Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying the Nest-Site Selection (NeSS) Algorithm for Yield Improvement in Disk Drive Manufacturing

This work applies a honeybees-inspired Nest-Site Selection (NeSS) optimization algorithm to a clustering problem. We study an actual data set collected from a hard disk drive (HDD) manufacturing process. The dataset contains attributes such as date of production, materials used, and manufactured location as well as machine parameters of the Head Gimbal Assembly (HGA) production lines. The HGA products can be classified as pass or fail based on the attributes and machine parameters. In this work, we first pre-process the data using the regular data cleaning techniques. Subsequently, a clustering technique is performed to group the machine parameters into two clusters based on a measure of data similarity such as distances between clusters or dense areas of data. In the beginning, data will be randomly assigned to the clusters. Then, in each iteration, the NeSS algorithm will reassign the data to more related clusters until a stopping criterion is reached. Finally, different profiles of the passed and failed HGA products will be created. The methodology and results in this work should help the data analysts in a HDD company to understand problems on the manufacturing line and improve yield by monitoring the right set of parameters and configuring the values to fit the “passed” profile.
The social negative mood index for social networks

With the wide application of social networks the interactions of users in social networks become more and more convenient, frequent and cheap for the people in real society. The related research of Behavioral science represents that individual emotions can profoundly affect individual behavior, the emotions of others,the group behavior,and decisionmaking of group.The public mood analysis for social networks have get attention by the early researchers about the social networks analysis,and further more they try to predict the financial markets from social networks to obtain the valuable information from the scial networks.This is a very important academic perspective that critical social intelligence information can be obtained through the analysis of social data from social networks and other social data sources.The public opinion analysis for social networks is an important research field. Nevertheless, the corresponding quantitative evaluation indexes of public opinion analysis trough social networks have not yet been developed.The corresponding social computing method is the base for the data analysis.This paper proposes both the concept of a social negative mood index for social networks and its calculation method, making it a fundamental work in the field of public opinion analysis.
Recognition of Working Condition in Sucker-Rod Pumping Wells Based on Multi-View Kernel Learning and Hessian-Regularized Logistic Regression

There are some research problems now in the existing working condition recognition methods of suckerrod pumping wells, such as false alarms easily caused by single information source, poor recognition effect and robustness arisen by traditional multiple information sources of feature connection, inferior engineering practicality induced by lots of labeled training samples. In order to overcome above problems and further improve the accuracy and practicality, in this paper, we propose a novel method for identifying the working condition of sucker-rod pumping wells based on multi-view kernel learning and Hessian-regularized logistic regression. The measured ground dynamometer cards, electrical power and wellhead temperature signal are combined together to recognize the working condition based on a model with the integration algorithm of multi-view kernel learning and Hessian-regularized logistic regression. The proposed method is applied to eleven kinds of working condition recognition of a certain block in Shengli oil field, and compared with traditional methods based on measured ground dynamometer cards, electrical power signal and multisource of feature connection respectively. Results show that the recognition rates increase by 2.4%, 11% and 13.8% respectively. The performance is even much better for cases of all marked and fewer marked training samples, especially in the case of 30% marked.
Modeling Direct Transmission Diseases Using Parallel Bitstring Agent-Based Models

Agent-based models (ABMs) are gaining importance over traditional epidemiological modeling due to advances in computing technology and catalyzed by the need for detailed epidemiological analysis of emergent diseases. Unfortunately, the advantages of ABMs are realized at the cost of significantly large execution times and high memory consumption for large-scale simulations. To address the memory issue, we designed and implemented an ABM using an innovative feature: the bitstring approach, in which the attributes of each agent are represented by an array of bits instead using traditional data structures. To cope with the high computational demands, we developed a parallel version of model aiming multicore CPUs and GPUs architectures using Thrust parallel algorithms library. The results of our model were validated comparing them with data of a spread of Influenza A in the Cascavel City, South Brazil, occurred in 2009. The model presented good qualitative results and an excellent performance on GPUs. The application of bitstring technique is proved to be relevant in economy of memory, allowing to store the same attributes using 41% less memory space and improving the data copy time between CPU and GPU up to 52%.
A Spectra-Based Equalization-Generation Combined Framework for Throat Microphone Speech Enhancement

