Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of ITLMS In-transit Logistics Monitoring System

A design of in-transit logistics remote video monitoring system is proposed based on cotex-A8 processor hardware and Android software systems platform, by the terminal USB infrared camera driver transplant, video streaming H.264 codec, and 3G wireless transmission network, the in-transit logistics remote video monitoring real-time display is achieved, which can prevent the loss of goods and cargo in logistics process and be served as the evidence for the responsibility distinguishing in the event of goods damage. Managers and customers can track the transport status of goods by computer or mobile phone in real-time, and provide better service to customer, therefore enhance the competitiveness of logistics enterprises.
Real Time Driver Fatigue Detection Based on SVM Algorithm

Among the causes of the traffic accidents driver drowsiness comes at one of the first places. According to the literature some work has been done in driver fatigue detection. This paper proposes a Real Time Driver Fatigue Detection Based on Support Vector Machine (SVM) Algorithm. Fatigue detection mainly focuses on drivers’ face expressions and behaviors. OpenCV and Dlib libraries were utilized to detect the expressions of drivers’ faces. The proposed system has five stages: PERCLOS, count of yawn, internal zone of the mouth opening, count of eye blinking and head detection to extract attributes from real time video. Subsequently, facial expressions were trained with SVM. In this study an SVM-based driver fatigue detection is recommended and the tests showed that the accuracy rate of fatigue detection is up to 97.93%.
Selective Ensemble Learning based Human Action Recognition Using Fusing Visual Features

The selection of motion feature directly affects the recognition effect of human action recognition method. Single feature is often affected by human appearance, environment, camera settings and other factors, and its recognition effect is limited. This paper propose a novel action recognition method by using selective ensemble learning, which is a special paradigm of ensemble learning. Moreover, this paper presents a fast and efficient action description feature and a novel recognition algorithm. Robust discriminant mixed features are learnt from behavioral video frames as behavioral descriptors, The recogniton algorithm using selective ensemble learning can achieve fast classification. Experimental results show that the proposed method achieves ideal recognition results on the self-built indoor behavior data set and public data set.
On the Application of Cross-Project Validation for Predicting Maintainability of Open Source Software using Machine Learning Techniques

Design and development of models to predict software maintenance effort is an impending research area as these models help to predict maintenance effort of software system at earlier stages of its development. The predictions of these models help in allocation of limited resources in an optimal way in the test and maintenance phases of software development. Although numeral software maintainability prediction models have been successfully developed in the past using machine learning (ML) and statistical techniques but there is always threat to generalizability of result have prevailed, as these models are validated on the same data set on which they are trained. This study endeavors to improve generalizability of the software maintainability prediction by cross-project validation where prediction model developed on one software project is validated against the other project. To meet our objective we have taken three open source projects written in java language.The performance of the models is evaluated using prevalent the performance measures. Based on the statistical tests; it is quite conclusive that cross project validation can be successfully applied to predict software maintenance effort of open source software.
Double-Talk Detection Based on Enhanced Geigel Algorithm for Acoustic Echo Cancellation

The role of a double talk detector (DTD) in acoustic echo cancelation (AEC) system is to detect the presence of near-end speech signal with the microphone signal and freeze the filter adaptation to avoid the adaptive algorithm divergence. This paper presents a DTD based on an enhanced Geigel algorithm where a modified form of decision variable is proposed. The aim is to improve the behavior of Geigel algorithm, evaluate performances of the proposed method, and compare it to conventional Geigel algorithm. Recursive Least Squares (RLS) algorithm is used in this case as an adaptive filter which it requires a good DTD due to its fast convergence and its sensitivity to double talk situations.
EEG-based Biometric Identification using Frequently Occurring Maximum Power Spectral Features

Biometric identification of persons based on unique features extracted from brain signals acquired using Electroencephalography (EEG) is an emerging area of research. EEG-based biometric identification exploits the subject-specificity of human neural activity. In this paper, we propose a biometric identification method using the frequently occurring maximum power spectral features derived from the 60-80 Hz band of resting state EEG signal. Power Spectral features derived from 109 subjects are used in this study. Our method obtains an equal error rate of 0.016 using only 10 EEG electrodes, which is better than the best method reported in literature using resting state EEG.
Estimating the 2D Static Map Based on Moving Stereo Camera

Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual information in any weather condition and they are expensive. Furthermore, they require intensive calculations. Therefore, they can't be easily processed online. The aim of this study is to provide a solution based on the low-cost, light, and low-power stereo camera. The proposed solution focuses on the spatial information about the driving environment which is represented as a 3D point cloud. These post-processed points are projected on the 2D rectangle grid and divided into identical square cells. Each cell is holding information about the 3D points that lie over it and this created what is called a height map. In the same time, a confidence map is built to reduce and discard scattered points because the produced 3D point cloud is noisy. Finally, an occlusion map is constructed to estimate the status of each cell as a border, free or occluded.
Control of Twin Rotor MIMO System using Robust Generalized Dynamic Inversion

This paper presents the control design approach based on Robust Generalized Dynamic Inversion (RGDI) for the Twin Rotor Multi-input-multi-output System (TRMS). In RGDI control, constraint dynamics is formulated in the particular part, by prescribing the linear time varying constraint differential equation based on the attitude deviation function. The constraint dynamics encapsulates the control objectives and is inverted by using Moore-Penrose Generalized Inverse (MPGI) to generate the required control deflections for stable attitude tracking. The involved null control vector in the auxiliary part, is constructed by designing a proportionality gain matrix, which guarantees asymptotic stability of the body rate dynamics. The singularity problem due to non-square inversion is avoided by implementing dynamic scaling factor. To provide robustness, Sliding Mode term is integrated with GDI to make it RGDI, that will guarantee semi-global practically stable attitude tracking. Numerical simulations are conducted on TRMS simulator to demonstrate the performance of RGDI control under nominal and perturbed flight conditions.
Content Harvest Network: Optimizing First Mile for Crowdsourced Live Streaming

Crowdsourced live streaming (CLS), such as Twitch.tv and Inke.tv, has emerged as an important multimedia application in recent years. Video delivery in such CLS service involves two steps: 1) video uploading—video streaming (i.e., a live channel) generated from a broadcaster is uploaded to the server, which we call the “first mile” network and 2) video distribution—the video streaming is then delivered to viewers in the channel. Today’s CLS services usually use conventional content delivery network solutions to address the video distribution problem, while little attention has been paid to improve the video uploading quality. Our measurement study shows that the first mile network causes 17% viewer rebuffers, and some viewers quit the channel once encountering rebuffer. In this paper, we propose a content harvest network (CHN) architecture to address the uploading problem in the CLS service. Specifically, the CHN architecture employs edge devices in the network as relays to receive the streaming uploaded by broadcasters and then forward to the central servers. On one hand, we need to reduce the latency since it is live streaming; on the other hand, we must provide sustainable upload bandwidth. It is challenging to achieve both at the same time, especially in such high dynamic system as CLS. In order to provide global optimal and real-time assignment, we propose a hybrid solution, i.e., centralized and distributed assignment. Specifically, we formulate the centralized relay assignment problem as an optimization problem to achieve both low latency and sustainable bandwidth. To cope with the frequent channel establishments, we use a multi-armed bandit method to characterize the time-variant network condition. Experiment results on a large-scale trace provided by Inke.tv show that our solution can reduce the overall viewer cost by 40% compared to state-of-the-art solutions. The viewers’ rebuffer can also be reduced by 50%.
Sentiment Analysis of Text Based on CNN and Bi-directional LSTM Model

In order to overcome the deficiency of sentiment analysis based on traditional machine learning, which difficulty of effective feature selection and inadequacy of marked training corpus will affect the performance of the classification system, we address the sentiment emotions analysis problem of Chinese product reviews text by combining convolutional neural network (CNN) with bidirectional long-short term memory network (BiLSTM) in this paper. The CNN can extract the sequence features from the global information, and it is able to consider the relationship among these features. The BiLSTM not only solves the long-term dependency problem, but also considers the context of the text at the same time. The result of numerical experiments shows that the proposed model achieves better metrics performance than the state-of-the-art methods.
Integrating Deep Learning for NLP in Romanian Psychology

