Miracle-dz/UniFAR-FTUs huggingface dataset:
Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform

A good condition of road was very needed by the people, because the movement of goods and services, and also a lot of people activities is indirectly depending on the road condition. So for keeps the road on good quality, early detection of road damage, especially pothole must be held. This thing is very important because a small damage that is not immediately handled can be large, so that the danger will decreased. It also make the cost that needed for repair it become greater. In the other hand, detecting road damage cannot be done with a manual checking, because it will take a lot of time and money. So supporting technology is needed to detect this kind of roads hazard. One of the technology easily used to detect road damage is computer vision. In this research, we build a system that can detect a pothole on the road which is captured by the camera. The camera used here is a wireless portable camera. Also for the location tagging, the GPS sensor is used here. The vision object detection system using imageZMQ library for stream the frames and process it in the processor PC. The capturing and streaming activity performed very well from the mini portable computer camera which is attached in the vehicle. the next step of this research is making the detection performed well in the more robust computational device.
Cross-Domain Sentiment Classification With Bidirectional Contextualized Transformer Language Models

Cross-domain sentiment classification is an important Natural Language Processing (NLP) task that aims at leveraging knowledge obtained from a source domain to train a high-performance learner for sentiment classification on a target domain. Existing transfer learning methods applied on cross-domain sentiment classification mostly focus on inducing a low-dimensional feature representation shared across domains based on pivots and non-pivots, which is still a low-level representation of sequence data. Recently, there have been great progress in the NLP literature in developing high-level representation language models based on Transformer architecture, which are pre-trained on large text corpus and fine-tuned for specific task with an additional layer on top. Among such language models, the bidirectional contextualized Transformer language models of BERT and XLNet have greatly impacted NLP research field. In this paper, we fine-tune BERT and XLNet for the cross-domain sentiment classification. We then explore their transferability in the context of cross-domain sentiment classification through in-depth analysis of two models’ performances and update the state-of-the-arts with a significant margin of improvement. Our results show that such bidirectional contextualized language models outperform the previous state-of-the-arts methods for cross-domain sentiment classification while using up to 120 times less data.
Service Assurance in 5G Networks: A Study of Joint Monitoring and Analytics

In 5G, network slicing is considered as an important enabler to support various verticals simultaneously, by providing dedicated and isolated slice services over a common infrastructure. Many verticals have specific Service Level Agreement (SLA), which should be guaranteed by mechanisms like service assurance (SA). In 5G, SA is required to collaborate with orchestration and management to automate the slice provisioning process. In this paper, we first propose a hierarchical, distributive, and modular SA architecture to assure the entire slice services from end to end (E2E), based on distributed modules assuring underlying services, functions, and resources. Secondly, a closed loop is formed between assurance and orchestration to enable automation by cooperation between monitoring, analytics, and orchestration. In addition, correlation across layers (infrastructure, network function, network service and E2E slice) is considered to form a closed loop through all layers to further improve efficiency. The joint approach also allows to optimize the tradeoff between the cost and the performance of SA. Simulation results illustratively demonstrate the benefit of the proposed approach.
Mucosal barrier, bacteria and IBD: possibilities for therapy

The mucosal barrier has three major components, the mucus layer, the epithelial glycocalyx, and the surface epithelium itself, whose integrity largely depends on tight junction function. In health, there is relatively little direct interaction between the luminal microbiota and the epithelium – the continuous mucus layer in the colon keeps the surface epithelium out of contact with bacteria and the ileo-caecal valve ensures that the distal small intestine is relatively microbe-free. Most interaction take place at the Peyer’s patches in the distal ileum and their smaller colonic equivalents the lymphoid follicles. Peyer’s patches are overlain by a “dome” epithelium, 5% of whose cells are specialized M (microfold) epithelial cells which act as the major portal of entry for bacteria. There are no goblet cells in the dome epithelium and M cells have a very sparse glycocalyx allowing easy microbial interaction. It is intriguing that the typical age range for CD onset is similar to the age at which the number of Peyer’s patches is greatest. Peyer’s patches are commonly the sites of the initial lesions in CD and the “anti-pancreatic” antibody associated with CD has been shown to have as its epitope the GP2 glycoprotein that is the receptor for type-1 bacterial fimbrial protein (fimH) on M cells. There are many reasons to believe that the mucosal barrier is critically important in IBD pathogenesis. These include (i) associations between both CD and UC with genes that are relevant to the mucosal barrier; (ii) increased intestinal permeability in unaffected relatives of CD patients; (iii) increased immune reactivity against bacterial antigens; (iv) animal models in which altered mucosal barrier – e.g. denudation of the mucus layer associated with oral dextran sulphate in rodents –
Laplace Nonnegative Matrix Factorization with Application to Semi-supervised Audio Denoising

