Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Investigation on the Trade-off between Smart Contract Readability and Gas Consumption

Blockchain technology is becoming increasingly popular, and smart contracts (i.e., programs that run on top of the blockchain) represent a crucial element of this technology. In particular, smart contracts running on Ethereum (i.e., one of the most popular blockchain platforms) are often developed with Solidity, and their deployment and execution consume gas (i.e., a fee compensating the computing resources required). Smart contract development frequently involves code reuse, but poor readable smart contracts could hinder their reuse. However, writing readable smart contracts is challenging, since practices for improving the readability could also be in contrast with optimization strategies for reducing gas consumption. This paper aims at better understanding (i) the readability aspects for which traditional software and smart contracts differ, and (ii) the specific smart contract readability features exhibiting significant relationships with gas consumption. We leverage a set of metrics that previous research has proven correlated with code readability. In particular, we first compare the values of these metrics obtained for both Solidity smart contracts and traditional software systems (written in Java). Then, we investigate the correlations occurring between these metrics and gas consumption and between each pair of metrics. The results of our study highlight that smart contracts usually exhibit lower readability than traditional software for what concerns the number of parentheses, inline comments, and blank lines used. In addition, we found some readability metrics (such as the average length of identifiers and the average number of keywords) that significantly correlate with gas consumption.
A Fully Convolutional Neural Network Based on 2D-Unet in Cardiac MR Image Segmentation

Cardiac MRI image segmentation is of great importance for evaluating cardiac function and diagnosing diseases. Manual segmentation is time-consuming and tedious, so automatic segmentation is very popular in practical applications. In this paper, we propose an improved full convolutional neural network based on 2D-Unet for automatic segmentation of the left ventricle, right ventricle and myocardium. Experiments were conducted on the ACDC 2017 Challenge Training dataset. The segmentation results were assessed by means of average vertical distance, Dice coefficient and Hausdorff distance. Our model reduces the amount of parameters, improves the training speed, uses the fusion loss function, and maintains a satisfactory segmentation accuracy of left ventricle, right ventricle and myocardium.
Used Car Price Prediction using Machine Learning: A Case Study

In many business fields that are related to statistics and machine learning (ML), multiple linear regression (MLR) models are often used to estimate and fit a linear relationship between a continuous response variable and other explanatory variables. In our case study, we applied several regression techniques based on supervised machine learning to predict the resale price of used cars given many factors such as mileage, fuel type, fiscal power, mark, model, and the production year of the car. In all tested models, gradient boosting regressor showed a high R-squared score and low root mean square error.
New Modeling Approach for Engine Cold Testing Driveline Torsional Vibration Analysis with Increased Accuracy

Internal combustion engine cold test is becoming one of the main tests performed during the late stage of the product development for defects inspection using the torque signatures and pressure levels in various engine components. This paper includes a lumped masses model generated for the cold test torsional system to calculate the driveline natural frequencies with a high accuracy. Calculating the natural frequency of the system is crucial for avoiding resonance excitation during the testing phase. Eigen value problem was solved; the natural frequencies and the mode shapes were obtained. The natural frequencies of the model were compared with the measured natural frequencies and 95% accuracy is achieved.
Screen-Printed Flexible Circular and Rectangular Silver Spirals for Planar Electrodynamic Loudspeakers: A Comparative Study of Pressure Frequency Response

We present the fabrication and characterization of flexible planar electrodynamic loudspeakers. Conductive spirals are fabricated on a flexible and transparent polyethylene terephthalate substrate via screen printing. Different geometries (circular and rectangular) and sizes of the conductive spirals are investigated to understand their impact on the performance. The optimized circular spiral allows achieving an average sound pressure level of 63 dB at 1m distance in 2kHz-20kHz band, proving the suitability of these devices as high-frequency loudspeaker drivers.
Default: Mutual Information-based Crash Triage for Massive Crashes

With the considerable success achieved by modern fuzzing in-frastructures, more crashes are produced than ever before. To dig out the root cause, rapid and faithful crash triage for large numbers of crashes has always been attractive. However, hindered by the practical difficulty of reducing analysis imprecision without compromising efficiency, this goal has not been accomplished. In this paper, we present an end-to-end crash triage solution Default, for accurately and quickly pinpointing unique root cause from large numbers of crashes. In particular, we quantify the “crash relevance” of program entities based on mutual information, which serves as the criterion of unique crash bucketing and allows us to bucket massive crashes without pre-analyzing their root cause. The quantification of “crash relevance” is also used in the shortening of long crashing traces. On this basis, we use the interpretability of neural networks to precisely pinpoint the root cause in the shortened traces by evaluating each basic block's impact on the crash label. Evaluated with 20 programs with 22216 crashes in total, Default demonstrates remarkable accuracy and performance, which is way beyond what the state-of-the-art techniques can achieve: crash de-duplication was achieved at a super-fast processing speed - 0.017 seconds per crashing trace, without missing any unique bugs. After that, it identifies the root cause of 43 unique crashes with no false negatives and an average false positive rate of 9.2%.
Lessons from Eight Years of Operational Data from a Continuous Integration Service: An Exploratory Case Study of CircleCI

