Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Impact of Flaky Tests in Automated Program Repair

The literature of Automated Program Repair is largely dominated by approaches that leverage test suites not only to expose bugs but also to validate the generated patches. Unfortunately, beyond the widely-discussed concern that test suites are an imperfect oracle because they can be incomplete, they can include tests that are flaky. A flaky test is one that can be passed or failed by a program in a non-deterministic way. Such tests are generally carefully removed from the repair benchmarks. In practice, however, flaky tests are available test suite of software repositories. To the best of our knowledge, no study has discussed this threat to validity for evaluation of program repair. In this work, we highlight this threat and further investigate the impact of flaky tests by reverting their removal from the Defects4J benchmark. Our study aims to characterize the impact of flaky tests for localizing bugs and the eventual influence on the repair performance. Among other insights, we find that (1) although flaky tests are few (≈0.3%) of total tests, they affect experiments related to a large proportion (98.9%) of Defects4J real-world faults; (2) most flaky tests (98%) actually provide deterministic results under specific environment configurations (with the jdk version influencing the results); (3) flaky tests drastically hinder the effectiveness of spectrum-based fault localization (e.g., the rankings of 90 bugs drop down while none of the bugs obtains better location results compared with results achieved without flaky tests); and (4) the repairability of APR tools is greatly affected by the presence of flaky tests (e.g., 10 state of the art APR tools can now fix significantly fewer bugs than when the benchmark is manually curated to remove flaky tests). Given that the detection of flaky tests is still nascent, we call for the program repair community to relax the artificial assumption that the test suite is free from flaky tests. One direction that we propose is to consider developing strategies where patches that partially-fix bugs are considered worthwhile: a patch may make the program pass some test cases but fail some (which may actually be the flaky ones).
Plant Disease Detection Using Deep Learning

In recent years, use of Convolutional neural networks has been explored in a wide range of applications whether its image classification, feature extraction or image segmentation. One of those applications is plant disease detection, since plant disease is one of the most significant factors that leads to poor yield in the agricultural sector. Over the period of time various deep learning approaches have been used to solve this problem of identification and classification of plant disease. But then to there are some limitation to these approaches. The recent application of transformer networks in computer vision tasks has shown great promise. This paper compares these approaches with traditional CNN approaches in the task of plant disease detection. The best validation accuracy that our transformer model achieves is 97.98%.
A $3.86\ \mu \mathrm{W}$ Miller-Compensated Inverter Transimpedance Amplifier for Photoplethysmography Sensing

This paper presents a design and simulation results of an ultra-low power inverter transimpedance amplifier. Transimpedance amplifiers require negative feedback, hence, stability should always be maintained. The design was able to achieve a 92.9 dB transimpedance gain, 81.77 $\mathbf{pA}_{\mathbf{RMS}}$ total input integrated noise from 0 to 10 KHZ, 2% total harmonic distortion, and 88 dB spurious-free dynamic range. The Miller capacitances were used to ensure stability by separating the poles and removing the region of instability. The circuit was implemented in $\mathbf{0.18}\ \mu\mathbf{m}$ technology with 1.2 V supply and a power dissipation of $\mathbf{3.86}\ \mu\mathbf{W}$.
HeronEye: Survey on Coordinated and Efficient Huge Page Management to Fine-grained OS Support

Successful HUGE page management working in with different systems is fundamentally require for address translation overheads. Be that as it may, this keeps on staying a troublesome zone in OS plan. The upcoming of latest data storage innovations that have huge and less expensive than product Dynamic Random Access Memory has restored enthusiasm for 2: layered fundamental data storage plans. Inconsistently got to application information can be put away in such recollections to accomplish significant memory cost saving. Past we have large page about on 2: layered primary data storage that need to expected a 32000 bits page extent. Be that as it may, 2000KiloBytes HUGE pages of data execution is been done at cloud applications and will appear enormous data storage impressions, particularly in virtualized cloud situations, where settled paging definitely builds the expense of 4KB page the board. Utilizing both page get to designs and found by the operating system portion which filter the information when hardware execution encounters. We uncover tricky parts of latest HUGE data storage page on board methodologies. In this survey many challenge are expose and to resolve the issue the author has suggest some methodology and named the process as HeronEye/Linux. Author also demonstrate different approaches to deliver issues identified with executing performance, page fault delay and data storage leakage are the challenges which need to track is idea behind HeronEye program. Our assessment shows that HeronEye is more execution, vigorous and more qualified to deal with assorted outstanding tasks at hand when contrasted and current best in class frameworks. We likewise would like to encourage the advancement of extra cloud benchmark suites that speak to different classes of utilizations by making our benchmark instrument accessible by means of open source.
Walking motions with high margin-of-stability values