This paper proposes two statistical models for the nonnegative matrix factorization (NMF) based on heavy-tailed distributions. In the NMF for acoustic signals, previous works justify the additivity of an observed spectrogram using the reproductive property of a probability density function. However, the effectiveness of these properties is not clear. Consequently, to construct a model robust to noise, statistical models based on heavy-tailed distributions are recently growing up. In this paper, as heavy-tailed models for the NMF, we introduce statistical models based on the complex Laplace distributions, and call them Laplace-NMF. Moreover, we derive convergence-guaranteed optimization algorithms to estimate parameters. From our formulation, a statistical interpretation of the Itakura-Saito (IS) divergence-based NMF is newly revealed. We confirm the effectiveness of Laplace-NMF in semi-supervised audio denoising.
Tuning KSM for dual core Automotive Systems

KSM(Kernel Same page Merging) is a kernel feature released in kernel version 2.6. It helps in saving memory but it consumes considerable amount of CPU in the process. In automotive systems, there is a limitation of both memory and CPU. Therefore, for an efficient use of KSM, it is important to have a balance between memory saved by KSM and the CPU consumed by it. A methodology has been conducted on how to tune KSM such that it will have considerable memory savings with limited CPU consumptions. Prior to discussing the problems, KSM and its working will be covered so that it will be easy to understand the problems associated with it. Later, the issues faced in enabling KSM and the solutions to those issues with data are presented to give more clarity in approach and the solution.
Experimental Study of Upper Limb Exoskeleton Control Based on IMUs Sensory System

This paper describes experimental study of controlling an upper limb exoskeleton to track the user’s arm. The main purpose of this paper is to discuss and evaluate the control strategies and sensory system with different users. Furthermore, this study has used IMU sensors which are put on the users arm to attain an outstanding interactive control to make the user’s arm imitation to the exoskeleton much comfortable, flexible and improved. To evaluate that, several experiments are performed. The results of these experiments indicate that the sensory system and control system are successful and have good performance to track user arm and control all the system.
Prediction of Axis Attitude Deviation and Deviation Correction Method Based on Data Driven During Shield Tunneling

Due to the complex shield construction characteristics and the complex effects of geological environment, it is difficult to control the direction of shield tunneling and to determine the reasonable tunneling parameters such as tunneling speed and so on. During the tunneling, shield tunneling machine may rise, shift and snake advance, which are not conducive to control tunnel axis. Aiming at the problem that it is difficult to accurately predict and correct the axis attitude deviation in the shield tunneling process, a prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling was put forward in this paper. Under certain geological conditions, the relationship between the attitude deviation of the construction axis and tunneling parameters during shield tunneling process with different tunneling mileages is established. Based on the tunneling historical data, the XGBoost prediction model is constructed, and the axis deviation variation is predicted and analyzed with the shield construction parameters. The multi-ring deviation correction parameter calculation model based on the fusion of geometric model and association rules is designed to obtain the internal correlation of the deviation amount, the number of deviation correction rings and the deviation correction parameters of each ring under the maximum deviation of different deviation correction sections, so as to realize the accurate prediction and deviation correction of shield axis deviation in the complex construction process. Under the verification of 155 ring data in a certain subway construction section, the method proposed in this paper has higher prediction accuracy, which is important for improving the safety and quality of shield tunneling. Results from the measured shield construction ring data verify the reasonability of the proposed axis attitude deviation prediction and multi-ring deviation correction method.
An Open-Source Speech Codec at 450 bit/s with Pseudo-Wideband Mode

Most modern digital speech codecs are proprietary and do not give insights into their implementations. "Codec 2" is an open-source codec and opens up the possibility of application-tailored adaptations. In this publication a new 450 bit/s mode is introduced. It includes a pseudo-wideband mode that can be optionally enabled at the receiver without resulting in a higher bit rate. By sorting the codebook vectors a bit error rate of 10−1 is still intelligible. An encoded audio transmission was tested with two low-power Sub-GHz transceivers in the 70 cm amateur radio band achieving a sensitivity of −130 dBm. For proofing the concept, a field test was conducted and compared to a propagation simulation.
Palm Vein Recognition Using Enhanced Symmetry Local Binary Pattern and SIFT Features

Recently, palm vein recognition is new biometric technology with a high degree of privacy and security because this technique uses the blood vessels under the palm skin to establish identification. This paper proposes a novel palm vein feature extraction method for contactless palm vein recognition based on combining enhanced center-symmetric local binary pattern (ECS-LBP) with SIFT, called EL-SIFT. The proposed method includes two steps: (1) applying ECS-LBP to detect stable and clear palm-vein lines; and (2) extracting SIFT feature on palm vein lines image. The experimental results on the public contactless palm vein databases (CASIA Multi-spectral Palm vein Image Database V1.0) show that our proposed method is accurate and robust for palm vein recognition in comparing with other approaches in the literature.
Online dictionary learning for single-subject fMRI data unmixing