Non-acoustic sensors are widely used in speech signal processing tasks, and their immunity to the background acoustic noise shows great benefits to traditional speech enhancement. To avoid using acoustic speech disturbed by strong noise, spectra mapping from throat microphone (TM) speech to acoustic microphone (AM) speech has been studied. However, there is a distinguished difference between the spectra of the two kinds of speech, and the mapping relationship is different in the low-band and high-band spectra, which limits the performance of the traditional full-band spectra mapping model. In this paper, to improve the perceived quality and intelligibility of TM speech, we investigate the low-band and high-band spectral structure between TM and AM speech, respectively, and propose a spectra-based band-division mapping framework for TM speech enhancement based on the investigation. In the framework, the low-band target spectra of AM speech are mapped based on the equalization method, and the high-band spectra are mapped from the full-band TM speech spectra, which are lack of high-frequency components. The overall framework can be viewed as a combination of spectra equalization in the low band and spectra generation in the high band. Both the objective and subjective evaluation results show clear advantages over the existing TM speech enhancement method based on the full-band spectra mapping.
Intelligent Vehicles' Effects on Chinese Traffic: A Simulation Study of Cooperative Adaptive Cruise Control and Intelligent Speed Adaption

With the rapid development and wide deployment of intelligent vehicles, they will exert significant impacts on traffic efficiency not only in developed countries, but also in developing countries such as China. This paper focuses on the effects of Cooperative Adaptive Cruise Control and Intelligent Speed Adaption on Chinese urban, highway and rural traffic. Through several parallel microscopic traffic simulations of Chinese contexts considering different road types and penetration rates, we find that these intelligent vehicle technologies can generate noticeable decrease in travel time and increase in travel speed for urban traffic, especially in the morning and evening peak time, but not quite effective on highways and in rural areas. The study also indicates the importance of their market penetration rate to technological effects. Furthermore, based on the characteristics and simulation results of Chinese traffic, we propose some suggestions for developing countries to make better use of these advanced technologies.
A user-adaptive deep machine learning method for handwritten digit recognition

The HWR (handwritten recognition) problem gains more attention with the development of machine learning. In this work, a user-adaptive HWR method is purposed for the application when only handwritten digits and few limited characters need to be recognized. Five types of CNN (Convolutional Neural Network) classifier are used in three steps: digits recognition, string-type classifier and string recognition. Experiment results show that the purposed method is capable of HWR for digits and few limited characters.
A Dictionary-Based Test Data Compression Method Using Tri-State Coding

With the rapid development of integrated circuit manufacturing processes, the degree of integration of system-on-chip(SoC) has increased dramatically. In this paper, a dictionary-based test data compression method with tri-state coding is proposed to reduce the increasing test data volume. Firstly the partial input reduction is used to preprocess the test set, and then the tri-state coding is presented to mark the index so that it can encode anywhere in the test set. Experimental results on ISCAS'89 benchmark circuits show that the average compression rate of the proposed scheme reaches 73.92% by increasing the hardware area overhead slightly.
Doppler effect mitigation over mobile underwater acoustic OFDM system

Orthogonal frequency division multiplexing (OFDM) has prevailed in high speed underwater acoustic(UWA) communications with obvious multipath effects. However, with the characteristic of being sensitive to the Doppler effect, the performance of an OFDM system is severely limited by the intercarrier interference (ICI) introduced by Doppler spread. In this paper, we build a hydroacoustic baseband defferentially encoded OFDM system and propose an effective approach to mitigate the Doppler effect for the system. After utilizing the Chirp-Z transform(CZT) to estimate a coarse common value of large scale Doppler factor a and null subcarriers for the estimation of residual Doppler am for each OFDM symbol, we use the method of polyphase filter resampling to compensate them. We also correct the phase rotation effect caused by timing sampling offset according to the residual Doppler estimation result. Simulation and experimental results show that while meeting the accuracy requirement of Doppler scaling factor estimation in mobile UWA communications, the proposed algorithm can be used to accurately and effectively estimate the Doppler scaling factor and requires lower computational complexity.
Application of “Well Clear” to Small Drones

Detect and avoid systems (DAA) play an important role in more and more areas of manned and unmanned aviation. Originally developed as an additional safety concept for manned aviation, today they represent a central concept of unmanned aviation, without which many deployment scenarios would not be conceivable. DAA systems perceive the environment using sensors such as lidar, radar or cooperative transponders to detect and avoid surrounding traffic. A core concept here is the notion of “Well Clear”. Originally this described the intuitive awareness of the pilot of a safe, conflict-free situation. In the context of semi-automatic systems, however, this term had to be formalized, not least by the standardization of ACAS / TCAS. This resulted in several, partly mutually compatible definitions. We present different approaches to the concept of “Well Clear”, including RTCA DO-365 and latest U-space developments. Further, we apply the notion to application scenarios involving small drones, and discuss to what degree the definitions can be applied or have to be modified in order to grant both safety and effectiveness.
Surge current capability of SiC MOSFETs in AC distribution systems

