Reviewerly/exHarmony huggingface dataset:
Demystifying the adoption of behavior-driven development in open source projects

Context:Behavior-Driven Development (BDD) features the capability, through appropriate domain-specific languages, of specifying acceptance test cases and making them executable. The availability of frameworks such as Cucumber or RSpec makes the application of BDD possible in practice. However, it is unclear to what extent developers use such frameworks, and whether they use them for actually performing BDD, or, instead, for other purposes such as unit testing. Objective:In this paper, we conduct an empirical investigation about the use of BDD tools in open source, and how, when a BDD tool is in place, BDD specifications co-evolve with source code. Method:Our investigation includes three different phases: (i) a large-scale analysis to understand the extent to which BDD frameworks are used in 50,000 popular open-source projects written in five programming languages; (ii) a study on the co-evolution of scenarios, fixtures and production code in a sample of 20 Ruby projects, through the Granger's causality test, and (iii) a survey with 31 developers to understand how they use BDD frameworks. Results:Results of the study indicate that ≃ 27% of the sampled projects use BDD frameworks, with a prevalence in Ruby projects (68%). In about 37% of the cases, we found a co-evolution between scenarios/fixtures and production code. Specifically, changes to scenarios and fixtures often happen together or after changes to source code. Moreover, survey respondents indicate that, while they understand the intended purpose of BDD frameworks, most of them write tests while/after coding rather than strictly applying BDD. Conclusions:Even if the BDD frameworks usage is widespread among open source projects, in many cases they are used for different purposes such as unit testing activities. This mainly happens because developers felt BDD remains quite effort-prone, and its application goes beyond the simple adoption of a BDD framework.
Behavioral Activity Recognition Based on Gaze Ethograms

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user’s behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.
A novel parallel chaotic system with greatly improved Lyapunov exponent and chaotic range

Lyapunov exponent (LE), chaotic range and complexity are the key considerations of a discrete chaotic system. A dynamic chaotic system with larger LE and wider parameter space will result in better statistical performance that can be used to generate pseudo random sequences and applied to encryption fields. At the same time, the combination of simple chaotic maps can generate more excellent chaotic behavior. This paper proposes a newly combined chaotic system called Parallel Chaotic System (PCS). Multiple simple chaotic maps are paralleled to construct the novel system. In this system, LE and chaotic range can be improved as much as possible by setting additional parameters. Compared with the existing models made up of same seed maps, PCS is able to get better chaotic behavior by means of a simple structure at the same time. Performance evaluation emphasizes that the chaotic maps generated by PCS are more unpredictable with better complexity.
Joint Phoneme-Grapheme Model for End-To-End Speech Recognition

This paper proposes methods to improve a commonly used end-to-end speech recognition model, Listen-Attend-Spell (LAS). The methods we propose use multi-task learning to improve generalization of the model by leveraging information from multiple labels. The focus in this paper is on multi-task models for simultaneous signal-to-grapheme and signal-to-phoneme conversions while sharing the encoder parameters. Since phonemes are designed to be a precise description of the linguistic aspects of the speech signal, using phoneme recognition as an auxiliary task can help guiding the early stages of training to be more stable. In addition to conventional multi-task learning, we obtain further improvements by introducing a method that can exploit dependencies between labels in different tasks. Specifically, the dependencies between phonemes and grapheme sequences are considered. In conventional multi-task learning these sequences are assumed to be independent. Instead, in this paper, a joint model is proposed based on "iterative refinement" where dependency modeling is achieved by a multi-pass strategy. The proposed method is evaluated on a 28000h corpus of Japanese speech data. Performance of a conventional multi-task approach is contrasted with that of the joint model with iterative refinement.
Zero-Shot Object Detection With Attributes-Based Category Similarity

