Reviewerly/exHarmony huggingface dataset:
Electricity Billing System using Ethereum and Firebase

Ethereum, a blockchain-based distributed computing platform, provides smart contract functionality. It also provides Ethereum virtual machine (EVM) that can execute peer-to-peer contracts across decentralized network. However, the gas consumption of smart contract is so costly that it becomes one of the important problems to be solved. The purpose of this paper is to provide a conceptual overview of blockchain based electricity billing system with the aim of lessening gas consumption of the smart contract. In this system, Firebase is employed as a data storage while Ethereum blockchain acts as both a cryptocurrency payment system and an authentication channel. Moreover, this paper illustrates two-factor authentication by utilizing Ethereum account and Firebase Authentication as an authentication channel. Results show that by utilizing Firebase with blockchain, the transaction cost of each transaction made on Ethereum is decreased by approximately 73%.
Round Robin Scheduling Based on Remaining Time and Median (RR_RT&amp;M) for Cloud Computing

Cloud computing is a system that is flexible to adjust infrastructure as needed with low costs. Quality of service is a challenge for a cloud service provider. A scheduling algorithm has a direct impact on the quality of service in cloud computing. Therefore, this work focuses on studying scheduling algorithms for cloud environments and proposes a new algorithm based on a round robin algorithm called round robin based on remaining time and median (RR_RT&M). It is compared with other algorithms, such as first-come, first-served (FCFS), and smarter round robin (SRR) algorithms. The performance metrics are makespan, execution time, and waiting time. The experiments were conducted in CloudSim simulator, and the results showed that RR_RT&M performed the best for all metrics and the percentage of improvement for makespan was between 16 and 72%. For execution time and waiting time, the improvement percentages were 31–73 and 0–73%, respectively.
PotSAC: A Robust Axis Estimator for Axially Symmetric Pot Fragments

The task of virtually reassembling an axially symmetric pot from its fragments can be greatly simplified by utilizing the constraints induced by the pot's axis of symmetry. This requires accurate estimation of the axis for each sherd, whose 3D data typically contain gross outliers arising from surface artifacts, noisy surface normals and unfiltered data along the break surface. In this work, we propose a simple two-stage robust axis estimator, PotSAC, which is based on a variant of the random sample consensus (RANSAC) algorithm followed by robust nonlinear least squares refinement. Unlike previous work which have either compensated the axis estimation accuracy for robustness against outliers or vice versa, our method can handle the aforementioned outlier sources without compromising its accuracy. This is achieved by carefully designing the method to combine and extend the advantage of each key prior work. Experimental results on real scanned fragments demonstrate the effectiveness of our method, paving the way towards high quality reassembly of symmetric potteries.
Careful Devices

This short paper offers a rationale and manifesto for a design-led research project called careful devices—domestic healthcare technologies that seek to bridge the gap between the lived experience of a person and the abstracted medical knowledge of a health practitioner. The rationale places careful devices at the intersection of contemporary trends in self-tracking and health care technology, and explains how and why this intersection is relevant for future interaction design. This is followed by a manifesto that articulates design goals for producing devices linking these trends, creating a space for interaction design research. We end with a discussion of Ovum, an example of a careful fertility tracking device.
Decouple co-adaptation: Classifier randomization for person re-identification

The Person Re-identification (ReID) task aims to match persons across cameras in a surveillance system. In the past few years, many researches are devoted to ReID and its performance has gained significant improvement. ReID models are usually trained as a joint framework comprising a person feature extractor and a classifier. However, there exists co-adaptation between the feature extractor and the classifier, which prevents the feature extractor from making effective and sufficient optimization and results in inferior retrieval performance. In this paper, we propose a very simple and effective training method, called DeAda, to decouple this co-adaptation. Our main motivation is to construct a series of weak classifiers during training by randomization of parameters, so that optimization on the feature extractor could be strengthened in the training stage. DeAda is easy, effective, and efficient, and could serve as a plug-and-play optimization tool for ReID models, without additional memory and time cost. We also analyze the theoretical property of DeAda and show that it could produce identical features for the same person under some simple assumptions. We demonstrate its effectiveness on three public ReID datasets: Market1501, DukeMTMC-reID and CUHK03 over different ReID models. With DeAda optimization, we finally obtain state-of-the-art results on all the three datasets.
Visualization of Twitter Sentiment during the Period of US Banned Huawei

