Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Edge Computing With Wireless Backhaul: Joint Task Offloading and Resource Allocation

Considered as a key technology in 5G networks, mobile edge computing (MEC) can support intensive computation for energy-constrained and computation-limited mobile users (MUs) through offloading various computation and service functions to the edge of mobile networks. In addition to MEC, wireless heterogeneous networks will play an important role in providing high transmission capacity for MUs in 5G, where wireless backhaul is a cost-effective and viable solution to solve the expensive backhaul deployment issue. In this paper, we consider a setting, where MUs can offload their computations to the MEC server through a small cell base station (SBS), the SBS connects to the macro BS through a wireless backhaul, and computation resource at the MEC server is shared among offloading MUs. First, we formulate a joint optimization problem with the goal of minimizing the system-wide computation overhead. This is a mixed-integer problem and hard to derive the optimal solution. To solve this problem, we propose to decompose it into two subproblems, namely the offloading decision subproblem and the joint backhaul bandwidth and computation resource allocation subproblem. An algorithm, namely JOBCA, is proposed to obtain a feasible solution to the original problem by solving two subproblems iteratively. Finally, numerical results are conducted to verify the performance improvement of the proposed algorithm over two baseline algorithms and the close performance of the proposed algorithm compared with the centralized exhaustive search.
An Empirical Study on Sentiments in Twitter Communities

Sentiment analysis and community detection are two popular research subjects in data mining. Lots of research have been published in recent years that aim to enhance the mining of text using sentiment analysis tools and to mine network structure to find cohesive and important communities in social networks. However, there is a lack of knowledge of the importance of understanding the sentiment and its changes on the community lifetime. In this paper, we aim to study the sentiments and its impact on user behaviour and the evolution of social network communities. To do that, we collect three Twitter datasets, two of which are based on the communications between people who share following links and the third dataset is based on people who talked about world cup subject. Next, we analyse the sentiments of communications to positive, negative or neutral. After that, we detect communities using k-core. Later, we track changes of sentiments in communities for an extended period of time. Our results showed that the positive sentiment is contagious because members of the communities increasingly share positive tweets more than the negative ones over time. Also, we found a strong correlation between positive sentiments and the size of the community in all three datasets. These results lay shed on the existence of like-minded users within the communities which attract social network companies for their viral marketing and recommendation systems.
Reliable Middleware for Wireless Sensor-Actuator Networks

Many important and critical applications use wireless sensor/actuator networks (WSAN). These applications are spanning widely that range in several environments, including industrial, commercial, and residential. Most of these applications need a certain level of real-time communication in addition to reliable data delivery service. Data distribution service (DDS) is a known standard for supporting real-time distributed systems based on the publish/subscribe model. The DDS specification offers two disparate quality-of-service levels of data reliability, namely: best-effort and fully-reliable. TinyDDS is a lightweight and partial porting of DDS middleware to sensor-based platforms, specifically for platforms with limited resources. However, TinyDDS in its current form lacks the reliability support for data delivery. In this paper, we extended the DDS data reliability service and integrated it into TinyDDS, which resulted in Reliable TinyDDS. Moreover, we provide a prototype and comprehensive performance evaluation of the reliability functions taking into account: number of hops, number of publishers, and several other network parameters. The achieved results indicate that reliable data delivery in real-time for WSAN is possible with optimized system parameters, such as retransmission time-out value of 400ms and number of hops not exceeding 10 for an end-to-end delay ranging from 10 to 100s of milliseconds.
Fixed-Time Attitude Tracking Control for Rigid Spacecraft With Actuator Misalignments and Faults

This paper investigates an attitude tracking maneuver problem for a rigid spacecraft with external disturbances, uncertain inertia parameters, actuator misalignments, and faults. First, all the uncertainty terms are split from the desired commanded control torque, and then, the nonsingular terminal sliding mode is implemented to design a fixed-time attitude tracking controller. Subsequently, an adaptive law is drawn into the attitude tracking controller to form a new control scheme, which eliminates the requirement of the upper bounds of all the uncertain terms. Besides, in the light of the concept of fixed-time stabilization, practical fixed-time convergence is achieved for attitude tracking errors. Finally, the numerical simulations are demonstrated to highlight the performance of the proposed controllers.
A Controller Placement Algorithm Based on Density Clustering in SDN