The accurate alignment between visual features and semantic concepts is a challenging problem in zero-shot object detection. To address that, this brief proposes a new algorithm using attributes based category similarity. An unsupervised learning method is utilized to evaluate and adjust an attribute table, which helps to establish a better synergy between visual and semantic domains. Based on that, the similarity between categories is exploited to simultaneously detect and recognize novel concept instances with visual attributes. Experimental results on the MS-COCO dataset show that the proposed algorithm can achieve the highest mean average precision (15.34%), compared with the state-of-the-art algorithms.
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction

We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image. These insights are: (1) denoise the "ground truth" surface normals in the training set to ensure consistency with the semantic labels; (2) concurrently train on a mix of real and synthetic data, instead of pretraining on synthetic and fine-tuning on real; (3) jointly predict normals and semantics using a shared model, but only backpropagate errors on pixels that have valid training labels; (4) slim down the model and use grayscale instead of color inputs. Despite the simplicity of these steps, we demonstrate consistently improved state of the art results on several datasets, using a model that runs at 12 fps on a standard mobile phone.
Low-Power RTL Code Generation for Advanced CNN Algorithms toward Object Detection in Autonomous Vehicles

In the implementation process of a convolution neural network (CNN)-based object detection system, the primary issues are power dissipation and limited throughput. Even though we utilize ultra-low power dissipation devices, the dynamic power dissipation issue will be difficult to resolve. During the operation of the CNN algorithm, there are several factors such as the heating problem generated from the massive computational complexity, the bottleneck generated in data transformation and by the limited bandwidth, and the power dissipation generated from redundant data access. This article proposes the low-power techniques, applies them to the CNN accelerator on the FPGA and ASIC design flow, and evaluates them on the Xilinx ZCU-102 FPGA SoC hardware platform and 45 nm technology for ASIC, respectively. Our proposed low-power techniques are applied at the register-transfer-level (RT-level), targeting FPGA and ASIC. In this article, we achieve up to a 53.21% power reduction in the ASIC implementation and saved 32.72% of the dynamic power dissipation in the FPGA implementation. This shows that our RTL low-power schemes have a powerful possibility of dynamic power reduction when applied to the FPGA design flow and ASIC design flow for the implementation of the CNN-based object detection system.
Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs

We study the problem of non-Bayesian social learning with uncertain models, in which a network of agents seek to cooperatively identify the state of the world based on a sequence of observed signals. In contrast with the existing literature, we focus our attention on the scenario where the statistical models held by the agents about possible states of the world are built from finite observations. We show that existing non-Bayesian social learning approaches may select a wrong hypothesis with non-zero probability under these conditions. Therefore, we propose a new algorithm to iteratively construct a set of beliefs that indicate whether a certain hypothesis is supported by the empirical evidence. This new algorithm can be implemented over time-varying directed graphs, with non-doubly stochastic weights.
SIAT-3DFE: A High-Resolution 3D Facial Expression Dataset

3D facial expression dataset plays an essential role in computer vision and computer graphics, especially to the data-driven machine learning algorithms like deep-learning etc. In contrast with traditional low-resolution and low-accuracy 3D face related datasets, an accurate and dense facial expression database was introduced in this paper. During the period from January to June 2019, we have collected 8,000 3D facial expression models and 32,000 texture images from 500 subjects. The 3D facial capture device works based on structured light principle. The procedure of 3D face capture and property of 3D samples are introduced in details. The proposed high-accuracy 3D face dataset can be used in many 3D applications, like recognition, animation, and 3D facial expression simulations.
Designing Graphs for Decision-Makers

Data graphics can be a powerful aid to decision-making—if they are designed to mesh well with human vision and understanding. Perceiving data values can be more precise for some graphical types, such as a scatterplot, and less precise for others, such as a heatmap. The eye can extract some types of statistics from large arrays in an eyeblink, as quickly as recognizing an object or face. But perceiving some patterns in visualized numbers—particularly comparisons within a dataset—is slow and effortful, unfolding over a series of operations that are guided by attention and previous experience. Effective data graphics map important messages onto visual patterns that are easily extracted, likely to be attended, and as consistent as possible with the audience’s previous experience. User-centered design methods, which rely on iteration and experimentation to improve a design, are critical tools for creating effective data visualizations.
VNDS: A Vietnamese Dataset for Summarization

