Reviewerly/exHarmony huggingface dataset:
Predict the required cost to develop Software Engineering projects by Using Machine Learning

Abstract Software project cost prediction is a very important task during building and developing software projects. This process helps software project engineers to accurately manage and plan their resources in terms of cost estimation. However, Need for accurate cost development prediction model for a software project is not a simple procedure. Predicting the cost required while developing software engineering projects is the most difficult challenge that attracts the attention of researchers and practitioners. This paper adopts a new model in estimating the cost of building or developing software engineering projects using a machine learning approach. The results proves that machine learning methods can be used to predict program cost with high accuracy rate compared with traditional software estimation techniques. The proposed model in this research was trained on the NASA (National Aeronautics and Space Administration) data set, which contains the characteristics of 60 projects in addition to the real cost of the projects. An analysis of the results of the implementation for the proposed methods showed that the cost Predicting process using K-Nearest Neighbours algorithm (KNN), Cascade Neural Networks (CNN) and Elman Neural Networks (ENN) It has the ability to predict the costs required to build or develop software engineering projects, K-Nearest Neighbours algorithm has shown high accuracy for Predict the required cost to develop Software Engineering projects Compared to Cascade Neural Networks and Elman Neural Networks ENN.
Karamad: A Voice-based Crowdsourcing Platform for Underserved Populations

Crowdsourcing enables the completion of large-scale and hard-to-automate tasks, while allowing people to earn money. However, 3.6 billion people – a workforce comprising 46.4% of the world population – who could benefit most from this source of income lack the access and literacy to use computers, smartphones, and the internet. In this paper we present Karamad, a voice-based crowdsourcing platform that allows workers in low-resource regions to complete crowd work using low-end phones and receive payments as mobile airtime balance. We explore the usefulness, scalability, and sustainability of Karamad in Pakistan through a 6-month deployment. Without any advertising, training, or airtime subsidies, Karamad organically engaged 725 workers who completed 3,939 tasks (involving 43,006 components) including translations, dataset generation, and surveys on demographics, accessibility, disability, health, employment, and literacy. Collectively, the workers produced a valuable service market for potential customers and included female, unemployed, non-literate, and blind users.
On the Naming of Methods: A Survey of Professional Developers

This paper describes the results of a large (+1100 responses) survey of professional software developers concerning standards for naming source code methods. The various standards for source code method names are derived from and supported in the software engineering literature. The goal of the survey is to determine if there is a general consensus among developers that the standards are accepted and used in practice. Additionally, the paper examines factors such as years of experience and programming language knowledge in the context of survey responses. The survey results show that participants very much agree about the importance of various standards and how they apply to names and that years of experience and the programming language has almost no effect on their responses. The results imply that the given standards are both valid and to a large degree complete. The work provides a foundation for automated method name assessment during development and code reviews.
Double Multi-Head Attention for Speaker Verification

Most state-of-the-art Deep Learning systems for speaker verification are based on speaker embedding extractors.These architectures are commonly composed of a feature extractor front-end together with a pooling layer to encode variable-length utterances into fixed-length speaker vectors.In this paper we present Double Multi-Head Attention pooling, which extends our previous approach based on Self Multi-Head Attention.An additional self attention layer is added to the pooling layer that summarizes the context vectors produced by Multi-Head Attention into a unique speaker representation.This method enhances the pooling mechanism by giving weights to the information captured for each head and it results in creating more discriminative speaker embeddings.We have evaluated our approach with the VoxCeleb2 dataset.Our results show 6.09% and 5.23% relative improvement in terms of EER compared to Self Attention pooling and Self Multi-Head Attention, respectively.According to the obtained results, Double Multi-Head Attention has shown to be an excellent approach to efficiently select the most relevant features captured by the CNN-based front-ends from the speech signal.
A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorithm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if each of the compared algorithms generates inaccurate mappings for a statement or its tokens. We invite 12 external experts to determine if three commonly used AST mapping algorithms generate accurate mappings for a statement and its tokens for 200 statements. Based on the experts' feedback, we observe that our approach achieves a precision of 0.98-1.00 and a recall of 0.65-0.75. Furthermore, we conduct a large-scale study with a dataset of ten Java projects containing a total of 263,165 file revisions. Our approach determines that GumTree, MTDiff and IJM generate inaccurate mappings for 20%-29%, 25%-36% and 21%-30% of the file revisions, respectively. Our experimental results show that state-of-the-art AST mapping algorithms still need improvements.
Recommendation Systems For E-commerce Systems An Overview

Abstract Due to the huge expand in global markets and financial transactions, the importance of E-commerce has grown significantly, so to achieve fully functioning, scalable, reliable, efficient, secure, and user-friendly E-commerce system, Adequate system analysis and design procedures are essential. On the other hand The internet vast growing has totally altered the way most companies work. The Internet has created a means for e-Commerce the internet has created a way to provide a unique avenue for companies and customers to sell and buy goods and services. When creating an e-commerce website, there are several goals that must be taken into account, one of them is how to Increase the site’s efficiency to ensure customer turnout and thus achieve the required material profits. There are several methods that are followed to increase the efficiency of the site. One of these methods is the recommender system. This paper produces a study overview of the value of recommended systems and thoroughly analyzed Collaborative Recommender System (CF) techniques with its techniques., which presents proposals to customers according to their interests, making it easier for the customer to search and thus choose the goods that suit him.
Verify It’s You: How Users Perceive Risk-Based Authentication

