression level of IGFBP2 increased after transfection of the IGFBP2-containing vector. The expression levels of PPARG, IGF1, and IGF1R also increased in cultured chicken hepatocytes after the overexpression of IGFBP2, whereas the expression of LFABP and APOAI decreased. Triglyceride production in primary cultured chicken hepatocytes increased after the overexpression of IGFBP2. These results suggest that IGFBP2 is involved in lipogenesis, increasing both the expression of fatty acid synthesis regulators, and triglyceride production in primary cultured chicken hepatocytes.
Diet and macronutrient niche of Asiatic black bear (Ursus thibetanus) in two regions of Nepal during summer and autumn

Abstract Relatively little is known about the nutritional ecology of omnivorous Asiatic black bears (Ursus thibetanus) in Nepal. We characterized the diet of black bears in two seasons (June–July, “summer”; and October–November “autumn”) and two study areas (Dhorpatan Hunting Reserve [DHR]; and Kailash Sacred Landscape [KSL]). We then conducted nutritional analysis of species consumed by black bears in each study area, in combination with nutritional estimates from the literature, to estimate the proportions of macronutrients (i.e., protein [P], lipid [L], and carbohydrate [C]) in the seasonal bear foods and diets, as well as their macronutrient niche breadth. We found that bamboo (Arundinaria spp.) had the highest relative frequency in both study areas and seasons. Ants and termites were found in DHR diets, but not KSL diets. One anthropogenic crop was found in DHR summer diets (Zea mays) and two were found in KSL summer diets (Z. mays; and Kodo millet [Paspalum scrobiculatum]). Other than insects, no animal prey was found in either diet. The proportions of macronutrients in diets (i.e., realized macronutrient niches) were relatively high in carbohydrate for both study areas and seasons: DHRsummer 24.1P:8.7L:67.2C; KSLsummer 16.7P:8.2L:75.1C; DHRautumn 21.1P:10.5L:68.4C; KSHautumn 19.0P:11.0L:70.0C. Macronutrient niche breadth was 3.1 × greater in the DHR than KSL during summer, and 4.0 × greater in the autumn, primarily due to the higher proportion of lipid in ants and termites relative to plant foods. Within‐study area differences in niche breadth were greater during summer than autumn; in the KSH the macronutrient breadth was 1.4 × greater in summer, while in the DHR it was 1.1 × greater in summer. Similarity in dietary macronutrient proportions despite differences in foods consumed and niche breadth are suggestive of foraging to reach a preferred macronutrient balance.
A Virtual Space Vector Model Predictive Control for a Seven-Level Hybrid Multilevel Converter

This article proposes a virtual space vector (VSV) model predictive control (MPC) for a three-phase seven-level (7L) hybrid multilevel converter (HMC), where each phase consists of an active-neutral-point-clamped converter with a floating H-bridge. To achieve the best current tracking, which is the primary goal of the proposed algorithm, a novel geometrical positioning approach is proposed to select the optimal voltage vector among all the real-space vectors and VSVs. Then, all the possible switching sequences that belong to the optimal voltage vector are evaluated to realize the dc capacitor voltage balancing and common-mode voltage reduction. Through an external modulator, the optimal voltage vector can be synthesized by using either one-, three-, or seven-segment switching sequence. Compared with the conventional MPC, the proposed VSV-MPC can reduce not only the computational burden but also the current THD. Both simulation and experimental results obtained on silicon carbide based 7L-HMC prototype are presented to validate the feasibility and effectiveness of the proposed VSV-MPC strategy.
Ultra-Low on-State Voltage IGCT for Solid-State DC Circuit Breaker With Single-Switching Attribute