We have seen a lot of interesting developments and research in text summarization. While numerous approaches for summarization have been widely studied and applied in various domains in English, it is still an early stage in Vietnamese due to a few number of papers, systems, and the lack of benchmark datasets. Inspired to contribute to make a progress in Vietnamese language research, firstly in this paper we create a standard dataset for document summarization. To the best our knowledge, we are the first to formally publish the large benchmark dataset of summarization. Secondly, we make a comparison of traditional and state-of-the-art extractive and abstractive summarization on our dataset. We strongly believe that the results of our work will facilitate studies of text summarization in Vietnamese for the future.
Transkribus. A Platform for Automated Text Recognition and Searching of Historical Documents

The Transkribus platform provides services and tools for the digitization, transcription, recognition and searching of historical documents. It is the only platform worldwide were non-technical users are enabled to train their own machine learning based neural networks and to apply them on their documents in order to generate an automated transcription and to make them searchable via keyword spotting. Transkribus is used by thousands of users and hundreds of archives, libraries, and research groups all over the world. In this paper we briefly describe the approach of the platform in terms of the underlying business and governance model, as well as the technical aspects of the platform.
A Longitudinal Study on Web-Sites Password Management (in)Security: Evidence and Remedies

Single-factor password-based authentication is generally the norm to access on-line Web-sites.While single-factor authentication is well known to be a weak form of authentication, a further concern arises when considering the possibility for an attacker to recover the user passwords by leveraging the loopholes in the password recovery mechanisms.Indeed, the adoption by a Web-site of a poor password management system makes useless even the most robust password chosen by the registered users.In this paper, building on the results of our previous work, we study the possible attacks to on-line password recovery systems analyzing the mechanisms implemented by some of the most popular Web-sites.In detail, we provide several contributions: (i) we revise and detail the attacker model; (ii) we provide an updated analysis with respect to a preliminary study we carried out in December 2017; (iii) we perform a brand new analysis of the current top 200 Alexa's Web-sites of five major EU countries; and, (iv) we propose Maildust, a working open-source module that could be adopted by any Web-site to provide registered users with a password recovery mechanism to prevent mail service provider-level attacks.Overall, it is striking to notice how the analyzed Web-sites have made little (if any) effort to become compliant with the GDPR regulation, showing that the objective to have basic user protection mechanisms in place-despite the fines threatened by the GDPR-is still far, mainly because of sub-standard security management practices.Finally, it is worth noting that while this study has been focused on EU registered Web-sites, the proposed solution has, instead, general applicability.
Spotting faked 5 stars ratings in E-Commerce using mouse dynamics

In the last decade, faked reviews have become a growing issue. While most studies have focused on text analysis to identify false reviews, the big companies are now switching to simpler review systems exclusively based on ratings (such as number of stars or like/dislike systems). In this paper, the possibility of detecting faked ratings from the analysis of mouse movements is explored for the first time. The participants were asked to evaluate twenty products on a five-star rating scale. For half of the products, they were encouraged to cheat to get an advantage. Results showed greater response times and wider mouse trajectories when users left false ratings then when they left true ones. Concerning the analysis of the ratings, results revealed an interaction between the type of rating (positive vs. negative) and deception (true vs. false rating). Moreover, the users’ attitudes toward the faked reviews has been investigated through a self-report questionnaire, pointing out that people are more prone to give neutral judgments rather than extremely positive or negative ratings when they are encouraged to cheat.
Combined estimation of the parameters and states for a multivariable state‐space system in presence of colored noise

Summary This article addresses the combined estimation issues of parameters and states for multivariable systems in the state‐space form disturbed by colored noises. By utilizing the Kalman filtering principle and the coupling identification concept, we derive a Kalman filtering based partially coupled recursive generalized extended least squares (KF‐PC‐RGELS) algorithm to jointly estimate the parameters and the states. Using the past and the current data in parameter estimation, we propose a Kalman filtering based multi‐innovation partially coupled recursive generalized extended least‐squares algorithm to enhance the parameter estimation accuracy of the KF‐PC‐RGELS algorithm. Finally, a simulation example is provided to test and compare the performance of the proposed algorithms.
What is the Lagrangian for Nonlinear Filtering?