Independent component analysis (ICA) and dictionary learning (DL) methods are widely used to analyse resting state functional Magnetic Resonance Imaging (rs-fMRI) in multi-subject studies. These methods aim at decomposing the multi-subject data into common spatial abundance maps and their related temporal signatures.We are interested here in such a decomposition for a single-subject rs-fMRI dataset. The above-mentioned methods often fail in this case because the problem becomes too ill-posed, requiring the use of additional prior information and the design of novel regularising constraints. The poor resolution of rs-fMRI data is an additional source of difficulty, yielding noisy and blurry spatial maps.In this paper, we propose a new DL formulation adapted to the unique subject by integrating high-resolution (HR) spatial information to constrain single-subject data unmixing. HR information is provided by the registration of an anatomical atlas on the data set. We show on a quasi-real dataset from mice, the benefit of using an HR spatial segmentation map in the decomposition of low-resolution rs-fMRI.
Emulation of a Navigation Processor with Physical Memristors Models

Self-organizing circuits are subject to current research with the respect to machine learning in which memristors are the essential devices to enable the desired functionalities. Since the inherent nature of a current is to flow along the path of least resistance, circuit-inspired solutions are good candidates to solve the problem of finding the shortest path through a maze. While this navigation problem has been realized with theoretical models of memristors before, it has not been addressed by using real memristors. The goal of this paper is to employ a physical model of a real HfO2-based RRAM memristive device to create a real navigation processor. We utilize the wave digital concept as an emulation technique to create a circuit-inspired navigation processor with underlying models of real memristors. We therefore provide the necessary tools for future hardware realizations of self-organizing circuits in this work.
Challenges and Trends of Changing from Hover to Forward Flight for a Converted Hybrid Fixed Wing VTOL UAS from Automatic Flight Control System Perspective

Capabilities of fixed-wing UAS to perform missions have been well studied and documented today. And so has for multirotor drones. It is also well accepted that both platforms have their strengths and limitations during operation. For this reason, one suggestion to address those limitations is to create a 'hybrid' UAS which will incorporate the best capabilities of both types of aircraft. This paper proposes retaining the best features of an already flying long endurance fixed wing UAS and then incorporate a vertical takeoff capability. The paper looks at the challenges and issues of transitioning from hovering to forward flight. From the 3 strategies proposed, Strategy 1 suggests to increase the speed of rotation of the 2 aft rotors therefore, causing the aircraft to pitch forward, hence generating a forward speed from hovering. For Strategy 2 during hovering, all 4 rotors are spinning at the same speed hence generating equal balanced thrust upwards for the hybrid aircraft. Then, the aircraft main petrol engine is set to full throttle to generate forward thrust to commence flight forward. For Strategy 3, the 4 rotors are tasked as in Strategy 2 but once a safe altitude is reached, the 4 rotors are disengaged to let the aircraft nose drop forward to commence a controlled dive. At the same time, the aircraft main petrol engine is set to full throttle to gain airspeed to continue with normal forward flight. The paper concludes with a brief description of the role of automatic flight control systems responsible for regulating the transition phase, setting up the future work for future work on modelling, simulation, fabrication and flight testing.
Comparative Analysis of Ensemble Learning Methods in Classifying Network Intrusions

Ensemble learning methods as compared to a conventional machine learning method can enhance the classification performance of a network intrusion detection system. The main idea of such methods is to emulate the human nature of gathering and weighing various viewpoints and combining them before making an important decision. However, in selecting the best ensemble learning method, proper assessment is imperative. Hence, this paper provides a comparative study of different ensemble learning methods such as Adaptive Boost, Gradient Boost, Random Forest, Extra Trees, and Logistic Regression based on classification performance and computational cost. The NSL-KDD dataset is selected for this research and is pre-processed to train and test the methods in classifying network intrusions. A comparative analysis of each method is evaluated using the performance metrics, accuracy, recall, precision, F1-score, and computational cost based on training and classification speed. The experimental results show that Extra Trees gives the best performance both in classification performance and computational cost.
Design of Double-tail Dynamic Latch Comparator for Low Power Application

The Analog to Digital Converter (ADC) is an important part of any signal processing system. It is used to convert the analog signal to digital signal. Power consumption and compactness are the main issues in an ADC. Among the different components of ADC comparator is the one which consumes more power. Dynamic latch comparators are the most suitable comparators for low power applications. A modified Double-tail latch comparator with reduced glitches for low frequency applications is presented in this paper. The total power consumed by the circuit is 296.171 pW at 1.8 V power supply. The simulation is carried out in Cadence Virtuoso environment using 180 nm CMOS technology.
A Novel Utility Based Metric and Routing for Energy Efficiency in Software Defined Networking