The performance of solid-state dc circuit breaker (SS-DCCB) is mainly determined by the <sc>on</sc>-state feature of power device. In this article, an optimized low <sc>on</sc>-state voltage integrated gate-commutated thyristor (LO-IGCT) for SS-DCCB is developed. First, the impacts of device parameters, including base length, carrier lifetime, and emitter injection efficiencies, on <sc>on</sc>-state voltage are presented. The boundary conditions raised by blocking voltage and safe operation area (SOA) are analyzed. Based on that, a three-step blocking-SOA-emitter optimization routine is proposed. Then, the optimization methodology for doping profiles in different regions, especially in <italic>n</italic>-drift, <italic>n</italic> buffer, <italic>p</italic><sup>+</sup> base, <italic>n</italic><sup>+</sup> emitter, and <italic>p</italic><sup>+</sup> emitter regions, is presented. The optimal values are derived with theoretical deduction or numerical calculation. After that, based the optimization result, samples of LO-IGCTs are fabricated and tested. The fabricated LO-IGCT sample shows a blocking voltage capability over 4500 V and an <sc>on</sc>-state voltage of 1.11 V at the current of 2000 A. The commercialized IGCT and IGBT optimized for high-frequency applications exhibits 1.23 and 1.85 times the <sc>on</sc>-state voltage under the same test configuration.
Influence of EMG Data from Different Days on Feature Selection Methods

Feature selection is an method which can effectively reduce the dimension of electromyography (EMG) feature vector. Previous studies only focused on the performance of feature selection method in the same day. However, the distribution of EMG data from different days is different. And few studies focused on the influence of different day’s EMG data on feature selection. We mainly discussed the influence of different days’ EMG data on feature selection methods in this study. We adopt three feature selection methods (none feature selection (NFS), sequential forward selection (SFS) and particle swam optimization (PSO)) in this study. Six subjects performed eleven upper limb motions, and the EMG signal was collect in consecutive five days. First, we use the data from the first day for feature selection. After feature selection, we used the EMG feature vectors after feature selection to train a support vector machine (SVM). We performed the same feature selection as the first day on the EMG data of remaining four days. Then, we used the SVM trained in the first day to classify the EMG data of each four days. Finally, we discussed the performance of SVM under three different feature selection methods in different days. The results indicated that EMG data from different days have insignificant influence on feature selection methods (p=0.218). SVM with PSO had a shorter time consumption when compare to NFS (p < 0.05) and SFS(p < 0.05). PSO feature selection method is more suitable to improve the real-time performance of EMG pattern recognition under different days.
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems

In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However, its performance on a wide variety of many-objective test problems has not been examined in the literature. It has been implicitly assumed by EMO researchers that NSGA-II does not work well on many-objective problems. As a result, NSGA-II has always been excluded from performance comparison with recently proposed many-objective EMO algorithms. Recently, it was pointed out that the performance of NSGA-II on many-objective problems is not always bad. In fact, the poor performance of NSGA-II on many-objective problems is mainly due to the existence of dominance resistant solutions. In this article, we show that the negative effect of the dominance resistant solutions can be remedied by slightly modifying objective values of many-objective problems in NSGA-II. Experimental results show that the modified NSGA-II works well on a wide variety of many-objective test problems.
Teleoperation of two non-isomorphic manipulators by Joint Space and Task Space Mapping

This paper presents real time teleoperation of a six degrees of freedom Serial manipulator with a six degrees of freedom Parallel manipulator. Generally for isomorphic manipulators joint space mapping is used where as for non-isomorphic manipulators task space mapping is performed. There are certain cases where even in non-isomorphic manipulators joint-space mapping is used, though it is not very intuitive. In this works teleoperation of the manipulators is implemented by both by Joint space and the Task space mapping of the manipulators. Teleoperation with both manipulators controllers in same PC and Teleoperation over the internet using TCP/IP communication is carried out and the experimental results are presented.
Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning

The prognosis of cardiovascular and cerebrovascular events for patients suffering from hypertension is considered of a high importance in preventing any further development of cardiac diseases. Despite of the ability of current gold standard techniques in predicting vascular events risks, they still lack the required clinical efficiency. In this vein, the study proposed herein provides an investigation on the feasibility of using heart rate variability (HRV) utilized through a machine learning approach to predict hypertensive patients at higher risk of developing vascular events. Initially, HRV features were extracted from all patient’s data using time-domain, frequency-domain, non-linear, and fragmentation metrics. The extraction of features was based on a 24-hour cycle analysis segmented into four time periods; namely late-night, early-morning, afternoon, and evening. Analysis of all features was performed using a one-way analysis of variance (ANOVA) test on period by period basis. Furthermore, the selection of best features was performed following a Chi-squared test for demographic and HRV features. Then, a model based on decision trees and random under-sampling boosting (RUSBOOST) was trained using demographic features, HRV features, and a combination of both features. The performance of the trained model achieved a maximum accuracy of 97.08% using the combined set of features during the afternoon time period. In addition, the precision and F1-score in predicting high risk patients reached 81.25% and 86.67%, respectively. The overall area under the curve for the model was at 0.98, suggesting a high performance in the sensitivity and specificity measures. This study paves the way towards utilizing machine learning models and heart rate variability for the prognosis of vascular events in hypertensive patient. Furthermore, it assists clinicians in decision making by providing a simple, yet effective, and continuous prediction approach when compared to other available techniques.
Centralized Coordination of Autonomous Vehicles at Intersections

Recent advances in autonomous vehicles present new opportunities in Intelligent transportation systems (ITS) to address urban transport challenges. Therefore, urban traffic scenarios, and in particular intersections as a bottleneck of transportation network, has received significant attention. In this paper we investigate intelligent traffic control mechanisms for autonomous vehicles at intersections as a replacement of traditional intersection control. An edge cloud controller is used to deliver services that provide traffic safety and efficiency to vehicles. Two well-cited optimization algorithms for cooperative vehicles are compared with realistic simulations in SUMO. We investigated the safety and possibility of implementing the proposed algorithms in the real world. This side by side comparison helps to gain insight into the strengths and limitations of these types of algorithms.
Multi-Beam Index Modulation With Cooperative Legitimate Users Schemes Based on Frequency Diverse Array

In this paper, a novel multi-beam transmission scheme is proposed to enhance physical layer (PHY) security. This scheme utilizes index modulation (IM) based on frequency diverse array (FDA) beamforming with cooperative legitimate users (LUs). By leveraging IM based on FDA, the indices associated with multiple LUs are directly used as additional sources to transmit information. Using this scheme, the confidential messages can be retrieved only if all the index information and the corresponding symbols are obtained. This adds an extra level of PHY security hence reduces the possibility of information interception by eavesdroppers (Eves). Here instead of maximizing the secrecy rate, we propose a minimum message transmission power criterion to design the FDA beamforming vector, subject to prescribed minimum received power at each of the LUs, in addition to the orthogonality constraints of the remaining LUs. This provides a reliable and accurate transmission assurance for the LUs to obtain the corresponding messages under minimum message transmission power. To further deteriorate the quality of the received signals at Eves, we also add artificial noise (AN). Moreover, by elaborating the time-variant characteristics, we provide a new and more accurate perspective for the range-time dimension secure transmission. Numerical simulations confirm the effectiveness of the proposed scheme.
Towards a Generic Manipulation Framework for Robots based on Model Predictive Interaction Control

This paper presents an optimization-based control framework for robotic manipulation tasks. A hierarchical and systematic decomposition is used in order to formulate the high-level task as a sequence of manipulation primitives. A key difficulty is that different primitives usually require different strategies to control motions and forces. However, based on the concept of model predictive interaction control (MPIC), it is possible to use the same control approach for all primitives. Instead of changing the controller, only the parameterization of the MPIC, i.e. the cost function and the constraints, is adapted for each primitive. The hierarchical task decomposition and the generic control concept are demonstrated for a screwing application using a robot arm with seven degrees of freedom.
Robust Audio-visual Enhancement elicited by Exogenous Bimodal Cue

