Reviewerly/exHarmony huggingface dataset:
When traceability goes awry: An industrial experience report

The concept of traceability between artifacts is considered an enabler for software project success. This concept has received plenty of attention from the research community and is by many perceived to always be available in an industrial setting. In this industry-academia collaborative project, a team of researchers, supported by testing practitioners from a large telecommunication company, sought to investigate the partner company's issues related to software quality. However, it was soon identified that the fundamental traceability links between requirements and test cases were missing. This lack of traceability impeded the implementation of a solution to help the company deal with its quality issues. In this experience report, we discuss lessons learned about the practical value of creating and maintaining traceability links in complex industrial settings and provide a cautionary tale for researchers.
Modeling N-ary relational data as gyro-polygons with learnable gyro-centroid

Binary Knowledge base (KB) embedding has been broadly studied, while n-ary relational KB embedding which aims to simultaneously map binary and beyond-binary facts into low-dimensional vector space is less explored. Existing n-ary KB embedding approaches typically treat entities in n-ary fact as equally important or decompose n-ary relational fact into subtuples. And they generally model n-ary relational KBs in the Euclidean space. However, n-ary facts are semantically and structurally intact, decomposition leads to intrinsic semantic information loss and undermines the structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space and have been greatly overlooked. To address these issues, we propose a gyro-polygon embedding framework to realize n-ary fact structure keeping, semantic integrity retaining, and hierarchy capturing, termed as PolygonE. Specifically, n-ary relational facts are modeled as gyro-polygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex–gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. We further extend PolygonE by unsupervisedly learning the weight of each entity, and strengthening the connection between the primary triple and the whole fact with the gyrocentroid–gyromidpoint distance. Extensive experiments demonstrate the superiority of PolygonE.
Phoneme based Kannada Speech Corpus for Automatic Speech Recognition System

Automatic speech recognition is a challenging task in languages which are not having many resources for research. The state-of-the-art technologies like Deep Neural Network approach require a standard dataset for training and testing the approach for a particular language. In this paper, development of a novel dataset for Kannada speech corpus is described. The speech dataset developed is useful for implementation of Automatic Kannada speech recognition system. The speech data for this dataset is crowd-sourced by using a website developed for the purpose. Since the data collection process is active in the internet the size of the dataset grows as more number of people contributes their voice. The system asks the user to enter their details when they open it for the first time. The user-data collected here is helpful to categorize the data in the dataset based on different parameters, which is the requirement for some types of ASR system. The phonetic representation of the word is stored in the database along with the numeric representation which optimizes the processing of speech to text conversion.
Code Implementation Recommendation for Android GUI Components

We present a prototype tool Icon2Code, targeted to helping app de-velopers more quickly implement the callback functions of complex Android GUI components by recommending code implementations learnt from similar GUI components from other apps. Given an icon or UI widget provided by designers, Icon2Code first queries a large pre-established database to locate similar icons that other apps have utilized. It then leverages a collaborative filtering model to suggest the most relevant APIs and their usage examples associated with the intended behaviours of these icons. Experimental results on 5,000 randomly selected real-world apps show that Icon2Code is useful and effective in recommending code examples for imple-menting the behaviours of complex GUI components. It has over 50% of success rate when only one recommended API is taken into account, and over 94% of success rate if 20 APIs are considered. The video demo can be found at https://youtu.be/pM3ZBGrQTdQ.
Mobile Passive Authentication through Touchscreen and Background Sensor Data

The security and usability shortcomings of current mobile user authentication systems based on PIN codes, fingerprint, and face recognition are well known. To overcome such limitations, the present work focuses on the comparative analysis of unimodal and multimodal behavioral biometric traits suitable for mobile passive authentication, such as touchscreen data during separate gestures (keystroke, scrolling, drawing a number, tapping on the screen), and background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, magnetometer).This paper carries out a performance evaluation over one of the most complete and challenging databases to date with mobile user interaction data, HuMIdb, with 600 subjects. For each individual modality, we propose a separate RNN (Recurrent Neural Network) trained with semi-hard triplet loss. In addition, we perform the fusion of the different modalities at score level. Our results show that the best performing tasks are keystroke and drawing a number, whereas the most discriminative background sensor is the magnetometer. Additionally, the fusion of modalities is very beneficial, consistently reducing the Equal Error Rates (EER) by half (ranging from 5% to 13% depending on the modality combination).
Motivation and Demotivation of Hackers in Selecting a Hacking Task

