Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning the semantics of change sequences for query expansion

The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.
Low Latency Displays for Augmented Reality

Peter C. Lincoln: Low Latency Displays for Augmented Reality (Under the direction of Henry Fuchs and Greg Welch) The primary goal for Augmented Reality (AR) is bringing the real and virtual together into a common space. Maintaining this illusion, however, requires preserving spatially and temporally consistent registration despite changes in user or object pose. The greatest source of registration error is latency—the delay between when something moves and the display changes in response—which breaks temporal consistency. Furthermore, the real world varies greatly in brightness; ranging from bright sunlight to deep shadows. Thus, a compelling AR system must also support High-Dynamic Range (HDR) to maintain its virtual objects’ appearance both spatially and temporally consistent with the real world. This dissertation presents new methods, implementations, results (both visual and performance), and future steps for low latency displays, primarily in the context of Optical See-through Augmented Reality (OST-AR) Head-Mounted Displays, focusing on temporal consistency in registration, HDR color support, and spatial and temporal consistency in brightness: 1. For registration temporal consistency, the primary insight is breaking the conventional display paradigm: computers render imagery, frame by frame, and then transmit it to the display for emission. Instead, the display must also contribute towards rendering by performing a post-rendering, post-transmission warp of the computer-supplied imagery in the display hardware. By compensating in the display for system latency by using the latest tracking information, much of the latency can be short-circuited. Furthermore, the low latency display must support ultra-high frequency (multiple kHz) refreshing to minimize pose displacement between updates. 2. For HDR color support, the primary insight is developing new display modulation techniques. DMDs, a type of ultra-high frequency display, emit binary output, which require modulation to produce multiple brightness levels. Conventional modulation breaks low latency guarantees, and modulation of bright
VR Props: An End-to-End Pipeline for Transporting Real Objects Into Virtual and Augmented Environments

Improvements in both software and hardware, as well as an increase in consumer suitable equipment, have resulted in great advances in the fields of virtual and augmented reality. Typically, systems use controllers or hand gestures to interact with virtual objects. However, these motions are often unnatural and diminish the immersion of the experience. Moreover, these approaches offer limited tactile feedback. There does not currently exist a platform to bring an arbitrary physical object into the virtual world without additional peripherals or the use of expensive motion capture systems. Such a system could be used for immersive experiences within the entertainment industry as well as being applied to VR or AR training experiences, in the fields of health and engineering. We propose an end-to-end pipeline for creating an interactive virtual prop from rigid and non-rigid physical objects. This includes a novel method for tracking the deformations of rigid and non-rigid objects at interactive rates using a single RGBD camera. We scan our physical object and process the point cloud to produce a triangular mesh. A range of possible deformations can be obtained by using a finite element method simulation and these are reduced to a low dimensional basis using principal component analysis. Machine learning approaches, in particular neural networks, have become key tools in computer vision and have been used on a range of tasks. Moreover, there has been an increased trend in training networks on synthetic data. To this end, we use a convolutional neural network, trained on synthetic data, to track the movement and potential deformations of an object in unlabelled RGB images from a single RGBD camera. We demonstrate our results for several objects with different sizes and appearances.
IEC 61131-3: Programming Industrial Automation Systems: Concepts and Programming Languages, Requirements for Programming Systems, Decision-Making Aids

This practical book gives a comprehensive introduction to the concepts and languages of the new standard IEC 61131 used to program industrial control systems. A summary of the special requirements in programming industrial automation systems and the corresponding features in the IEC 61131-3 standard makes it suitable for students as well as PLC experts. The material is presented in an easy-to-understand form using numerous examples, illustrations and summary tables. There is also a purchaser's guide and two CD-ROMs containing two reduced but functional versions of programming systems. These increase the value of the book for PLC programmers and for those in charge of purchasing software in industrial companies.
A comparison of machine learning methods for detecting right whales from autonomous surface vehicles

