Reviewerly/exHarmony huggingface dataset:
Consensus-Based Secret Sharing in Blockchain Smart Contracts

Smart contracts on a blockchain permit the performance of credible transactions without the involvement or oversight of a third party. In some industries such as finance, confidentiality is paramount to protect business-critical information from becoming public; however, in a smart contract, information needs to be known and independently verified. A zero-knowledge proof (ZKP) can be used to obscure this information when there are only two parties in the contract; however, ZKPs do not have a mechanism to handle situations in which multiple parties are involved. We introduce, discuss, and simulate a consensus-based secret sharing protocol which provides zero knowledge of personal information, only allows members to participate if they have a key, and only works when a specified number of members “vote.” This enhances the ability for smart contracts to work in situations where majority votes are needed, but the confidentiality of members needs to be preserved.
Abduction and comparative weighing of explanatory hypotheses: an argumentative approach

Abstract This paper makes use of the concepts and theoretical framework developed within the field of Argumentation Theory to account for the structure and characteristics of abduction and of the comparative processes of weighing explanatory hypothesis. It elaborates an analysis of abduction based on its consideration as a meta-explanatory argumentation scheme while elucidating its relations with abductive reasoning and inference. The conceptualization of comparative processes of weighing explanatory hypothesis as complex and varied argumentative structures is presented as an alternative to the idea of providing a formally rigid and supposedly universal account of ‘inference to the best explanation’.
Fuzzy-Based 3-D Stream Traffic Lightweighting Over Mobile P2P Network

Supporting the huge amount of network traffic from three-dimensional (3-D) streaming is an extremely important challenge, as 3-D streaming has the potential to cause network congestion and longer waiting times. These problems can also lead to 3-D video being blurred, and negatively affecting the user experience. This article uses the front camera of a mobile device to track a user's viewing angle, and then calculates the currently needed 3-D stream to find the most suitable peer for video source supply. This article also considers several impact factors to choose supply partners with different data flow via fuzzy theory. Consequently, the proposed method is able to determine the best way to download the desired 3-D stream without unnecessary congestion, thus maintaining the overall peer-to-peer network quality. Simulation results show that the proposed method can reduce the quantity of the video flow, while maintaining video quality, thus avoiding bottlenecks like bandwidth and network speed.
Requirements for the Linguistic Quality Control of Wikipedia Article

The article focuses on key Wikipedia issues. One of them concerning Wikipedia article quality management has been considered. The importance of professional linguistic skills has been emphasized. Taking into account information quality model and crucial characteristics of well-written text, the linguistic quality control model has been provided. Three groups with the relevant features of the model have been allocated. The basic requirements for the linguistic quality control model have been suggested.
Studying the impact of CI on pull request delivery time in open source projects—a conceptual replication

Nowadays, continuous integration (CI) is indispensable in the software development process. A central promise of adopting CI is that new features or bug fixes can be delivered more quickly. A recent repository mining study by Bernardo, da Costa &amp; Kulesza (2018) found that only about half of the investigated open source projects actually deliver pull requests (PR) faster after adopting CI, with small effect sizes. However, there are some concerns regarding the methodology used by Bernardo et al., which may potentially limit the trustworthiness of this finding. Particularly, they do not explicitly control for normal changes in the pull request delivery time during a project’s lifetime (independently of CI introduction). Hence, in our work, we conduct a conceptual replication of this study. In a first step, we replicate their study results using the same subjects and methodology. In a second step, we address the same core research question using an adapted methodology. We use a different statistical method (regression discontinuity design, RDD) that is more robust towards the confounding factor of projects potentially getting faster in delivering PRs over time naturally, and we introduce a control group of comparable projects that never applied CI. Finally, we also evaluate the generalizability of the original findings on a set of new open source projects sampled using the same methodology. We find that the results of the study by Bernardo et al. largely hold in our replication. Using RDD, we do not find robust evidence of projects getting faster at delivering PRs without CI, and we similarly do not see a speed-up in our control group that never introduced CI. Further, results obtained from a newly mined set of projects are comparable to the original findings. In conclusion, we consider the replication successful.
Analytical evaluation of resource allocation algorithms and process migration methods in virtualized systems