To build a solid foundation on which to understand and combat threats to information systems, researchers need to look past technical security issues and explore why hackers do what they do. Based on General Deterrence Theory and the Theory of Reasoned Action, a structural model is proposed and validated that examines attraction and detraction factors towards a hack. From a motivational perspective, individual characteristics (mastery and curiosity), peer influence and the nature of the task itself are shown to impact hacker’s attitudes. Specifically, we uncover an interesting non-linear relationship between hacking task complexity and a hacker’s attitude towards a hack. From a deterrence perspective, while hackers consider the likelihood of being caught, the severity of punishment/sanctions does not have a significant effect on hackers’ intention to engage in a hacking task. When we better understand what motivates and demotivates these highly skilled users, we gain insights to avoid becoming targets.
A Novel Light Field Image Compression Method Using EPI Restoration Neural Network

Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane image (EPI) contains a lot of low rank information, which is suitable for recovering the complete EPI from a part of EPI. In this paper, a light field image coding framework based on EPI restoration neural network has been proposed. Compared with previous algorithms, the proposed algorithm further takes advantage of the inherent similarity in light field images, and the proposed framework has higher performance and robustness. Experimental results show that the proposed method has superior performance compared to the state-of-the-art both in quantitatively and qualitatively.
An High Speed Area Efficient Implementation of Prime Field based Twisted Edwards Curve Point Multiplication using FPGA Architecture

On the digital side, data transfer is constantly subject to attack. A cypher strength study is an important part of any corporate or academic security assessment. A strong encryption mechanism is required for data security. For secure information transmission, System-On-Chips (SoCs) for Internet of things (IoT) applications require hardware-based coordinated arbitrary number generators. They don’t have a confined hardware and power spending plan, intends to the use of particular Twisted Edwards Curve (TEC). A symmetric encryption with incorporated TEC is proposed in this paper. A regular 40 nm CMOS innovation is utilized to accomplish the proposed plan. The aftereffects of the post-design recreation uncover that it gives great irregularity at a modest energy-per-bit cost. Moreover, the circuit finished all NIST assessments with no post-handling. When contrasted with the conventional encryption, it has an extremely low region increment of just 0.14 percent. As a proof of idea, a FPGA execution is likewise shown, which checks the simulated results. To show the double utilization of the proposed TEC, the Advanced Encryption Standard (AES) key extension method is also evolved. The AES technique depends solely on the substitute-permute network plan rule, and it functions admired in both programming and hardware. The procedure introduced here utilizes a solitary indistinguishable mystery key for encryption and disentangling. It can't be utilized in public sector or private, business, or different projects. The AES calculation is executed on two FPGAs in this research, and it was found that the Spartan-6 FPGA conveys better throughput and lower time delay than FPGA-based IoT gadgets.
Cross-Platform Testing of Quantum Computing Platforms

Quantum computing has been attracting the attention of both applied research and companies. Continuous progress on fundamental hardware technology promises to bring us more reliable and large-scale quantum computers on which to run the next generation of quantum algorithms. These programs are compiled and executed on dedicated platforms, and similarly to classical programs, a large effort is required to test these platforms and create a robust software toolchain.Unlike previous studies which focused on cross-optimization and cross-backend testing, this dissertation aims to create the first approach for cross-platform testing which compares execution on diverse quantum computing platforms. To inform the design of the method, we will first perform an empirical study of bugs in quantum computing platforms and a review of the characteristics of realistic quantum programs.The final approach for cross-platform testing will include three components: a learning-based method to generate realistic quantum programs, an approach to map and run them on multiple platforms, and finally a quantum-specific statistical test to compare two multivariate binary distributions returned as the output of quantum programs.
Coloring an Image Using Generative Adversarial Networks (GAN)

Past few years ago there were only gray scale images videos, as technologies has improved those gray scales images are now colorized, which depicts the exact color as it is visible to human eye. Now these colorization techniques are done using deep learning and are automated because of which they have impressive performance. In 2002 a calculation was suggested that colorized pictures through surface combination. Colorization was finished by matching luminance and surface data between a current shading picture and the grayscale picture to be colorized. A technique was proposed as an elective detailing to the colorization issue in 2004. This plan followed a converse methodology, where the expense work was planned by the contrast between each pixel and a weighted normal of its adjoining pixels. To overcome abnormal colors and to improve image quality with good colorization we are using Generative Adversarial Network (GAN). Studies shows that GAN outperforms existing methods
Taking Care With Caregiving Robots