Many researchers studied on gait parameters, such as step width and speed, correlated with gait stability indices. In contrast, to understand stable gait motions against fall, we computed the time-series of joint angles that are correlated with a popular kinetic gait stability index: margin of stability (MoS). A time-series extension of the partial least square regression was applied on the joint angles to construct the principal motions, i.e., motion elements included in gait motions. These principal motions were linearly independent of each other and correlated with MoS. The linear combination of three principal motions produced a quantity that was correlated with the minimal MoS values having a coefficient of 0.87. Interpretation of the obtained principal motions suggests that the stable gait is of the small takeoff and step lengths.
Magnetic and electronic properties of Mn doped SrISi(111)-(3 × 2) HCC surface

Magnetic semiconductors, can be formed by transition metal on silicon surface, have drown much attention due to the great promise in the application of spintronic devices. Here, we propose two robust antimagnetic semiconductor structures by doping Mn atom on a template that Sr/Si(111)-(3 × 2) honeycomb-chain channel (HCC) surface, using ab initio calculations. Mn atoms are located at insertion site between honeycomb Si chain and Si(111) surface in these two magnetic structures.
Design and Analysis of Deep-Learning Based Iris Recognition Technologies by Combination of U-Net and EfficientNet

In this paper, the effective deep-learning based methodology is developed for iris biometric authentication. Firstly, based on the U-Net model, the proposed system uses the semantic segmentation technology to localize and extract the region of interest (ROI) of iris. After the ROI of iris in the eye image is revealed, the inputted eye image will be cropped to the small-size eye image with the just-fitted ROI of iris. Then, the iris features of the cropped eye image are strengthened optionally by adaptive histogram equalization or Gabor filtering process. Finally, the cropped iris image is classified by the EfficientNet model. By the Chinese Academy of Sciences Institute of Automation (CASIA) v1 database, the proposed deep-learning based iris recognition scheme reaches the recognition accuracies up to 98%. Compared with the previous works, the proposed technology can provide the effective iris recognition accuracy for the biometrics applications with iris information.
Is the Ground Truth Really Accurate? Dataset Purification for Automated Program Repair

Datasets of real-world bugs shipped with human-written patches are intensively used in the evaluation of existing automated program repair (APR) techniques, wherein the human-written patches always serve as the ground truth, for manual or automated assessment approaches, to evaluate the correctness of test-suite adequate patches. An inaccurate human-written patch tangled with other code changes will pose threats to the reliability of the assessment results. Therefore, the construction of such datasets always requires much manual effort on isolating real bug fixes from bug fixing commits. However, the manual work is time-consuming and prone to mistakes, and little has been known on whether the ground truth in such datasets is really accurate.In this paper, we propose DEPTEST, an automated DatasEt Purification technique from the perspective of triggering Tests. Leveraging coverage analysis and delta debugging, DEPTEST can automatically identify and filter out the code changes irrelevant to the bug exposed by triggering tests. To measure the strength of DEPTEST, we run it on the most extensively used dataset (i.e., Defects4J) that claims to already exclude all irrelevant code changes for each bug fix via manual purification. Our experiment indicates that even in a dataset where the bug fix is claimed to be well isolated, 41.01% of human-written patches can be further reduced by 4.3 lines on average, with the largest reduction reaching up to 53 lines. This indicates its great potential in assisting in the construction of datasets of accurate bug fixes. Furthermore, based on the purified patches, we re-dissect Defects4J and systematically revisit the APR of multi-chunk bugs to provide insights for future research targeting such bugs.
Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks With Genetic Algorithm

Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal behavior can help investors adjust their strategy and reduce investment risks. We proposed a spatiotemporal convolutional neural network–based relational network (STCNN-RN) model that can learn the complex correlations between multiple financial time-series data sets, and we used genetic algorithms with a constrained gene to discover the time points for outlier companies by fitting the STCNN-RN model; we used these outlier points to identify abnormal situations. Most research on identifying anomalous patterns has been unable to sufficiently explain the reason for anomalies to investors. We applied an interpretability model to enable investors to understand these anomalous time points in relation to companies and discover the key factors giving rise to the anomalies. The experiment results revealed that the proposed model can be used to model multiple financial time-series data sets and to capture anomalous situations in relevant companies. Because this study explored the discovery of anomaly phenomena in all transaction data and the explanation of these abnormalities, investors can understand a stock market situation holistically.
The Anti-Inflammatory Potency of Vitamin E and Coconut Oils on Ligature-induced Periodontitis in Albino Rats (Histological and Immunohistochemical Study)