Software defined networking (SDN) is a rapidly growing networking paradigm in both industry and research areas, with network programmability as its powerful feature which enables propagating changes in the network easily. However, the links and switches are designed to accommodate maximum traffic load and their power consumption is not traffic aware. The logically centralized control in SDN enables dynamically minimizing the energy consumption of the links and the switches by diverting paths of packets. Energy efficiency and performance are opposite objectives that have to be addressed simultaneously. As the main contributions in this study, we first propose an energy efficiency metric Energy Profit Threshold (EPT) that is applicable to SDN. Then, we provide Integer Programming (IP) formulation with the objective of maximizing the EPT of a software defined network environment. Experimental results show that maximizing the EPT value exhibits energy saving of more than 35 % as compared to other utility based energy saving algorithms.
Multi-Connectivity Management for 5G V2X Communication

With the advent of automated driving functions the need for cooperation among vehicles becomes increasingly necessary. The availability of high quality connectivity is an important factor for successful and uninterrupted cooperative and autonomous services. Flexible selection of available connectivity options, increases the probabilities to achieve and maintain the required Quality of Service (QoS). Future vehicles will support multiple 5G communication interfaces e.g., cellular (Uu), sidelink (PC5). A 5G RAN-based solution is proposed in this paper to dynamically select and change the communication interface that is suitable for a V2X service, considering radio and road conditions. The combination of different interfaces, for data packets duplication or splitting, increases the multi-connectivity capabilities and expected QoS benefits. The simulation results show the improvement that multi-connectivity could bring to V2X services and specifically to packet reception ratio (PRR) in an urban environment.
Evaluation of Speech Quality Degradation due to Atmospheric Phenomena

Modern communication systems, such as 5G networks promise to meet the crescent demand of users for high data rates in order to support new technologies. However, the frequency range of operation of these networks deserves special attention, since the high frequencies are more susceptible to interferences caused by atmospheric phenomena. The recommendations ITU-R P.838-3 and ITU-R P.676-11 describe methodologies to estimate signal degradations caused by phenomena related to rain and atmospheric gases, respectively. In this work, the impact of such phenomena on speech signal quality is investigated. The perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.862. The experimental results show that the higher the frequency of operation, the higher the level of signal degradation for the same atmospheric conditions. Based on these results a parametric speech quality assessment model is proposed that uses an artificial neural network. Performance evaluation results demonstrated a high correlation between the proposed model and subjective test results, reaching an PCC and an RMSE of 0.9869 and 0.4773, respectively. Hence, the proposed model intends to be useful for network planning tasks.
An 8-bit 2.8 GS/s Flash ADC with Time-based Offset Calibration and Interpolation in 65 nm CMOS

An 8-bit 2.8 GS/s flash ADC with time-based offset calibration and interpolation is realized in 65 nm CMOS. The proposed time-based offset calibration uses intentional timing skew for offset cancellation without adding extra load to comparators, thus avoiding the speed penalty. The time-based 4x interpolation reduces the number of comparators to 1/4 and provides calibration capability for 8-bit accuracy through SR latches and delay lines. At 2.8 GS/s, the prototype consumes 51 mW from a 1-V supply and achieves Nyquist SNDR of 43.3 dB, effective resolution bandwidth (ERBW) of 1.52 GHz, and Walden figure-of-merit (FoM) of 153 fJ/conv-step, reporting a higher Nyquist ENOB than state-of-the-art single-channel flash ADCs with comparable FoM.
Association of GATA5 methylation with clinocopathological characteristics in non-muscle invasive bladder cancer

terized epigenetic change in human cancer. Recently, the association between GATA-binding protein 5 (GATA5) methylation and carcinogenesis of various types of tumors was investigated. The aim of the present study was to evaluate the effect of GATA5 methylation status on clinicopathological features and prognosis in primary non-muscle invasive bladder cancer (NMIBC) patients with a long-term followup period. The GATA5 methylation status was determined for 171 human bladder specimens (eight normal controls [NCs] and 163 primary NMIBC patients) using quantitative pyrosequencing analysis. The primary NMIBC tissues were obtained from patients who underwent transurethral resection (TUR) for histologically diagnosed transitional cell carcinomas between 1995 and 2012 at Chungbuk National University Hospital. GATA5 methylation was significantly higher in NMIBC patients than in NCs and was significantly associated with higher grade and more advanced stage of cancer. Kaplan-Meier estimates showed significant differences in tumor recurrence and progression according to GATA5 methylation status (each p<0.05). Our results show that increased methylation of GATA5 was significantly associated with not only aggressive characteristics but also poor prognosis in primary NMIBC patients. Alteration of GATA5 methylation might be used as a biomarker for prognosis of NMIBC patients. However, prospective and functional investigations are necessary to clarify the role of GATA5 methylation in future clinical management of patients with NMIBC.