In this paper, analytical models, based on the stochastic reward nets (SRNs), are proposed to analyze the impact of resource allocation algorithms and process migration methods on the power consumption and performance of virtualized systems. In the proposed models, each computer offers a certain capacity of computational, data, and communication resources and resource requesters, namely processes, are categorized into three main categories: compute-intensive, data-intensive and communication-intensive, according to their required resources. Since the processing rate of a computer reduces when the number of the processes running on the computer increases, we apply the migration methods to keep the performance of the system at a high level and reduce the power consumption as much as it is possible. The proposed SRNs appropriately model the migration of processes among computers by applying two types of migration methods: power-aware and performance-aware. Furthermore, by applying the proposed models, different resource allocation algorithms, e.g. First-fit, Best-fit, Worst-fit, and Random, can be compared in terms of the power consumption and performance. The numerical results, cross-validated with the CloudSim framework, show that the proposed models can be appropriately used to analyze different resource allocation algorithms and migration methods, and thereby, help system providers to make treatment decisions with confidence.
Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.
Towards Summarization for Social Media - Results of the TL;DR Challenge

With most summarization research focused on the news domain and scientific papers, little is known about the capabilities of the state of the art at summarizing more informal text.Today, the vast majority of text on the web is informally written on social media, and staying on top of the fast-paced stream of posts originating from one's subscriptions and followees is a burden to many.The TL;DR challenge marks a first step towards developing new summarization technology for social media, focusing on abstractive summarization.This paper reports the results of the challenge and describes our manual evaluation of the submissions.Finally, we discuss the expected properties of a good summary after analyzing the comments provided by human annotators.
Building the role of ICT coordinators in primary schools: A typology based on task prioritisation

Abstract Although the school ICT coordinator’s role has been institutionally defined by the Andalusian Education Administration (Spain), individual factors may be key in the emerging role‐building process. Multidimensional scaling (MDS) of the priority given by coordinators to their different functions and a subsequent cluster analysis of the MDS solution were used to identify role orientations among ICT coordinators in primary schools of Andalucía (Spain). Three orientation clusters were identified: “support of ICT use in the classroom” (67.1%), “promote ICT use in the classroom” (17.8%) and “planning and maintenance of ICT equipment in the school” (15.1%). The interest in separating technical and pedagogical duties into two profiles and the perceived organisational support from the Teachers Centres (TCs) differed in the three role orientation types. Finally, strategies are suggested to reduce role ambiguity and role conflict in ICT coordinators. A clearer definition of the role would improve their leadership in ICT implementation in schools.
A SECURE STEGANOGRAPHY CREATION ALGORITHM FOR MULTIPLE FILE FORMATS

Nowadays the skin cancer has become a more dangerous and an unpredictable disease among the humans. Nearly one million of people all over the world every year are been affected by the skin cancer and left with no treatment due to the lack of early diagnosis. Besides the usual types of cancer such as the melanoma, basal cell carcinoma and squamous cell carcinoma that could be identified easily there are certain types of unusual skin cancer such as the Merkel cell skin cancer that are rare and difficult to diagnose. As the identification of the Merkel cell skin cancer at the early stage would be very useful in deciding the necessary treatment for its cure, the paper has put forward preprocessing techniques to improve the image quality to make the further image processing procedure easy in the identification of the skin cancer. The proposed method applies the combined image enhancement and the restoration (CIEIR) on the input skin lesion images and makes it more presentable with the improved quality for the further image processing steps in the identification of the normal skin and the skin affected by the Merkel cell tumor. The CIEIR is implemented in the MATLAB and the parameters such as the PSNR, SSIM and the MSE are measured.
PRNU-based finger vein sensor identification: On the effect of different sensor croppings

In this work, we study the applicability of PRNU-based sensor identification methods for finger vein imagery. We also investigate the effect of different image regions on the identification performance by looking at five different crop-pings with different sizes. The proposed method is tested on eight publicly available finger vein datasets. For each finger vein sensor a noise reference pattern is generated and subsequently matched with noise residuals extracted from previously unseen finger vein images. Although the final result strongly encourages the use of PRNU-based approaches for sensor identification, it can also be observed that the choice of image region for PRNU extraction is crucial. The result clearly shows that regions containing biometric trait (varying content) should be preferred over background regions containing non-biometric trait (identical content).
Some aspects of functional modeling in the IDEF0 standard as the initial stage of TPPs design

