Reviewerly/exHarmony huggingface dataset:
The sensitivity of a quantum PageRank

In this study, we discuss the sensitivity of a quantum PageRank. By utilizing the finite-dimensional perturbation theory, we estimate the change of the quantum PageRank under a small analytical perturbation on the Google matrix. In addition, we will show the way to estimate the lower bound of the convergence radius and the error bound of the finite sum in the expansion of the perturbed PageRank.
Address Authentication Method for Sustainable Social Qualification

This paper proposes an address authentication method based on a user’s location history. Address authentication refers to actual residence verification, which can be used in various fields such as personnel qualification, online identification, and public inquiry. In other words, accurate address authentication methods can reduce social cost for actual residence verification. For address authentication, existing studies discover the user’s regular locations, called location of interest (LOI), from the location history by using clustering algorithms. They authenticate an address if the address is contained in one of the LOIs. However, unnecessary LOIs, which are unrelated to the address may lead to false authentications of illegitimate addresses, that is, other users’ addresses or feigned addresses. The proposed method tries to reduce the authentication error rate by eliminating unnecessary LOIs with the distinguishing properties of the addresses. In other words, only few LOIs that satisfy the properties (long duration, high density, and consistency) are kept and utilized for address authentication. Experimental results show that the proposed method decreases the authentication error rate compared with previous approaches using time-based clustering and density-based clustering.
Deep Diamond Re-ID

Re-identification neural networks are widely used in numerous applications such as crowd control, crime investigations, safety systems and even in most smartphones to unlock the phone with a picture of the owner. These techniques are mostly used to re-identify faces or persons but in this paper we investigate the possibility to adapt these to also re-identify similar looking objects such as diamonds. Since polished diamonds are very similar to the naked eye, it is difficult to distinguish one diamond from another. We have indications that diamonds are sometimes switched by trained switchers with fake or less expensive stones, while they pretend to inspect the stone. A solution to this is diamond fingerprinting. We therefore propose a technique to generate a unique ID for each stone, which allows to re-identify the diamond solely based on an image of the gem. Since each diamond is assigned a unique ID it is even possible to keep track of the diamonds over time. This allows the seller to verify his stones before and after trading while switchers don't stand a chance. For this task we trained and adapted a classification network optimized for both speed and accuracy.
A Mean Model Based Incremental Learning Technique for Extreme Learning Machine

Development of efficient methods for processing the online data is interesting as well as challenging in the computing scenario of data intensive applications, especially in streaming. Because of the inherent computational nature of Extreme Learning Machine, the non-iterative learning mechanism, it has undergone serous studies and exploitation especially in the domain of online data analysis. This paper gives a simple and effective strategy for updating the learned parameters, the knowledge of a machine learning system, in a computationally better perspective comparing with the existing state-of-the-art learning techniques. The performance of different measures for the central tendency is analyzed in this work. It is found that the results are comparable in respect of normal models. The overall performance of the proposed method with LIBSVM data sets gives an interesting direction for the improvement of learning machines for online data processing.
Expected Number of Dice Rolls for the Sum to Reach <i>n</i>

"Expected Number of Dice Rolls for the Sum to Reach n." The American Mathematical Monthly, 127(3), p. 257
Capability Assessment of Networking Information-Centric System of Systems: Review and Prospect

This paper reviews the research progress of capability assessment of the networking information-centric system of systems. The concept, characteristics and capability structure of the networking information-centric system of systems are summarized, and the challenges of capability assessment are analyzed. For the problem that the nonlinear characteristics of networking information-centric system of systems capabilities are difficult to evaluate, the idea of using machine learning methods to solve this problem is proposed, and the characteristics and applications of these methods are analyzed.
Separable Binary Convolutional Neural Network on Embedded Systems

We have witnessed the tremendous success of deep neural networks. However, this success comes with the considerable memory and computational costs which make it difficult to deploy these networks directly on resource-constrained embedded systems. To address this problem, we propose TaijiNet, a separable binary network, to reduce the storage and computational overhead while maintaining a comparable accuracy. Furthermore, we also introduce a strategy called partial binarized convolution which binarizes only unimportant kernels to efficiently balance network performance and accuracy. Our approach is evaluated on the CIFAR-10 and ImageNet datasets. The experimental results show that with the proposed TaijiNet, the separable binary versions of AlexNet and ResNet-18 can achieve 26× and 6.4× compression rates with comparable accuracy when comparing with the full-precision versions respectively. In addition, by adjusting the PCA threshold, the xnor version of Taiji-AlexNet improves accuracy by 4-8 percent comparing with other state-of-the-art methods.
Armenia: Communicating to World Community in Electronic Test and Design