Studies found that the exogenous cue influences the interaction between auditory stimulus and visual stimulus, however, the current studies rarely investigate how cue modality influences the interplay between audio-visual integration and exogenous attention. To make clear the different effect of bimodal cue and unimodal cue on audio-visual integration, an auditoryvisual discrimination task was conducted. The analysis for the response time analysis illustrated that the response to audio-visual stimuli was obviously faster than that to auditory and vosual stimuli (all p<0.011) under all of the cue conditions, and a faster response to the valid cued condition than that to invalid cued condition and no cue condition (all p< 0.010), indicating significant audio-visual integration and cueing effect were occurred. The further analysis showed significant greater audiovisual enhancement under bimodal cue condition than unimodal cue conditions (all ${p}\leq 0.040$) regardless of the cue validity. Together with previous studies, this result indicated exogenous bimodal cue can capture more constant and greater attentional resource.
Adaptive Resource Allocation and Consolidation for Scientific Workflow Scheduling in Multi-Cloud Environments

The emerging multi-cloud environments (MCEs) empower the execution of large-scale scientific workflows (SWs) with sufficient resource provisioning. However, due to complex task dependencies in SWs and various cost-performance of cloud resources, the SW scheduling in MCEs faces huge challenges. To address these challenges, we propose an Online Workflow Scheduling algorithm based on Adaptive resource Allocation and Consolidation (OWS-A2C). In OWS-A2C, the deadline reassignment is first executed for SW tasks based on the execution performance of instance resources, which enhances resource utilization from a local perspective when executing an SW. Next, the execution instances are allocated and consolidated according to the performance requirements of multiple SWs, which improves resource utilization and reduces the total costs of executing multiple SWs from a global perspective. Finally, the SW tasks are dynamically scheduled to the execution instances with the earliest-deadline-first (EDF) discipline and completed before their sub-deadlines. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed OWS-A2C on SW scheduling in MCEs, which outperforms three baseline scheduling methods with higher resource utilization and lower execution costs under deadline constraints.
On Recent Security Issues in Machine Learning

In recent years, applications of machine learning have grown rapidly in various areas. With the accelerating rate of data generation, and recent developments in big data analytics, machine learning has become a de facto standard in many applications that benefited the society in many areas. However, with the increasing number and types of machine learning applications, it has become a target for an increasing number of malicious actors. Security challenges became more complex and diverse in machine-learning-based systems.In this paper, we present a concise survey and discussion of the mechanisms employed by attackers to exploit vulnerabilities in machine learning algorithms or injecting malicious data. The paper focuses on most recent attacks reported in literature and discusses the methods proposed to counter these attacks and reduce their impact.
Node Energy Consumption Balanced Multi-Hop Transmission for Underwater Acoustic Sensor Networks Based on Clustering Algorithm

With the advancement and implementation of marine strategies in various countries, it is of great significance to collect marine data by using underwater acoustic sensor networks (UWA-SNs). Due to the difficulties in charging or replacing batteries, how to prolong the lifetime of underwater sensor nodes is one of the key problems to be solved. In this paper, we propose a new energy consumption balanced protocol, named as dynamic clustering K-means (DC-K-means) based simplified balanced energy adaptive routing (S-BEAR), to prolong the lifetime of a UWA-SN by balancing the energy consumption of underwater sensor nodes and avoiding the energy hole. First, by considering the residual energy of the underwater sensor nodes, we propose the DC-K-means algorithm to cluster the underwater sensor nodes and optimize the topology of data transmission among underwater sensor nodes so that the overall energy consumption of the system can be reduced and the problem of energy consumption balance of underwater sensor nodes can be solved. Then, the S-BEAR protocol is designed for multi-hop transmissions, where the data collected by each cluster head is transmitted to the surface receiving terminal equipment, and thus further balances and saves the energy consumption of the system. The simulation results demonstrate that the proposed DC-K-means based S-BEAR scheme has the advantages of more balanced energy consumption among sensor nodes and higher overall residual energy as compared to the state-of-the-art schemes.