Continuous Integration (CI) is a popular practice that enables the rapid pace of modern software development. Cloud-based CI services have made CI ubiquitous by relieving software teams of the hassle of maintaining a CI infrastructure. To improve these CI services, prior research has focused on analyzing historical CI data to help service consumers. However, finding areas of improvement for CI service providers could also improve the experience for service consumers. To search for these opportunities, we conduct an empirical study of 22.2 million builds spanning 7,795 open-source projects that used CircleCI from 2012 to 2020. First, we quantitatively analyze the builds (i.e., invocations of the CI service) with passing or failing outcomes. We observe that the heavy and typical service consumer groups spend significantly different proportions of time on seven of the nine build actions (e.g., dependency retrieval). On the other hand, the compilation and testing actions consistently consume a large proportion of build time across consumer groups (median 33%). Second, we study builds that terminate prior to generating a pass or fail signal. Through a systematic manual analysis, we find that availability issues, configuration errors, user cancellation, and exceeding time limits are key reasons that lead to premature build termination. Our observations suggest that (1) heavy service consumers would benefit most from build acceleration approaches that tackle long build durations (e.g., skipping build steps) or high throughput rates (e.g., optimizing CI service job queues), (2) efficiency in CI pipelines can be improved for most CI consumers by focusing on the compilation and testing stages, and (3) avoiding misconfigurations and tackling service availability issues present the largest opportunities for improving the robustness of CI services.
Fast Frequency Regulation Method for Power System With Two-Stage Photovoltaic Plants

The full utilization of solar energy is of great significance in reducing carbon emissions and alleviating environmental problems. Fast frequency regulation plays an important role in the power system with grid-connected two-stage photovoltaic (PV) plants. The presented fast frequency regulation method is composed of droop control, virtual inertia control and de-loading control. This work focuses on improving droop control and virtual inertia control for PV plants connected to grid. Due to non-Gaussian disturbance induced by solar irradiance, an adaptive control strategy is proposed by minimizing an improved performance index which combines the mean square error (MSE) and the minimum error entropy (MEE) criteria. Based on the collected frequency deviation series data, the entropy of the frequency deviation is estimated with the aid of a quantizer. The droop control coefficient and inertia gain are then solved by an improved gravitational search algorithm (IGSA). Finally, the effectiveness of the proposed strategy is testified by an illustrative power system with a two-stage grid-connected PV plant.
A Review Paper on Facial Recognition Techniques in E-business

Facial recognition is a biological biometric feature that allows a person to be identified from a digital image. The face is known as the most recognizable aspect of human anatomy and acts like a human being’s first distinguishing feature. There are different techniques that can be used for the classification of data, two widely used techniques for data classification and dimension reduction are Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Facial recognition techniques have been comprehensively studied and applied in e-business. To reduce the False Rejection Rate (FRR) and False Acceptance Rate (FAR) during the recognition process, this review looks at the methods and the parameters that affect the facial recognition. Furthermore, we outline the strengths and challenges of these techniques. This comprehensive study serves as a starting point and a guide for everyone interested in exploring facial recognition techniques research area. The paper presents the conclusion and future work.
Immunochemical Identification of Brucella abortus Lipopolysaccharide

NORMAN ROJAS, 2 ENRIQUE FREER,2'3 ANDREJ WEINTRAUB,2 MARGARITA RAMIREZ,4,5 SIV LIND,2 AND EDGARDO MORENO45* Facultad de Microbiologia' and Departamento de Fisiologia, Facultad de Medicina Universidad de Costa Rica, San Jose, Programa de Investigaci6n en Enfermedades Tropicales (PIET), Escuela de Medicina Veterinaria, Universidad Nacional, Heredia,4 and International Center for Medical Research and Training, Louisiana State University, INCIENSA, Tres Rios, Cartago,5 Costa Rica, and Karolinska Institute, Department of Clinical Bacteriology, Huddinge Hospital, Huddinge, Sweden2
Energy-Efficient System Design Through Adaptive Voltage Scaling

