Reviewerly/exHarmony huggingface dataset:
The Evolution and Emerging Trends of Cloud Computing Adoption Research

With the continuous promotion and application of cloud computing, the issue of its adoption has increasingly attracted widespread attention from the theoretical and practical circles at home and abroad. Therefore, it is of great significance to summarize and analyze the research status and hotspots of cloud computing adoption and promote the subsequent research on cloud computing adoption. This paper uses CiteSpace, a visualized literature analysis software, to draw core authors, core journals, key words cluster and other knowledge maps in the field of cloud computing adoption based on 668 documents related to cloud computing adoption from 2011 to 2019 included in the Web of Science database. The research status and trend of cloud computing adoption are visualized and analyzed. The results of the study indicate that the attention paid to cloud computing adoption is gradually increasing. It shows that the research on the security, risk and other technical characteristics of cloud computing, the research on the influencing factors of cloud computing adoption and user behavior, and the research on the cost and performance of cloud computing are the three main research contents in the field of cloud computing adoption. The research hotspots in the field of cloud computing adoption have experienced the evolution process from specific research issues such as cloud computing business and technology, to the application and implementation of cloud computing in enterprises, to the integration of cloud computing and other emerging technologies.
An Optimization Method for Blockchain Electronic Transaction Queries Based on Indexing Technology

Due to the advantages of non-tampering, non-forgery and anonymity of blockchain, it is gradually being used in electronic trading systems. However, there is a problem of low efficiency of querying historical information in the traditional blockchain electronic transaction system, which cannot meet the user’s basic query needs. To better solve this problem, we propose an index-based blockchain electric transaction query scheme in this paper. We construct the index directory of the BKV (B-Key-Value) Tree storage structure by modifying the storage of the B-tree, and based on the BKV tree, we store the transaction order number and the corresponding block number in form of key-value pairs by combining the characteristics of the blockchain system. At the same time, we also design the blockchain structure and query algorithm based on the BKV index directory. Theory and experiments show that the index-based blockchain electrical transaction query scheme can reduce the electrical transaction query time, thus effectively improve the efficiency of the electrical transaction query and improve the user’ experience with the blockchain electronic trading system.
Tuning ANNs Hyperparameters and Neural Architecture Search Using HPC

Rapid development of deep ANNs made the number of hyperparameters constantly grow. As a sequence various aspects of ANNs, such as inference time, efficient resources utilization, losses and even training time were strongly influenced. In general methods of hyperparameters tuning are used for adapting well-known ANN models to new tasks or to tasks in similar areas without pre-training or for synthesis of new particular architectures for particular problems. In this article we compare different types of hyperparameters tuning like CoDeepNEAT, Naive Evolution, Tree-Parzen estimation, structured annealing with MorphNet post-tuning. We apply these methods to particular network architectures for image processing and HRM signal estimation. The process of adaptation this technology to big networks requires a lot of computational resources, so it’s necessary to use parallel implementations. It can be done by utilizing HPC with hybrid computational nodes. Also we propose new type of tool based on Microsoft NNI. It is used for tuners comparison, convergence analysis, and runs different tuners in parallel mode on cluster nodes.
Naïve Approaches to Deal With Concept Drifts

A common problem in machine learning is to find representative real-world labeled datasets to put the methods to test. When developing approaches to deal with concept drifts, some datasets such as the Forest Covertype and Nebraska Weather are common choices for testing, even though there is no consensus on whether these exhibit concept drifts or not. We argue that some well-known real-world concept drift datasets present a high serial dependence in the target class and may have only minor changes. With this in mind, we propose the use of Naïve methods that should be used for comparison with methods that deal with concept drifts. The experimental results using six real-world well-known concept drift datasets show that the Naïve approaches can be better than some methods to deal with possible concept drifts in datasets such as the Forest Covertype, Electricity, and Nebraska Weather. These results suggest that some widely used datasets may be trivial from the concept drift standpoint, and thus, should be avoided, or at least the results should be compared with the proposed Naïve methods.
Use of multivariate statistical techniques to determine the source apportionment of heavy metals in soils and sediments