Whereas short circuit current and time ratings for power converter applications are often stated by SiC device manufacturers, surge current capability for AC electrical apparatus applications are rarely available. This paper present the experimental validation of surge current capability of selected SiC devices for low voltage AC distribution system applications. Because AC electrical apparatus, such as relays, contactors, and circuit breakers, have a different set of system parameters and requirements compared to traditional power converter applications, power semiconductor devices need to be validated from a different point of view. In fact, robustness to inductive short circuit currents, inrush currents, short and long time overload are some of the basic requirements for the utilization of WBG devices for these applications. In this paper, we compare the performance of different SiC MOSFETs under different types of AC waveform conditions.
AIRPORTS Metrics: A Big Data application for computing flights' performance indexes based on flown trajectories

A research line of the AIRPORTS Project, a partnership between Boeing Research and Technology-Europe (BR&TE) and several Spanish institutions, is focused on the measurement and assessment of performance indicators involved in ATM systems from surveillance data. As primary source of information we have different ADS-B (Automatic Dependent Surveillance - Broadcast) providers and, possibly, other sources that can enrich the raw data with other flight-related information. Previous work has already proposed and described a Big Data-based architecture, referred to as AIRPORTS DL, to manage in a scalable way the huge available collection of data. The conceptual data model is built around a sequence of ADS-B messages to reconstruct flight trajectories and, if possible, relates each trajectory with the departure and arrival airports, the aircraft being used, the corresponding airline or its flight-plan. In this paper, we describe a first attempt to develop an end-user application under the AIRPORTS DL framework. The application computes different metrics or performance indexes that depend only on the flown trajectory (e.g., traffic density, peak load or number of conflicts). Details about the proposed workflow for computing in advance these metrics are given and then, some examples of computed metrics for reference volumes (ATC sectors, FIR regions or ASMA circles around major airports) of the Spanish airspace during the year 2016 are used to illustrate novel visualization components in the AIRPORTS dashboard.
High-Speed Software Data Plane via Vectorized Packet Processing

In the last decade, a number of frameworks started to appear that implement, directly in userspace with kernel-bypass mode, high-speed software data plane functionalities on commodity hardware. Vector Packet Processor (VPP) is one of such frameworks, representing an interesting point in the design space in that it offers, in userspace networking, the flexibility of a modular router (Click and variants), with the benefits provided by techniques such as batch processing that have become commonplace in high-speed networking stacks (such as netmap or DPDK). Similarly to Click, VPP lets users arrange functions as a processing graph, providing a full-blown stack of network functions. However, unlike Click, where the whole tree is traversed for each packet, in VPP each traversed node processes all packets in the batch (called vector) before moving to the next node. This design choice enables several code optimizations that greatly improve the achievable processing throughput. This article introduces the main VPP concepts and architecture, and experimentally evaluates the impact of design choices (such as batch packet processing) on performance.
Game Theoretic Approach for Virtual Machines in IoT Fog Networks

Fog computing (FC) is new architecture that enables a set of low-power fog nodes (FNs) to provide service for cloud centers clients. FNs are managed by fog nodes owners (FNO). FC extends cloud computing services and support economical and low latency data centers for future Internet of Things (IoT). In this paper, we consider FC network consisting of a set of data service agents (DSAs) who manage a set of FNSs that provide the required service to cloud clients. Setting service price based on dynamic demand is an important application in the cloud computing. In this work, we propose new scheme to efficiently capture client's consumption behavior based on service price. Each client accepts the service price based on his requirements and the budget constraint. We model the problem of allocating FNO's resources to clients as Stackelberg game where FNO aims to maximize his profit, while DSA try to pay the lowest price and be served in time. After submitting the bids of all DSAs, FNO sets an optimal service price based on DSAs' bids. Simulation results stress that the FNO can maximize his profit and the DSA requests will be served in time.
Dynamic Taxi Service Planning by Minimizing Cruising Distance Without Passengers