“AI is likely to be either the best or worst thing to happen to humanity”—Stephen Hawking <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> .
Comparison and Performance Analysis of Various Despeckling Methods on Echocardiographic Images

The echocardiographic images consist of multiplicative noise due to heart motion, therefore despeckling in echocardiographic images is required. The performance of the median filter on various window sizes, Fourier ideal and Butterworth filter on various cut-off frequencies, wavelet filter with or without eliminated bands and Homomorphic Fourier Ideal Filters has been tested on the echocardiographic image. When the lower band is removed, the wavelet transform produces the best despeckled images. The despeckled results have significantly improved visualization. The eliminated lower band wavelet transform gives highest values of peak signal to noise ratio, signal to noise ratio and lowest mean square error.
Disaster Risk Evaluation of Superlong Highways Tunnel Based on the Cloud and AHP Model

Comprehensive evaluation of the geological disaster risk of super long tunnels is significant for safety construction. In this paper, we establish a comprehensive evaluation system based on the cloud model and analytic hierarchy process (AHP) theory, considering four kinds of geological hazards, such as water and mud inrush, large deformation, rock burst, and collapse. Then, based on a super long highway tunnel of the Chizhou-Qimen expressway in China, the construction risk evaluation is carried out. The results show that the evaluation system can accurately judge the disaster risk level of the tunnel under different conditions surrounding a rock. The cloud model theory can realize the mutual transformation of qualitative indicators and quantitative indicators and avoid the problem of intense subjectivity of evaluation indicators. The evaluation system can be used as a new approach to evaluate tunnel risks in similar projects.
PHYSICAL ACTIVITY TECHNIQUE MONITORING (PATMo) BASED POSE ESTIMATION USING CNN

Exercising is a physical activity to increase the quality of life. However, exercising may come with various injuries ranging from minor to an injury that can cause fatality. Exercising requires gathering information to perform a specific physical activity with extreme caution and safety. Immediate feedback on human pose on performing a physical activity is a prime of importance to avoid harm to the human body. This study emphasizes pose estimation using a Convolution Neural Network model to identify a person’s key points. A prototype called Physical Activity Technique Monitoring (PATMo) embedded with Mobilenet-YOLOv3 and Simple Pose Tesnet18 v1b models is developed. PATMo focuses on a single movement and angle for receiving feedback for the physical activity. PATMo utilizes two optimizers and several batch sizes for parameter tuning. The batch size 32 with Adaptive Moment Estimation optimizer has the highest accuracy of 82.66% to Stochastic Gradient Descent, but the computational time took about 12 hours. More evaluations are expected with more powerful computer and Convolution Neural Network models variants. It is a starting point for further investigation to improve the feedback time during physical.
A Secure method for Image Signaturing using SHA-256, RSA, and Advanced Encryption Standard (AES)

Images are the most sensitive and easily editable form of data that is available right now. Because of all the Image manipulation programs which are more powerful and some are Open-source like GIMP (GNU Image Manipulation Program) and paid ones like Adobe Photo-shop have made image manipulation easier and undetectable with the human eye and hence conveying wrong information, this information can be destructive or misleading in some situations. So, there is a need to secure images that are sensitive and need to be secured and verified by the viewer that they are original and not being touched by any middlemen. There are two options either to encrypt the images or to digitally sign them. In this method, The signature of the image is done through 3 steps. The three steps include SHA256 (Secure Hash Algorithm 256), RSA (Rivest-Shamir-Adleman), and AES (Advanced Encryption Standard). The signature of the image is stored in a binary file which is sent to the person who needs to verify the image that is already present near the viewer. The method has been tested y artificially applying attacks such as blurring the image greyscale the image and changing one pixel in the image. The images which have PSNR less than 30% are also being detected as manipulated. The method can successfully detect even a change in one pixel of the original image and mark it as manipulated image.
Efficiently and Precisely Searching for Code Changes with DiffSearch