Software-defined networks(SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. The reasonable placement of the controller in SDN would effectively improve the performance of entire network. In this paper, we study clustering-based controller placement algorithm. The clustering by fast search and find of density peaks (CFSFDP) algorithm is a density-based clustering algorithm with less initial parameters and faster execution speed. However, the CFSFDP algorithm does not consider other performance indicators in the network and the number of the clustering centers needs to be manually determined. In this paper, we propose a controller placement algorithm in SDN to reduce propagation delay and communication consumption in the intra-controller and inter-controller. And the number and the location of controllers are discovered automatically through the inflection point of decision graph. We evaluate the performance of the proposed algorithm on internet2 OS3E network topology. The experimental results show that the proposed algorithm provides better performance than the traditional approaches in terms of communication consumption and propagation delay.
Improving FFTNet Vocoder with Noise Shaping and Subband Approaches

Although FFTNet neural vocoders can synthesize speech waveforms in real time, the synthesized speech quality is worse than that of WaveNet vocoders. To improve the synthesized speech quality of FFTNet while ensuring real-time synthesis, residual connections are introduced to enhance the prediction accuracy. Additionally, time-invariant noise shaping and subband approaches, which significantly improve the synthesized speech quality of WaveNet vocoders, are applied. A subband FFTNet vocoder with multiband input is also proposed to directly compensate the phase shift between subbands. The proposed approaches are evaluated through experiments using a Japanese male corpus with a sampling frequency of 16 kHz. The results are compared with those synthesized by the STRAIGHT vocoder without mel-cepstral compression and those from conventional FFTNet and WaveNet vocoders. The proposed approaches are shown to successfully improve the synthesized speech quality of the FFTNet vocoder. In particular, the use of noise shaping enables FFTNet to significantly outperform the STRAIGHT vocoder.
Achieving Performance and Programmability for MapReduce(-Like) Frameworks

Programmability and performance are often considered alternatives in the context of HPC programming systems. For example, general purpose frameworks like MPI are associated with high performance, and though MapReduce and similar frameworks have demonstrated high programmability, it is also well accepted that they fall short in terms of performance. Providing abstractions that maintain high programmability and performance remains an open question. In this paper, we introduce two different variations of the original MapReduce API, We demonstrate efficient implementations of the three APIs, focusing on how the API differences impact middleware implementation, and examine the resulting performance. Furthermore, to understand how application characteristics impact relative performance of the three systems, we develop and validate a performance model. Overall, we show that a MapReduce-like AP that only requires small additional effort from programmers can provide high performance, outperforming Spark significantly.
Spectrum Occupancy Reconstruction in Distributed Cognitive Radio Networks Using Deep Learning

Spectrum occupancy reconstruction is an important issue often encountered in collaborative spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the spectrum sensing data that are collaborated by secondary users have gaps of missing entries. Many data imputation techniques, such as matrix completion techniques, have shown great promise in dealing with missing spectrum sensing observations by reconstructing the spectrum occupancy data matrix. However, matrix completion approaches achieve lower reconstruction resolution due to the use of standard singular value decomposition (SVD), which is designed for more general matrices. In this paper, we consider the problem of spectrum occupancy reconstruction where the spectrum sensing results across the CRN are represented as a plenary grid on a Markov random field. We formulate the problem as a magnetic excitation state recovery problem, and the stochastic gradient descent (SGD) method is applied to solve the matrix factorization. SGD is able to learn and impute the missing values with a low reconstruction error compared with SVD. The graphical and numerical results show that the SGD algorithm competes favorably SVD in the matrix factorization by taking advantage of correlations in multiple dimensions.
Multi-Temporal-Resolution Technique for Action Recognition using C3D: Experimental Study

In any given video containing an action, the motion conveys information complementary to the individual frames. This motion varies in speed for similar actions. Therefore, it is a promising approach to train a separate deep-learning model for different versions of action speeds. In this paper, two novel ideas are explored: single-temporal-resolution single-model (STR-SM) and multi-temporal-resolution multi-model (MTR-MM). The STR-SM model is trained on one specific temporal resolution of the action dataset. This allows the model to accept a longer temporal frame range as input and therefore, a faster action classification. On the other hand, the MTR-MM is a set of STR-SM models, each trained on a different temporal resolution with a late fusion using majority voting achieving more accurate action recognition. Both models have improvements over the traditional training approach, 3.63% and 6% video-wise accuracy respectively.
Rectifiers Based on Quadrature Hybrid Coupler with Improved Performance for Energy Harvesting

