Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Semantic Features for Software Defect Prediction by Code Comments Embedding

Software Quality Assurance (SQA) is essential in software development and many defect prediction methods based on machine learning have been proposed to identify defective modules. However, most existing defect prediction models do not provide good defect prediction results, and the semantic features reflecting the detective patterns may not be well-captured via traditional feature extraction methods. More information such as code comments should be also be embedded to generate semantic features respecting the source code functionality. Therefore, how to embed code comments for defect prediction is a big challenge, and another problem is that many comments of source code are missing in real-world applications. In this paper, we propose a novel defect prediction model named CAP-CNN (Convolutional Neural Network for Comments Augmented Programs), which is a deep learning model that automatically embeds code comments in generating semantic features from the source code for software defect prediction. To overcome the missing comments problem, a novel training strategy is used in CAP-CNN that the network encodes and absorb comments information to generate semantic features automatically during training process, which does not need testing modules to contain comments. Experimental results on several widely-used software data sets indicate that the comment features are able to improve defect prediction performance.
Knowledge Based Hierarchical Decomposition of Industry 4.0 Robotic Automation Tasks

Robotic automation has made significant inroads into industrial manufacturing and supply chains. With Industry 4.0 requirements proposing further autonomy to robotic participants, it is necessary to reason about robotic tasks within a knowledge dependent software framework. In this work, we model robotic automation tasks using hierarchical decomposition models, that are used to extract action plans to satisfy end goals. By abstracting components as intelligent agents that have perception, action, goal and knowledge base elements, we provide a reusable model to abstract robotic automation behavior. Through the use of the formal typed specification language Orc, implementations that confirm to the goal decomposition process are formulated. We demonstrate our techniques over Smart Warehouse deployments, with domain specific ontologies to ensure accurate type based descriptions of knowledge elements. This provides a generic framework for deploying intelligent automation systems across a host of industrial settings.
Research on Harmonic Instability of Wind Farms Based on Nyquist Stability Criterion

The wind-power generation is a kind of renewable energy technology with the good development prospects at present. As the problem of wind farm resonance becomes more and more serious. Moreover, because of the negative damping of the VSC(voltage source converter) output impedance in partial frequency domain, sometimes there is a problem of harmonic instability in wind farms. The purpose of this paper is to analyze the mechanism of harmonic instability in wind farms. Firstly, the harmonic model of wind farms is built in this paper. The VSC impedance model base on the control parameters is established to explore the output characteristics of the VSC in the wind turbine. The transmission and amplification rules of harmonics in the VSC control loop are explored. Further, the influence factors of harmonic instability are analyzed by using Nyquist stability criterion. Meanwhile, passive damping is used to suppress harmonic instability.
Short Text Mining for Fault Diagnosis of Railway System Based on Multi-Granularity Topic Model

Vehicle equipment is one of the core equipments of train control system in the high-speed railway, and it mainly depends on the experience of maintenance personnel to perform fault diagnosis when it fails, which is ineffective. The maintenance text data of vehicle equipment, which contains fault category information, is not fully utilized. The classification of maintenance text can well assist fault diagnosis of vehicle equipment. However, shortness and imbalanced class distribution of maintenance texts, which hinder the application of conventional text representation models and classification algorithms, pose challenges for classification task. In this paper, we propose a novel text feature selection algorithm based on multi-granularity latent Dirichlet allocation (LDA) to overcome shortness characteristic of maintenance text. To solve the problem of class imbalance, a cost-sensitive Support Vector Machine (SVM) is utilized to construct fault diagnosis model. Finally, we compare our proposed method with the state-of-the-art baseline over a vehicle maintenance text data set collected by Guangzhou railway corporation, and it outperforms traditional methods.
IoT for Healthcare: An architecture and prototype implementation for the remote e-health device management using Continua and LwM2M protocols

The advent of the IoT and increased attention on the quality of the remote e-healthcare services intensified the focus on the management and interoperability of personal health devices. In order to operate remote e-health services over a long period of time, it is important to remotely manage and monitor the status of the personal health devices and gateways. Existing standards have limitations for IoT healthcare services, e.g., the ISO/IEEE 11073 standard does not support IP communication. In this article, we propose a solution for introducing OMA LwM2M into Continua architecture to ensure the remote management of personal health devices and gateway. A software prototype was implemented for the personal health gateway on an Android smartphone using Eclipse LeShan. The tests performed showed that integrating the LwM2M in Continua architecture into a personal health gateway represents a valid approach.
On Multi-query Local Community Detection

