Reviewerly/exHarmony huggingface dataset:
A Novel XOR-Based Threshold Visual Cryptography with Adjustable Pixel Expansion

A ( k , n ) visual cryptography (VC) scheme encodes a secret image into n shadows that are printed on transparencies distributed among a group of n participants secretly, and reveal the secret image by stacking no less than k of them. Its decryption requires no computation and attracts much attention in image security applications. The pixel expansion and contrast are two important characteristics to evaluate the visual quality of the revealed secret image for a ( k , n ) -VC scheme. The ( k , n ) XOR-based VC (XVC) schemes can greatly improve the visual quality including both pixel expansion and contrast. Previous methods require complex computation and result in high pixel expansion when they are used to construct such schemes. In this paper, we propose a pixel expansion adjustable ( k , n ) -XVC scheme, which allows pixel expansion to be changed among 2 k - 1 - 1 different values. It can ensure each pixel being exactly recovered with the same average contrast no matter it takes any pixel expansion value. The least pixel expansion is much smaller than previous schemes. Our scheme can be easily implemented based on any conventional OR-based ( k , n ) -VC (OVC) scheme.
An adaptive integral separated proportional–integral controller based strategy for particle swarm optimization

Particle swarm optimization algorithm (PSO), which updates the particle by the linear summation of the particle's past momentum and current search direction, has demonstrated its power in many optimization applications. However, few researches have focused on the overshoot problem of PSO caused by past momentum, which may result in an oscillation search and slow convergence speed on complex optimization problems. Based on the connection between the PSO optimization process and the PID controller based control system, we first analyze the effect of the momentum in PSO, then we find that the oscillation problem relates to the gathering of the momentum term and the current search direction. Inspired by the conditional integration in automatic control, we propose an adaptive search direction learning approach for PSO, namely ISPSO (Integral Separated PI controller-based PSO). The ISPSO separates momentum term adaptively when the current search direction is consistent with historical momentum direction, and then the IS strategy will guide particles to fly to better and steady directions by eliminating the integral gathering of historical momentum. We select seven main-stream approaches as control group, and conduct experiments on benchmark CEC2013 test suite. The experimental results of ISPSO, providing the faster steady global convergence and higher solution accuracy, show a significant improvement on PSO algorithm. Compared with the results of the seven methods, the performance of ISPSO is also promising in general, especially for unimodal and composition functions. Furthermore, our results validate the generalization and effectiveness of IS strategy by the designed experiments of PSO variants with and without IS strategy, which also indicates that the IS strategy can be applied to PSO variants with any topological structures.
Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems

Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce `allotaxonometry' along with `rank-turbulence divergence' (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of `type calculus', for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.
A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.
Feature Scoring using Tree-Based Ensembles for Evolving Data Streams

Assigning scores to individual features is a popular method for estimating the relevance of features in supervised learning. An accurate feature score estimation provides essential insights in sensitive domains, which is decisive to explain how features influence a given decision, contributing to the interpretability of the model. Learning from streaming data adds several challenges to machine learning tasks, including limited resources and changes to the underlying data distribution (i.e., evolving data streams). In this work, we introduce and analyze methods to efficiently estimate the Mean Decrease in Impurity (MDI) and COVER measures using ensembles of incremental decision trees. To achieve current scores in evolving data streams, we employ tree-ensembles that incorporate active drift detection. Experimental results show how MDI and COVER can be used to track the feature scores when their importance to the ensemble model shift over time. On top of that, we present the impact on the feature scores when the learning problem includes a non-negligible verification latency for the arrival of the labels. We also present a counter-intuitive experiment using a standard benchmark dataset where the feature scores correctly illustrate the importance of two features to the ensemble model. However, these features are prioritized due to biased split decisions, and in their absence, the model increases in predictive performance. We conclude that the presented measures can be used to understand the impact of features in the ensemble model better, still, such measures should be used with caution as they are limited by the underlying tree building and ensemble model biases.
The impact of human resource development practices on employee engagement and performance in Jordanian family restaurants