It is a long-standing challenge to recommend routes to a group of taxis at different locations to minimize the overall mileage spent without customs. This paper studies route recommendation to a group of taxis by minimizing the overall mileage spent without customers. A new recommendation algorithm and evaluation model are proposed. The algorithm recommends the current optimal route and updates both the capacity and the probability along the route after getting a passenger. The new evaluation model is adapted to estimate the performance of each candidate route. A simulator that imitates the recommendation process on the real-world datasets and virtual taxis based on our recommendation system is developed. The experimental results demonstrate the effectiveness of the proposed evaluation model. The proposed method using the potential-cruising-distance model effectively reduces the global cruising distance of multiple taxis, especially in the applications with a large number taxis.
Mechanisms Involved in the Neuroprotection of Electroacupuncture Therapy for Ischemic Stroke

Stroke is one of the main causes of death all over the world. As the combination of acupuncture and electric stimulation, electroacupuncutre is a safe and effective therapy, which is commonly applied in ischemic stroke therapy in both experimental studies and clinical settings. The review was performed via searching for related articles in the databases of OVID, PUBMED, and ISI Web of Science from their respective inceptions to May 2018. In this review, we summarized the mechanism of EA for ischemic stroke via a series of factors, consisting of apoptosis related-factors, inflammatory factors, autophagy-related factors, growth factors, transcriptional factors, cannabinoid CB1 receptors, and other factors. In summary, EA stimulation may effectively alleviate ischemic brain injury via a series of signal pathways and various other factors.
Analyze Students Performance of a National Exam Using Feature Selection Methods

Recently, educational institutions are generating the mass of data and interesting to analyze these data for their applications. This purpose is achieved by data mining methods to extract knowledge required by the systems. This kind of dataset is usually huge and include many samples and unnecessary features. The nature of dataset implies that the analysis of data leads to inaccurate results without preprocessing. In this study, we want to find and evaluate the most important features by different feature selection methods. These methods give different results based on their nature. Therefore in the following, we evaluate obtained feature subsets with applying some machine learning methods. Here we use one educational dataset of an exam and want to construct a reliable model to predict the final outcome of this exam. We survey different feature selection and machine learning algorithms and find out the Information Gain and Gain Ratio yield better performance.
A Trade-off Between Accuracy and Complexity: Short-term Traffic Flow Prediction with Spatio-temporal Correlations

Considering spatio-temporal correlation between traffic in different roads has benefit for building an accurate spatio-temporal model for traffic prediction. However, it implies high computational complexity for model building in the context of a complicated network topology, e.g., urban network. Hence, this paper develops a method for capturing and quantifying the intricate spatio-temporal correlations. The contributions of this paper are as follows. First, we offer a physically intuitive approach to capture the spatio-temporal correlation between traffic in different roads, which is related to the road network topology, time-varying speed, and time-varying trip distribution. With this approach, only the parameters, namely time-varying lags, in our STARIMA (Space-Time Autoregressive Integrated Moving Average) based model should be adjusted in different time periods of the day. It guarantees the prediction accuracy and makes the predictor readily amendable to suit changing road and traffic conditions. Second, a metric named traffic transition probability calculated based on trip distribution, as well as a threshold $\varepsilon$ are applied for selecting the most spatio-temporally correlated neighbors of a target road. Thus, the complexity of model building will be reduced dramatically. Trace-driven experiments are conducted from two aspects. First, our proposed prediction method has superior accuracy compared with ARIMA and the back propagation neural network model (BPNN) based method, but has much reduced computational complexity. Second, the results show that the prediction accuracy is not always proportional to the increase in the number of spatial neighbors considered for a target road. The trade-off between accuracy and complexity depends on the configuration of $\varepsilon$.
Supporting Independent Living for Older Adults; Employing a Visual Based Fall Detection Through Analysing the Motion and Shape of the Human Body

Falls are one of the greatest risks for older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body. The proposed approach employs a new set of features to detect a fall. Motion information of a segmented silhouette when extracted can provide a useful cue for classifying different behaviours, while variation in shape and the projection histogram can be used to describe human body postures and subsequent fall events. The proposed approach presented here extracts motion information using best-fit approximated ellipse and bounding box around the human body, produces projection histograms and determines the head position over time, to generate 10 features to identify falls. These features are fed into a multilayer perceptron neural network for fall classification. Experimental results show the reliability of the proposed approach with a high fall detection rate of 99.60% and a low false alarm rate of 2.62% when tested with the UR Fall Detection dataset. Comparisons with state of the art fall detection techniques show the robustness of the proposed approach.