Miracle-dz/UniFAR-FTUs huggingface dataset:
YOLO Acceleration using FPGA Architecture

In recent years, convolutional neural networks (CNN) have achieved breakthrough developments. However, with the spatial and time complexity of CNN gradually increasing, it becomes a great challenge to implement larger CNN for mobile applications. Accelerated platforms based on FPGAs are gradually being studied due to its advantages such as high performance, low power consumption, reconfigurability, etc. In this paper, we implement the YOLO platform based on the Zynq board (which is released by Xillinx) and optimize its architecture combined the FPGA feature. The use case of pedestrian and cars recognition is demonstrated in real time, which outperforms the traditional CPU-based YOLO networks.
Piggyback Game: Efficient Event Stream Dissemination in Online Social Network Systems

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffic due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server communications for event stream dissemination. Different sub-graph structures are exploited for achieving approximated optimal solutions. However, such schemes incur prohibitively high cost of either computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the highly complex graph computation problem into rational decision making of every individual social link. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existence of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimum. We conduct comprehensive experiments using large-scale real-world traces from popular OSN systems as well as implement a prototype system to evaluate the performance of this design. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.
Rapid Speaker Adaptation of Neural Network Based Filterbank Layer for Automatic Speech Recognition

Deep neural networks (DNN) have achieved significant success in the field of automatic speech recognition. Previously, we proposed a filterbank-incorporated DNN which takes power spectra as input features. This method has a function of VTLN (Vocal tract length normalization) and fMLLR (feature-space maximum likelihood linear regression). The filterbank layer can be implemented by using a small number of parameters and is optimized under a framework of backpropagation. Therefore, it is advantageous in adaptation under limited available data. In this paper, speaker adaptation is applied to the filterbank-incorporated DNN. By applying speaker adaptation using 15 utterances, the adapted model gave a 7.4% relative improvement in WER over the baseline DNN at a significance level of 0.005 on CSJ task. Adaptation of filterbank layer also showed better performance than the other adaptation methods; singular value decomposition (SVD) based adaptation and learning hidden unit contributions (LHUC).
There are Trillions of Little Forks in the Road. Choose Wisely! - Estimating the Cost and Likelihood of Success of Constrained Walks to Optimize a Graph Pruning Pipeline

We have developed [Reza et al. SC'18] a highly scalable algorithmic pipeline for pattern matching in labeled graphs and demonstrated it on trillion-edge graphs. This pipeline: (i) supports arbitrary search patterns, (ii) identifies all the vertices and edges that participate in matches - offering 100% precision and recall, and (iii) supports realistic data analytics scenarios. This pipeline is based on graph pruning: it decomposes the search template into individual constraints and uses them to repeatedly prune the graph to a final solution. Our current solution, however, makes a number of ad-hoc intuition-based decisions with impact on performance. In a nutshell these relate to (i) constraint selection - which constraints to generate? (ii) constraint ordering - in which order to use them? and (iii) individual constraint generation - how to best verify them? This position paper makes the observation that by estimating the runtime cost and likelihood of success of a constrained walk in a labeled graph one can inform these optimization decisions. We propose a preliminary solution to make these estimates, and demonstrate - using a prototype shared-memory implementation - that this: (i) is feasible with low overheads, and (ii) offers accurate enough information to optimize our pruning pipeline by a significant margin.
Share-a-GPU: Providing Simple and Effective Time-Sharing on GPUs

Time-sharing, which allows for multiple users to use a shared resource, is an important and fundamental aspect of modern computing systems. However, accelerators such as GPUs, that come without a native operating system do not support time sharing. The inability of accelerators to support time-sharing limits their applicability especially as they get deployed in Platform-as-a-Service and Resource-as-a-Service environmen ts. In the former, elastic demands may require preemption where as in the latter, fine-grained economic models of service cost can be supported with time sharing. In this paper, we extend the concept of time sharing to the GPGPU computational space using cooperative multitasking approach. Our technique is applicable to any GPGPU program written in Compute Unified Device Architecture (CUDA) API provided for C/C++ programming languages. With minimal support from the programmer, our framework incorporates process scheduling, light-weight memory management, and multi-GPU support. Our framework provides an abstraction where, in a round-robin manner, every workload can use a GPU(s) over a time quantum exclusively. We demonstrate the applicability of our scheduling framework, by running many workloads concurrently in a time sharing manner.
Design of the Lorentz Force Based Resonant Magnetic Sensor for SiGe MEMS on CMOS Process