Given the organizational positive outcomes of human resource development (HRD) practices like organizational effectiveness, sustainable competitive advantage, and organizational commitment, the current study was designed to detect the impact of HRD practices (employee training, employee empowerment, employee promotion) as independent variables on employee engagement and employee performance as dependent variables and to notify those who are interested. A quantitative descriptive-analytical method was adopted for the current research paper. Data were collected conveniently via a questionnaire from employees of family restaurants. Usable responses were analyzed using IBM SPSS and AMOS on the strength of structural equation modeling (SEM).The current data identified employee training, employee empowerment, and employee promotion as key predictors of both employee engagement and employee performance. Employee empowerment has the highest impact on employee engagement, followed by employee promotion, while employee training has the highest effect on employee performance. Furthermore, employee engagement is positively related to employee performance.
Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user–item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but- not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.
An efficient framework for ensemble of natural disaster simulations as a service

Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors. Such large ensembles cannot typically be run on a single computer due to the limited computational resources available. Cloud Computing offers an attractive alternative, with an almost unlimited capacity for computation, storage, and network bandwidth. However, there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources. As such, this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud. The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models. We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements. The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.
Peak Power Shaving for Reduced Electricity Costs in Cloud Data Centers: Opportunities and Challenges

An electricity bill of a data center (DC) is determined not only by how much energy the DC consumes, but especially by how the consumed energy is spread over time during the billing cycle. More specifically, these electricity costs are essentially made up of two major charges: the Energy Charge, a cost based on the amount of consumed energy (in kWh), and a Peak Charge, a cost based on the maximum power (in kW) requested during the billing cycle. The latter charge component is forced to encourage DCs to balance and regulate their power demands over the billing cycle, allowing the utility company to manage congestion without increasing supply. This billing model has thus called for the development of peak power shaving approaches that reduce costs by smoothing peak power demands over the billing cycle to minimize the Peak Charge component. In this paper, we investigate peak power shaving approaches, and begin by using Google data traces to quantify and provide a real sense of how much electricity cost reduction can peak power demand shaving achieve on a Google DC cluster. We then discuss why peak power shaving is well-suited for reducing electricity costs of DCs, and describe two commonly used peak shaving approaches, namely energy storage and workload modulation. We finally identify and describe key research problems that remain unsolved and require further investigation.
Rise and Rise of Blockchain: A Patent Statistics Approach to Identify the Underlying Technologies

Researchers have always tried to identify the technological evolution of any particular technology with the help of patent statistics. Blockchain is one such technology which, although emerged more than a decade ago, but has been aggressively applied and studied within the past five years only. Therefore, this study is an attempt to identify the underlying technologies that eventually constitute the Blockchain technology. This paper identifies the literature related to the primary Blockchain technologies and then tries to perform basic patent statistics to identify the earliest innovation that conforms to the idea of blockchain that we know today. Critical Cooperative Patent Classification (CPCs) codes to analyze Blockchain patents in the future are identified. Specific and detailed analysis methods are suggested for the future Blockchain patent research.
Grid Map Construction and Terrain Prediction for Quadruped Robot Based on C-Terrain Path

In general, grid map based path planning algorithms are employed in the robotics arena. The algorithm uses a grid map to represent environmental information, standardized. Compared with feature maps and topological maps, the algorithm realizes the construction of environmental maps in a more direct way, and has the characteristics of fast, simple and efficient.The integration and prediction of terrain is an unavoidable problem and the traditional raster map prediction method is based on the research of the terrain data itself, and lacks dynamic supplement for the path planning process. When the environmental data changes, the classification algorithm can only be re-executed, and the past data is completely discarded. Since the planned path is unlikely to change, the terrain tends to be stable. To solve this problem, this paper proposes a concept of C(circular)-terrain band following path nodes and terrain construction and prediction methods. The C-Terrain method first obtains an ordered set of passing points at the initial moment, based on the complete path planning. Then an ordered sequence of influence function values is obtained, which depends on the selection of the terrain band and the adjustment of related parameters. Finally, regression methods such as machine learning are used to complete the prediction of the path and location terrain, and the unknown path and terrain are predicted. The experimental results prove the accuracy and practical value of the C-T method.
Science, data, and case-studies under the Third Science Revolution: some theoretical considerations

