Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey on VANET Security Services in Traffic Management System

Recently, vehicular ad hoc networks (VANETs) embark a great deal of attention in the area of wireless and communication technology and are becoming one of the prominent research areas in the intelligent transportation system (ITS) because they provide safety and precautionary measures to the drivers and passengers, respectively. VANETs are quite different from the mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and their application. In this paper, we summarize the recent state-of-the-art methods of VANETs by discussing their architecture, security, and challenges. Secondly, we discuss the detailed analysis of security schemes and the possible measures to provide secure communication in VANETs. Then, we comprehensively cover the authentication schemes, which is able to protect the vehicular network from malicious nodes and fake messages. Thus, it provides security in VANETs. Thirdly, we cover the mobility and network simulators, as well as other simulation tools, followed by the performance of authentication schemes. Finally, we discuss the comfort and safety applications of VANETs. In sum, this paper comprehensively covers the entire VANET system and its applications by filling the gaps of existing surveys and incorporating the latest trends in VANETs.
An Efficient Multilevel Fast Multipole Algorithm to Solve Volume Integral Equation for Arbitrary Inhomogeneous Bi-Anisotropic Objects

A volume integral equation (VIE) based on the mixed-potential representation is presented to analyze the electromagnetic scattering from objects involving inhomogeneous bi-anisotropic materials. By discretizing the objects using tetrahedrons on which the commonly used Schaubert-Wilton-Glisson (SWG) basis functions are defined, the matrix equation is derived using the method of moments (MoM) combined with the Galerkin’s testing. Further, adopting an integral strategy of tetrahedron-to-tetrahedron scheme, the multilevel fast multipole algorithm (MLFMA) is proposed to accelerate the iterative solution, which is further improved by using the spherical harmonics expansion with a faster implementation and low memory requirement. The memory requirement of the radiation patterns of basis functions in the proposed MLFMA is several times less than that in the conventional MLFMA.
Articulation Rate as a Metric in Spoken Language Assessment

Automated evaluation of non-native pronunciation provides a consistent and more cost-efficient alternative to human evaluation. To that end, there is considerable interest in deriving metrics that are based on the cues human listeners use to judge pronunciation. Previous research reported the use of phonetic features such as vowel characteristics in automated spoken language evaluation. The present study extends this line of work on the significance of phonetic features in automated evaluation of L2 speech (both assessment and feedback). Predictive modelling techniques examined the relationship between various articulation rate metrics one the one hand, and the proficiency and L1 background of non-native English speakers on the other. It was found that the optimal predictive model was one in which the phonetic details of phoneme articulation were factored in the analysis of articulation rate. Model performance varied also according to the L1 background of speakers. The implications for assessment and feedback are discussed.
Do university residential experiences contribute to holistic education?

ABSTRACT Can residential experiences help undergraduates develop holistic competencies? To answer this question, a study was conducted based on the insights of 211 residents in Hong Kong. Deductive and inductive content analyses were respectively used to examine residence life outcomes, particularly in holistic competencies, and to categorise the types of residential experiences deemed to be effective for these outcomes. Results revealed that participation in hall activities led to the development of residence life outcomes, including justice, humanity, wisdom, courage, transcendence, and wellbeing. Furthermore, planning/organising hall activities greatly improved students’ sense of justice while interpersonal dynamics facilitated the improvement of humanity only. The analyses indicated that students without specific engagement in hall activities were also able to develop temperance, particularly in self-regulation. Recommendations are suggested based on the analyses. It is hoped that this study might help inform policy-making processes to provide better residential environment that is beneficial to student holistic development.
Augmented Experiences: Investigating the Feasibility of Virtual Reality as Part of a Workplace Wellbeing Intervention

This exploratory experiment tested the effect of two virtual reality (VR) relaxation interventions on measures of physiological arousal and affect, compared to a control. Forty-nine participants were randomly assigned to one of three conditions, all using the Oculus Rift. Participants wore a heart rate (HR) monitor and completed pre- and post-test surveys, including the positive and negative affect schedule, Likert-type and open feedback questions. Mixed repeated measures ANOVAs revealed significant reductions in HR and affect over time across all conditions, with a significant interaction identified for the breathing condition’s effect on negative affect. Participants were able to identify feeling relaxation, as well as influencing factors. Further, the majority expected, and supported, the introduction of VR interventions in their workplace and/or college, with privacy being a common concern. These results demonstrate the potential of VR augmented relaxation interventions with recommendations for further study provided.VR can potentially provide wellbeing interventions with a uniquely immersive quality Results suggest interactive and immersive VR experiences may facilitate relaxation and reduce stress Participants believe there may be substantial benefits to having VR wellbeing interventions at work and college.
Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures

Fuzzy Clustering is one of the mining techniques that have been used to extract information from Data Streams. The dFuzzStream algorithm is a fuzzy version of the Online-Offline Framework, which consists of two steps: an online step, where a summary structure formed by fuzzy microclusters is built and an offline step, where the micro-clusters are clustered in batch mode. The quality of the data summary depends on the criteria used to decide whether an example starts a new micro-cluster or is absorbed by the existing ones; and whether two microclusters became similar enough to be merged. In d-FuzzStream algorithm such decisions are based on concepts of fuzzy dispersion and a distance-based fuzzy clusters similarity. In this paper we investigate the behavior of different fuzzy similarity measures on the decision of merging two fuzzy micro-clusters during the online step. Experiments were run using five synthetic data sets and four fuzzy similarity measures. The results obtained are analyzed and discussed through informative and purity measures.
A Preliminary Study of Charismatic Speech on YouTube: Correlating Prosodic Variation with Counts of Subscribers, Views and Likes

This paper is a first investigation into the influence of the pitch range and the intensity variation on the number of subscribers, views and likes of YouTube Creators. A total of ten minutes of speech material from five English and five North-American YouTubers was analyzed. The results for pitch range and intensity variation suggest that an increase in both parameters results in higher subscriber counts. For views, there was no influence of pitch range, but an increase in intensity variation results in a lower number of views. Pitch range and intensity variation had no influence on the like count. Furthermore, both origin and gender had an influence on the results. Ultimately, this study will provide further information for the phonetic research of charisma (i.e., the perceived charm, competence, power, and persuasiveness of a speaker), as it is suspected that the acoustic features that have so far been connected to charisma also play an important role in the success of a YouTuber and their channel.
Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data

Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents’ transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.
Trimming Extreme Opinions in Preference Aggregation

The use of trimmed mean mechanisms in collective decision-making is motivated by the perception that they constitute a remedy for strategic misreporting. This work focuses on the strategic calculus of voting under such mechanisms and –contrary to the above presumption– it demonstrates both formally and experimentally that: a) voters persistently resort to strategic polarization for all but the most extreme levels of trimming and b) the outcome is more extreme and closer to the ideal policy of the median voter compared to when trimming does not take place. These so far uncharted properties of trimming provide novel insights –and call for caution– regarding its implementation.
Analysis of Control Valves Stiction Quantification Tool

Control valve stiction is considered as one of the main sources of control loops nonlinearities which impacts plants profitability. In turn, this phenomenon hinders the plant from being operated at optimal conditions. Therefore, an efficient and accurate stiction quantification algorithm is required for accurate stiction compensation and timely scheduling of control valve maintenance. This research investigates the robustness and recommends improvements to the previously developed stiction quantification approach by Zabiri et al. The approach was tested under several operating conditions which were simulated in five case studies by using MATLAB software. The case studies investigated the impact of a wide range of stiction values, controller tuning, disturbance, time delay and noise on the quantification approach. The algorithm was found to be robust since it quantified the correct values of stiction regardless of the operating conditions. It was found that the accuracy of the quantification results depends on the process model accuracy, number of data samples and the search resolution. A number of improvements were recommended and validated by simulation in order to further enhance the current quantification approach. As conclusion, the algorithm can be applied on any type of process due to its robustness.
An Energy-Efficient Deep Reinforcement Learning Accelerator With Transposable PE Array and Experience Compression

In this letter, we propose a low power deep reinforcement learning (DRL) SoC, supporting CNN and learning-optimized RNN, and fully connected layer. The adaptive reuse of weights and inputs, and data encoding/decoding techniques reduces power consumption and peak memory bandwidth of DRL processing by 31% and 41%, respectively. The 65-nm 16-mm2 chip achieves a peak 2.16 TFLOPS/W at 0.73 V and 204 GFLOPS at 1.1 V with 16-bit data.
Translational Pharmacologic Efficacy Studies of Glial Growth Factor 2 (GGF2) in Spinal Cord Injury Models and in the Veterinary Clinical Setting