The design and operation of thermal power plants (TPPs) in the Russian Federation are standardized by a set of regulatory and technical documents. At the initial design stage, it is necessary to coordinate legal, environmental and other indicators of TPPs with specialists of various profiles, develop a general concept and structure of the station. In the traditional technical standards of the Russian Federation there are no documents regulating this process. At the end of the twentieth and the beginning of the twenty-first century, a methodology and a family of IDEF standards covering all the stages and aspects of design were developed and adopted as national standards in a number of countries, including individual items in Russia. The IDEF methodology has a visual graphical language, which allows it to be used as a means of interprofessional communication. In this paper, some aspects of the application of the IDEF0 standard at the initial design stage of thermal power plants are considered.
Local Wisdom in Malay Kampung Semarang as Representatives of Smart Environment

Abstract One of the smart city indicators is a smart environment. Smart environment is a setting which can be a catalyst for its citizens, support them to be aware of a local engagement in managing their existing natural resources. The local engagement is not only shaped by formal strategies, but also by local wisdom. Local wisdom is a community understanding that is used as a reference in daily life activities. Based on its history, the Malay Kampong (Kampung Melayu) Semarang which was formed in 1743 has strong local wisdom as a basis for arranging the district. Settlement patterns based on ethnicity and culture, building patterns that are sensitive to the floods and tidal ranges, multicultural-based district, make this region able to survive hundreds of years to maintain its existence and can be a representation of the smart multicultural environment in Semarang. The current environmental degradation in this area is the basis for the research. Is local wisdom still maintained here? Or is there a shifting meaning of local wisdom at this time? This study aims to explore the role of local wisdom of Malay Kampung Semarang as a representation of a smart environment. This study combines quantitative and qualitative approaches. To achieve this goal, it is necessary to identify the influence of local wisdom in Malay Kampung Semarang on flood resilience and tidal. The analysis of the smart environment model is based on the interaction of wisdom and influence on the district arrangement. The result of the study shows that local wisdom is an effort to improve the living place to be more sustainable in the event of flooding and tidal. Efforts made to elevate the floor of the building, height of buildings, elevate the street, build houses with a stage model, and make the second floor of the house represent a smart environment in the form of sustainable urban planning in the Malay Kampung Semarang.
Predictive Modeling of Trait-Aging Invariant Face Recognition System Using Machine Learning

There is an ever increasing demand for face recognition systems for a wide range of applications. But face recognition systems have a unique challenge, Trait Aging. Trait Aging makes the recognition of individuals' faces less accurate with time lapse. In this study, a Convolutional Neural Network (CNN) called Inception-Resnet-V2 was adapted using transfer learning to develop a trait aging invariant face recognition system. The adapted neural network model was tested and trained using image datasets from the MORPH database. The MORPH dataset used in this study had 55,000 unrepeated images of over 13,000 persons. The images were acquired between the year 2003 and 2007. The median age of the dataset was 33, while the minimum and maximum ages in the dataset were 16 and 77 respectively. The mean separation in days between the time each image in the dataset was acquired was 164 days with 1 day and 1681 days as the minimum and maximum separation in days respectively. The standard deviation of separation in days for all images in the dataset was 180 days. Training and testing accuracies reached as high as 100% and experimental results showed training and testing loss of as low as 0%. All these, notwithstanding the trait aging factor present in the pre-processed MORPH database.
Demonstration actor critic

We study the problem of Reinforcement Learning from Demonstrations (RLfD), where the agent has access to not only reward signals from the environment, but also some available expert demonstrations. Recent works absorb ingredients from imitation learning and utilize demonstration data as reward reshaping. Despite their success, these methods update policy over these states seen in the demonstration data, in the same way as other states in the state space, overlooking the validity of direct supervision signals on these states. To address this issue, we propose a novel RLfD objective function with a new shaping reward, by optimizing which can directly leverage the supervision signal on these demonstrated states. We propose a general framework for policy optimization of the proposed objective, with convergence guarantees under the classic tabular setting. Based on that, we further make some approximations based on deep neural networks, and then introduce a new practical algorithm, called Demonstration Actor Critic (DAC) in large continuous domains. Extensive experiments on a range of popular benchmark sparse-reward tasks show that our method can lead to significant performance gains over several strong and off-the-shelf baselines.
Hi-D Maps: An Interactive Visualization Technique for Multi-Dimensional Categorical Data