During its 4000 years old history, Armenia was situated at a cultural, historical, and religious intersection and located at the crossroads between Europe and Asia. Today, it is located in the southern Caucasus between the Black and Caspian Seas, bordered on the north and east by Georgia and Azerbaijan, and on the south and west by Iran and Turkey. Armenia has a strong cultural and scientific heritage for thousands of years. It was the first state to adopt Christianity as a state religion, in 301 AD. The Ararat Valley of Armenia is believed to be inhabited since the Stone Age, making it is one of the oldest settled regions in the world.
Collaborative Semantic Segmentation with Image Labels

Weakly-supervised semantic segmentation has recently received much attention since it needs less fine-grained annotations than fully-supervised learning. Most existing studies use attention maps from the classification network as supervision, which suffers from only locating small discriminative parts of objects and lacking precise boundaries. In this study, we propose a collaborative segmentation network consisting of a localization sub-network and a segmentation sub-network. The localization sub-network takes only image-level labels as supervision. The pseudo masks generated by the segmentation sub-network and the localization sub-network, providing rough localization information of objects and the shape information respectively, are mixed-up adaptively and used as supervision for the segmentation sub-network. The two sub-networks share the same backbone and are shown to mutually enhance each other during training. We evaluate the proposed method on PASCAL VOC 2012 Semantic Segmentation benchmark and achieve new state-of-the-art.
Vitality Analysis of the Linguistic Atlas of Brazil on Twitter

The diversity of the linguistic reality of Brazil is analyzed through the Linguistic Atlas of Brazil (ALiB). ALiB’s first publication covered 25 capitals of Brazil into the volume 1 and 2 of the Atlas. Taking into account the semantic-lexical map from ALiB, this paper analyzes the vitality of the ALiB terms through Twitter. Two methods are proposed: one lexical that deals with the lexical variations of ALiB terms on Twitter, and the other reflects the disambiguation of Twitter terms with similar meaning to those of the project. The results highlight that some terms are no longer used or not employed in a social network. On the other hand, some terms cataloged by ALiB continue to be used by Brazilians even in social media.
Establish a Medical Image Analysis and Identification Platform

The proportion of China’s 65-year-old population has increased year by year. As we enter old age, people’s average life expectancy increases year by year. The risk of disease in the elderly population increases. According to the statistics of the Ministry of Health and Welfare of Taiwan, most people over the age of 65 suffer from cancer and heart disease. Pneumonia, cerebrovascular disease, and diabetes. With medical advancement, more and more disease characteristics can be found in medical imaging. With the development of technology, the higher the resolution of the image, the more information that can be contained, and the doctor will spend more time marking and diagnosing. There is currently no open imaging training and recognition platform for users to use, so this study uses deep learning CNN technology to construct an image analysis and recognition platform with analysis and recognition interface. In the future, retraining and batch identification capabilities can be added to complete the integrity of the platform.
A Modified Differential Evolution Algorithm for Constrained Optimization Problems

Many engineering design problems can be transformed into constrained optimization problems (COPs) which are usually very challenging for optimization algorithms. Differential Evolution (DE) has better global searching ability than traditional optimization algorithms. However, the drawbacks are also obvious: (1) The performance of DE depends largely on parameters. (2) Constraint handle technique (CHT) is necessary for COPs since DE is essentially an unconstrained optimization algorithm. This study sets up a stable optimization algorithm for COPs by combining Differential Evolution Algorithm with Strategy Adaptation (SADE) and a novel CHT. This paper proposes a simple and easy repair strategy (RP), which needn't to introduce other parameters or change the structure of algorithm. The specific operation is to repair the non-feasible solution by constructing sub-problems. To verify its superiority, the proposed Repair Strategy-Differential Evolution Algorithm with Strategy Adaptation (RP-SADE) was applied to the 2018 IEEE Congress on Evolutionary Computation (CEC2018) and one engineering application. The results show that the proposed RP-SADE has good comprehensive performance by considering computational efficiency and convergence accuracy.
Distributed Adaptation of the Functional Integration Structure of a Multi-agent System in a Dual-tasking Environment