Local community detection, which aims to find a target community containing a set of query nodes, has recently drawn intense research interest. The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, we may not have any prior knowledge about the community memberships of the query nodes, and different query nodes may be from different communities. To address this limitation of the existing methods, we propose a novel memory-based random walk method, MRW, that can simultaneously identify multiple target local communities to which the query nodes belong. In MRW, each query node is associated with a random walker. Different from commonly used memoryless random walk models, MRW records the entire visiting history of each walker. The visiting histories of walkers can help unravel whether they are from the same community or not. Intuitively, walkers with similar visiting histories are more likely to be in the same community. Moreover, MRW allows walkers with similar visiting histories to reinforce each other so that they can better capture the community structure instead of being biased to the query nodes. We provide rigorous theoretical foundation for the proposed method and develop efficient algorithms to identify multiple target local communities simultaneously. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.
Intelligent Salary Benchmarking for Talent Recruitment: A Holistic Matrix Factorization Approach

As a vital process to the success of an organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data, which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement. To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data. Specifically, we first construct a salary matrix based on the large-scale recruitment data and creatively formalize the salary benchmarking problem as a matrix completion task. Along this line, we develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the missing salary information in the salary matrix. Indeed, by integrating multiple confounding factors, such as company similarity, job similarity, and spatial-temporal similarity, HSBMF is able to provide a holistic and dynamic view for fine-grained salary benchmarking. Finally, extensive experiments on large-scale real-world data clearly validate the effectiveness of our approach for job salary benchmarking.
Performance Study of the Genetic Algorithm and the Levenverg-Marquardt Optimization Algorithm for the Calibration of a Rotating Camera

Calibrating the camera is one of the fundamental problems of computer vision. It aims to determine the parameters of the intrinsic and extrinsic camera. During the camera calibration process, the initial camera settings are acquired by solving linear equations. Then these parameters are refined by a nonlinear optimization procedure. In this paper, a comparative study between Levenberg Marquardt and the genetic algorithm for the optimization of nonlinear system parameters is presented. Our main objective is to show the merits of the optimization of Genetic Algorithms and to determine its suitability in the field of camera calibration. Experimental results demonstrate that the proposed method is effective.
Wakeup: Designing Multimodal Alerts for Drowsy Drivers with SmartPhones

This paper aims to improve warning signals of the existing drowsiness detection systems in terms of efficiency and effectiveness. For this purpose, firstly, we exploit visual, audio and tactile modalities to discover the most suitable signal for alerting the car drivers in drowsiness detection systems. Secondly, the resultant signals are merged into various combinations to form multimodal warnings. From this, we got four different combinations. Furthermore, we have done an experimental evaluation in order to identify the most effective and efficient multimodal warnings for drowsiness detection systems. Results from our observational studies showed that the most suitable unimodal signals are as follows: (1) 40 ms on and 50 ms off the flashlight of smartphone for visual warning, (2) Apple iOS Ringtones-Alarm for the audio warning, and (3) 1 pulse in a second with 500 ms inter-stimulus interval for the vibrotactile warning. From the experimental study, we found that the most efficient and preferable warning for alerting the drowsy driver is multimodal signal consisting of visual, audio, and tactile modalities. This paper is helpful for the car industries to develop the effective warnings of drowsiness detection systems, furthermore, it helps in adopting these systems and in preventing accidents.
Study on a Novel Deadbeat Predictive Torque Control Strategy with Flux and Torque Decoupling for PMSM

Considering the disadvantages such as large torque ripple and unfixed switching frequency of the traditional direct torque control (TDTC), a deadbeat predictive torque control strategy (DB-PTC) based on stator flux coordinate was proposed. First of all, the control principle of TDTC and the reason of large flux and torque ripple were analyzed. Secondly, due to the fact that the parameters setting the controllers are complex and the control effect is not ideal in the SVM-DTC and the complex strategy with large computation in the model predictive torque control (MPTC) and DB-MPTC, the decoupling control of the stator flux and the torque was realized based on the stator flux coordinate, which combined the deadbeat theory to calculate the voltage vector and simply realize the decoupling control. Finally, the simulation results show that the strategy has the advantages of simple strategy and low computation while can effectively eliminate the torque ripple.
Carrier Phase Shift Modulation for Reducing the Common Mode Voltage in a Two-Level Three-Phase Inverter

