Miracle-dz/UniFAR-FTUs huggingface dataset:
VISCOSITY DEPENDENCE OF PRIMARY PHOTOPROCESSES OF 124

To characterize the nature of primary photoprocesses of phytochrome which serves as the red-far red reversible photoreceptor for photomorphogenesis in plants, viscosity dependence of the fluorescence lifetimes of phytochrome isolated from etiolated oat seedling (Avena sativa L.) has been investigated. The fluorescence decay of phytochrome exhibited approximately two components, one with lifetime in the range of 50-70 ps and another with 1.1-1.2 ns in phosphate buffer with or without 40-67% glycerol. However, relative amplitudes of these decay components were found to be strongly viscosity dependent. Thus, the longer decay component increased from 2-5% in phosphate buffer to -20% in 67% glycerol-phosphate buffer. These results have been interpreted in terms of primary reaction from the excited singlet state of phytochrome, yielding a photoreversible intermediate whose rate of formation and decay were apparently viscosity-dependent. Further, the viscosity dependence is consistent with the primary reaction involving conformational changes of the chromophore/its apoprotein environment.
MalDeWe: New Malware Website Detector Model based on Natural Language Processing using Balanced Dataset

The increasing use of the Internet in everyday life and the huge number of users leads to increasing the number of malicious websites, which aim is to damage a computer system or compromise data without the owner’s consent. In this paper we propose a Natural Language Processing (NLP) model, called Malware Website Detector (MalDeWe), for malware web page identification that is trained on a domain-specific corpus. The major goal of this model is to transfer English word knowledge from the pre-trained model RoBERTa into a dataset of JavaScript codes that included the website’s text context as well as certain JavaScript expressions. With this model, we obtain a Roc Auc score of 0.95. Therefore, we can conclude that our model is doing admirably in terms of identifying a malicious web page. On the other hand, we may infer that one of the most essential aspects to consider while training a classification model is dataset balance. Whereas the model trained on the initial unbalanced dataset failed to detect harmful websites, the model trained on the balanced dataset, correctly identified 95% of dangerous websites. In that sequence, we may deduce that the metrics used for the model evaluation are critical. Therefore, we recommend using the Roc Auc score, Recall, Precision, and Confusion matrix as evaluation metrics. So, in this paper we propose a new NLP model, and we discuss the reasons why the choice of evaluation metrics are important and how the dataset balance makes changes on the model efficacy.
Pandemic Agility: Towards a theory on adapting to working from home

Since the world was taken over by the coronavirus pandemic, professionals from all areas have had to adapt to the changes imposed by health measures, in a sudden process, which points to permanent changes in the forms of consumption and work. In the present study, we aimed to document the main practical challenges faced by agile software development teams in this adaptation process. We conducted an interview based qualitative study, using techniques from Grounded Theory, and collected data on the experience of five agile software development teams, in three different countries. Besides bringing a rich description of the journeys of the studied teams, our results highlighted four main challenges in the adaptation processes: (1) the full digitization of agile ceremonies, (2) a loss of project awareness, (3) the administration of work-life balance, and (4) the self-management transition in less mature teams. Overall, the participants reported increased levels of perceived productivity, and we share along practices and recommendations that may be useful for other teams facing similar challenges.
Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy by Random Forests

Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while simultaneously sparing healthy tissue. One promising method of real-time imaging during treatment is the use of a Compton camera, which can image prompt gamma rays that are emitted along the beam’s path through the patient. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the reconstructed images are often noisy and unusable for verifying proton treatment delivery. Machine learning ensemble methods like random forests are able to automatically learn patterns that exist in numerical data, making them a promising method to analyze Compton camera data for the purpose of reducing noise in the reconstructed images. We conduct a hyperparameter search to find an optimal random forest model. We then present the results of the best performing random forest model, which demonstrate that this ensemble method is less effective than competing machine learning techniques for this application.
Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques

Log message template identification aims to convert raw logs containing free-formed log messages into structured logs to be processed by automated log-based analysis, such as anomaly detection and model inference. While many techniques have been proposed in the literature, only two recent studies provide a comprehensive evaluation and comparison of the techniques using an established benchmark composed of real-world logs. Nevertheless, we argue that both studies have the following issues: (1) they used different accuracy metrics without comparison between them, (2) some ground-truth (oracle) templates are incorrect, and (3) the accuracy evaluation results do not provide any information regarding incorrectly identified templates. In this paper, we address the above issues by providing three guidelines for assessing the accuracy of log template identification techniques: (1) use appropriate accuracy metrics, (2) perform oracle template correction, and (3) perform analysis of incorrect templates. We then assess the application of such guidelines through a comprehensive evaluation of 14 existing template identification techniques on the established benchmark logs. Results show very different insights than existing studies and in particular a much less optimistic outlook on existing techniques.
Operationalizing Threats to MSR Studies by Simulation-Based Testing