The paper considers the problem of distributed adaptation of the functional integration structure of a multi-agent system in a dual-tasking environment from the point of view of organizing multi-agent search and use of the functional emergence effect provided by different structures of functional integration.The considered problem belongs to a wider class of problems of structural adaptation and self-organization.Models of functional integration, in particular, models based on general quantitative characteristics of the functional roles distribution of agents and models based on local qualitative characteristics of the functional roles distribution of agents, taking into account the specifics of functional links established between agents have been considered in the paper.The problems of the distributed adaptation of the functional integration structure have been analyzed, including the problem of the functional specialization of agents in a multitasking environment.Various ways of organizing structural changes have been considered, including multi-agent parametric adaptation based on a local structural parameter.Multi-agent structural adaptation based on reinforcement learning methods, in particular, multi-agent structural adaptation based on the normalized exponential function method (MSA-softmax) and multi-agent structural adaptation based on the upper confidence bound method (MSA-UCB) has been proposed.The distributed adaptation methods simulation results have been presented, which showed the advantage of multi-agent structural adaptation over multi-agent parametric adaptation.
Active Learning Strategies for Hierarchical Labeling Microtasks

This paper reports the result of a preliminary experiment on active learning strategies for the hierarchical labeling microtasks. A typical example of hierarchical labeling microtask consists of a set of labeling tasks for partitions of a large image; starting from the whole image, the workers choose to give a label or divide it into smaller ones. This paper shows the result of an experiment to compare several strategies for active learning in the setting. The result suggests that the difference in the strategies affects the performance in the early stage.
Towards ontology editing, querying and visualization in .net environment

In this paper, a .net based ontology editor has been proposed. Despite the growing interest in ontology engineering, a review of current literature reveals that most of the developments have been mainly concentrated on open source platforms. The feedback obtained from .net developers show their disappointment over current progress in the domain of semantic web and raises the need for urgent response for catering to current demands. Realizing this research gap, this paper proposed a .net based tool for ontology development and editing (TODE). Based on the developer's feedback, it has been identified that visualization and query management are one of the most important demands of .net semantic web community. Therefore, TODE provides integrated support for these features. The evaluation of proposed tool has been performed by different usability experts, which concluded that TODE provides a convenient approach for ontology development to end-users.
QualiFood: An Intelligent Quality Food Evaluation Using Logical Satisfiability Reasoning On Spark

There is an urgent need to address unhealthy dietary patterns for people. Therefore, having a high food quality is essential to health by adopting good eating habits. Furthermore, the evaluation of food quality is complex because it needs huge numbers of information related to food, for example its ingredients, the environmental conditions of food productions, the consumer state of health. Such information are scattered on multiple and non communicating systems including food producer systems, open data, medical data, etc. Applying the semantic to a very large collection of information from different data sources can highly contribute to enhance the food quality score. Hence, it will improve the individual diet/health by giving adequate nutritional recommendation and avoiding inconsistent food mixing by following set of rules.In this paper, we propose an intelligent and scalable approach to ensuring the food quality for meal healthiness query answering over big data related to food in a distributed way on a Spark ecosystem. For that, the cleaning inconsistent and contradictory big data approach is built by following the steps (1)modeling the consistency rules including inference and inconsistent rules (2) detecting inconsistency through rule evaluation on Apache Spark framework to discover the minimally subset of inconsistent data (3) cleaning the inconsistency through finding the cleaned meals for consistent query answering.
Cyber Landscape of Trust, Fear and Surveillance Concerns: How Slovenians Around the Globe Perceive the Cyberspace

Purpose:The purpose of this paper is to study the differences between countries regarding their residents’ trust in government, fear of government intrusions into their privacy and government surveillance concerns in the cyberspace.Design/Methods/Approach:A survey has been conducted to capture the perceptions of Slovenians around the globe. Respondents from 58 countries were reached (n = 629) although the results were reported only for countries with at least three respondents. Descriptive statistics were used to describe the sample and measured variables. Graphic illustrations made with MapChart are used to visualize the results.Findings:The findings of our study show that perceptions of trust in government, fear of government intrusions into the privacy of country residents and government surveillance concerns vary from country to country. Countries are ranked according to these three criteria. The average trust in government seems to be relatively low. It appears that respondents moderately fear government intrusions into their social network accounts and seem to be concerned about government surveillance over their online activities.Research Limitations / Implications:The research contributes to an understanding of the perceptions of Slovenians around the world of trust in government, fear of government intrusions and government surveillance concerns. Although a limited number of countries was reached, the results present some interesting insights into different regions of the world. The study targeted the population of Slovenians around the world thus the readers should be extremely cautious when trying to generalize the results, also due to snowball sampling employed.Originality/Value:This paper presents one of the first studies on perceptions of Slovenians around the world regarding their trust in the government of the country of their residence, fear of government intrusions into their privacy and their government surveillance concerns.
A New Approach for Matrix Completion Using Arrow Relation of FCA in Recommender Systems