Risk-based authentication (RBA) is an adaptive security measure used to strengthen password-based authentication against account takeover attacks. Our study on 65 participants shows that users find RBA more usable than two-factor authentication equivalents and more secure than password-only authentication.
Event detection in finance using hierarchical clustering algorithms on news and tweets

In the current age of overwhelming information and massive production of textual data on the Web, Event Detection has become an increasingly important task in various application domains. Several research branches have been developed to tackle the problem from different perspectives, including Natural Language Processing and Big Data analysis, with the goal of providing valuable resources to support decision-making in a wide variety of fields. In this paper, we propose a real-time domain-specific clustering-based event-detection approach that integrates textual information coming, on one hand, from traditional newswires and, on the other hand, from microblogging platforms. The goal of the implemented pipeline is twofold: (i) providing insights to the user about the relevant events that are reported in the press on a daily basis; (ii) alerting the user about potentially important and impactful events, referred to as hot events, for some specific tasks or domains of interest. The algorithm identifies clusters of related news stories published by globally renowned press sources, which guarantee authoritative, noise-free information about current affairs; subsequently, the content extracted from microblogs is associated to the clusters in order to gain an assessment of the relevance of the event in the public opinion. To identify the events of a day d we create the lexicon by looking at news articles and stock data of previous days up to d-1 Although the approach can be extended to a variety of domains (e.g. politics, economy, sports), we hereby present a specific implementation in the financial sector. We validated our solution through a qualitative and quantitative evaluation, performed on the Dow Jones' Data, News and Analytics dataset, on a stream of messages extracted from the microblogging platform Stocktwits, and on the Standard & Poor's 500 index time-series. The experiments demonstrate the effectiveness of our proposal in extracting meaningful information from real-world events and in spotting hot events in the financial sphere. An added value of the evaluation is given by the visual inspection of a selected number of significant real-world events, starting from the Brexit Referendum and reaching until the recent outbreak of the Covid-19 pandemic in early 2020.
Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram

Cross-lingual voice conversion (VC) is an important and challenging problem due to significant mismatches of the phonetic set and the speech prosody of different languages. In this paper, we build upon the neural text-to-speech (TTS) model, i.e., FastSpeech, and LPCNet neural vocoder to design a new cross-lingual VC framework named FastSpeech-VC. We address the mismatches of the phonetic set and the speech prosody by applying Phonetic PosteriorGrams (PPGs), which have been proved to bridge across speaker and language boundaries. Moreover, we add normalized logarithm-scale fundamental frequency (Log-F0) to further compensate for the prosodic mismatches and significantly improve naturalness. Our experiments on English and Mandarin languages demonstrate that with only mono-lingual corpus, the proposed FastSpeech-VC can achieve high quality converted speech with mean opinion score (MOS) close to the professional records while maintaining good speaker similarity. Compared to the baselines using Tacotron2 and Transformer TTS models, the FastSpeech-VC can achieve controllable converted speech rate and much faster inference speed. More importantly, the FastSpeech-VC can easily be adapted to a speaker with limited training utterances.
Multi-Object Tracking Using Poisson Multi-Bernoulli Mixture Filtering For Autonomous Vehicles

The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects’ states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-based MOT framework for 3D LiDAR data. In partiuclar, we propose a Poisson multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for autonomous driving applications. To the best of our knowledge, this represents a first attempt for employing an RFS-based approach in conjunction with 3D LiDAR data for MOT applications with comprehensive validation using challenging datasets made available by industry leaders. The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT applications.
Physical Warm-up Games

Warm-up games are widespread practices in multiple activities across domains, yet little scholarly work can be found about their role in physical training. Here, we study potential goals and benefits of warm-up games, and explore opportunities for technology inclusion through investigating a collection of warm-up games gathered: online, from a survey of online warm-up games curated, described, and used by Physical Education teachers; and in person, from an ongoing design research work as part of a technology-supported circus training course. Further, in the context of the latter, we conducted explorative design interventions, augmenting a range of the warm-up games with wearable technology. Our work surfaces major goals and benefits of warm-up games, which can be broadly classified as preparing participants physically, socially, and mentally. We also show how the inclusion of open-ended technology can support these goals and discuss broader opportunities for technology inclusion in warm-up games.
Community Supported Agriculture: The Concept of Solidarity in Mitigating Between Harvests and Needs

There is a developing recognition of the social and economic costs entailed in global supply chains. In this paper, we report on efforts to provide alternative, more sustainable and resilient models of production. Community Supported Agricultures (CSAs) address this problem but require new means of exchange which, we suggest, offer a design opportunity for sustainable HCI research. This paper presents a two months participatory observation in a food movement, a German CSA which developed a distribution system involving their own currency. Based on our ethnographic observations, we focus our discussion on (1) the solidaristic principles upon which the movement is based and (2) techniques of mediating between consumers' wishes and the constraints of local agricultural production. By relating to the continued development of CSAs, we identify three interrelated innovation gaps and discuss new software architectures aimed at resolving the problems which arise as the movement grows.
Private Wireless Federated Learning with Anonymous Over-the-Air Computation

