Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation of the Relationship between Extract Method and Change Metrics: A Case Study of JEdit

Extract Method is one of the most widely used refactoring patterns. So far, low quality of source code has been regarded as an indicator for Extract Method opportunities. However, recent studies showed that there is no clear relationship between source code quality and Extract Method. Change metrics can be indicators for Extract Method because the characteristics of software evolution strongly affect software quality. However, there has been no study that investigated the relationship between change metrics and Extract Method. In this study, we conducted two studies investigating the relationship between Extract Method and change metrics. As a result, we found that (1) change metrics have a clear relationship with Extract Method and (2) both product and change metrics are necessary to recommend candidates for Extract Method with high accuracy.
Estimation of non-linear effects for Capacitive DAC in SAR ADC: An Analytical Model

The efficiency in terms of accuracy, power consumption, and on-chip area of Successive Approximation Register (SAR) Analog to Digital Converter (ADC) depends on the charge-redistribution Digital to Analog Converter (DAC). Thus, the optimal design of SAR ADC demands accurate design and analysis of the DAC unit. In this work, a MATLAB model of charge-redistribution DAC based on Binary Weighted with attenuation capacitor array has been presented for the design of a SAR ADC. This MATLAB based model performs statistical as well as parametric simulations including the effects of parasitic capacitances and capacitor mismatch which help in the accurate estimation of static and dynamic non-linearity of the DAC unit.
Reliability Challenges with Materials for Analog Computing

Specialized hardware for deep learning using analog memory devices has the potential to outperform conventional GPUs by a large margin. At the core of such hardware are arrays of non-volatile-memory (NVM) devices that can perform the simple matrix operations needed for deep learning in parallel and in constant time. Several implementations can be found in the literature that use different materials as memory elements, including phase-change-memory (PCM), resistive-random-access-memory (RRAM), electrochemical-random-access-memory (ECRAM), and ferroelectric devices. While the current focus is to demonstrate functionality, there is an increasing concern about the reliability margins of this emerging technology. In this paper we will briefly describe operation and device requirements, and then focus on possible reliability exposure in terms of variability, stability and drift, retention and durability.
A Low-Power Approximate Multiply-Add Unit

Modern applications such as image processing and deep learning are error tolerant in some level of inaccuracy. Approximate computing is one of the promising techniques that benefit applications in such domain by trading power for accuracy. This paper supposes applications that prioritize power and area over accuracy and proposes lower-power approximate multiply-add (MAC) unit with reduced area. The proposed MAC unit utilizes an approximate tree compressor (ATC), which was proposed in the previous study. From the experimental designs, it is unveiled that the proposed MAC unit consumes 48.7% less power and its area is 49.7% smaller than the conventional MAC unit.
Tracking the Dynamics of SPOC Discussion Forums: A Temporal Emotion-Topic Modeling Approach

Small private online courses (SPOCs) have drawn widespread attention due to their adaptability to blended learning in formal education. As a type of interactive tool, many SPOC forums have stored rich textual data including focused learning content and feedback. However, currently, this information is used mainly for measuring students' activity levels instead of academic emotion and their evolutionary trends throughout a teaching period. This paper presents an unsupervised forum understanding model, namely the temporal emotion-topic model (TETM), to model time jointly with emotions and topics. This model is applied to track the evolutionary trends of pairs of {emotion, topic}, i.e., emot-topics. Especially, for each emotion, the most significant topic can be extracted and tracked across a semester. Finally, we investigate the emot-topics differences of different achievement levels of students, and examine the dynamics of emot-topics over course weeks to gain some insights about the relationship between time, emotion and learning achievement. The tracking of emot-topics could be valuable if users are informed of what students are concerned about and how these topics evolve in courses.
Reliability Perspective on Neuromorphic Computing Based on Analog RRAM

A perspective is provided to compare the reliability requirements of Resistive Random Access Memory (RRAM) for digital memory application and neuromorphic computing application. Retention and endurance are considered as the two key metrics to evaluate the impact on neural network training. Statistical measurements and network simulation demonstrate that the retention and endurance degradation behaviors of analog RRAM are quite unique when implementing on a neuromorphic computing system. New evaluation method is therefore needed for reliability test.
View-Dependent Omnidirectional Video Encapsulation Using Multiple Tracks

In recent years, there has been a significant interest towards virtual reality applications, especially omnidirectional videos. Due to the huge resolutions, omnidirectional videos are difficult to codec and stream in term of traditional multimedia tools. In this paper, a view-dependent video encapsulation solution is proposed. Videos with different resolutions are partly extracted to combine to encapsulate mixed-resolution file according to user's viewport, which can effectively save bandwidth without much noticeable quality impacts. Our solution results can decrease file size to 75% with a 110° × 110° viewport and provide full resolution video inside the FOV.
GIDL Increase Due to HCI Stress: Correlation Study of MOSFET Degradation Parameters and Modelling for Reliability Simulation