Quantitative studies on the border between Mining Software Repository (MSR) and Empirical Software Engineering (ESE) apply data analysis methods, like regression modeling, statistic tests or correlation analysis, to commits or pulls to better understand the software development process. Such studies assure the validity of the reported results by following a sound methodology. However, with increasing complexity, parts of the methodology can still go wrong. This may result in MSR/ESE studies with undetected threats to validity. In this paper, we propose to systematically protect against threats by operationalizing their treatment using simulations. A simulation substitutes observed and unobserved data, related to an MSR/ESE scenario, with synthetic data, carefully defined according to plausible assumptions on the scenario. Within a simulation, unobserved data becomes transparent, which is the key difference to a real study, necessary to detect threats to an analysis methodology. Running an analysis methodology on synthetic data may detect basic technical bugs and misinterpretations, but it also improves the trust in the methodology. The contribution of a simulation is to operationalize testing the impact of important assumptions. Assumptions still need to be rated for plausibility. We evaluate simulation-based testing by operationalizing undetected threats in the context of four published MSR/ESE studies. We recommend that future research uses such more systematic treatment of threats, as a contribution against the reproducibility crisis.
Improving the Accuracy of Progress Indication for Constructing Deep Learning Models

For many machine learning tasks, deep learning greatly outperforms all other existing learning algorithms. However, constructing a deep learning model on a big data set often takes days or months. During this long process, it is preferable to provide a progress indicator that keeps predicting the model construction time left and the percentage of model construction work done. Recently, we developed the first method to do this that permits early stopping. That method revises its predicted model construction cost using information gathered at the validation points, where the model’s error rate is computed on the validation set. Due to the sparsity of validation points, the resulting progress indicators often have a long delay in gathering information from enough validation points and obtaining relatively accurate progress estimates. In this paper, we propose a new progress indication method to overcome this shortcoming by judiciously inserting extra validation points between the original validation points. We implemented this new method in TensorFlow. Our experiments show that compared with using our prior method, using this new method reduces the progress indicator’s prediction error of the model construction time left by 57.5% on average. Also, with a low overhead, this new method enables us to obtain relatively accurate progress estimates faster.
Algorithm Identification in Programming Assignments

Current autograders of programming assignments are typically program output based; they fall short in many ways: e.g. they do not carry out subjective evaluations such as code quality, or whether the code has followed any instructor specified constraints; this is still done manually by teaching assistants. In this paper, we tackle a specific aspect of such evaluation: to verify whether a program implements a specific algorithm that the instructor specified. An algorithm, e.g. bubble sort, can be coded in myriad different ways, but a human can always understand the code and spot, say a bubble sort, vs. a selection sort. We develop and compare four approaches to do precisely this: given the source code of a program known to implement a certain functionality, identify the algorithm used, among a known set of algorithms. The approaches are based on code similarity, Support Vector Machine (SVM) with tree or graph kernels, and transformer neural architectures based only source code (CodeBERT), and the extension of this that includes code structure (GraphCodeBERT). Furthermore, we use a model for explainability (LIME) to generate insights into why certain programs get certain labels. Results based on our datasets of sorting, searching and shortest path codes, show that GraphCodeBERT, fine-tuned with scrambled source code, i.e., where identifiers are replaced consistently with arbitrary words, gives the best performance in algorithm identification, with accuracy of 96-99% depending on the functionality. Additionally, we add uncalled function source code elimination to our pre-processing pipeline of test programs, to improve the accuracy of classification of obfuscated source code.
Sequential Recommendation for Online Consumption by ACFNN*

Online consumption promotes more and more users to purchase stuff and receive service by the Internet. Digging out users’ underlying preferences chronologically can benefit both customers and servicers. While several approaches have been built on the basis of the Markov chain method and convolutional neural network, they still insufficiency unveil tangled relationships that are involved in users’ historical behavior. In this paper, we deliver an Attention-based Convolutional Fusion Neural Network (ACFNN) for the sequential recommendation. Specifically, we first conduct pairwise encoding between items and then put them into two branches of CNN block, i.e., one is the naive CNN module and another is combined with the attention mechanism. In this way, we can extract more rich representations of sequential features and capture useful item-item correlations. Comparative experiments on real datasets show that ACFNN outperforms the state-of-the-art methods in the sequential recommendation task.
Shape-Analysis Driven Memory Graph Visualization