Soil quality can be classified as the multiphase system of the Earth’s surface including the function of living organisms. Some of the human activities correlated to the exploitation of minerals can lead to soil pollution and degradation. Furthermore, an extensive approach for soil and sediment monitoring can be evaluated using various statistical approaches. Over the past decade, different multivariate approaches in different affected areas have been evaluated continuously, in order to characterize any abnormal changes in elemental distribution in automorphic and alluvial soil samples. Therefore monitoring activities have been implemented in a very specifically affected area (with mining and ore processing activities). This area has been monitored for the soil contents of 69 elements: Ag, As, Al, Au, B, Ba, Be, Bi, Br, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Hf, Hg, Ho, I, In, Ir, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, Os, P, Pb, Pd, Pr, Pt, Rb, Re, Rh, Ru, Sb, Sc, Se, Sm, Sn, Sr, Ta, Tb, Te, Ti, Th, Tl, Tm, V, W, Y, Yb, Zn, and Zr. Multielement contents were determined in top/bottom samples of automorphic and alluvial soil. Basic descriptive statistics of the data set was applied for primary data processing. A bivariate statistics methodology was applied for elements content correlations. Scatter plots were applied in order to examine the relations between two data sets. Multivariate statistics was also used in order to extract the geochemical associations of the dominant chemical elements distributed in the surface soil. Factor analysis was applied to the data sets, which were standardized to a zero mean. The main goal of the applied processing techniques was to extract the dominant marker groups. Box-Cox data transformation was applied for the total data set. Statistical data processing software Statistica 11.0 was applied for all of the mentioned processing methods.
Quantifying the Impact of Complementary Visual and Textual Cues Under Image Captioning

Describing an image with natural sentence without human involvement requires knowledge of both image processing and Natural Language Processing (NLP). Most of the existing works are based on unimodal representations of the visual and textual contents using an Encoder-Decoder (EnDec) Deep Neural Network (DNN), where the input images are encoded using Convolutional Neural Network (CNN) and the caption is generated by a Recurrent Neural Network (RNN). This paper dives into a basic image captioning model to quantify the impact of multimodal representation of the visual and textual cues. The multimodal representation is carried out via an early fusion of encoded visual cues from different CNNs, along with combined textual features from different word embedding techniques. The resultant of the multimodal representation of the visual and textual cues are employed to train a Long Short-Term Memory (LSTM)-based baseline caption generator to quantify the impact of various levels of complementary feature mutations. The ablation study involves two different CNN feature extractors and two types of textual feature extractors, shows that exploitation of the complementary information outperforms the unimodal representations significantly with endurable timing overhead.
Analyzing Machine Learning Algorithms for Sentiments in Arabic Text

The studies addressing the application of machine and deep learning models to analyze the sentiments of Arabic online reviews related to the real-estate and automobile fields are not mature. To fill this gap, this research has focused on classifying three types of sentiments in Arabic real-estate and automobile online reviews, which are negative, positive, and mixed sentiments. The research focused on analyzing the reviews written in both Gulf Cooperation Council (GCC) dialects and modern standard Arabic (MSA). The research also explained the natural language processing strategies that were adopted to prepare the text for classification. The research discussed the details of collecting and annotating the data, preprocessing procedures, and feature selection methods. Following this, the research highlighted the adopted strategies for balancing and splitting the datasets, and it showed the analysis of the classification results for both machine and deep learning models. Finally, the suggestions for future work were provided in this research.
Development of an Automatic Robotic Procedure for Machining of Skull Prosthesis

The project presented in this paper develops within the field of automation in the medical-surgical sector. It aims at automating the process for the realization of prosthetic devices for the skull in cranioplasty, following a craniotomy intervention for brain tumor removal. The paper puts emphasis on the possibility to create the prosthetic device in run-time during the surgery, in order to ease the work that surgeons have to do during the operation. Generally, a skull prosthesis is realized before the day of the intervention, based on the plan of the medical operation, on the results of computed tomography, and through image processing software. However, after the surgery is performed, a non-negligible geometrical uncertainty can be found between the part of the skull actually removed and the cut planned during the preliminary analysis, so that the realized prosthesis (or even the skull, at worse) may need to be retouched. This paper demonstrates the possibility to introduce a fully automated process in a hospital environment, to manufacture in runtime the prosthetic operculum, relying on the actual geometry of the incision of the skull detected during the intervention. By processing a 3D scan of the skull after the craniectomy, a digital model of the prosthesis can be created and then used as an input to generate the code to be run by a robotic system in charge of the workpiece machining. Focusing on this second step, i.e., the manufacturing process, the work describes the way the dimensions of the raw material block are automatically selected, and the way robot trajectories for milling operation are automatically generated. Experimental validation demonstrates the possibility to complete the prosthesis within the surgery time, thus increasing the accuracy of the produced prosthesis and consequently reducing the time needed to complete the operation.
The Extreme Value Evolving Predictor