This work presents a correlation study between gate-induced drain leakage (GIDL) increase and standard n-MOSFET degradation parameters (IDlin and IDsat) due to Hot Carrier Injection (HCI) both for SiON and SiO2 gate nodes. The same mechanism is responsible for the changes in these n-MOSFET parameters, so we propose the use of a unique HCI lifetime model, including the GIDL increase in accumulation. The GIDL parameter degradation can be implemented into aging models in CAD libraries along with the degradation models for standard n-MOSFET parameters. We also discuss model limitations for high-K metal gate (HKMG) devices.
A Bird's Eye View on Requirements Engineering and Machine Learning

Machine learning (ML) has demonstrated practical impact in a variety of application domains. Software engineering is a fertile domain where ML is helping in automating different tasks. In this paper, our focus is the intersection of software requirement engineering (RE) and ML. To obtain an overview of how ML is helping RE and the research trends in this area, we have surveyed a large number of research articles. We found that the impact of ML can be observed in requirement elicitation, analysis and specification, validation and management. Furthermore, in these categories, we discuss the specific problem solved by ML, the features and ML algorithms used as well as datasets, when available. We outline lessons learned and envision possible future directions for the domain.
Spatial-Temporal Editing for Dynamic Hair Data

Hair plays a unique role in depicting a person's character. Currently, most hair simulation techniques require a lot of computation time, or rely on complex capture settings. Editing and reusing of existing hair model data are very important topics in computer graphics. In this paper, we present a spatialtemporal editing technique for dynamic hair data. This method can generate a longer or even infinite length sequence of hair motion according to its motion trend from a short input. Firstly, we build spatial-temporal neighborhood information about input hair data. We then initialize the output according to the input exemplar and output constraints, and optimize the output through iterative search and assignment steps. To make the method be more efficient, we select a sparse part of the hair as the guide hair to simplify the model, and interpolate a full set of hair after the synthesis. Results show that our method can deal with a variety of hairstyles and different way of motions.
An update on pharmaceutical film coating for drug delivery

Introduction: Pharmaceutical coating processes have generally been transformed from what was essentially an art form in the mid-twentieth century to a much more technology-driven process. Areas covered: This review article provides a basic overview of current film coating processes, including a discussion on polymer selection, coating formulation additives and processing equipment. Substrate considerations for pharmaceutical coating processes are also presented. Expert opinion: While polymeric coating operations are commonplace in the pharmaceutical industry, film coating processes are still not fully understood, which presents serious challenges with current regulatory requirements. Novel analytical technologies and various modeling techniques that are being used to better understand film coating processes are discussed. This review article also examines the challenges of implementing process analytical technologies in coating operations, active pharmaceutical ingredients in polymer film coatings, the use of high-solids coating systems and continuous coating and other novel coating application methods.
High Voltage Tolerant Design with Advanced Process for TV Application

High voltage tole rant IO interface provides an efficient and flexible solution for integrated multimedia application. In this paper, we studied the design for reliability of IO interface in advanced technology node. Three different approaches, which is from device, layout and circuit point of view, to enhance the IO overdrive circuit (IP) reliability are investigated. We successfully develop a LDMOS that is compatible with advanced process and provides good reliability. However, new device development is a time consuming process. To reduce time-to-market, two solutions are proposed. 1) Taking the advantage of reverse body bias effect to reduce HCI damage on IP level. It can be achieved by implementing deep N-well. 2) Reducing the overstress voltage on devices by bias tracing circuit. Finally, we compared the IO interface performance and area of each solution. The solution with bias tracing circuit exhibits good reliability to sustain 5V stress and provides 66% area reduction.
Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance

It is a pervasive problem to obtain the human body size without contacting for apparel application. In this paper, a new approach is proposed which can be applied to calculate human body size such as shoulder width, bust, hips and waist girth with single depth camera. First, single depth camera as the 3D model acquisition device was used to get the 3D human body model. Then an automatic extraction method of focal features on 3D human body via random forest regression analysis of geodesics distance is used to extract the predefined feature points and lines. Finally, the individual human body size is calculated according to the feature points and lines. The method is an automatic data-driven way. The scale-invariant heat kernel signature is exploited to serve as feature proximity. So it is insensitive to postures and different shapes of 3D human body. These main advantages of our method lead to a robust and accurate feature extraction technique and size measurement for 3D human bodies in various postures and shapes. The experiment results show that the average relative error of feature points extraction is 0.0617cm. The average relative errors of shoulder width and girth are 1.332cm and 0.7635cm, respectively. Overall, the algorithm has a better detection effect for 3D human body size, and it is stable with better robustness.
Full ($V_{\mathrm{g}},\ V_{\mathrm{d}}$) Bias Space Modeling of Hot-Carrier Degradation in Nanowire FETs