In this digital world, the Internet is the major source of rich amount of data where there are large numbers of choices available to the internet users. It requires the attention to scrutinize, itemize, and effectively project the essential information in order to violate the dispute of information overload. The information overload is an obstacle for many of the internet users. Recommender systems aim to provide solution to such obstacles by refining and seeking the large volume of exponentially growing information to provide the internet users with the personalized content and services. Here finding or predicting the user-item rating with the sparse data available in the rating matrix of the input data becomes a challenging task. This paper explores the problem of recommendation by presenting a FCA-based methodology which uses the mathematical tool in reducing the dimensionality of the user-item rating thereby determining the rating value of the unknown user with its corresponding item. The proposed method is simple and faster explained with an illustration to complete the user-item rating matrix from which the rating can be predicted by applying any of the recommendation algorithms.
Scalable Data Parallel Approaches to Anomaly Detection in Climate Data using Gaussian Processes

Anomaly detection on large scale spatio-temporal data such as climate data is a challenging task depending on the spatial and temporal resolution and autocorrelation of the data. When considering global gridded daily temperature data, the number of locations and the length of time period considered makes anomaly detection a big data problem. Gaussian Process (GP) Learning is a method that is well-suited to identify the complex spatial and temporal autocorrelation properties of spatio-temporal data. One of the primary challenges with using GP is the computational complexity associated with inverting a covariance matrix. This is further compounded when considering data on a national/global scale and performing anomaly detection using such methods often requires dedicated high performance computing platforms. In this paper, we describe a purely temporal scalable anomaly detection technique for gridded temperature data based on GP Learning that ignore the spatial autocorrelation between neighboring grids and perform anomaly detection on each of the grids in parallel, thereby reducing the execution time. We introduce three methods: a standalone data parallel approach using a single GPU, a distributed memory version on multi-node clusters using MPI, and a mixed parallel approach using multiple GPUs. In comparison to a sequential approach, they are 17.2x, 47.1x, and 88.9x faster, respectively.
Design and Performances of Quasi-logarithmic Quantizer for Mean Coding Bit Rates

This paper investigates quasi-logarithmic (QL) quantizers with two approaches: first is for constant variance σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =1 and second is switched QL quantizer, that implies splitting the variance range into subranges and designing a separate quantizer for each of them. For both approaches our goal is to design the optimal quantizer with predetermined mean coding bit rate, i.e. to find the quantizer parameters that lead to the highest Signal-to-Quantisation-Noise-Ratio (SQNR).
Salient Object Detection With Residual Refinement Network

The salient object detection task has recently been further developed based on deep convolution neural networks. However, it's still challenging to extract effective features and obtain clear boundaries of the salient objects. An often-used way to enhance performance is to directly aggregate multilevel convolutional features. In this paper, We put forward a prediction-refinement architecture to detect salient regions. The saliency estimation network(E) integrates low-level structural feature and high-level context information to make estimations. While the high-level feature is capable of locating salient regions, the low-level feature can capture more detailed information to estimate salient pixels correctly. Furthermore, We bring forward a residual refinement network(R) with novel downsample and upsample layers to optimize the saliency prediction generated from E. R refines the coarse saliency detection results by learning the residual between the saliency prediction and ground-truth. The prediction-refinement architecture progressively generates saliency maps with high-quality boundaries. Experiments indicate that this method notably outperforms most previous saliency approaches.
Tuning as convex optimisation: a polynomial tuner for multi-parametric combinatorial samplers

Abstract Combinatorial samplers are algorithmic schemes devised for the approximate- and exact-size generation of large random combinatorial structures, such as context-free words, various tree-like data structures, maps, tilings, RNA molecules. They can be adapted to combinatorial specifications with additional parameters, allowing for a more flexible control over the output profile of parametrised combinatorial patterns. One can control, for instance, the number of leaves, profile of node degrees in trees or the number of certain sub-patterns in generated strings. However, such a flexible control requires an additional and nontrivial tuning procedure. Using techniques of convex optimisation, we present an efficient tuning algorithm for multi-parametric combinatorial specifications. Our algorithm works in polynomial time in the system description length, the number of tuning parameters, the number of combinatorial classes in the specification, and the logarithm of the total target size. We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called Pólya specifications. We show how our method can be adapted to a broad range of less typical combinatorial constructions, including symmetric polynomials, labelled sets and cycles with cardinality lower bounds, simple increasing trees or substitutions. Finally, we discuss some practical aspects of our prototype tuner implementation and provide its benchmark results.