Abstract : The work performed under this grant evaluated the efficacy of Glial Growth Factor 2 (GGF2), a member for the neuregulin family, as a spinal cord injury (SCI) therapeutic. The research expanded on previous work and extended these studies to determine if GGF2 should be advanced into human clinical studies for SCI. GGF2 s optimal dosage was examined using a model of rodent SCI and testing several dose levels delivered either subcutaneously (SC) or intravenously (IV). Optimized dosing was determined to be by a SC route, and subsequent studies evaluated recovery of function by monitoring bladder function, muscle volume changes, and motor function in SCI rodent models of various severities. Additionally, safety and tolerability was explored in naive canines, and followed by a clinical trial for safety in dogs with SCI.
Construction Conflict Management and Resolution

This paper attempts to draw together the principal themes of the UMIST First International Conference on Construction Conflict: Management and Resolution and to identify the most important perspectives of the papers presented. The subjects covered include the phenomena of conflict and their management, experiences of traditional resolution mechanisms ie litigation and arbitration. Alternative Dispute Resolution (ADR) and education/attitude changes.
Simultaneous Management of Peak-Power and Reliability in Heterogeneous Multicore Embedded Systems

Analysis of reliability, power, and performance at hardware and software levels due to heterogeneity is a crucial requirement for heterogeneous multicore embedded systems. Escalating power densities have led to thermal issues for heterogeneous multicore embedded systems. This paper proposes a peak-power-aware reliability management scheme to meet power constraints through distributing power density on the whole chip such that reliability targets are satisfied. In this paper, we consider peak power consumption as a system-level power constraint to prevent system failure. To balance the power consumption, we also employ a Dynamic Frequency Scaling (DFS) method to further reduce peak power consumption and satisfy thermal constraints on the chip. We illustrate the benefits of our scheme by comparing it with state-of-the-art schemes, resulting in average in 26.5 percent less peak power consumption (up to 54.3 percent).
Inter-Metropolitan Land-Price Characteristics and Patterns in the Beijing-Tianjin-Hebei Urban Agglomeration in China

The continuous expansion of urban areas in China has increased cohesion and synergy among cities. As a result, the land price in an urban area is not only affected by the city’s own factors, but also by its interaction with nearby cities. Understanding the characteristics, types, and patterns of urban interaction is of critical importance in regulating the land market and promoting coordinated regional development. In this study, we integrated a gravity model with an improved Voronoi diagram model to investigate the gravitational characteristics, types of action, gravitational patterns, and problems of land market development in the Beijing-Tianjin-Hebei urban agglomeration region based on social, economic, transportation, and comprehensive land-price data from 2017. The results showed that the gravitational value of land prices for Beijing, Tianjin, Langfang, and Tangshan cities (11.24–63.35) is significantly higher than that for other cities (0–6.09). The gravitational structures are closely connected for cities around Beijing and Tianjin, but loosely connected for peripheral cities. Further, various types of radiation, conduction, and convection actions exist in relation to urban land prices. In terms of gravitational patterns, the range of influence of land prices is not limited to the administrative boundaries of each city. Five clusters of urban land prices can be identified based on the gravitational structure. The land-price gravity value of the city cluster around Beijing accounted for 66.4% of the total. The polarizing effect of land-price levels and influence is clearly evident in Beijing and Tianjin, while a lock-in effect is evident in Xingtai and Handan in the south of the region.
A Light Convolutional GRU-RNN Deep Feature Extractor for ASV Spoofing Detection

The aim of this work is to develop a single anti-spoofing system which can be applied to effectively detect all the types of spoofing attacks considered in the ASVspoof 2019 Challenge: text-to-speech, voice conversion and replay based attacks. To achieve this, we propose the use of a Light Convolutional Gated Recurrent Neural Network (LC-GRNN) as a deep feature extractor to robustly represent speech signals as utterance-level embeddings, which are later used by a back-end recognizer which performs the final genuine/spoofed classification. This novel architecture combines the ability of light convolutional layers for extracting discriminative features at frame level with the capacity of gated recurrent unit based RNNs for learning long-term dependencies of the subsequent deep features. The proposed system has been presented as a contribution to the ASVspoof 2019 Challenge, and the results show a significant improvement in comparison with the baseline systems. Moreover, experiments were also carried out on the ASVspoof 2015 and 2017 corpora, and the results indicate that our proposal clearly outperforms other popular methods recently proposed and other similar deep feature based systems.
Masking‐Level Differences as a Function of Interaural Disparities in Intensity of Signal and of Noise