Analyzing heap dumps containing complex dynamic data structures is essential when debugging modern software systems. However, existing tools for visualizing memory graphs can neither deal with corrupt structures such as binary trees exhibiting cycles, nor do they offer adequate abstractions when being confronted with large heaps. This paper presents MGE (Memory Graph Explorer), a memory analyzer and visualizer that combines a novel memory graph abstraction with an interactive visualization. MGE borrows ideas from separation logic and shape analysis to reveal relationships between memory nodes, name recognized structures such as doubly-linked lists and binary trees, and summarize complex structures. This summarization works for corrupt data structures, too, and is particularly powerful for large, nested structures due to its support for interactive (un)folding. MGE's utility for aiding program comprehension is illustrated by real-world and textbook examples and contrasted with existing debuggers.
Design of Rogowski Switch-Current Sensor With Offset Compensation for Three-Phase SiC Inverter

This article proposes a Rogowski switch-current sensor (RSCS) with the offset compensation for the current control of the three-phase SiC inverter. The RSCS is built into the four-layer print circuit board (PCB) gate drive board. The coil parameters are analyzed with the Ansys software (Maxwell and Q3D) and then compared with the measurement results. The analog signal processing circuits are implemented with the operational amplifier (op amp) to reconstruct the switch current and the phase current. Unfortunately, the RSCS suffers from the op amp offset voltage and bias current integration, which varies according to the duty cycle and switching frequency. This article proposes an offset compensation software (S/W) to address this issue. Moreover, an RC discharge factor is proposed to make up for the discharging phenomenon of the capacitor in the integrator feedback path. A three-phase SiC inverter prototype is built and tested with the three-phase inductor load to verify the feasibility of the method. Experimental results at 10–30 kHz, 600 V, 150 A show the proposed method to have less than 2% sensing error.
An Exploratory Study of Deep learning Supply Chain

Deep learning becomes the driving force behind many contemporary technologies and has been successfully applied in many fields. Through software dependencies, a multi-layer supply chain (SC) with a deep learning framework as the core and substantial down-stream projects as the periphery has gradually formed and is constantly developing. However, basic knowledge about the structure and characteristics of the SC is lacking, which hinders effective support for its sustainable development. Previous studies on software SC usually focus on the packages in different registries without paying attention to the SCs derived from a single project. We present an empirical study on two deep learning SCs: TensorFlow and PyTorch SCs. By constructing and analyzing their SCs, we aim to understand their structure, application domains, and evolutionary factors. We find that both SCs exhibit a short and sparse hierarchy structure. Overall, the relative growth of new projects increases month by month. Projects have a tendency to attract downstream projects shortly after the release of their packages, later the growth becomes faster and tends to stabilize. We propose three criteria to identify vulnerabilities and identify 51 types of packages and 26 types of projects involved in the two SCs. A comparison reveals their similarities and differences, e.g., TensorFlow SC provides a wealth of packages in experiment result analysis, while PyTorch SC contains more specific framework packages. By fitting the GAM model, we find that the number of dependent packages is significantly negatively associated with the number of downstream projects, but the relationship with the number of authors is nonlinear. Our findings can help further open the “black box” of deep learning SCs and provide insights for their healthy and sustainable development.
On the Computational Complexity of 2D Filtering by Winograd Method

Increasing the speed of digital image processing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for image processing based on the Winograd method have been proposed. The theoretical analysis results showed that an increase in the number of pixels in the resulting image fragment leads to a decrease in multiplications and the computational complexity of digital filtering. Winograd methods reduced multiplications to 4 times by increasing additions to 2.88 times using filter $3\mathrm{x}3$. The results obtained can be used in various image processing fields that require real-time processing using modern microelectronic devices.
CodePanorama: a language agnostic tool for visual code inspection

Software projects change hands frequently. Oftentimes, developers are interested in the quality of the code before taking over responsibility on a project. This quality is commonly assessed using various code metrics, reducing the code into a handful of numbers. While useful, these numerical reductions quickly become detached from the real code. CodePanorama uses an alternative approach to summarize code not into numbers, but into images. By generating zoomed-out images of the code-base, the human eye can quickly spot anomalies without the need to rely on numerical metrics and statistics. This paper describes the tool CodePanorama, the images it generates, and the insights that can be gained from these images. We finally invite the software engineering community to start using it.
Measurement of Circuit Parasitics of SiC MOSFET in a Half-Bridge Configuration