Duality between estimation and optimal control is a problem of rich historical significance. The first duality principle appears in the seminal paper of Kalman-Bucy, where the problem of minimum variance estimation is shown to be dual to a linear quadratic (LQ) optimal control problem. Duality offers a constructive proof technique to derive the Kalman filter equation from the optimal control solution.This paper generalizes the classical duality result of Kalman-Bucy to the nonlinear filter: The state evolves as a continuous-time Markov process and the observation is a nonlinear function of state corrupted by an additive Gaussian noise. A dual process is introduced as a backward stochastic differential equation (BSDE). The process is used to transform the problem of minimum variance estimation into an optimal control problem. Its solution is obtained from an application of the maximum principle, and subsequently used to derive the equation of the nonlinear filter. The classical duality result of Kalman-Bucy is shown to be a special case.
Efficient Smartphone-Based Human Activity Recognition Using Convolutional Neural Network

Human activity recognition primarily targets the identification of individual/group activities from acquired data, which can be captured commonly using smartphone based sensory units. Existing sensor based human activity recognition, including smartphone based activity recognition, has suffered from efficient performances to accurately recognize physical activities. In this paper, an efficient human activity recognition system, based on the new simplified convolutional neural network framework, is proposed. The proposed system also improves the overall recognition accuracy of human activities given as one-dimensional time-series dataset based on accelerometers and gyroscopes of smartphones. In particular, the proposed system accurately recognizes six types of physical activities, viz. walk, walk-upstairs, walk-downstairs, sit, stand and lay. The proposed system has been thoroughly experimented to show that its overall recognition accuracy increases to as high as 93.926%, with significant improvement in training time and testing time performances, which reach to a minimum of 3.4274 seconds and 372.6 milliseconds respectively. Consequently, the proposed system achieves time bound performances of accurate activity recognition, thereby advocating many societal benefits including disaster management.
Caching Techniques to Improve Latency in Serverless Architectures

Serverless computing has gained a significant traction in recent times because of its simplicity of development, deployment and fine-grained billing. However, while implementing complex services comprising databases, file stores, or more than one serverless function, the performance in terms of latency of serving requests often degrades severely. In this work, we analyze different serverless architectures with AWS Lambda services and compare their performance in terms of latency with a traditional virtual machine (VM) based approach. We observe that database access latency in serverless architecture is almost 14 times than that in VM based setup. Further, we introduce some caching strategies which can improve the response time significantly, and compare their performance.
Dynamic partial reconfiguration enchanced with security system for reduced area and low power consumption

Field-programmable gate arrays (FPGAs) have travelled far from just being utilized as glue logic to an entire system solution. This is mostly due to their generalized re-configurable nature, lower non-recurring engineering (NRE) expense, and also fast time to market. Owing to the reconfigurable nature of FPGA, a new field called reconfigurable computing that can change the circuit configuration after hardware production came into existence. Application of re-configurable computing for self-adaptive hardware allows hardware to get adapt to various environmental conditions and different needs by swapping or loading disparate computational modules. This work proposes an effectual design methodology (enhanced DPR security system (EDPRSS)) utilized to execute high performance FPGA device in respect of low power consumption along with security for the area reduction. In the proposed technique, hash code generation (HCG) and encryption hardware accelerators can well be dynamically produced on FPGA utilizing partial re-configuration as stated by the application requisites. The system is competent to swap in or swap out the equivalent hardware accelerator during run time, which in turn diminishes the power and area. Here, 2 re-configurable partitions are produced for encryption and also HCG algorithm. Experiential outcomes proved that the proposed technique proffers better performance when contrasted to the other conventional systems.