This paper presents a method for enhancing RF-dc conversion efficiency of the rectifier based on inkjet-printed quadrature based hybrid coupler (QHC) for wireless energy transfer systems. The input matching network and output load of the rectifier are optimized for optimal RF-dc conversion efficiency. Improvement of about 16 dB input power range is achieved for RF-dc conversion efficiency of 60%. The simulated efficiencies of the rectifiers with and without coupler for 3 MHz LTE signals and output power spectra densities at 1.5 GHz are illustrated.
Explainable Predictions of Adverse Drug Events from Electronic Health Records Via Oracle Coaching

Information about drug efficacy and safety is limited despite current research on adverse drug events (ADEs). Electronic health records (EHRs) may be an overcoming medium, however the application and evaluation of predictive models for ADE detection based on EHRs focus primarily on predictive performance with little emphasis on explainability and clinical relevance of the obtained predictions. This paper therefore aims to provide new means for obtaining explainable and clinically relevant predictions and medical pathways underlying ADEs, by deriving sets of rules leading to explainable ADE predictions via oracle coaching and indirect rule induction. This is achieved by mapping opaque random forest models to explainable decision trees without compromising predictive performance. The results suggest that the average performance of decision trees with oracle coaching exceeds that of random forests for all considered metrics for the task of ADE detection. Relationships between many patient features present in the rulesets and the ADEs appear to exist, however not conforming to the causal pathways implied by the models - which emphasises the need for explainable predictions.
Sequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment

A high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored.
Approaching Rutted Road-Segment Alert using Smartphone

Driving on unfamiliar poorly paved roads is risky even if the vehicle speed is kept under limits. A driver may lose control if his vehicle suddenly comes into road anomalies, especially at night. In developing countries, road anomalies are not only common, but also new ones usually exist without precautions. We present an alerting system that detects and localizes road ruts in order to release a prior-rut notification to the driver using no additional devices but his smartphone. Our system uses crowd-sourcing techniques to collect labeled data from smartphones built-in sensors that describe road ruts. We use this data to feed a machine learning engine to build models that can detect new ruts. Our system localizes identified ruts on the map via GPS coordinates and alerts drivers when they approach a rutted road. Our experiments show that the accuracy of the system can be raised from 59% up to 99% if the learning technique is carefully selected and the sensors data set size is increased to 100000 samples.
Optimization of the permittivity-based BE SOI biosensor

This paper explores for the first time how the electrical characteristic of the BE (Back Enhanced) SOI MOSFETs (n-and p-type) is affected by the introduction of materials with different permittivities, aiming the biological sensing application. The BE SOI MOSFET is a planar undoped SOI device whose working principle is based on the electrical field interaction between the substrate and the front gate. The biomaterial is deposited on the underlap region between source/drain and gate to be sensed. Simulations of the BE SOI were performed using the experimental data. In the dimensions studied range, the best biosensor sensitivities as a function of the relative permittivity are obtained for the thinnest silicon film (5nm) and shortest underlap length (100nm). Other parameters influence on sensitivities like front and back gate oxide thicknesses and transistor channel dimensions are negligible.
Design Considerations for Integrated Radar Chirp Synthesizers

Phase-locked loops (PLLs) effectively generate frequency chirps for frequency-modulated continuous-wave (FMCW) radar and are ideal for integrated circuit implementations. This paper discusses the design requirements for integrated PLLs used as chirp synthesizers for FMCW radar and focuses on an analysis of the radar performance based on the PLL configuration. The fundamental principles of the FMCW radar are reviewed, and the importance of low synthesizer phase noise for reliable target detection is quantified. This paper provides guidance for the design of chirp synthesizer PLLs by analyzing the impact of the PLL configuration on the accuracy and reliability of the radar. The presented analysis approach allows for a straightforward study of the radar performance and quantifies the optimal settings of a PLL-based chirp synthesizer for a given application scenario, while the developed methodology can be easily applied to other scenarios. A novel digital chirp synthesizer PLL design that meets the requirements of FMCW radar is presented. The synthesizer prototype fabricated in 65-nm CMOS drives a radar testbed to verify the effectiveness of the synthesizer design in a complete FMCW radar system.
Ego-Lane Position Identification With Event Warning Applications