Simulations of hot-carrier degradation of nanowire field-effect transistors are reported. The simulations rely on the carrier energy distribution function, obtained by solving the Boltzmann transport equation. To model the contribution of impact ionization, the hydrodynamic scheme is employed. A range of pertinent physical mechanisms is invoked and discussed to adequately reproduce HCD measurements in the full ($V_{\mathrm{g}}, V_{\mathrm{d}}$) bias space. Impact ionization is discussed as an important ingredient of HCD at low gate, high drain voltages.
Fully-Integrated CMOS DC-DC Boost Converter

This paper presents a boost DC-DC power converter, which consists an RF oscillator at 2.3 GHz serving as DC-to-AC converter with an embedded DC rectifier to provide the first-stage DC-DC converter, and an RF-DC charge pump serving AC-to-DC converter with an input from the DC rectifier. Two differential, cross-coupled rectifiers as the charge pump circuit mitigate the reverse-leakage problem and they are cascaded to double the output voltage. The boost converter was implemented in the TSMC standard 0.18 um CMOS process. The die area is 0.992×1.118 mm2. The DC/DC converter can boost 0.6 V supply voltage to drive a red LED with normally 1.7 V operated bias for solar-cell and photovoltaics (PV) unit application.
Phase I trial of a humanized, Fc receptor nonbinding anti-CD3 antibody, hu12F6mu in patients receiving renal allografts

Hu12F6mu is an Fc-mutated, humanized anti-CD3 antibody developed in our lab. The aim of this study was to assess single dose escalation pharmacokinetics (PK) and safety profile of hu12F6mu and to measure the effects of the antibody on levels of circulating T cells over time. Twenty-seven patients receiving renal allografts were randomized to receive hu12F6mu intravenously at a single-dose of 2.5, 5 or 10 mg. The concentration-time data obtained by a validated ELISA method were subjected to non-compartmental PK analysis by DAS 2.1 software. Subgroups of CD2+, CD3+, CD4+ and CD8+ lymphocytes were monitored periodically by flow cytometry. Our results showed that hu12F6mu exhibited linear PK over the dose range of 2.5 to 10 mg. A significant decline in the proportion of T cells was observed immediately after the infusion, followed by a progressive increase occurring over the ensuing days of therapy. A significant negative correlation was observed between serum concentration of hu12F6mu and CD3+ cell proportion. Intravenous infusion of hu12F6mu was well-tolerated in patients receiving renal allografts. These results suggest that hu12F6mu may have potential as a therapeutic agent, although further studies are needed.
Blind Source Separation for Face Image based on Deep Leaming

Deep learning is a branch of machine learning, and it is also a particular type of machine learning. JADE-MTCNN-FaceNet algorithm is proposed by combining JADE, MTCNN and FaceNet. TheMTCNN deep learning convolution neural network exists the problem that the observation data has high dimension of feature information. Considering this problem, the dimension reduction of the feature information, face verification and experimental simulation on LFW database are realized by using FaceNet network. Experimental results show the algorithm can further improve the accuracy of the face detection and separation. The accuracy rate on LFW database is 0.98.
Progressive Refinement Bilateral Filter

In this paper, a coarse-to-fine framework for image noise removal is proposed. The bilateral filter is redefined by the manner of progressive refining to effectively eliminate noise, thus forming progressive refinement bilateral filter (PRBF), which estimates the pixel values of a noisy image through the method of progressive refinement with gradual recursion and its result retains more details, infinite close to the original image. PRBF is further integrated into the denoising model for the details processing again after denoising of the major part of the image. Our algorithm is evaluated and compared on two large standardized datasets and a number of selected images. The experimental results show that our algorithm consistently outperforms other approaches for image denoising and demonstrates the effectiveness of our framework for stereo matching.
Performance Analysis of Apache Hadoop for Generating Candidates of Acronym and Expansion Pairs and Their Numerical Features