Energy-Efficient System Design Through Adaptive Voltage Scaling by Benjamin Andrew Keller Doctor of Philosophy in Engineering – Electrical Engineering and Computer Sciences University of California, Berkeley Professor Borivoje Nikolić, Co-chair Professor Krste Asanović, Co-chair Improving the energy efficiency of processor systems-on-chip (SoCs) is key to improving the performance and utility of thermally-limited servers, battery-constrained mobile devices, and energy-harvesting Internet-of-Things nodes. Adaptive voltage scaling adjusts voltage levels at runtime in response to changes in workload, reducing voltage when additional performance is not required. The granularity of this voltage scaling greatly impacts its effectiveness. Fine-grained adaptive voltage scaling (FG-AVS) in time reduces wasted energy by tracking microsecond-scale changes in workload; FG-AVS in space independently adjusts voltage levels in many different portions of an SoC, saving additional energy. FG-AVS has the potential to save considerable energy, but it has not yet seen adoption in commercial systems due to several challenges that complicate its implementation. Integrated voltage regulators are required to supply the many voltages required and switch quickly between modes, but efficient implementations impose high area overhead. Clocking, synchronization, and power management have their own requirements and difficulties. This work presents key components of fully-featured SoCs that enable demonstration of FG-AVS. Integrated simultaneous-switching switched capacitor voltage regulators are presented that achieve high conversion efficiency when coupled with an adaptive clock generator. Power management is accomplished with programs run on a dedicated power management unit (PMU), and a pausible bisynchronous FIFO circuit that can achieve low-latency communication between asynchronous voltage domains is described. These components are integrated into systems that demonstrate the potential energy savings of FG-AVS. The Raven-3 testchip demonstrates efficient voltage regulation supplying a complicated digital load; the system achieves 26.2 GFLOPS/W while operating its processor under the generated supply voltage and adaptive clock. The Raven-4 testchip includes integrated power management circuits that allow fully integrated feedback for FG-AVS. The programmable PMU can run a wide variety of power management algorithms, including an AVS algorithm that saves 39.6% energy in a synthetic microbenchmark with minimal performance penalty. The Hurricane-1 testchip implements multiple independent voltage domains and hardware counters that can
Determining Performance of Computer Vision Models using Generalizability, Robustness & Elasticity Score

Performance measurement of computer vision models provides information about their ability to classify objects. However, their performance gets affected in the real-world environment. We propose a modification for the metric called Generalizability, Robustness, and Elasticity score (GRE), which is used to determine the efficiency of the computer vision models. Specifically, we use unaltered Visual Question Answering (VQA) datasets and develop three new datasets for each attribute of the GRE score. The new datasets pass through three novel serial processes designed to enhance the quality of the datasets. The new datasets have a better distribution of feature information of the objects in the original dataset. Their performance is measured by running the datasets on three models specifically modified for our experiment. Two out of three models perform better on our new datasets and provide a better GRE score. We prove that our system works and can provide better results than the conventional method of measuring the performance of computer vision models.
Neighborhood Base Matched Morphological Filters: Cross-fertilization with Linear Lowpass Filtering

The manuscript introduces Neighborhood Base Matched Morphological Filters (NBM-MF) by fusion of linear lowpass filtering process into the mathematical morphology structure of the operators. This internal cross-fertilization is gained via the deployment of a dynamic structuring element that adaptively matches itself to the neighborhood base of the signal/image. The neighborhood base is indeed the base-line/base-surface of the signal/image approximated by linear lowpass filtering called hereafter ‘base’. This cross-fertilization enables NBM-MF for modifying geometrical features of the signal/image more adaptively to the local geometry, and thereof having stronger filtering efficiency and less side effects of disturbing the original structure of the signal/image. The morphological smoothing is deployed for the efficiency evaluation of NBM-MF compared to the classical MF. Using three different numerical evaluation criteria, the morphological smoothing of signals with different noise and different base structure approves the higher efficiency of NBM-MF with respect to the classical one.
Windranger: A Directed Greybox Fuzzer driven by Deviation Basic Blocks