The Third Science Revolution described by Kristian Kristiansen (2014) has been openly embraced and is currently underway in archaeology. It has brought considerable improvement in terms of scientific methods and approaches, but at the same time, it brings with it the risk of transforming archaeology into something that is methodologically uniform, inflexible, and oversimplified, or in other words, a methodologically monistic discipline. This is particularly evident when it comes to Big Data: the Third Science Revolution has inaugurated a new understanding of data, one that reduces archaeological reality exclusively to those elements that are quantifiable. The aim of this paper is to demonstrate that archaeology needs to go beyond Big Data, and the Third Science revolution in general, and embody qualitative research. This can be done by incorporating methods and theories from history and anthropology that contextualize the purposeful character of past human action. This requires (re)embracing case-study research, but also recognizing a meaning of ‘case-study’ that has been largely ignored: as a paradigmatic example of a Zeitgeist - a context where different institutions, power relations, and ideologies are all entwined.
Visually guided classification trees for analyzing chronic patients

Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights.In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses.We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.
The Sparsity and Activation Analysis of Compressed CNN Networks in a HW CNN Accelerator Model

In this paper, we present the result of sparsity increase by CNN compression on 6 representative CNN networks including a famous localization CNN network VGG16-SSD-300. We will also show activation analysis result by applying the compressed CNN networks to a CNN HW accelerator model. Finally, this paper will be ended up with processing time estimation result of the CNN HW accelerator model which reflects the reduced transmission time of sparse weights.
Poster: Multilingualism as a Means of Students’ Technocommunicational Competence Forming at Engineering University

The accelerated rate of scientific development and technology contributes the expansion of products of intellectual activity exchange between countries and the formation of international scientific and technical relations. Scientific and technological progress has gone beyond the national wealth of a single state and has acquired an inter-ethnic character. With the development of modern forms of economic cooperation, the need for specialists with new communication capabilities is increasing. That is why an important task of engineering education is to ensure high quality language training based on multilingual approaches to training and considering the current and future needs of science and industry. In the framework of foreign language training at Kazan National Research Technological University the technocommunicational competence on the base of multilingualism is organized for developing the professional and technical communications. The technology of technocommunicational competence forming can be used in any engineering university, as well as in the system of supplementary vocational education.
Rethinking Object Detection in Retail Stores

The conventional standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in the context of warehouses due to severe occlusions among groups of instances of the same categories. In this paper, we propose a new task, i.e., simultaneously object localization and counting, abbreviated as Locount, which requires algorithms to localize groups of objects of interest with the number of instances. However, there does not exist a dataset or benchmark designed for such a task. To this end, we collect a large-scale object localization and counting dataset with rich annotations in retail stores, which consists of 50,394 images with more than 1.9 million object instances in 140 categories. Together with this dataset, we provide a new evaluation protocol and divide the training and testing subsets to fairly evaluate the performance of algorithms for Locount, developing a new benchmark for the Locount task. Moreover, we present a cascaded localization and counting network as a strong baseline, which gradually classifies and regresses the bounding boxes of objects with the predicted numbers of instances enclosed in the bounding boxes, trained in an end-to-end manner. Extensive experiments are conducted on the proposed dataset to demonstrate its significance and the analysis is provided to indicate future directions. Dataset is available at https://isrc.iscas.ac.cn/gitlab/research/locount-dataset.
Automatic drawing of customized removable partial denture diagrams based on textual design for the clinical decision support system

Qualified diagrams of removable partial denture (RPD) designs created by dentists provide technicians with clear and dynamic information. Generating RPD design in the clinical decision support system (CDSS) can be achieved by producing the RPD design in a textual format and then transferring the design onto diagrams. The drawing of RPD diagrams automatically and efficiently for the given textual designs is still under investigation. A new workflow consisting of three major steps is developed to produce and visualize two-dimensional RPD design diagrams. Annotations and orientations of teeth are established from the base maps in the first step, and built-in rules are then incorporated to describe the variations caused by the interactions of the RPD components. Finally, the software draws each component using a series of curve functions. To validate the performance of the software, 112 RPD clinical design plans are randomly selected as inputs for the software, and the outputs are independently verified by experienced clinicians. The proposed methods are proven to be efficient and accurate and thus can be used to improve clinical quality.