In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained by injecting additional noise to local model updates before transmitting to the parameter server (PS). In the wireless FL scenario, we show that the privacy of the system can be boosted by exploiting over-the-air computation (OAC) and anonymizing the transmitting devices. In OAC, devices transmit their model updates simultaneously and in an uncoded fashion, resulting in a much more efficient use of the available spectrum. We further exploit OAC to provide anonymity for the transmitting devices. The proposed approach improves the performance of private wireless FL by reducing the amount of noise that must be injected.
Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.
GarmentGAN: Photo-realistic Adversarial Fashion Transfer

The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN takes two images as input: a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. To make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques [18]. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body keypoint information. We demonstrate GarmentGAN outperforms current fashion transfer techniques through qualitative evaluation and standard image synthesis metrics.
Fast Region-Adaptive Defogging and Enhancement for Outdoor Images Containing Sky

Inclement weather, haze, and fog severely decrease the performance of outdoor imaging systems. Due to a large range of the depth-of-field, most image dehazing or enhancement methods suffer from color distortions and halo artifacts when applied to real-world hazy outdoor scenes, especially those with the sky. To effectively recover details in both distant and nearby regions as well as to preserve color fidelity of the sky, in this study, we propose a novel image defogging and enhancement approach based on a replaceable plug-in segmentation module and region-adaptive processing. First, regions of the grayish sky, pure white objects, and other parts are separated. Second, a luminance-inverted multi-scale Retinex with color restoration (MSRCR) and region-ratio-based adaptive Gamma correction are applied to non-grayish and non-white areas. Finally, the enhanced regions are stitched seamlessly by using a mean-filtered region mask. Extensive experiments show that the proposed approach not only outperforms several state-of-the-art defogging methods in terms of both visibility and color fidelity, but also provides enhanced outputs with fewer artifacts and halos, particularly in sky regions.
Proposing an International Standard Accident Number for Interconnecting Information and Communication Technology Systems of the Rescue Chain

The rapid dissemination of smart devices within the internet of things (IoT) is developing toward automatic emergency alerts which are transmitted from machine to machine without human interaction. However, apart from individual projects concentrating on single types of accidents, there is no general methodology of connecting the standalone information and communication technology (ICT) systems involved in an accident: systems for alerting (e.g., smart home/car/wearable), systems in the responding stage (e.g., ambulance), and in the curing stage (e.g., hospital).We define the International Standard Accident Number (ISAN) as a unique token for interconnecting these ICT systems and to provide embedded data describing the circumstances of an accident (time, position, and identifier of the alerting system).Based on the characteristics of processes and ICT systems in emergency care, we derive technological, syntactic, and semantic requirements for the ISAN, and we analyze existing standards to be incorporated in the ISAN specification.We choose a set of formats for describing the embedded data and give rules for their combination to generate an ISAN. It is a compact alphanumeric representation that is generated easily by the alerting system. We demonstrate generation, conversion, analysis, and visualization via representational state transfer (REST) services. Although ISAN targets machine-to-machine communication, we give examples of graphical user interfaces.Created either locally by the alerting IoT system or remotely using our RESTful service, the ISAN is a simple and flexible token that enables technological, syntactic, and semantic interoperability between all ICT systems in emergency care.
Revisiting Gendered Web Forms: An Evaluation of Gender Inputs with (Non-)Binary People

Gender input forms act as gates to accessing information, websites, and services online. Non-binary people regularly have to interact with them, though many do not offer non-binary gender options. This results in non-binary individuals having to either choose an incorrect gender category or refrain from using a site or service—which is occasionally infeasible (e.g., when accessing health services). We tested five different forms through a survey with binary and non-binary participants (n = 350) in three contexts—a digital health form, a social media website, and a dating app. Our results indicate that the majority of participants found binary "male or female" forms exclusive and uncomfortable to fill out across all contexts. We conclude with design considerations for improving gender input forms and consequently their underlying gender model in databases. Our work aims to sensitize designers of (online) gender web forms to the needs and desires of non-binary people.
DAG-GAN: Causal Structure Learning with Generative Adversarial Nets

Learning Directed Acyclic Graph (DAG) from purely observational data is a critical problem for causal inference. Most existing works tackle this problem by exploring gradient-based learning methods with a smooth characterization of acyclicity. A major shortcoming of current gradient based works is that they independently optimize SEMs with a single sample and neglect the interactions between different samples. In this paper, we consider DAG structure learning from the perspective of distributional optimization and design an adversarial framework named DAG-GAN to detect the DAG structure from data. We theoretically analyze the Nash equilibrium property of DAG-GAN and propose a novel score function to exploit the interactions between different samples. In addition, extensive experiments are conducted to validate the efficiency of DAG-GAN against several state-of-the-art DAG learning methods.