Due to the ever increasing use of Variable Frequency Drives (VFDs) which involves fast switching converters, focus on reducing the Common Mode Voltage (CMV) is picking up pace. Moreover, the application of SiC power electronics switches, which can switch at much higher switching frequencies than conventionel IGBTs, has refocused the attention on converter redesign to mitigate EMI/EMC issues. The reduction in CMV is necessary to limit the Common Mode Currents (CMC) which in turn reduces the inverter induced bearing currents and shaft voltage. This paper introduces a new scheme, which modulates the shift in the carrier wave for eliminating the highest peaks in CMV. Design, analysis, and simulations have been performed and will be discussed here in details. This new method is compared with the conventional PWM methods and to other similar methods for reducing CMV.
Qu'est-ce que la Nouvelle Vague?

the abdication of any right to explanation at all. It may be that the concentration on action and technique, at the cost of explanation and motivation in the work of French directors, is itself a sort of avoidance of content, a begging the question even, and that on this plane they meet their less advanced brethren elsewhere. But it is characteristic that, of all of them, the one who had most to say about forbidden things like war and peace, atom bombs and human suffering, is also the one who has, in effect, said most, and most interestingly, in (and about) the cinematic idiom itself. Perhaps what Resnais understood (consciously or not) is that the problem today rests in the synthesis of man's personal and social concerns. Social criticism is good for a book, and so is adultery, and so is first love. But in life they go together, mixed as the elements of a cake; for those who live them are men and women, complex and complicated, hungry and happy and apprehensive at the same time, working and loving and buying a newspaper and shooting a glance at a passing blonde all at the same time. A slice of reality-since that is all the artist can hope to cope with-a slice of reality is a slice of a mixture, not the artificial isolation of one of its many components.
Face recognition across pose for PTZ camera video surveillance applications

Face recognition has prodigious success in biometric and surveillance sectors. The key drawback in face recognition is dealing with different poses i.e., recognition of faces in arbitrary in-depth rotations (which depends upon camera rotation/person face movement). This paper deals with aligning the pose variation taken in non-overlapping camera views and the recognition is attained using this aligned face. Face fitting is done by Relevance Metric Learning with List-wise Constraints (RMLLC). Subsequently, Constrained Local Models (CLMs) framework otherwise Discriminative Response Map Fitting (DRMF) method, is adopted for fitting. Afterwards, the adjacent faces are aligned by means of transformation. Finally, all pairwise similarities for query frame against database frames is used to recognize face. The proposed work exemplifies substantial improvement which is validated by matching rate. The results obtained using CVL database shows the efficacy of the proposed algorithm under different pose variations.
Replication Strategies for Distributed IEC 61499 Applications

The IEC 61499 standard defines a framework for the development of distributed control applications, including a modeling language and its associated execution semantics. Traditionally industrial control applications run on a single computing device. When moving to a distributed solution new failure models need to be taken into account. In previous work the authors have this proposed a framework to support the development of fault tolerant distributed IEC 61499 applications, based on the use of active replicas. Special communication protocols were considered in order to guarantee replica determinism and synchronization. In this paper we relax the replica determinism requirement and explore other possible implementations for the replication framework that do not require special communication protocols and are easier to implement in practice.
Cultural Influences on the Requirements Engineering Process: Lessons Learned from Practice

The Requirements Engineering (RE) process is crucial for identifying users' requirements and the success of the software development process. As RE is a socio-technical activity in nature, an individuals' culture might influence its activities deeply. The aim of this study is to observe and evaluate the influence of culture on the RE process. In total, we conducted 16 interviews with requirements engineer practitioners in Saudi Arabia and Australia. We found that both cultures have an impact on the RE process. This paper describes the lessons learned from practice on how to improve RE practices, taking into account cultural aspects.
Harmonic elimination procedure for cascaded multilevel inverters having a particular even number of dc sources

This paper proposes an efficient analytical harmonic elimination procedure valid for cascaded multilevel converters having $s$ dc sources and a number of level $l=2s+1$ with $s=2^{n} n=1,2,3, \ldots$. This procedure eliminates $n+1$ harmonics and their respective multiple; this implies a very low Total Harmonic Distortion. A 9-level inverter prototype is built and experimental results are obtained. The accuracy of the presented procedure is proved by the agreement between computed and experimental results.or, is observed.
A 16-bit 2.0-ps Resolution Two-Step TDC in 0.18- $\mu$ m CMOS Utilizing Pulse-Shrinking Fine Stage With Built-In Coarse Gain Calibration

