Reviewerly/exHarmony huggingface dataset:
Target search for multiple mobile robots based on A* path planning algorithm

In this paper, we show that multiple mobile robots are necessary to search for targets in clutter environments. In order to avoid the obstacles and to find the shortest route of the hunting goal, an algorithm for route planning for mobile robots is used. The algorithm uses an evaluation function (including cost functions and heuristic functions) to determine the path. Finally, the simulation result by MATLAB showed good performance of the algorithm.
한·중 대학 무용학과 교과과정으로 본 민족무용 교육 비교 연구

The purpose of this study is to present characteristics by comparing the operation of the ethnic dance curriculum among the curriculum of the department of dance in Korea and China. The ethnic dance curriculum at Korean universities is established as a step-by-step in-depth course, and creative dance courses using ethnic dance are operated. The ethnic dance curriculum at Chinese universities has a variety of ethnic dance courses and courses on teaching methods for each ethnic dance. The Korean ethnic dance curriculum is a curriculum for the transmission of future-oriented national dance. The ethnic dance curriculum in China is a past-oriented curriculum.
PenShaft: Enabling Pen Shaft Detection and Interaction for Touchscreens

PenShaft is a battery-free, easy to implement solution for augmenting the shaft of a capacitive pen with interactive capabilities. By applying conductive materials in a specific pattern on the pen’s shaft, we can detect when it is laid on a capacitive touchscreen. This enables on-pen interactions, such as button clicks or swiping, and whole-shaft interactions, such as rotating and dragging the stylus. PenShaft supports at least six interactions, freeing the pen from having to interact with the layers or menus of a conventional user interface. We validate a device’s capability to detect these six interactions with all but two interactions achieving a success rate above 95%. We then present a series of applications to demonstrate the flexibility and utility of these interactions when using the pen.
Measurement of the angular motion parameters of the mirror of the scanning device

A scanning device for a space-based environmental monitoring system has been investigated. The main attention is paid to the study of the parameters of the angular motion of the mirror of the scanning device, the uniformity of rotation of which largely determines the quality of the image of the Earth's surface. The principle and results of measuring the parameters of the mirror rotation carried out in a wide angular range are considered. The measurements were performed using a dynamic goniometer-autocollimator, which has been calibrated at the State Standard of Plane Angle Unit GET 22-2014. The repeatability of the average angular velocity of the scanning device mirror and the repeatability of the initial scanning angle are calculated. Nonstationarity in mathematical expectation and variance in random deviations of the angular motion of the mirror from the linear law of scanning is noted. The use of wavelet analysis revealed the frequency of excitation of oscillations in the low-frequency region of the spectrum. The possibility of using the a dynamic goniometer-autocollimator for measuring not only the angular position of the scanning device mirror, but also the angular velocity is shown.
A New ID-Based Threshold Proxy Signature Scheme

The existing ID based threshold proxy signature schemes have the problem of key escrow. Based on identity based signature without trusted PKG, an identity based threshold proxy signature is constructed. This scheme prevents PKG from forging the signature of legitimate users, because PKG only generates part of the private key. The new ID based threshold proxy signature scheme solves the key escrow problem, it also meets all the security requirements of the threshold proxy signature scheme.
Research on English Informatization Practical Teaching in Independent Colleges Based on Constructivism Learning Theory under the Background of Internet Big Data: A case of the Informatization Practical Teaching of Integrated English Course for English Major

Combining the characteristics of the integrated English course, the author adopted ANOVA to analyze the students' using status of online teaching and learning platforms. Based on constructivist teaching concept, the author constructs an informatization practical teaching model under the background of Internet big data, and explores a new way to reform curriculum teaching of integrated English course in order to effectively improve the quality of curriculum teaching.
A complete characterization of the optimal unitary attacks in quantum cryptography with a refined optimality criteria involving the attacker’s Hilbert space only

Fuchs et al. (Phys Rev A 56:1163, 1997, https://doi.org/10.1103/PhysRevA.56.1163 ) suggested an optimal attack on the BB84 protocol, where the necessary and sufficient condition for optimality involves the joint Hilbert space of the sender and the attacker. In this work, we propose a refined optimality criteria involving the Hilbert space of the attacker only. It reveals that the optimal (non-zero) overlaps between the attackers post-interactions states must be equal and numerically the same as the difference between the fidelity and the disturbance at the receiving end. That amount turns out to be the same as the reduction (factor) in Bell violation when estimated for the equivalent entanglement-based protocol. Further, a series of necessary and sufficient conditions unveil the structure of the optimal states which therefore are the only and all possible optimal interactions. These optimal states are the same as the outputs of an optimal phase-covariant cloner. We also demonstrate various methods to derive optimal unitary evolutions that an eavesdropper is interested to know in order to mount an optimal attack. The final key-rate is computed and plotted as well.
Obstacle avoidance based on skeleton for the redundant Manipulator