In this paper, we present Hi-D maps, a novel method for the visualization of multi-dimensional categorical data. Our work addresses the scarcity of techniques for visualizing a large number of data-dimensions in an effective and space-efficient manner. We have mapped the full data-space onto a 2D regular polygonal region. The polygon is cut hierarchically with lines parallel to a user-controlled, ordered sequence of sides, each representing a dimension. We have used multiple visual cues such as orientation, thickness, color, countable glyphs, and text to depict cross-dimensional information. We have added interactivity and hierarchical browsing to facilitate flexible exploration of the display: small areas can be scrutinized for details. Thus, our method is also easily extendable to visualize hierarchical information. Our glyph animations add an engaging aesthetic during interaction. Like many visualizations, Hi-D maps become less effective when a large number of dimensions stresses perceptual limits, but Hi-D maps may add clarity before those limits are reached.
Swarm Methods of Image Segmentation

A distinctive feature of the thematic image segmentation of onboard optoelectronic surveillance systems is the search for rational solutions in the multidimensional space of alternatives. Searching for rational solutions in a multidimensional space of alternative is peculiarized by non-linearity, non-differentiation, multi-extremality, ravine surface, lack of the analytic expression of objective functions, high computational complexity, high dimensionality of the search space, and the complex topology of the region of acceptability. Finding a solution by exact methods of optimization is complicated by non-linearity, non-differentiation and lack of the analytic expression of the objective function. High computational complexity, high dimensionality of the search space, and the complex topology of the region of acceptability lead to wasting valuable time when using precise optimization methods. Currently, methods of searching for global extremum are being developed, which provide the convergence to the exact solution of the optimization problem, provide the optimal (minimum or maximum) value of the objective function. Such methods include meta-heuristic optimization methods, which, unlike classical optimization methods, can be used even if there is no information about the nature and properties of the objective function. Meta-heuristic methods have the following features: they manage the process of finding the optimal solution; they efficiently study the search space to find the optimal solution; they use simple local search procedures and complex training processes; they are approximate methods and, as a rule, non-deterministic; they take into account the probability of trapping in a limited search space; they are universal (solve various application problems); they use a priori information to find the optimal solution. Let us detail the use of the variety of meta-heuristic optimization methods (ant colony optimization (ACO) and artificial bee colony optimization (ABC) for the thematic image segmentation of onboard optical and electronic surveillance systems.
Efficient MPI‐AllReduce for large‐scale deep learning on GPU‐clusters

Summary Training models on large‐scale GPUs‐accelerated clusters are becoming a commonplace due to the increase in complexity and size in deep learning models. One of the main challenges for distributed training is the collective communication overhead for large message sizes: up to hundreds of MB. In this paper, we propose two hierarchical distributed memory multileader AllReduce algorithms optimized for GPU‐accelerated clusters (named lr_lr and lr_rab ), in which GPUs inside a computing node perform an intra‐node communication phase to gather and store results of local reduced values to designated GPUs (known as node leaders). Node leaders then keep a role as an inter‐node communicator. Each leader exchanges one part of reduced values to the leaders of the other nodes in parallel. Hence, we are capable of significantly reducing the time for injecting data into the inter‐node network. We also overlap the inter‐node and intra‐node communication by implementing our proposal in a pipelined manner. We evaluate those algorithms on the discrete‐event simulation Simgrid. We show that our algorithms, lr_lr and lr_rab , can cut down the execution time of an AllReduce microbenchmark that uses the logical ring algorithm ( lr ) by up to 45% and 51%, respectively. With the pipelined implementation, our lr_lr_pipe achieves 15% performance improvement when compared with lr_lr . In addition, the simulation result also projects power savings for the network devices of up to 23% and 32%.