This paper proposes a time-to-digital converter (TDC) that achieves wide input range and fine time resolution at the same time. The proposed TDC utilizes pulse-shrinking (PS) scheme in the second stage for a fine resolution and two-step (TS) architecture for a wide range. The proposed PS TDC prevents an undesirable nonuniform shrinking rate issue in the conventional PS TDCs by utilizing a built-in offset pulse and an offset pulsewidth detection schemes. With several techniques, including a built-in coarse gain calibration mechanism, the proposed TS architecture overcomes a nonlinearity due to the signal propagation and gain mismatch between coarse and fine stages. The simulation results of the TDC implemented in a 0.18- $\mu \text{m}$ standard CMOS technology demonstrate 2.0-ps resolution and 16-bit range that corresponds to ~130-ns input time interval with 0.08-mm2 area. It operates at 3.3 MS/s with 18.0 mW from 1.8-V supply and achieves 1.44-ps single-shot precision.
The Impact of Social Network: Understand Consumer’s Collaborative Purchase Behavior

With the popularity of social networking, consumer often relies on social network when making purchase decisions. Despite the growing importance of social network in supporting online purchase behavior, there has been limited research focusing on the effect of social network on consumer collaborative purchase behavior. This paper proposes a framework for understanding consumer’s collaborative purchase behavior within online social groups. It is one of the first studies to our knowledge that explore the combined effect of social network structure and content on consumer’s collaborative purchase. In this study, we first extract and analyze adjacent matrix, which representing related social networks, from a large dataset. This is then combined with interaction-based content analysis to identify the structure of social network. After verifying the impact of network attributes and structure on consumer purchases, we conducted content analysis based on chat records and identified productrelated interaction structures and content composition information based on the language action perspective. Finally, we combine content analysis with network topology analysis to construct a weighted social network and calculate the impact of these social networks on consumer purchasing behavior. Our research proposes a research framework to effectively collect, extract and analyze the structure and content of social networks.
Challenges Faced by Educational Institution in Middle East to Accept and Implement Cloud-Based Data Storage

Education is a one of the most important pillar to play a key role in a consistent growth of any economy, especially for developing and under-developed countries. Today, the education has come out from the traditional class room scenario and spread its wings to collaborate with technology. The faculties and students have become more tech savvy as the learning and teaching process is utilizing the advance technology on regular basis. Due to digitalization and virtualization of teaching resources through the Internet, and adoption of Cloud Computing by educational institutions has been significantly increased. Despite the heavily increasing pressure of financial obligations for educational institutions to deliver state of art facilities with minimum resources and maximum efforts the Educational institutions, both public and private must utilize the potential advantage of Cloud Computing to ensure high quality service regardless of the minimal resources available the cloud computing is growing and acting as a game changer in academia because of its various advantages and usages. Now Educational institutions are using various cloud-based applications provided by the various service providers to ensure that their faculties, wards and other stack holders can perform both academic as well as non-academics tasks. Thus, this research will seek to establish the benefits associated with the use of cloud computing in Educational Institution. The solutions provided by the cloud technology ensure that the research and development, as well as the teaching is more sustainable and efficient, thus positively influencing the quality of learning and teaching within Educational Institution. This has also led to all the educational institutions to adopt cloud technology as a solution to various technological challenges they face on a daily routine.
Intelligent Mechatronic System with Decentralised Control and Multi-Agent Planning

Flexibility and reconfigurability of production systems requires intelligent devices and products that enables easy integration and reconfiguration eliminating the need of explicit programing the functionality of resulting system. This lead to the development of such concept as Intelligent Mechatronic Component. However, coordinating such distributed self-contained components into the desired logic of operation is a challenging task. Multi-agent systems (MAS) architecture provides necessary features for seamless integration of individual functionalities of agents into system's behaviour by self-configuration. The presented study explores an approach where MAS realizes high level coordination tasks while IMCs provides embedded services. MAS is realized in GORITE goal-oriented team programming framework deployed as web service in the Cloud. Low-level control of IMCs is developed in IEC 61499. The paper presents a case study of a Pick and Place manipulator composed of intelligent cylinders.
Image Hash Minimization for Tamper Detection

Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area.