Plaque induced periodontitis is the second most common oral disease worldwide after dental caries. Adjunctive methods such as mouthwashes have been developed as an aid to routine scaling for treating periodontal inflammation. Aim: The aim of this study was to evaluate the anti-inflammatory potency of vitamin E and coconut oils when used for oil gum massage therapy in periodontitis and compare them to the conventionally used chlorhexidine. Methods: 105 adult male albino rats were divided into 5 groups of 21 rats each: The negative control group (group 1) that received no intervention, the positive control group (group 2) that received ligature induced periodontitis without any treatment, the chlorhexidine experimental group (group 3) that received ligature induced periodontitis and chlorhexidine for 10 days, the vitamin E experimental group (group 4) that received ligature induced periodontitis and vitamin E for 10 days. And the coconut oil group (group 5) that received ligature induced periodontitis and coconut oil for 10 days. Groups 2-5 were further divided into subgroups A,B, and C according to day of termination (day 3, 7, &10). After rats were terminated, maxillary molar regions were excised and stained with H&E and Masson’s trichrome stain. Specimens were also examined immunohistochemically for the expression of MMP-1 antigen. Results: Histological and immunohistochemical examination showed an inflammatory reaction with ligature induced periodontitis that decreased gradually from day 3 until day 10. Statistical analysis regarding the immunohistochemical expression of MMP-1 inflammatory marker showed that it gradually decreased over the course of the treatment in all experimental groups with chlorhexidine showing the greatest decrease in inflammation while there was no significant difference between vitamin E and coconut oil. Conclusion: Oil gum therapy using either chlorhexidine, vitamin E, or coconut oil was associated with variable grades of statistically significant reduction of inflammation.
EVALUATING THE INTENSITY OF THE 10-MINUTE SNATCH TEST DURING A HARDSTYLE KETTLEBELL

Aim. Over the last few years, an increasing number of people have started training with kettlebells (KBs) all over the world. Unfortunately, there is very little scientific data on the effect of training with KBs, and essentially, no research has been done on the various kettlebell sports which are dynamically developing. The main aim of this paper was to analyse the intensity of physical effort during the 10-minute Snatch Test (10-min ST) at the HardStyle Kettlebell Polish Championship (HSKPC). Our research also included an analysis of the contestants’ body composition on the day of competition. Basic procedures. Ten male contestants and five female contestants were examined. Physical effort intensity was measured using Polar Team Pro. Body composition was evaluated with Tanita SC-330 immediately before the beginning of the competitions. The mean values of the analysed parameters in the study contestants were compared with the best male and female contestants of the Championship. Results. Heart rate during intensive exertion for the 10 min ST was at a consistently high level (80-89% HRmax) or very high level (90-100% HRmax). Such a high HR was maintained for 98% of the exercise performance, and the mean relative energy expenditure was 14.4 METs among the women and 15.5 METs among the men. Analysis of body composition showed that participants of the HSKPC were characterized by an athletic build. Conclusions. Even though the contestants lift relatively heavy loads during the 10 min ST (men 24 or 28 kg, women 16 kg), the effort can be described as an endurance test. Considering all the competitions taking place during the HSKPC and the results of the participants, it can be stated that these competitions are unusually demanding and require versatile physical fitness, as well as strength, endurance and coordination.
Methodology for assessment of the cost effectiveness of simple energy efficient investments

The development of energy efficient buildings has been on the increase in recent years. This trend in architectural engineering reflects both the binding legal regulation and the rational approach of investors to the construction or refurbishment of buildings. When planning such an investment, it is necessary to scrutinize the underlying guidelines and the conditions in which the building will be developed, as well as its future useful life. Economic analysis of the effectiveness of investments in the building sector employs simple and discounted methods. Depending on the scope and complexity of envisaged construction work, it is possible to apply methods from both groups. However, simple methods should suffice for simple building projects, an example of which will be discussed in the article. Three variants of planned additional thermal insulation of external walls of a residential building will be presented. The results of our calculations showed that the payback period for such an investment would be too long, which is why it is recommended to combine the thermal insulation of outer walls with some broader measures so as to shorten the time needed to achieve a good return on investment ratio.
Vowel Intelligibility Analysis of Female Saudi Spoken English