Directed grey-box fuzzing (DGF) is a security testing technique that aims to steer the fuzzer towards predefined target sites in the program. To gain directedness, DGF prioritizes the seeds whose execution traces are closer to the target sites. Therefore, evaluating the distance between the execution trace of a seed and the target sites (aka, the seed distance) is important for DGF. The first directed grey-box fuzzer, AFLGo, uses an approach of calculating the basic block level distances during static analysis and accumulating the distances of the executed basic blocks to compute the seed distance. Following AFLGo, most of the existing state-of-the-art DGF techniques use all the basic blocks on the execution trace and only the control flow information for seed distance calculation. However, not every basic block is equally important and there are certain basic blocks where the execution trace starts to deviate from the target sites (aka, deviation basic blocks). In this paper, we propose a technique called Windranger which leverages deviation basic blocks to facilitate DGF. To identify the deviation basic blocks, Windranger applies both static reachability analysis and dynamic filtering. To conduct directed fuzzing, Windranger uses the deviation basic blocks and their related data flow information for seed distance calculation, mutation, seed prioritization as well as explore-exploit scheduling. We evaluated Windranger on 3 datasets consisting of 29 programs. The experiment results show that Windranger outperforms AFLGo, AFL, and FAIRFuzz by reaching the target sites 21%, 34%, and 37% faster and detecting the target crashes 44%, 66%, and 77% faster respectively. Moreover, we found a 0-day vulnerability with a CVE ID assigned in ffmpeg (a popular multimedia library extensively fuzzed by OSS-fuzz) with Windranger by supplying manually identified suspect locations as the target sites.
A Sentiment Analysis Approach for Abusive Content Detection using Improved Dataset

The rapid growth of information and communications technologies has led to the generation of enormous amount of information daily. Consequently, there has been an increased interest in effective data processing. The nature of the information is varied and can in some cases include users’ emotions and opinions. Faced with this situation, the need of proposing social media monitoring and content filtering is a major asset for the community. The specific case of abusive content is becoming more relevant these recent years leading to the proposition of many models which unfortunately suffer from the unbalanced nature of the dataset used. We propose a sentiment analysis approach which classifies social media posts according to three categories: hate, abusive and neutral. The approach is based on a constructed dataset which reduces unbalancing and improves classification results.
High Power Factor Bridgeless Integrated Buck-Type PFC Converter With Wide Output Voltage Range

Due to the input current dead zones, the buck power factor correction converter has to set a fixed output voltage for a tradeoff between the total harmonic distortion (THD<inline-formula><tex-math notation="LaTeX">${_\mathit{i}}$</tex-math></inline-formula>) and efficiency. Thus, many converter-cell integrated buck-type converters have been proposed to eliminate the dead zones, which unfortunately deteriorate efficiency due to the extra components.This article proposes a group of bridgeless buck-type integrated topologies. Then, a dead-zone-free bridgeless buck-type topology with a wide output voltage range is targeted for further study. The studied converter operates in the buck+flyback mode and can automatically change to flyback mode when the dead zones occur, which only requires a simple control. Moreover, an integrated transformer based on an E-E core is investigated to reduce cost and volume. The bridgeless operation modes are introduced, followed by the PF and THD<inline-formula><tex-math notation="LaTeX">${_\mathit{i}}$</tex-math></inline-formula> analysis to optimize its performance. Prototypes are built and tested, which confirm the effectiveness of the converter and the integrated magnetic.
UXO characterization: comparing cued surveying to standard detection and discrimination approaches: report 2 of 9 - ground penetrating radar for unexploded ordnance characterization

The objective of project W912HZ-04-C-0039, “UXO Characterization: Comparison of Cued Surveying to Standard Detection and Standard Discrimination Approaches,” was to research, develop, optimize, and evaluate the efficiencies of different modes of unexploded ordnance (UXO) characterization and remediation as a function of the density of UXO and associated clutter. This report, one of nine written for the project, focused on an overview of the fundamentals of ground penetrating radar (GPR) with specific emphasis on the detection and discrimination potential of the technology. Part A (Chapters 1–6) includes a brief overview of general GPR concepts, data, and survey procedures. Factors that determine the site-specific applicability of GPR are identified and existing tools for assessing the applicability of GPR to a particular site are reviewed. Part B (Chapters 7–10) describes the types of GPR instrumentation and reviews previous UXO and landmine specific work involving GPR. In both Parts A and B, GPR concepts introduced are discussed and illustrated with data acquired at the Sky Research Ashland (Oregon) test plot using commercial off the shelf (COTS) GPR sensors. In Part A, single GPR profiles over a row of emplaced UXO targets acquired using a singlechannel 250-megahertz (MHz) COTS GPR system are displayed. Further examples illustrate how multiple survey lines collected in a grid can be combined to create two-dimensional (2D) and three-dimensional (3D) visualizations of the subsurface and the targets contained therein. In Part B, examination of GPR data collected at the Ashland test site continues, but in this case, focuses on a range of frequencies (250, 500, 1000 MHz) and the comparative information that can be obtained from these respective frequencies. Discussion of the results observed from these initial measurements, limitations of the methods used, and a proposal for use of GPR in a cued-interrogation mode along with the types of information that are obtainable are presented. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/EL TR-08-33 iii