With the emergence of efficient word embeddings for free text, there has been a bloom of Natural Language Processing (NLP) breakthroughs using deep learning techniques. However, the literature is skewed towards the languages that offer large corpuses, and little research has been done in the direction of Romanian text. In this paper, we propose a Deep Learning (DL)-based system for classifying free sentences in the context of psychological surveys, automatically discovering whether respondees are talking about the expected subject in their answers (thoughts, emotions or behaviors) or not. We test several new architectures of convolutional and recurrent neural networks using pre-trained word embeddings from a very large corpus consisting of Wikipedia and Common Crawl data sets. We also present the benefits of transfer learning applied to NLP, allowing general language models trained on large data sets to be applied to problems that have a small amount of data, as in our case, allowing applications of these techniques in many fields.
Reduction in Ambient Noise Levels using Low Cost Scalable ANC Device

Significant increase in ambient noise levels have resulted in deterioration of lifestyle of people as well as working environment of many industries. Also, this increase is responsible for many medical conditions. Hence, there arises demand of possible ways to provide reduction in ambient noise levels in both domestic and industrial field of operation. This paper proposes an economic and scalable way to provide reduction in ambient noise levels efficiently using Active Noise Cancellation technique. To provide environment specific calibration, proposed model also offers manual calibration control. Moreover, we analyze the possibility of improving the efficiency to reduce ambient noise levels using such multiple devices and their amplitude and position with respect to each other. The evaluation shows significant reduction in ambient noise levels when using the proposed model in domestic environment setup.
Real-Time Intelligent Traffic Light Monitoring and Control System to Predict Traffic Congestion Using Data Mining and WSN

— Traffic monitoring and control are becoming more and more important as number of vehicles and traffic jams grow. Strategically placed video cameras are predominantly used to perform this task by visual means. To improve traffic monitoring and control this paper presents Real-time Intelligent traffic light monitoring and control automated system, to predict traffic congestion using Iterative Dichotomiser 3 (ID3) data mining algorithm and Wireless Sensor Network (WSN). At a junction, infrared sensor is used to count the number of vehicles on each road and send the report to Traffic Monitoring and Control (TMCM) for traffic estimation[1]. Along with infrared sensors, this system consists of light, temperature and gas sensors to measure day-night, temperature and pollution at the junction. Traffic and climatic data obtained from these sensors are aggregated in TMCM to form data logs stored in database, which is further used to extract traffic information as per system requirement. TMCM consist of micro controller (AVR ATmega32), Bluetooth (HC-05) and an android device for connecting server to different sensors like IR sensor, temperature sensor(LM35), light sensor and gas sensor with the help of MAX-232 and ADC. Proposed system not only measures the traffic flow and different climatic scenarios through wireless sensor nodes but also predicts the possibility of traffic congestion for relevant junction by using CSE5230 ID3 Data mining algorithm on collected database. Using historically collected data from database system intelligently decides the delay timer of Red-Green-Orange traffic signal light. This delay timer is automatically set based on the flow of traffic on each road of associated junction and data logs maintained in database. Traffic congestion information and climatic scenario are employed for early warning with the use of server to android-based mobile phones or smart phones connected via a web service
A Review of Smartphone's Text Entry for Visually Impaired

Smartphones are becoming more popular among the visually impaired people due to its inbuilt screen readers (e.g. TalkBack, VoiceOver). These screen readers enable visually impaired people to access some of the smartphone's features. There are other functions which can be inaccessible, time-consuming, and mental-workload inducing. Among these aforementioned functions is text entry. The text entry on smartphones using a virtual keyboard is inherently an ocular task as it requires target hitting accuracy. There has been a significant amount of research regarding text entry on smartphones for visually impaired. The purpose of this study is to conduct a new research from HCI perspective about the various types of text entry methods in smartphones for visually impaired. The systematic search is carried out in 6 databases to find the relevant papers, during the time frame of 2010–2017, to study the recent developments in smartphone text entry methods for visually impaired. This search resulted in 16 research papers, which helped in answering the research questions adapted from Siqueira et al. [1], because this paper not only serves as a guideline for SLRs related to braille-based text entry, but it is also a significant contribution in this field, and the main author of this study has more than 21000 citations. Our study not only presents a concise description of various text entry methods, but it also lists 22 research and design consideration for text entry solutions.
BlockZoom: Large-Scale Blockchain Testbed