This work compares a range of machine learning methods applied to the problem of detecting right whales from autonomous surface vehicles (ASV). Maximising detection accuracy is vital as is minimising processing requirements given the limitations of an ASV. This leads to an examination of the tradeoff between accuracy and processing requirements. Three broad types of machine learning methods are explored - convolution neural network (CNNs), time-domain methods and feature-based methods. CNNs are found to give best performance in terms of both detection accuracy and processing requirements. These were also tolerant to downsampling down to 1kHz which gave a slight improvement in accuracy as well as a significant reduction in processing time. This we attribute to the bandwidth of right whale calls which is around 250Hz and so downsampling is able to capture the sounds fully as well as removing unwanted noisy spectral regions.
In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) is an extremely heterogeneous malignant disorder; AML has been reported as one of the main causes of death in children. The objective of this work was to classify the most deleterious mutation in CCAAT/enhancer-binding protein-alpha (CEBPA) and to predict their influence on the functional, structural, and expression levels by various Bioinformatics analysis tools. Methods: The single nucleotide polymorphisms (SNPs) were claimed from the National Center for Biotechnology Information (NCBI) database and then submitted into various functional analysis tools, which were done to predict the influence of each SNP, followed by structural analysis of modeled protein followed by predicting the mutation effect on energy stability; the most damaging mutations were chosen for additional investigation by Mutation3D, Project hope, ConSurf, BioEdit, and UCSF Chimera tools. Results: A total of 5 mutations out of 248 were likely to be responsible for the structural and functional variations in CEBPA protein, whereas in the 3′-untranslated region (3′-UTR) the result showed that among 350 SNPs in the 3′-UTR of CEBPA gene, about 11 SNPs were predicted. Among these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived alleles created a new site of miRNA. Conclusions: In this study, the impact of functional mutations in the CEBPA gene was investigated through different bioinformatics analysis techniques, which determined that R339W, R288P, N292S, N292T, and D63N are pathogenic mutations that have a possible functional and structural influence, therefore, could be used as genetic biomarkers and may assist in genetic studies with a special consideration of the large heterogeneity of AML.
Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique

In order to perform operation management tasks, including state monitoring and control strategy optimization, of a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system, a data-driven dynamic model based on deep learning technique of long short term memory (LSTM) network is developed to predict the behaviours of fuel utilization. In addition, a LSTM model with unsupervised deep auto-encoder (DAE) method was developed to extract the feature from input data. The comparison performance between the common LSTM model and DAE-LSTM model was investigated. The results show that the DAE-LSTM model can enhance the prediction performance. Moreover, the effect of data size was investigated. The results demonstrate that the unsupervised DAE-LSTM model trained by large data size can further improve the prediction performance. The maximum error is only 0.00529, and average error decreases to 0.00025. In conclusions, the unsupervised DAE-LSTM model is an effective approach to predict dynamic behaviours.
Simple Convolutional Neural Network for Left-Right Hands Motor Imagery EEG Signals Classification

This article proposes a classification method of two-class motor imagery electroencephalogram (EEG) signals based on convolutional neural network (CNN), in which EEG signals from C3, C4 and Cz electrodes of publicly available BCI competition IV dataset 2b were used to test the performance of the CNN. The authors investigate two similar CNNs: a single-input CNN with a form of 2-dimensional input from short time Fourier transform (STFT) combining time, frequency and location information, and a multiple-input CNN with 3-dimensional input which processes the electrodes as an independent dimension. Fisher discriminant analysis-type F-score based on band pass (BP) feature and power spectra density (PSD) feature are employed respectively to select the subject-optimal frequency bands. In the experiments, typical frequency bands related to motor imagery EEG signals, subject-optimal frequency bands and extension frequency bands are employed respectively as the frequency range of the input image of CNN. The better classification performance of extension frequency bands show that CNN can extract optimal feature from frequency information automatically. The classification result also demonstrates that the proposed approach is more competitive in prediction of left/right hand motor imagery task compared with other state-of-art approaches.
3D rigid pose tracking based on new distance function of line segments