This paper describes the resonant type magnetic sensor completely compatible to the micro electromechanical systems (MEMS) batch fabrication process of SiGe. The resonant vibration of the sensor is excited by the Lorentz force that is proportional to the driving current and applied magnetic field. Since the sensitivity of the sensor reaches the order of geomagnetic field, the magnetic sensor can be incorporated into inertial measurement unit (IMU), which is fabricated with accelerometer and gyroscope. In addition, low deposition temperature of SiGe is advantageous to realize the monolithic integration of IMU combo sensors onto CMOS circuit. For inspection, the test device was fabricated with Si-SOI.
Precise Target Selection Techniques in Handheld Augmented Reality Interfaces

Augmented reality (AR) capabilities on handheld mobile devices are increasing; therefore, interaction with a virtual scene is a key feature to explore the full potential of mobile AR. One of the primary interaction tasks is 3D target selection. Because touch screens provide a direct means of interaction with AR content by directly touching and manipulating AR elements, touch-based interaction is a natural and appealing style of input for AR applications. However, developing AR selection techniques for handheld devices using touch screens is not a straightforward task. One issue is that while interacting with AR targets, users occlude the targets with their fingers. Furthermore, because the user’s finger covers a large area of the screen, the size of the target users can touch is limited. In addition, in a 3D AR environment, it is difficult to select targets by touch when they are occluded from each other due to differences in spatial depth. In this paper, we present four novels AR selection techniques for handheld mobile devices that can precisely select targets with small sizes even when they are occluded. Then, we present the results of an experimental study in which we evaluated the usability, performance, and error rate of our proposed and existing AR selection techniques.
Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT

In the mobile computing-based Internet of Things, the computational complexity of applications is constrained by the capacity of the user equipment. In order to reduce the computational complexity of compressed sensing (CS)-based magnetic resonance image (MRI) reconstruction algorithms, we propose a novel multi-resolution-based parallel MRI reconstruction framework in this paper. We break down CS-based MRI reconstruction problem into four independent low-resolution image reconstruction sub-problems. Compared with the original problem, each sub-problem has a lower computational complexity. Assigned to four cores of the central processing unit (CPU), the sub-problems are solved simultaneously, and therefore the MRI reconstruction is accelerated. The combination of reconstructed low-resolution images achieves high-resolution image reconstruction. The proposed framework is applicable to the state-of-the-art CS-based MRI reconstruction algorithms to compute low-resolution images and involves multi-resolution processing. Compared with conventional serial computing, the proposed MRI reconstruction framework speeds at least four times up. Therefore, the parallel computation framework is especially suitable for widely used mobile devices with lower computational capability than workstations. To validate and evaluate the proposed scheme, when selecting the outstanding MRI reconstructing algorithm fast dictionary learning method on classified patches for numerical simulation, peak-signal-to-noise-ratio values of parallel reconstruction results are at least 0.929 dB higher than that of serial computation reconstruction results calculated by classical MRI reconstruction algorithm.
Content-Insensitive Blind Image Blurriness Assessment Using Weibull Statistics and Sparse Extreme Learning Machine

Most of the existing image blurriness assessment algorithms are proposed based on measuring image edge width, gradient, high-frequency energy, or pixel intensity variation. However, these methods are content sensitive with little consideration of image content variations, which causes variant estimations for images with different contents but same blurriness degrees. In this paper, a content-insensitive blind image blurriness assessment metric is developed utilizing Weibull statistics. Inspired by the property that the statistics of image gradient magnitude (GM) follows Weibull distribution, we parameterize the GM using <inline-formula> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula> (scale parameter) and <inline-formula> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula> (shape parameter) of Weibull distribution. We also adopt skewness (<inline-formula> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula>) to measure the asymmetry of the GM distribution. In order to reduce the influence of image content and achieve more robust performance, divisive normalization is then incorporated to moderate the <inline-formula> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula>. The final image quality is predicted using a sparse extreme learning machine. Performances evaluation on the blur image subsets in LIVE, CSIQ, TID2008, and TID2013 databases demonstrate that the proposed method is highly correlated with human perception and robust with image contents. In addition, our method has low computational complexity which is suitable for online applications.
Fast but Not Deep: Efficient Crowd Abnormality Detection with Local Binary Tracklets