The polarity analysis of sentiments based on users' expressions on several events has been in much interest for the research. Recently, social media has been popular and it is widely used as a proxy platform to gauge public opinions in real-time. With the growth of microblog sites on the Web, people have started using blog sites like Twitter and other similar social services to express their opinions and emotions on a wide variety of topics. We proposed a visual sentiment analysis framework for US-China trade war related with US banned on Chinese telecoms giant Huawei Technologies. The proposed framework consists of two components, sentiment analysis modeling and geographic visualization. We focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis by applying lexicon-based approach. This geographic visualization system can help people for better understanding the changes of public sentiment reactions along with the duration and mostly interested regions of Twitter users on this case. In our research, we worked with English language; however, the proposed technique can also be used with any other language.
Child Face Recognition with Deep Learning

The following topics are dealt with: feature extraction; learning (artificial intelligence); data mining; face recognition; pattern classification; text analysis; cloud computing; natural language processing; image colour analysis; Internet.
Straggler Resilient Serverless Computing Based on Polar Codes

We propose a serverless computing mechanism for distributed computation based on polar codes. Serverless computing is an emerging cloud based computation model that lets users run their functions on the cloud without provisioning or managing servers. Our proposed approach is a hybrid computing framework that carries out computationally expensive tasks such as linear algebraic operations involving large-scale data using serverless computing and does the rest of the processing locally. We address the limitations and reliability issues of serverless platforms such as straggling workers using coding theory, drawing ideas from recent literature on coded computation. The proposed mechanism uses polar codes to ensure straggler-resilience in a computationally effective manner. We provide extensive evidence showing polar codes outperform other coding methods. We have designed a sequential decoder specifically for polar codes in erasure channels with full-precision input and outputs. In addition, we have extended the proposed method to the matrix multiplication case where both matrices being multiplied are coded. The proposed coded computation scheme is implemented for AWS Lambda. Experiment results are presented where the performance of the proposed coded computation technique is tested in optimization via gradient descent. Finally, we introduce the idea of partial polarization which reduces the computational burden of encoding and decoding at the expense of straggler-resilience.
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories

Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, such as the seminal work of Li and Durbin, attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure—the history of multiple subpopulations that merge, split, and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations. Along the way and of independent interest, we essentially determine the optimal number of samples needed to learn an exponential mixture distribution information-theoretically, proving the upper bound by analyzing natural (and efficient) algorithms for this problem.
Towards the Systematic Testing of Virtual Reality Programs

Background: Software testing is a critical activity to ensure that software complies with its specification. However, current software testing activities tend not to be completely effective when applied in specific software domains in Virtual Reality (VR) that has several new types of features such as images, sounds, videos, and differentiated interaction, which can become sources of new kinds of faults. Aims: This paper presents an overview of the main VR characteristics that can have an impact on verification, validation, and testing (VV&T). Furthermore, it analyzes some of the most successful VR open-source projects to draw a picture concerning the danger of the lack of software testing activities. Method: We compared the current state of software testing practice in open-source VR projects and evaluate how the lack of testing can be damaging to the development of a product. We assessed the incidence of code smells and verified how such projects behave concerning the tendency to present faults. We also perform the same analyses on projects that are not VR related to have a better understanding of these results. Results: The results showed that the practice of software testing is not yet widespread in the development of VR applications. It was also found that there is a high incidence of code smells in VR projects. Analyzing Non-VR projects we noticed that classes that have test cases tend to produce fewer smells compared to classes that were not tested. Regarding fault-proneness analysis, we used an unsupervised approach to VR and Non-VR projects. Results showed that about 12.2% of the classes analyzed in VR projects are fault-prone, while Non-VR projects presented a lower fault-proneness rate (8.9%). Conclusions: Regarding the application of software testing techniques on VR projects, it was observed that only a small number of projects are concerned about developing test cases for VR projects, perhaps because we still do not have the necessary tools to help in this direction. Concerning smells, we concluded that there is a high incidence in VR projects, especially regarding implementing smells and this high incidence can have a significant influence on faults. Finally, the study related to fault proneness pointed out that the lack of software testing activity is a significant risk to the success of the projects.
Information Hiding Using 3D-Printing Technology