This paper proposes a high-performance advanced driver assistance system that analyses driving scenes based on monocular cameras. The system identifies the ego-lane position and indicates if the vehicle is driving on an inner or outer lane. Dense optical flow analysis is performed and a fuzzy system is designed to achieve ego-lane position identification. An event warning application is implemented based on a hierarchical classifier and the results of ego-lane position identification. The proposed event warning system accurately issues events without having to detect vehicles first, making the system more responsive to potential approaching dangers. Also, the proposed system has a comprehensive ability to generate warnings on various types of events. The experimental results validate the effectiveness of the proposed schemes.
Implanted Knee Kinematics Prediction: comparative performance analysis of machine learning techniques

Knee implantation is a popular knee surgery to replace damaged knee joint in Total knee arthroplasty (TKA). It is essential to predict postoperative knee kinematic before the surgery for patient-specific TKA surgical planning because outcome of the TKA operation strongly depends on types of prosthesis and surgical methods. Previously, we proposed postoperative kinematics (A-P and i-e patterns) prediction method based on generalized linear regression (GLR). However, this study mainly focuses on comparative performance analysis of the two popular machine learning methods (SVR and NN) in predictive model construction for postoperative kinematics prediction using PCA-based feature extraction, then compared with GLR method. It was found that predictive model’s prediction performance slightly varies from each other’s because the characteristics features of the kinematic patterns differs from each type. Therefore, this study recommends the best ML method (NN for A-P pattern and GLM for i-e pattern) with high prediction performance for predicting TKA outcome.
Improving Very Deep Time-Delay Neural Network With Vertical-Attention For Effectively Training CTC-Based ASR Systems

The very deep neural network has recently been proposed for speech recognition and achieves significant performance. It has excellent potential for integration with end-to-end (E2E) training. Connectionist temporal classification (CTC) has shown great potential in E2E acoustic modeling. In this study, we investigate deep architectures and techniques which are suitable for CTC-based acoustic modeling. We propose a very deep residual time-delay CTC neural network (VResTD-CTC). How to select a suitable deep architecture optimized with the CTC objective function is crucial for obtaining the state of the art performance. Excellent performances can be obtained by selecting deep architecture for non-E2E ASR systems modeling with tied-triphone states. However, these optimized structures do not guarantee to achieve better or comparable performances on E2E (e.g., CTC-based) systems modeling with dynamic acoustic units. For solving this problem and further leveraging the system performance, we introduce the vertical-attention mechanism to reweight the residual blocks at each time step. Speech recognition experiments show our proposed model significantly outperforms the DNN and LSTM-based (both bidirectional and unidirectional) CTC baseline models.
Interpreting Traffic Congestion Using Fundamental Diagrams and Probabilistic Graphical Modeling

Traffic congestion is a major economic, environmental and social issue that affects cities throughout the world. This research explains the complex associations of traffic flow based in an empirical-theoretical framework using real-world datasets. We propose a data fusion method to infer well-defined microscopic fundamental diagrams in dense urban areas making use of inductive loop detectors and taxi trajectory data. We also present a semi-naive Bayesian modeling approach to extract causality knowledge built on previous discriminated congestion in different road segments. A realistic empirical evaluation allows us to identify and quantify causalities between congestion and diverse confounding variables (e.g. meteorological conditions). Our aim is to contribute to efficient traffic flow by uncovering the tangled traffic congestion in an urban geographical area.
Optimised Headway Distance Moving Block with Capacity Analysis

The railway control system is designed to act as a safety system that prevents collisions between trains and that maximises the utilisation of the rail network. Different signalling systems are widely used to achieve safety, but to achieve higher capacity, these existing systems may have to be updated or replaced in the future. Combining moving block and aspects of relative distance braking, a novel signalling system is proposed in this paper. The simulation shows that the novel concept has an excellent capacity outcome. The safety risk is also discussed to ensure its feasibility.
Corpus and Annotation Towards NLU for Customer Ordering Dialogs

Ordering products and services through virtual agents is possible but suffers limitations on the kind of ordering that is possible or on the naturalness of the conversation. We address these limitations by collecting a corpus of human-human dialogs in the food ordering domain. We create a food focused annotation scheme that is tailored for this corpus but customizable for other applications. After annotating the corpus, we find corpus characteristics that may make it more natural, such as complexity of food item mentions and use of multiple intent utterances. Furthermore, we train and evaluate preliminary statistical item and intent models using the annotated corpus.