This article describes the acoustic characteristics of female Saudi-accented English vowels and uses acoustic phonetic measurements to assess the intelligibility of their vowels. Peterson & Barney’s (1952) and Hillenbrand et al.’s (1995) methodology is slightly modified. Whereas their studies extracted various measurements, including F1 and F2 of vowels in General American English (GAE), the current study examines measurements extracted from 11 monophthong phonemic English vowels in running speech produced by 23 female Saudi EFL teachers. Intelligibility assessments of their vowels are based on Koffi’s (2019) Acoustic Masking and Intelligibility (AMI) theory. He contends that intelligibility of vowels can be measured instrumentally by comparing the F1 of vowels because this formant carries 80% of the acoustic energy found in vowels. The AMI theory also combines Just Noticeable Differences (JND) thresholds and Relative Functional Load (RFL) calculations to gauge severity of masking and intelligibility. The findings in this article are based on 1,518 measured tokens. The study reveals that the fleece vowel [i], the kiss vowel [ɪ] and the face vowel [e] are the most problematic for Saudi speakers of English.
Polydopamine Functionalized 3D Printed Scaffolds for Bone Tissue Engineering

3D printing offers unique solutions for bone tissue regeneration with an opportunity to engineer scaffolds with the desired structure, shape, and porosity. Scaffold’s surface-to-cell interaction is critical for better tissue integration and regeneration. To achieve this, we have surface modified the 3D printed scaffolds with polydopamine (PDA) to evaluate the potential of the surface modification for bone regeneration.
Potential of Moringa oleifera seeds and fuel wood ash as adsorbent of dye and organic matter in wastewater from batik producing enterprises

Treatment of effluents from batik producing enterprises to removal dyes and other impurities is inevitable as these enterprises are becoming ubiquitous in many cities in Tanzania. Wastewater from batik production processes are discharged haphazardly without treatment. In this study, preliminary results on adsorption potential of defatted Moringa oleifera crude seed extract (DMOCSE) and fuel wood ash (FWA) for dye and chemical oxygen demand (COD) in batik processing wastewater (BPW) are presented. Batch and column adsorption studies were conducted using varied dye concentrations, adsorbents dosage, contact time and pH. 7% (w/v) DMOCSE at the dosage of 15 to 25 ml/L produced minimum residual dye in BPW with initial color ranging from 480 to 675 mgpt-Co/L. Optimum contact time for DMOCSE ranged from 1.5 to 2.5 h and pH 7.5 to 9.5 with 75.5 to 93% dye removal efficiency. FWA at optimum dosage of 25 g/l, contact time of 1.5 to 3 h and pH 8.5 to 11.5 removed 78.8 to 84% of dye. FWA and DMOCSE reduced 98 and 64% of COD. The FWA packed column with depth of 20 and 25 cm achieved 99% dye removal after 4 to 6 h of retention time. The findings reveal that DMOCSE and FWA have potential to treat BPW in both batch and column mode. Key words: Dye-rich wastewater, impurities removal, natural adsorbent.
Volumetric appearance stylization with stylizing kernel prediction network

This paper aims to efficiently construct the volume of heterogeneous single-scattering albedo for a given medium that would lead to desired color appearance. We achieve this goal by formulating it as a volumetric style transfer problem in which an input 3D density volume is stylized using color features extracted from a reference 2D image. Unlike existing algorithms that require cumbersome iterative optimizations, our method leverages a feed-forward deep neural network with multiple well-designed modules. At the core of our network is a stylizing kernel predictor (SKP) that extracts multi-scale feature maps from a 2D style image and predicts a handful of stylizing kernels as a highly non-linear combination of the feature maps. Each group of stylizing kernels represents a specific style. A volume autoencoder (VolAE) is designed and jointly learned with the SKP to transform a density volume to an albedo volume based on these stylizing kernels. Since the autoencoder does not encode any style information, it can generate different albedo volumes with a wide range of appearance once training is completed. Additionally, a hybrid multi-scale loss function is used to learn plausible color features and guarantee temporal coherence for time-evolving volumes. Through comprehensive experiments, we validate the effectiveness of our method and show its superiority by comparing against state-of-the-arts. We show that with our method a novice user can easily create a diverse set of realistic translucent effects for 3D models (either static or dynamic), neglecting any cumbersome process of parameter tuning.
The tax compliance decision of the individual in business in the sharing economy

Digital innovation is creating completely new ways to do business, unlocking a new generation of micro entrepreneurs who are progressively engaging with the sharing economy. One of the most pressing challenges tax authorities face is the tax compliance of these individuals that are new to business in the sharing economy. Kamleitner, Korunka and Kirchler (2012) developed a conceptual framework that depicts key aspects that distinguish small business owners’ perceptions of their tax obligation. Using Kamleitner et al.’s framework as a basis and analysing evidence from the literature, this article aims to point out additional considerations in order to develop a revised and extended framework of the factors influencing tax compliance of individuals in business in the home-sharing economy. The results of the study propose a framework depicting the four main factors that influence the tax compliance decision, namely: (1) perceived opportunity; (2) tax knowledge; (3) decision-making, and (4) person.