To track and estimate the pose of known rigid objects efficiently in complex environment, we propose a method based on 3D particle filter (PF) with M-estimation optimization. A similarity observation model is put forward according to a new distance function of line segments firstly; secondly, the correspondences between 3D-2D line segments are obtained based on the tracking results of PF. Then, the pose is optimized using M-estimation to minimize the objective function defined according to our new distance metric which integrating the midpoint distance. Finally, the optimized particles are fused into the PF framework according to the importance sampling theory. Experiments indicate that the proposed method can effectively track and accurately estimate the pose of freely moving objects in unconstrained environment. Comparisons on synthetic images demonstrate that our method greatly outperforms the state-of-art method in accuracy and efficiency.
Antenna design and characterization for biomedical applications

The use of radiofrequency in biomedical devices is taking on increasing significance due to its contributions to the prevention, diagnosis and treatment of diseases, either for minimally invasive remote monitoring of physiological data or for other applications such as medical imaging and thermal treatments. The advantages provided by radiofrequency fit perfectly inside the new paradigm of predictive, preventive, personalized and participatory medicine (P4 medicine) in which the individual patient is the central focus of the healthcare system. Although the development of biomedical devices with radiofrequency has been studied in depth in recent years, several challenges still remain. In this context, this thesis deals with one of the most important issues: the design and characterization of antennas in the presence of the human body. The main goal of this thesis is to develop new antenna designs that overcome some of the current limitations, such as miniaturization, efficiency, frequency detuning and integration. This thesis also aims to provide tools and new insights for the development of antenna designs in the presence of the human body by analyzing the interaction between the antennas and the surrounding biological medium. The research carried out has two main focuses: the wireless transmission of physiological data (biotelemetry) and microwave hyperthermia for cancer treatments. For the first, the study presents a theoretical framework review of inbody antennas followed by an exhaustive study of their miniaturization process, the manufacturing and the experimental characterization, as well as the phantom influence. Based on these studies, a novel multilayered broadband antenna and a miniaturized RFID tag are presented. And with regard to thermal treatment of breast cancer, an on-body, compact and configurable applicator with a phased antenna array has been developed. In conclusion, the outcomes of this thesis make contributions in two main areas: (1) the antenna design and characterization for use in the presence of human body and (2) the research of new solutions for in-body biomedical devices with biotelemetry and for the treatment of breast cancer through microwave hyperthermia.
Context and Privacy Concerns in Friend Request Decisions

Friend request acceptance and information disclosure constitute 2 important privacy decisions for users to control the flow of their personal information in social network sites (SNSs). These decisions are greatly influenced by contextual characteristics of the request. However, the contextual influence may not be uniform among users with different levels of privacy concerns. In this study, we hypothesize that users with higher privacy concerns may consider contextual factors differently from those with lower privacy concerns. By conducting a scenario‐based survey study and structural equation modeling, we verify the interaction effects between privacy concerns and contextual factors. We additionally find that users' perceived risk towards the requester mediates the effect of context and privacy concerns. These results extend our understanding about the cognitive process behind privacy decision making in SNSs. The interaction effects suggest strategies for SNS providers to predict user's friend request acceptance and to customize context‐aware privacy decision support based on users' different privacy attitudes.
A Discounted Stochastic Multiplayer Game Approach for Vehicle-to-Grid Voltage Regulation

Along with the diversification of electricity market, the voltage regulation (VR) service is opened up to various qualified providers to meet the enormous demand, among which, the electric vehicle (EV) aggregators (AGGs) can integrate the scattered EVs and play the role of VR sources with a high response speed and low cost. In order to coordinate the VR service providers to satisfy the total VR requirement, auction mechanisms are widely used to select VR sources to achieve better performance. Yet, it is challenging to optimize the strategy of each EV AGG in the auction process due to the stochastic EV mobility, various distribution network topology, and the competition mechanism. To address these challenges, we proposed a discounted stochastic multiplayer game (DSMG) approach to analyze the competition among EV AGGs. Due to the constraint of distribution network topology, the efficiency of the VR sources at different locations can be different. Thus, the impact of distribution network topology on the VR efficiency is investigated by DSO when evaluating the capacity of AGGs. The randomness of EV numbers is considered when predicting the AGGs’ available VR capacity so that the tendency for the AGGs to follow the optimal strategies can be modeled accurately. Accordingly, a linear power flow analysis approach and a battery pool model are developed to address the distribution network topology and EV mobility, respectively. Then, the DSMG approach is used in the VR auction process to optimize the AGGs’ strategies. The existence proof of the stationary Markov perfect equilibrium is presented, and the corresponding algorithms to obtain the equilibrium is proposed. The performance of the proposed DSMG approach is evaluated and compared with other approaches based on the IEEE 33-bus test feeder, IEEE 123-bus test feeder, and the real-world generation and load data from PVWatts Calculator and Market Analysis and Information System, respectively.
A 4D Trajectory Prediction Model Based on the BP Neural Network