For use in unstructured domains, highly redundant robotic systems need both deliberative and compliant control schemes, to avoid collision and safely interact with the dynamic environment. Aiming at the shortcoming of the traditional method of path planning using merely on the typical structure of the manipulator, a new algorithm, named the “skeleton extraction based on 3D-sensor”, is proposed for the real-time generation of collision avoidance motions. The algorithm is applied to get the distances of the multiple possible collision points and to establish a repulsive force. Then, the Robot Operating System (ROS) framework and a collaborative robot manipulator validate the proposed method. The method was tested by tests by a 7DOF C++ model of the collaborative robot manipulator in the Vrep and ROS environment. The results show that the proposed method can avoid local minima traps and eliminate oscillations effectively.
Workload Volatility Management in Datacenters and Cloud

With increasing adoption of cloud by enterprises, resource management has never been more important than it is now. Efficient utilization of infrastructure can not only improve application performance but also help enterprises save significantly on their operational expenditure. For features like Cluster Autoscaling, inefficient resource management can lead to cluster resources getting scaled in or out more frequently, thereby increasing operational expenses. Traditional resource management engines distribute workloads across the nodes based on their utilization. This approach is most effective when the workloads are more or less stable. If the workloads are unstable or volatile, this approach of distributing the load is not optimal as workloads keep varying and the resource management engine can end up moving workloads from one node to another continuously to keep the load distributed. This results in inefficient resource utilisation, overloading of hosts and wastage of resources due to continuously moving workloads. With new workloads like container applications and Kubernetes, clusters are more prone to workload volatility since these workloads are short-lived and can frequently switch between the containers. In the future, handling this workload volatility will be critical to support these new container workloads. To address this problem, in this paper, we introduce a new Volatility Balancer which can work with any Load Balancer and distribute workload volatility along with workload utilization. Volatility Balancer, together with a Load Balancer, can provide an efficient resource management model for all the new generation workloads.
Parameters Estimation of a Reliability Model Based on Bath-Shaped Failure Rate Function Using Meta-Heuristic Algorithms

The main objective of using the reliability analysis and assessment approaches in many industrial application is to avoid breakdown of their operation mode by extending their lifespan. In this context, some searchers have proposed a reliability model based on bath shaped rate failure function, it uses a graphical method which ensures the ease estimation of the parameters of the concerned model. However, it was found that in this proposed graphical model, a minor reading error will lead to poor estimation of the parameters of the reliability model. In this paper, an objective function based on root-mean-square error (RMSE) is proposed to achieve the best selection of the parameters of the previously proposed model. Indeed, the minimization of this objective function have been carried out using two algorithms such as the grey wolf optimizer (GWO) and particles swarm optimization (PSO). The obtained results for the both algorithms show that they can ensure better accuracy in parameters estimation compared the graphical method.
Adaptive Differential Evolution based on Exploration and Exploitation Control

Search operator design and parameter tuning are essential parts of algorithm design. However, they often involve trial-and-error and are very time-consuming. A new differential evolution (DE) algorithm with adaptive exploration and exploitation control (AEEC-DE) is proposed in this work to tackle this challenge. The proposed method improves the performance of DE by automatically selecting trial vector generation strategies (both mutation and crossover operators) and dynamically generating the associated control parameter values. A probability-based exploration and exploitation measurement is introduced to estimate whether the state of each newly generated individual is in exploration or exploitation. The state of historical individuals is used to assess the exploration and exploitation capabilities of different generation strategies and parameter values. Then, the strategies and parameters of DE are adapted following the common belief that evolutionary algorithms (EAs) should start with exploration and then gradually change into exploitation. The performance of AEEC-DE is evaluated through experimental studies on a set of test problems and compared with several state-of-the-art adaptive DE variants.
The asymmetric evolution of economic institutions: evidence from dynamic panel quantile regression with IV and fixed effects

Conditional mean regressions show that economic institutions (insteco) correlate positively to regime durability, the quality of political institutions, human capital and GDP per capita. However, using dynamic panel quantile regressions with fixed effects and instrumental variables, we detect that insteco evolves asymmetrically. Adverse shocks worsen insteco of countries with better political institutions and more durable regimes, while positive innovations cause insteco of countries with better political institutions and longer political regime to improve even further. Among advanced economies, negative shocks cause a stronger effort to ameliorate insteco the higher the human capital and GDP per capita. Our exercises include 129countries.
Gradient direction analysis for contour tracking and local non maximum suppression