In this paper, an efficient method for crowd abnormal behavior detection and localization is introduced. Despite the significant improvements of deep-learning-based methods in this field, but still, they are not fully applicable for the real-time applications. We propose a simple yet effective descriptor based on binary tracklets, containing both orientation and magnitude information in a single feature. The results of the proposed method are comparable with deep-based methods while it performs more efficiently. The evaluation of our descriptors on three different datasets yields a promising result in abnormality detection, which is competitive with the state-of-the-art methods.
Context-Aware Wireless-Protocol Selection in Heterogeneous Public Safety Networks

Modern models for public safety networks (PSN) utilize commercial user equipment (UE) and exclusively rely on cellular architecture. This paper introduces a heterogeneous PSN mechanism where multiple wireless protocols are used to establish resilient communications. Specifically, each UE acts as a learning automaton and using a reinforcement-learning approach selects the most appropriate operation protocol, in terms of environment/topology needs, protocol specifications, and PSN battery-life extension. In addition, we follow a UE coalition formation approach, using a Chinese restaurant process that considers UEs’ communication interest, physical distance, and energy availability. Finally, each UE's optimal transmission power is obtained using the formulation of a utility-based power control problem as a non-cooperative, distributed game among UEs, which converges to a unique Nash equilibrium. The performance of the proposed approach is evaluated using modeling and simulation under several scenarios; also, its superiority is demonstrated.
E-Learning Readiness Assessment of Medical Students in University of Fallujah

Learning in the medical school of universi-ty of Fallujah in Iraq is still by the classical face-to-face approach with minimal e-communication. Studies show that there are numerous models that have been devel-oped, however, they are used in developed countries whose e-Readiness is high hence not applicable in devel-oping countries. The aim of this paper is to assess medi-cal student’s perspectives of E-learning readiness. Stu-dents responded to a semi-structured self-administered questionnaire adapted from literature. Analysis was with SPSS version 19, using descriptive statistics, ANOVA, and independent samples T-test. The results obtained indicate that cultural readiness is the most important factor followed by technological readiness. In addition, the study results show that there are no significant rela-tionship between age, gender, and level of education, also it was reported in this study that the lowest mean for e-learn readiness is among students in a higher level of study. In conclusion, the students are not ready for eLearning, one of the reasons might be the ICT infra-structure is not adequate enough to support the use of e-learning. Further study with qualitative methodology will help in preparing for this evolution in learning.
Aspect Extraction from Bangla Reviews using Convolutional Neural Network

The extensive customer reviews in web assist customers for purchase-decision-making as well as providers for business planning. Summarization of reviews is desirable as reading all reviews is not feasible to evaluate properly. To find aspect categories from reviews is a sub-task of summarization known as Aspect Based Sentiment Analysis (ABSA). In this paper, we present two Bangla datasets to perform ABSA task. We collected user comments on cricket game and annotated manually. The other dataset consists of consumer reviews of restaurants. A model to extract aspect category based on Convo-lutional Neural Network (CNN) is presented. The model shows the convincing performance of the proposed datasets compared to the conventional classifiers.
Strategy-Based Gain Ratio Power Allocation in Non-Orthogonal Multiple Access for Indoor Visible Light Communication Networks

Visible light communication (VLC) is an evolving green communication technology for indoor wireless communication networks. VLC utilizes light-emitting diodes (LEDs) for both illumination and communication. To achieve economic and energy-efficient communications, non-orthogonal multiple access (NOMA) improves the system throughput by enhancing spectral efficiency. In indoor VLC networks, the gain ratio power allocation (GRPA) of NOMA allocates greater signal power to the users of poor channels to achieve both fairness and high data rate. However, current GRPA strategies developed for radio frequency channels are not necessarily effective in all VLC channel and illumination cases. This study makes the following contributions. First, a GRPA strategy based on VLC channels is proposed that modifies the power counting and channel proportions enhance the NOMA-VLC throughput. Second, owing to difficulties of directly analyzing NOMA-VLC throughput, an alternative lower bound based on a VLC channel model to facilitate throughput comparisons is proposed. It is proved that the proposed alternative lower bound is asymptotic and compact with respect to NOMA-VLC throughput. Third, for the case of LED heights greater than 1 m, a local extremum solution using the proposed strategy is analytically demonstrated based on the alternative bound of VLC network throughput. Then, for the case of LED heights smaller than 1 m, the necessary but not sufficient condition under which the proposed strategy outperforms previous strategies is provided in VLC scenarios. Finally, the experimental results demonstrate the compactness of the proposed alternate lower bound and the advantages of the proposed strategy.
A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM

Due to their location, malignant brain tumors are one of humanity’s greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is time-consuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems.
Ventilatory efficiency response is unaffected by fitness level, ergometer type, age or body mass index in male athletes

The aim of this study was to evaluate the ventilatory efficiency (V E/VCO2 slope) and the respiratory control (Vt/Ti slope) in a wide range of athletes and describe the influence of fitness level, age, ergometer type or BMI on these parameters. Ninety-one males (30.4±10.53 years; 175.52±7.45 cm; 71.99±9.35 kg) were analysed retrospectively for the study. Ventilatory efficiency reacted similarly in athletes independently of the fitness level, age, BMI or the ergometer used for testing. No significant differences were found in VE/VCO2 slope and the Vt/Ti slope between variables analyzed (P>0.05). The slope of the predictive equations was similar in all cases studied in VE/VCO2 slope and the Vt/Ti slope. Moreover, the central control impulse of respiration was not affected by the variables studied. These observations suggest that ventilatory efficiency (VE/VCO2 slope) could be a variable fixed by the respiratory system which tends to respond similarly in athletes.
Combining De-noising Auto-encoder and Recurrent Neural Networks in End-to-End Automatic Speech Recognition for Noise Robustness

In this paper, we propose an end-to-end noise-robust automatic speech recognition system through deep-learning implementation of de-noising auto-encoders and recurrent neural networks. We use batch normalization and a novel design for the front-end de-noising auto-encoder, which mimics a two-stage prediction of a single-frame clean feature vector from multi-frame noisy feature vectors. For the backend word recognition, we use an end-to-end system based on bidirectional recurrent neural network with long short-term memory cells. The LSTM-BiRNN is trained via connectionist temporal classification criterion. Its performance is compared to a baseline backend based on hidden Markov models and Gaussian mixture models (HMM-GMM). Our experimental results show that the proposed novel front-end de-noising auto-encoder outperforms the best record we can find for the Aurora 2.0 clean-condition training tasks by an absolute improvement of 1.2% (6.0% vs. 7.2%). In addition, the proposed end-to-end back-end architecture is as good as the traditional HMM-GMM back-end recognizer.
Hybrid Alternating Precoding and Combining Design for mmWave Multi-User MIMO Systems

This paper investigates the hybrid beamforming for multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems under the sub-connected architecture where each antenna is only connected to a unique radio frequency (RF) chain. Based on the perfect knowledge of the channel state information (CSI), the analog combiner for each user is achieved by using the singular value decomposition (SVD), and the analog and digital precoder is achieved by minimizing the Frobenius norm of the matrix of difference between the unconstrained precoder matrix and products of the hybrid RF and baseband precoders via the alternating optimization approach. Simulation results show that the performance of our proposed method over the existing SVD-based approaches in terms of achievable sum rate with different scales of transmit and receive antenna array, especially in the low signal to noise ratio (SNR) regime.
GPS and IMU Require Visual Odometry for Elevation Accuracy

Currently, self-localization of vehicles is primarily carried out by GPS receivers of different price and performance classes, which are permanently installed in vehicles. Known limitations of GPS-based localization include signal disturbances due to multipath propagation, as it may occur in narrow street canyons or tree alleys, as well as signal losses in tunnels or entrances to buildings, for example. It may happen that horizontal GPS channels still support a useful 2D positioning with low uncertainties while the GPS altitude channel has very large deviations or error uncertainties. Additional sensors (IMU or cameras) help to detect sections of trajectories where the GPS signal becomes unusable. Based on a traditional navigation strategy (i.e. integration of IMU and GPS), we also study the possible integration of visual odometry obtained from a stereo camera system. The paper reports about a large-scale project studying multi-sensor integration for very accurate and robust self-localization of vehicles.