Fast switching transient of SiC <sc>mosfet</sc> reduces switching loss. However, it excites device and circuit parasitics resulting in prolonged oscillation, high device stress, spurious turn <sc>on</sc>, EMI related issues, and higher switching loss. Behavioral or analytical models are used to estimate switching loss <inline-formula><tex-math notation="LaTeX">$(dv/dt)$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$(di/dt)$</tex-math></inline-formula> rates, etc., and it requires the value of circuit parasitics as input. Measurement is the only way to accurately estimate some device package-dependent circuit parasitics when the internal package geometry is unknown. Hence, measurement of circuit parasitic is essential for optimal design. This article presents a set of simple experimental measurement techniques to determine circuit parasitic inductance and capacitances relevant for switching transient study of SiC <sc>mosfet</sc> in a half-bridge configuration. The accuracy of the proposed technique is verified through simulation and experimental results of the hard turn <sc>off</sc>and capacitor assisted soft turn-<sc>off</sc> dynamics of SiC <sc>mosfet</sc> over a range of operating conditions for two 1.2-kV discrete SiC <sc>mosfet</sc> of different current ratings and two different PCB layouts. Furthermore, the proposed technique is also validated with the existing frequency response analysis-based impedance measurement.
A Novel Integrated Matrix Magnetics for Isolated Single-Stage DC–DC Converter

With the development of data center, promotion for efficiency and power density of the voltage regulator module (VRM) is paid considerable attention. Isolated VRM is suitable for the high-conversion-ratio dc–dc application. However, isolated VRM has many magnetic components, which take up a large proportion of the total volume. In this article, a novel integrated matrix magnetics structure is proposed, which integrates two elemental transformers and four output inductors into a single core. By integrating several discrete magnetic components, the volume of passive components, and high-current interconnections are reduced, leading to high power density and high efficiency. The integrated matrix magnetics is employed in an isolated single-stage converter, which can provide high output current. In addition, primary switches can achieve zero-voltage-switching (ZVS), and synchronous rectifiers of secondary side can achieve ZVS and zero-current-switching, so efficiency can be further improved. Besides, equivalent output inductance, voltage conversion ratio and AP calculation are derived to design the integrated matrix magnetics. Finally, a 48 V/1.8 V 144 W prototype of the isolated single-stage converter with integrated matrix magnetics is made and tested to verify the analysis.
On the Latency and Complexity of Semi-Parallel Decoding Architectures for 5G NR Polar Codes

Recently adopted in the New Radio (NR) standard for 5G control channel, polar codes represent one of the latest additions to the family of forward error correction (FEC) codes. However, the stringent requirements introduced by the 5G standard in terms of block length and code rate flexibility, together with low end-to-end latency and high error correction performance, pose a major challenge for their hardware implementation. In this context, we study the impact of main code and decoder design parameters on the latency and the hardware complexity of semi-parallel decoding architectures. Detailed analytical and logic synthesis results are provided and compared for a large range of values in order to constitute a reference for the implementation of flexible, yet efficient FEC decoders for polar codes.
Repairing Order-Dependent Flaky Tests via Test Generation

Flaky tests are tests that pass or fail nondeterministically on the same version of code. These tests can mislead developers concerning the quality of their code changes during regression testing. A common kind of flaky tests are order-dependent tests, whose pass/ fail outcomes depend on the test order in which they are run. Such tests have different outcomes because other tests running before them pollute shared state. Prior work has proposed repairing order-dependent tests by searching for existing tests, known as “cleaners”, that reset the shared state, allowing the order-dependent test to pass when run after a polluted shared state. The code within a cleaner represents a patch to repair the order-dependent test. However, this technique requires cleaners to already exist in the test suite. We propose ODRepair, an automated technique to repair order-dependent tests even without existing cleaners. The idea is to first determine the exact polluted shared state that results in the order-dependent test to fail and then generate code that can modify and reset the shared state so that the order-dependent test can pass. We focus on shared state through internal heap memory, in particular shared state reachable from static fields. Once we know which static field leads to the pollution, we search for reset-methods in the code-base that can potentially access and modify state reachable from that static field. We then apply an automatic test-generation tool to generate method-call sequences, targeting these reset-methods. Our evaluation on 327 order-dependent tests from a publicly available dataset shows that ODRepair automatically identifies the polluted static field for 181 tests, and it can generate patches for 141 of these tests. Compared against state-of-the-art iFixFlakies, ODRepair can generate patches for 24 tests that iFixFlakies cannot.