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.
Building a Semantic Health Data Warehouse: Evaluation of a search tool in Clinical trials

Background: The huge amount of clinical, administrative and demographic data recorded and maintained by hospitals can be consistently aggregated into Health Data Warehouses (HDWs) with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a Semantic Health Data Warehouse (SHDW) enabling both semantic description and retrieval of
Real-time lane detection and tracking for autonomous vehicle applications

This article proposes an improved lane detection and tracking method for autonomous vehicle applications. In real applications, when the pose and position of the camera are changed, parameters and thresholds in the algorithms need fine adjustment. In order to improve adaptability to different perspective conditions, a width-adaptive lane detection method is proposed. As a useful reference to reduce noises, vanishing point is widely applied in lane detection studies. However, vanishing point detection based on original image consumes many calculation resources. In order to improve the calculation efficiency for real-time applications, we proposed a simplified vanishing point detection method. In the feature extraction step, a scan-line method is applied to detect lane ridge features, the width threshold of which is set automatically based on lane tracking. With clustering, validating, and model fitting, lane candidates are obtained from the basic ridge features. A lane-voted vanishing point is obtained by the simplified grid-based method, then applied to filter out noises. Finally, a multi-lane tracking Kalman filter is applied, the confirmed lines of which also provide adaptive width threshold for ridge feature extraction. Real-road experimental results based on our intelligent vehicle testbed proved the validity and robustness of the proposed method.
Data Science in Public Mental Health: A New Analytic Framework

Understanding public mental health issues using data science and finding solutions based on the findings from the data science projects can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that project associates are competent and have enough knowledge to implement the data science process. Therefore, this paper presents a new framework that mental health professionals can use to solve challenges they face using data science. Although a large number of research papers have been published on public mental health, few have addressed the use of data science in public mental health. Recently, Data Science has changed the way we manage, analyze and leverage data in healthcare industry. Data science projects differ from conventional data analysis, primarily because of the scientific approach used during data science projects. One of the motives for introducing a new framework is to motivate healthcare professionals to use "Data Science" to address the challenges of mental health. Having a good data analysis framework and clear guidelines for a comprehensive analysis is always a plus point. It also helps to predict the time and resources needed in the early in the process to get a clear idea of the problem to be solved.
“Standardising by running code”: the Signal protocol and de facto standardisation in end-to-end encrypted messaging

Abstract After Edward Snowden’s revelations, encryption of online communications at a large scale and in a usable manner has become a matter of public concern. The most advanced and popular among recently-developed encryption protocols is currently the Signal protocol. While the Signal protocol is widely adopted and considered as an improvement over previous ones, it remains officially unstandardised, even though there is an informal draft elaborated towards that goal. The analysis of how this protocol was introduced and swiftly adopted by various applications, and of subsequent transformations of the encrypted messaging ecosystem, sheds light on how a particular period in the history of secure messaging has been marked by a “de facto standardisation. ” What can we learn about existing modes of governance of encryption and the histories of traditional standardisation bodies, when analysing the approach of “standardisation by running code” adopted by Signal? And finally, how does the Signal protocol challenge a “linear,” evolution-based vision of messaging history? Drawing from a three-year qualitative investigation of end-to-end encrypted messaging, from a perspective informed by science and technology studies (STS), we seek to unveil the ensemble of processes that make the Signal protocol a quasi-standard.