In this work, information hiding using 3D-printing technology was investigated. It is a new direction of technical steganography. Information data is converted into a digital 3D model of elementary physical objects that are placed inside 3D model of a container product. After printing, a solid object physically contains hidden information that cannot be deleted or distorted without damaging the container product. In addition, the methods used do not reduce the operational, aesthetic and any other properties of the finished product. The possibility of extracting hidden information from a solid-state object through laser scanning was also investigated. We present the results of experimental studies of low-power laser irradiation of test samples of ABS 3D plastic in yellow and blue. The data obtained confirm the possibility of identifying the embedded content of a solid-state object and reliable extraction of hidden information. The proposed complex is invariant to the method of layer-by-layer growing, i.e. it can be equipped with any peripheral devices of 3D-printing of various manufacturers with any materials and principles of layer-by-layer creation.
Personalized Code Recommendation

The current state-of-the-art methods in code recommendation mostly take the crowd-based approach. The basic idea is to collect and extract code patterns from a large pool of available source code and use those code patterns for recommendations. However, different programmers have different coding styles, levels of experience, and knowledge about libraries and frameworks, all of which cause different uses of variable names, classes, and methods. When code of different programmers is combined, such differences are blurred, which could hurt the performance of the code recommendation tool for a specific programmer. In the paper, we explore a new research direction in code recommendation which focuses on personal coding patterns of programmers. As a proof of concept, we have developed a personalized code recommendation model for suggesting variable declaration and initialization code. Our techniques learn personalized code patterns for each programmer based on their coding history. The preliminary evaluation shows that our recommendation model is highly effective. For example, when evaluating on a programmer, our approach has top-1 accuracy of 62% and top-3 accuracy of 70% on recommending declaration types. Our approach has top-1 and top-3 accuracy of 67% and 76%, respectively, on recommending initialization method sequences. Furthermore, our model also outperforms the baselines significantly in these experiments.
Secure and Effective Key Management Using Secret Sharing Schemes in Cloud Computing

Security is a crucial problem in Cloud computing. Storing and accessing the data in the Cloud is very popular nowadays but the security of data is still lagging behind. Secret sharing schemes are widely used to improve the security of data. In this article, a threshold secret sharing scheme using Newton divided difference interpolating polynomial (TSSNIP) is proposed in a distributed Cloud environment to enhance security of keys used for encryption. The proposed method uses a Newton divided difference interpolating polynomial for key splitting and key reconstruction. A threshold value is used to reconstruct the shares in secret sharing schemes. The proposed work made use of dynamic and random threshold generation method to ensure security of key. The experimental output shows reduced execution time, better security, efficiency, and robustness in the proposed scheme. Furthermore, the proposed scheme also outperformed other secret sharing schemes.
Uncertainty-Based Pattern Mining for Maximizing Profit of Manufacturing Plants With List Structure

Products in manufacturing plants are not always manufactured without defects. The probability that commodities are produced without defects is uncertain. Uncertainty-based pattern mining can discover information about a set of goods by considering the possibilities. Besides, products have different importance due to the diverse characteristics of goods. Therefore, we propose a list-based pattern mining method over uncertain data considering an important condition in this article. The proposed method extracts commodities with large values that take into account the importance of merchandise and probability that can be as nondefective products. A list structure is efficient to be created and store a database as a minimal expression. The proposed approach is able to find results more accurately and faster than the existing techniques. We compare the performance of our proposed method with those of the state-of-the-art approaches through real datasets and synthetic datasets. Through these performance tests, we prove that the technique presented in this article has a more excellent performance than the latest algorithms in terms of execution time, memory usage, and scalability.
A Case Study about Startups' Software Development Practices