This article introduces a new evolving fuzzy-rule-based algorithm for online data streams, named extreme value evolving predictor (EVeP). It offers a statistically well-founded approach to define the evolving fuzzy granules that form the antecedent and the consequent parts of the rules. The evolving fuzzy granules correspond to radial inclusion Weibull functions. They are interpreted by the extreme value theory as the limiting distribution of the relative proximity among the rules of the learning model. Regarding the parameters of the Takagi–Sugeno term at the consequent of the rules, the algorithm enhances the already demonstrated benefits of multitask learning by replacing a binary version with a fuzzy structural relationship among the rules. The pairwise similarity among the rules is automatically provided by the current interaction of the evolving fuzzy granules at the antecedent and at the consequent parts of their corresponding rules. Several computational experiments, using artificial and real-world time series, attest to the dominating prediction performance of EVeP when compared to the state-of-the-art evolving algorithms.
Privacy in Society: Jewish Law Insights for the Age of Big Data

This Article makes the counterintuitive argument that Jewish law’s millennia-old approach to regulating visual and aural surveillance, the protection of communications, and information collection, sharing, and use, offers important frameworks for protecting privacy in an age of big data and pervasive surveillance. Judaism views privacy as a societal obligation, and employs categorical behavioral and architectural mandates that bind all of society’s members. It limits waiver of these rules, and rejects both technological capacity and the related notion of “expectations” as determinants of privacy’s content. It assumes the absence of anonymity, and does not depend on the confidentiality or secrecy of information or behavior witnessed or overheard; whether or not knowledge is later used or shared; or whether the privacy subject can show concrete personal harm. And when certain types of sensitive information is publicly known, or can’t help but be visible, Jewish law still provides rules against its use.The modern approach to privacy has failed. Notions of individual “rights to be left alone” and “informational self-determination,” offer little defense against rampant data collection and aggregation. The substantive promise of a “fundamental human right” of privacy has largely been reduced to illusory procedural safeguards of “notice” and “consent”—manipulable protections by which individuals “agree” to privacy terms with little understanding of the terms of the bargain, or power to negotiate or opt out. Jewish law offers a language that can enrich ongoing policy debates. It suggests a move from individual control over information as the mechanism for shaping privacy’s meaning and its enforcement, to a regime of substantive obligations on all societal members—personal and organizational—to protect privacy. It recognizes the interconnected nature of human interests, and comprehends the totality of the harm pervasive surveillance wreaks on both individuals and social relations. It offers a conceptual basis for extending traditional privacy protections to online spaces and new data uses. And it provides a language of dignity that recognizes unequal bargaining power; rejects the aggregation and use of information to create narratives and produce judgments that confine personal growth and free choice; and demands equal protection for all humans.
Residual dense U‐Net for abnormal exposure restoration from single images

Digital imaging devices sometimes capture images with abnormal exposure because of the complex lighting conditions and limited dynamic range of luminance. In this work, a new residual dense U-Net is proposed to predict the information that has been lost in saturated image areas, to enable abnormal exposure restoration from a single image. Full advantage of the multi-level features is taken from all the convolution layers in the restoration process. Specifically, the densely connected convolutional layers are used in a contracting encoder net to extract abundant local features. The transition layer and local residual learning after each dense block is then applied to adaptively learn more effectively from prior with present local features. Further, an expanding decoder net with dense layers is used and added with skip connections to preserve low-level information and existing details. Finally, multiple global residual learning is used to adaptively extract hierarchical features and help train the network. It is shown that such a network can be trained end-to-end from abnormal exposure images and outperform the prior best method on image enhancement. Experimental results show that the proposed model can greatly enhance the dynamic range of an abnormal exposure image.
Towards a Simulation Framework for Smart Indoor Spaces

The effectiveness of sensor-based applications for smart homes and smart buildings is conditioned upon the deployment configuration of their underlying sensors. Real-world evaluation of alternative possible sensor-deployment configurations is labor-intensive, costly, and time-consuming, which implies the need for a simulation-based methodology. In this work, we report on such a methodology that supports the modeling of indoor spaces, the activities of their occupants, and the behaviors of different types of sensors. We argue that, in order for a simulation to be useful for the purpose of evaluating a sensor deployment configuration, it has to generate realistic event streams of individual sensors over time, as well as realistic compositions of sensor events within a time window. We have evaluated our simulator for smart indoor spaces, SIMsis toolkit, in the context of our Smart-Condo ambient-assisted living platform, supporting the observation and analysis of activities of daily living (ADLs). Our findings indicate that SIMsis produces realistic agent traces and sensor readings, and has the potential to support the process of developing and deploying sensor-based applications.
Building Stereoscopic Zoomer via Global and Local Warping Optimization