Future blockchain applications are anticipated to serve millions of users. Thus the evaluation of new blockchain applications have to consider large-scale assessment of the technologies behind the scene. Most of current testing approaches have been done either on simulators or via local small blockchain networks. Hence, the performance in real world conditions is unpredictable. This demonstration introduces BlockZoom, a large-scale blockchain testbed that runs on top of a highly reconfigurable and controllable HPC platform. BlockZoom presents a reproducible environment for experimenting distributed ledgers technologies and smart contract applications. Through different configuration scenarios developers can evaluate the applications performance and the blockchain behavior at a scale comparable to the production environment. The target audience of this demonstration includes researchers and developers in blockchain technology.
Scalable BFT Consensus Mechanism Through Aggregated Signature Gossip

In this paper, we present a new BFT consensus mechanism which enables thousands of nodes to participate in the consensus process, and supports very high transaction throughput. This is achieved via an aggregated signature gossip protocol which can significantly reduce the messaging complexity and thus allows a large number of nodes to reach consensus quickly. The proposed aggregated signature gossip protocol leverages a non-interactive leaderless BLS signature aggregation scheme. We have proven the correctness of the protocol, and analyzed its efficiency. In our analysis, each node only needs to send and receive $O(\log n)$ messages to reach agreement, where each message just contains a couple kilobytes of data.
Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results

Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets’ transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets’ addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM’s addresses pay higher fees than others, which significantly improves the classification.
Polar Codes Analysis of 5G Systems

In this paper, we investigate the achievements and challenges of using polar codes in 5th generation New Radio (5G-NR) communications systems. Polar codes are considered the promising technique to solve the problems that emerged in previous generation wireless communications systems such as latency, complexity, error correction capability, network infrastructure and Quality of Service (QoS). Polar codes have been chosen as the standard control channels in 5G-NR by the 3rd Generation Partnership Project (3GPP). In this work, we compare polar codes with the two competitor codes that might be used in 5G-NR, which are turbo codes and Low Density Parity Check (LDPC) Codes. The comparison between them focused on advantages and disadvantages of each code scheme to make it easy understanding the performance, achievements, and challenges of each code.
A Different Approach to Maximum Clique Search

The way we tackle NP-hard problems in practical setting has experienced a major shift in recent years. Our view has became more sophisticated with the emergence of the parameterized complexity paradigm. We may distinguish subclasses inside the NP-hard complexity class. The complexity of the problems in different subclasses maybe quite different. The overall conservative estimate of the running time is replaced by a more optimistic estimate. In addition the approach of parameterized algorithms is sometimes able to deal with the more complex problems by dividing the problem into harder and a simpler parts. The easier instance at many times reduces to mere preprocessing step leaving us with only the harder part. In this paper we single out the so-called maximum clique problem as a typical representative of the NP-hard complexity class. We propose an algorithm to solve the maximum clique problem motivated by the above ideas. Many of the available maximum clique solvers are descendants or refined versions of the Carraghan–Pardalos algorithm. (Patric \"Osterg\aa rd's cliquer is being as an exception.) The maximum clique problem as a maximization problem can be reduced to a series of k-clique problems as decision problems. Our main observation is that this route offers a number of advantages. The structure of a k-clique decision problem is simpler than the structure of a maximization problem. It affords additional pruning opportunities based on the available value of k. A large scale numerical experiment indicates that in many occasions the combined search space of the k-clique problems is smaller than the search space of the maximization problem. The solver we propose turns out to be rather efficient. In a number of test problems it beats the best available solvers.
Design of General Purpose Microprocessor with an Improved Performance Self-Sleep Circuit

As the technology advances leakage power is a major concern to be addressed. MTCMOS technique has been the finest technique to decrease significant power in standby mode, giving decent performance in active mode. MTCMOS entails additional design exertion to address the design challenges. An improved performance self-sleep circuit that avoids the necessity of distribution of sleep signal is presented. This circuit helps to simplify the implementation of MTCMOS sleep-wake up scheduling. This work goals the tests and intricacies associated to sleep signal distribution in a distributed MTCMOS design. The self-sleep circuit that consumes low power, occupies less area with less delay has been designed. In a 90nm technology, at 2-Giga Hertz clock frequency, the self-sleep circuit gives good performance in active mode and eliminates the sleep distribution network overheads, providing fast transitions between active to-standby-to-active modes.