Mining information from web pages to automatically determine the acronym and its expansion is an important and challenging task. It is considered not easy to do because the acronyms writing rules and forms are very diverse for each language. The task consists of several important steps i.e. generating candidates of an acronym and its expansion, creating their numerical features, and determining the correct acronyms and expansions using machine learning algorithm. In this work, we evaluate and compare the performance analysis, in terms of speed, to generate the candidates of acronym and expansion pairs and create their numerical features when the processes are running on Hadoop cluster with different data nodes and on a single machine. The results show that Hadoop cluster outperforms a single machine when generating almost 52 million candidates of acronym and expansion pair and their numerical features. When Hadoop cluster was set up from two to three data nodes, the performance improved on average by 65.81%.
Action Recognition Based on Dense Trajectories and Human Detection

Dense Trajectories have shown outstanding performance for action recognition and attract more attention in recent years. Dense trajectories are generated by tracking the points which are densely sampled, so part of the trajectories is generated by the background movement. Those trajectories not only take up computing resources but also have a bad effect on the classification. To solve this problem, a novel algorithm is proposed to remove those trajectories based on human detection. Faster R-CNN is utilized to detect the human in the frames of videos. Furthermore, Fisher vector is used to encode the features and linear kernel SVM with one-against-rest approach to classify actions. The proposed algorithm is evaluated on KTH and YouTube datasets. Experimental results show that the proposed algorithm achieves comparative performance compared with the state-of-the-art methods.
Near Duplicate Image Pairs Detection Using Double-Channel Convolutional Neural Networks

Measuring the image pair similarity is a fundamental task in computer vision. This paper illustrates a neural network model to accomplish the task and decide if the input pair is a near duplicate pair. Authors explore several convolutional neural networks and adopt the double-channel network on this task. The model achieves comparable results on benchmark datasets and well performs on the closely similar images pairs among them. Comparing with the conventional approaches, the network provides a straightforward function to measure the pair-wise similarity and utilizes the strong correlation meanwhile.
Affective Virtual Reality System (AVRS): Design and Ratings of Affective VR Scenes

The present affective stimulation systems have shortages in terms of inefficient emotion evocation and poor immersion. This paper presents the design, instructions and ratings of a novel Affective Virtual Reality System (AVRS), which includes a large set of emotionally-evocative VR scenes and their affective ratings. It can provide more objective and direct affective stimuli of basic emotions (happiness, sadness, fear, relaxation, disgust, and rage) by shielding the environmental interferences. In this study, affective VR scenes have been designed by using various standard affective picture, video and audio materials as references. To assess the three dimensional emotion indices of valence, arousal and dominance, each scene of the system is rated and standardized by Self-Assessment Mainikin. AVRS is the first released VR version affect stimuli materials, which sets a precedent for future interdisciplinary work bridging the gap between VR and cognitive psychology.
Interpretive Structural Modeling Analysis of Reverse Supply Chain Critical Success Factors in Air Industry

The air industry is an integral part of the world economy. Based on this, understanding the key factors of success will create a competitive advantage and success in this industry. This research with Understanding the subject and identifying 159 factors by studying the literature of research, reached to 73 factors with an initial sieve. Ultimately, 24 critical success factors were identified by Lawshe technique and with the Structural Interpretative Modeling approach and utilizing experts' opinions we categorized the main success factors of the air industry. The results of the research show that the most influential factors are related to inventory management, transportation, materials and information integration, communications, information technology, mutual trust of supply chain partners, supply chain performance, supply chain flexibility, awareness of inverse support, Knowledge management, standardization, supply chain reliability, collection of return products, monitoring systems for return, cooperation of supply chain components, return and protection policies, and commitment of beneficiaries as interface factors which any partial changes on these factors leading to fundamental changes in the system.
PyReload: Dynamic Updating of Python Programs by Reloading

Dynamic Software Updating (DSU) is a promising technique for updating running software systems without incurring downtime. It is particularly useful to those systems which need to provide 24x7 services. Many efforts have been made to dynamic updating of the programs developed in mainstreaming languages such as C and Java. With the popularity of Python, many software servers are developed using Python and therefore they are desired to be dynamically updatable. However, there are few studies on dynamic updating of Python. To our knowledge, there exists only one updating approach for Python programs. The approach requires a dedicated Python interpreter in order to manipulate threads for updating, and consequently, it cannot be directly applied to mainstreaming Python interpreters. In this paper, we propose a novel dynamic updating approach to Python based on the built-in reloading mechanism that is supported by most of Python interpreters. Our approach is generic in that it is applicable to ordinary Python interpreters without any change to them. We implement a prototype called PyReload based on the proposed approach. PyReload is compatible with several mainstreaming Python interpreters such as CPython, Pypy and Jython. A case study is conducted to show the usage of PyReload. For performance, experimental results show that PyReload needs a shorter time for updating than the existing approach does.