Magnifying left and right images by increasing focal length of optical zoom lenses is a direct way for stereoscopic zoom. However, without adjusting the baseline distance, the optical zoom may distort the three-dimensional (3D) perception. To tackle this problem, this article presents a stereoscopic zoomer that directly operates on image plane by synthesizing a virtual camera in depth direction to simulate the zoom process. Specifically, we propose a two-stage warping optimization framework for zooming, which is solved by imposing global and local energy constraints. Different from the existing 3D rendering methods, the important innovation of our method is to take content zoom, depth adaptation and user editability into account in mesh optimization. Experimental results demonstrate that our method can achieve promising zoom effect on daily person scenes and street views.
Compress Blocks or Not: Tradeoffs for Energy Consumption of a Big Data Processing System

Currently, in addition to the performance, the energy consumption (hereinafter EC) of jobs running in a big data processing system is also of interest to academia and industry because it grows rapidly as an increasing amount of data is processed. Many studies focus on the EC optimization of jobs from the perspective of computation, which is specific to the algorithms in each job. However, the part of EC involved in I/O operations, which is general and universal, is mostly ignored in optimization. In this paper, we concentrate on the EC optimization of jobs from the perspective of I/O operations. To save energy, we argue that data compression could be exploited. On one hand, energy is saved by processing compressed data with less I/O cost. On the other hand, extra EC is incurred from the necessary data compression/decompression process, which may offset the saved energy. Therefore, there are tradeoffs to consider when determining whether to compress data for these jobs. In this paper, such tradeoffs and boundary conditions are studied. We first abstract a paradigm for the runtime environment of big data processing jobs. Then, we establish the power, jobs, compression, and I/O models in detail. Based on these models, we discuss the compression tradeoffs and derive the boundary conditions for EC optimization. Finally, we design and conduct experiments to validate our proposition. The experimental results confirm that the tradeoffs and boundary conditions exist for typical jobs in MapReduce and Spark. As explained, first, the EC of a job is reduced using data compression. Second, whether or not such optimization occurs is related to the specification of both the compression algorithm and the job and is determined by corresponding boundary conditions. Third, for a compression algorithm, the larger its compression/decompression speed and the better its compression ratio, the more likely it is to achieve EC optimization.
Constant-Round Blind Classical Verification of Quantum Sampling

In a recent breakthrough, Mahadev constructed a classical verification of quantum computation (CVQC) protocol for a classical client to delegate decision problems in $$\mathsf {BQP}$$ to an untrusted quantum prover under computational assumptions. In this work, we explore further the feasibility of CVQC with the more general sampling problems in BQP and with the desirable blindness property. We contribute affirmative solutions to both as follows. Applying our compiler to (a parallel repetition of) Mahadev’s CVQC protocol for $$\mathsf {BQP}$$ and our CVQC protocol for $$\mathsf {SampBQP}$$ yields the first constant-round blind CVQC protocol for $$\mathsf {BQP}$$ and $$\mathsf {SampBQP}$$ respectively, with negligible and inverse polynomial soundness errors respectively, and negligible completeness errors.
MPQ-YOLACT: Mixed-Precision Quantization for Lightweight YOLACT

Segmentation network, which are widely used in various multimedia applications in recent years, are capable of precise image processing in units of pixels, but have higher computational complexity compared to other computer vision algorithms such as classification and object detection. In this paper, we propose a technique that applies mixed-precision quantization to the existing YOLACT network, which performs accurate instance segmentation. By adaptively applying quantization according to the parameter size and the effect on the accuracy of modules in YOLACT, it is possible to significantly reduce the network size while maintaining the accuracy of YOLACT as much as possible. The experimental results show the parameter size of the entire network is reduced by 75.4% with only a negligible drop in accuracy of less than 0.1%.
MONETARY POLICY, FINANCIAL DEVELOPMENT AND INCOME INEQUALITY IN DEVELOPING COUNTRIES

The paper empirically examines the redistributive effect of monetary policy and assesses whether financial development plays any role in shaping monetary policy — inequality relations in developing countries. We uncover evidence supporting the redistributive consequences of monetary policy especially in more financially developed countries. We further note that while financial development raises income inequality in countries with low financial development, it leads to a reduction in income inequality in high financial development countries. As a side result, economic growth contributes favorably to income equalization in these countries. Finally, such financial indicators as financial access, financial efficiency and financial stability also condition the impacts of monetary policy on income distribution, although they are independently insignificant. Our results hint that the improvements in information and in efficiency rather than depth and access that would attenuate the negative impacts of contractionary monetary policy on income distribution.
Gaussian processes centered at their online average, and applications

We establish various properties of Gaussian processes centered at their online average and discuss their application to goodness-of-fit testing.