Version histories of code contain useful information and these data are public, thanks to open source software. However, searching through large repository histories can be complex, because there is no specific tool to search for code changes. This paper presents DiffSearch, the first efficient and scalable search engine for code changes. Given a list of repositories and a query, DiffSearch can retrieve specific code changes in a few seconds. We design a language-agnostic approach that we test on three popular programming languages: Java, JavaScript, and Python, and we design a query language that is an extension of the supported languages. We evaluate DiffSearch in three steps. First, we measure a recall of 81.8%, 89.6%, and 90,4% for Java, Python, and JavaScript, respectively, and an average response time lower than five seconds. Second, we demonstrate its scalability with a large dataset of one million code changes. Last, we perform a case study to show one of its possible applications, where DiffSearch gathers a dataset of 74,903 Java bug fixes.
Wafer Geometry Technique for Blank 300mm Silicon Wafers

In this paper we introduce a new optical metrology technique for measuring wafer geometry on full 300 mm blank and patterned silicon wafers. Wave Front Phase Imaging (WFPI) is presented that acquires 7.65 million data points in 5 seconds on a full 300mm silicon test wafer allowing for a lateral resolution of 96&#x03BC;m. The wafer geometry was measured 30 times on a blank 300mm silicon wafer front and backside while the wafer was resting on 3 pins close together and the deflection was found to be in close agreement to estimations for gravity pull. The system has repeatability with root-mean-square standard deviation (&#x03C3;<inf>RMS</inf>) of 4.75nm on the front side and 6.51nm on the backside of a 300mm silicon test wafer. Using a double Gaussian filtering technique with a 100&#x03BC;m lateral cutoff frequency, nanotopography was revealed on the full silicon wafer showing a repeatability of 0.265&#x00C5; for the frontside and 0.204&#x00C5; for the backside of the full 300mm silicon wafer.
RE-3DLVNet: Refined estimation of the left ventricle volume via interactive 3D segmentation and reinforced quantification

The reliable estimation of left ventricle (LV) volume from 3D medical data enables both visible shape exhibition and accurate volume report, for efficiently clinical cardiac diagnosis. However, it is still open challenging as: 1) intermediate volume error caused by fully depending on segmentation, 2) non-visualized result via solo using direct regression, 3) ambiguous conclusion from the unmatching multi-type outputs, 4) large spatial scene of stereo domain, 5) unbalanced information in 3D medical data between depth and slice plane, 6) large regression output space. We proposed RE-3DLVNet the first powerful work for the refined volume estimation of 3D LV in a novel way segmentation volume ± reinforced volume . It uses segmentation with advantage of intuitive voxel-to-voxel optimization to fast stabilize a rough volume. Reciprocally, it makes reinforced quantification (ReinQuan) with advantage of global inference to focus on checking scene misunderstanding and precisely remedy accumulative error caused by massive local optimization, for refining the volume. 1) In segmentation, a probe convolution creatively perceives global scene distribution, and a low-to-high depth segmentation net with high depth connection releases latent structure in low-resolution depth. 2) In ReinQuan, a regression net is conducted to refine estimating the reinforced volume of the segmentation, with focused contracted output space. 3) Interactively, a consistent constraint transfers the ReinQuan deviation between segmented result and its ground truth for penetratively penalizing. Extensive experiments show RE-3DLVNet clinical potential in assessing cardiac function and anatomy. It 1) enables exact 3D LV volume estimation with mean absolute error down to 1.03ml , increasing measurement precise by 30.41% compared to segmentation volume, and 2) achieves accurate visualization segmentation with Dice coefficient up to 95.13% .
CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code

Recent work has focused on using machine learning to automate software engineering processes, such as code completion, code migration, and generating code from natural language description. One of the challenges faced in these tasks is evaluating the quality of the predictions, which is usually done by comparing the prediction to a reference solution. BLEU score has been adopted for programming languages as it can be easily computed for any programming language and even incomplete source code, while enabling fast automated evaluation. However, programming languages are more verbose and have strict syntax when compared to natural languages. This feature causes BLEU to find common n-grams in unrelated programs, which makes distinguishing similar pairs of programs from dissimilar pairs hard. This work presents CrystalBLEU, an evaluation metric based on BLEU, that mitigates the distinguishability problem. Our metric maintains the desirable properties of BLEU, such as handling partial code, applicability to all programming languages, high correlation with human judgement, and efficiency, in addition to reducing the effects of the trivially matched n-grams. We evaluate CrystalBLEU on two datasets from previous work and a new dataset of human-written code. Our results show that CrystalBLEU differentiates similar and unrelated programs better than the original BLEU score and also a variant designed specifically for source code, CodeBLEU.