Recently software startups have been the focus of intense research, especially in the Software Engineering community. However, we need more empirical evidence that addresses how software startups perform their software development practices. This paper presents a case study of software startups to understand their software development practices. We conducted a multiple case study in ten software startups, incubated in industrial environments at All Saints Bay Ecosystem. The case study consisted of questionnaires, semi-structured interviews, non-participative observations, and archiving data analyses covering software development practices. The preliminary results allowed us to build an initial industrial startup model capturing the software startups' practices and the relationship among them. Early-stage startups do not adopt any software development methodology in its completeness. Instead, they work focusing on speed-up software development and guide development based on consumers' feedback. The findings of this study is a preliminary result, which needs to be tested through other interviews with practitioners that work in other startups to validate the model at its current stage.
PursuitPass: A Visual Pursuit-Based User Authentication System

As our lives get more deeply submerged in digital format, ubiquitous access to sensitive data requires more secure and efficient user authentication procedures. Methods that solely relied on password entry were lately enhanced with the use of biometrics. Yet, these techniques can still be tricked by, for example, recordings of the face, voice, and fingerprint cloning. In this paper we introduce PursuitPass, a compact, robust, and efficient visual pursuit-based authentication system. PursuitPass is a user calibration-free method that requires the user to enter a password by visually pursuing moving targets on a small screen, such as a public ATM or a personal mobile phone. Because eye movements are used as input, passwords are better protected against shoulder surfing. Also, since targets can potentially move in unpredictable ways, it naturally imposes a liveness feature that cannot be counterfeited by recordings of the eyes. We investigated four pattern-matching algorithms to match visual pursuit user data with the movement of the targets. Two experiments were conducted. The first experiment aimed to define the best performing matching algorithm and configuration for PursuitPass. The second experiment aimed to evaluate the performance of our prototype. PursuitPass achieved a 96.82% accuracy with an average time of 10.42 s on a series of 4-digit PIN entry trials.
EmoD: An End-to-End Approach for Investigating Emotion Dynamics in Software Development

Emotions are an integral part of human nature. Emotion awareness is critical to any form of interpersonal communication and collaboration, including these in the software development process. Recently, the SE community starts having growing interests in emotion awareness in software development. While researchers have accomplished many valuable results, most extant research ignores the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach to capture and model emotion dynamics rather than focuses on extracting isolated emotion states. In this paper, we proposed such an approach-EmoD. EmoD is able to automatically collect project teams' communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We developed a prototype tool that instantiates the EmoD approach by assembling state-of-the-art NLP, SE, and time series techniques. We demonstrate the utility of the tool using the IPython's project data on GitHub and a visualization solution built on EmoD. Thus, we demonstrate that EmoD can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation.
On Convergence Rate of Adaptive Multiscale Value Function Approximation for Reinforcement Learning

In this paper, we propose a generic framework for devising an adaptive approximation scheme for value function approximation in reinforcement learning, which introduces multiscale approximation. The two basic ingredients are multiresolution analysis as well as tree approximation. Starting from simple refinable functions, multiresolution analysis enables us to construct a wavelet system from which the basis functions are selected adaptively, resulting in a tree structure. Furthermore, we present the convergence rate of our multiscale approximation which does not depend on the regularity of basis functions.
Joint Extraction of Entities and Relations Based on Multi-label Classification

Relations extraction is an important semantic analysis task in the field of natural language processing. We propose a new end-to-end model for joint extraction of entities and relations. In this model, we use a matching mechanism to match the relations and entities, transforming the joint extraction task into a combination of multi-label classification and sequence labeling task, and we use the dilated convolution combined with LSTM to encode words, more fully extract the semantics in the text. Firstly, we detect relations and entities separately, where we consider relations extraction as a multi-label classification task. Then we match the detected relations with the detected multiple entities. We conduct experiments on a dataset generated by distant supervision method. The accuracy of our experimental results reach 0.693, the recall rate is 0.430, and the F1 is 0.531, which exceed the experimental results of most models. The experimental results show that our method is better than most of the current joint extraction methods and can effectively extract overlapping relations.