A marked release from masking, or masking‐level difference (MLD), results when a sinusoid is presented monaurally Sm rather than binaurally and in phase S0 with correlated (in phase) noise N0. If the sinusoid in one ear, rather than being removed entirely, is reduced in intensity relative to that in the other, a smaller MLD should result. MLD's were determined as a function of the ratio of the energies of the right‐ and left‐ear sinusoids. This signal (500 cps, 0.25 sec) was presented in one or the other of two temporal intervals against a background of white noise N0. Also, MLD's result when Nm‐Sm is changed to N0‐Sm. Thus, MLD's were determined as a function of the relative intensity of the noise in the nonsignal ear with noise correlations of N0, Nπ, and NU. For N0, there is some release from masking even when the noise in the nonsignal ear is as much as 40 dB down from that in the signal ear. [Work supported by the U. S. Air Force Office of Scientific Research. ]
Speaker-Invariant Feature-Mapping for Distant Speech Recognition via Adversarial Teacher-Student Learning

Feature mapping (FM) jointly trained with acoustic model (AFM) is commonly used for single-channel speech enhancement. However, the performance is affected by the inter-speaker variability. In this paper, we propose speaker-invariant AFM (SIAFM) aiming at curtailing the inter-talker variability while achieving speech enhancement. In SIAFM, a feature-mapping network, an acoustic model and a speaker classifier network are jointly optimized to minimize the feature-mapping loss and the senone classification loss, and simultaneously min-maximize the speaker classification loss. Evaluated on AMI dataset, the proposed SIAFM achieves 4.8% and 7.0% relative word error rate (WER) reduction on the overlapped and non-overlapped condition over the baseline acoustic model trained with single distant microphone (SDM) data. Additionally, the SIAFM obtains 3.0% relative overlapped WER and 4.2% relative nonoverlapped WER decrease over the multi-conditional (MCT) acoustic model. To further promote the performance of SIAFM, we employ teacher-student learning (TS), in which the posterior probabilities generated by the individual headset microphone (IHM) data can be used in lieu of labels to train the SIAFM model. The experiments show that compared with MCT model, SIAFM with TS (SIAFM-TS) can reach 4.2% relative overlapped WER and 6.3% relative non-overlapped WER decrease respectively.
The effects of blending and segmenting decoding strategies on reading

This study investigated the effects of a reading intervention that integrated blending and segmenting decoding strategies on the word recognition performance of five first grade students who were considered below grade level expectations set by the school district. The intervention evaluated students’ abilities to decode short vowel closed syllable words with digraphs through blending and segmenting phonemes. Six assessments were used to analyze the effects of the decoding performance of the five students. All students demonstrated significant gains in their ability to decode short vowels and digraphs, and segment phonemes. Students also increased their accuracy when reading words in isolation, and spelling words. The effects suggest the impact of incorporating blending and segmenting strategies when designing an intervention for students reading below grade level expectations. 4 EFFECTS OF BLENDING AND SEGMENTING STRATEGIES Table of
Evaluation of Block-Wise Parameter Generation for Statistical Parametric Speech Synthesis

In this study, we evaluate the spectral distortion caused by a block-wise parameter generation method in statistical parametric speech synthesis. Although an entire utterance is optimized in the conventional calculation algorithm, in this study, calculation blocks equally divided from the entire sequence are sequentially optimized to reduce required RAM size. To reduce distortion, several extra frames following the calculation block are taken into account in the calculation ﬁrst, then the extra frames are discarded after the calculation. In addition, two methods are also proposed for computational load leveling. In the experiment, the mean and maximum of mel-cepstral distortion (MCD) were examined for synthetic sounds of 503 Japanese sentences using HMMs trained from 10.5 hours of male speech sounds. The results show that the MCD from the conventional method rapidly decreases by increasing the number of discarded extra frames.