One of the most used techniques for edge refinement, once a convolution mask has been used, is the local non-maximum elimination method proposed by Canny. This technique uses the magnitude and direction of the gradient to determine whether a pixel is a local maximum. In addition, the direction of the gradient is used in a subsequent step, to perform contour enclosure in case any discontinuity is present. The local maximum detection technique gives good results when using masks of size 3 x 3 or larger. Deriche introduced an extension of Canny's edge detection method, which obtains state-of-the-art results, using a recursive method that only considers pixels in the vertical or horizontal direction for the calculation, which is equivalent to using linear operators of size 3 x 1 or larger. This technique allows good edge detection, in a shorter time, regardless of the filter size. Deriche mentions the need to employ a smoothing technique, obtained by integrating his derivative operator, prior to the use of the derivative operator. In this work the result obtained with this operator is compared with the Prewitt and Sobel and Feldman operators in the determination of the direction of the gradient to find the local maxima.
Dynamic Dictionaries for Multisets and Counting Filters with Constant Time Operations

We resolve the open problem posed by Arbitman, Naor, and Segev [FOCS 2010] of designing a dynamic dictionary for multisets in the following setting: (1) The dictionary supports multiplicity queries and allows insertions and deletions to the multiset. (2) The dictionary is designed to support multisets of cardinality at most n (i.e., including multiplicities). (3) The space required for the dictionary is $$(1+o(1))\cdot n\log \frac{u}{n} + \varTheta (n)$$ bits, where u denotes the cardinality of the universe of the elements. This space is $$1+o(1)$$ times the information-theoretic lower bound for static dictionaries over multisets of cardinality n if $$u=\omega (n)$$ . (4) All operations are completed in constant time in the worst case with high probability in the word RAM model. A direct consequence of our construction is the first dynamic counting filter (i.e., a dynamic data structure that supports approximate multiplicity queries with a one-sided error) that, with high probability, supports operations in constant time and requires space that is $$1+o(1)$$ times the information-theoretic lower bound for filters plus O(n) bits. The main technical component of our solution is based on efficiently storing variable-length bounded binary counters and its analysis via weighted balls-into-bins experiments in which the weight of a ball is logarithmic in its multiplicity.
Activation-Based Cause Analysis Method for Neural Networks

As neural networks are ubiquitous in the real-world environment, we encounter a variety of situations in which neural network-based systems fail. To address these failures, we need to identify their root causes. However, due to the black-box nature of neural networks, it is considered a difficult task to explain/understand internal functions, and cause analysis is usually conducted based on personal experience. To alleviate these problems, we propose a method to compute an element-wise contribution of inputs on current decisions. To this end, we focus on the physical meaning of neuron activations, and formulate an optimization problem that finds partial activations that support current decisions. Then, by accumulating these partial activations in the backward direction, we evaluate the contributions of each element in an input vector to the current decision. Experimental results have shown that the proposed method outperforms conventional methods in terms of cause localization for a range of failure scenarios.
Canonical noise distributions and private hypothesis tests

f-DP has recently been proposed as a generalization of differential privacy allowing a lossless analysis of composition, post-processing, and privacy amplification via subsampling. In the setting of f-DP, we propose the concept of a canonical noise distribution (CND), the first mechanism designed for an arbitrary f-DP guarantee. The notion of CND captures whether an additive privacy mechanism perfectly matches the privacy guarantee of a given f. We prove that a CND always exists, and give a construction that produces a CND for any f. We show that private hypothesis tests are intimately related to CNDs, allowing for the release of private p-values at no additional privacy cost, as well as the construction of uniformly most powerful (UMP) tests for binary data, within the general f-DP framework. We apply our techniques to the problem of difference-of-proportions testing, and construct a UMP unbiased (UMPU) “semiprivate” test which upper bounds the performance of any f-DP test. Using this as a benchmark, we propose a private test based on the inversion of characteristic functions, which allows for optimal inference on the two population parameters and is nearly as powerful as the semiprivate UMPU. When specialized to the case of (ϵ,0)-DP, we show empirically that our proposed test is more powerful than any (ϵ/ 2)-DP test and has more accurate type I errors than the classic normal approximation test.
Towards a NATO Lessons Learned Ontology

Lessons capture knowledge about events, actions taken and their consequences. They describe errors committed in the past that shall not be repeated in the future as well as successes and how they can be repeated. In both cases lessons may provide an advantage in future missions. They can be shared within an organisation, and across cooperating organisations providing decision makers with relevant knowledge of past experience. Being able to offer the right lessons in the right situation to the right decision makers is crucial for unlocking their benefits. However, much of the relevant content in lessons is stored is in the form of unstructured text that leads to inconveniences in their re-use. The Lessons Learned ontology project aims at enriching these lessons semantically, hence upgrading keyword-based search to semantic search, enabling the use of context information and leveraging organisational learning. This article presents potential use cases and requirements for a Lessons Learned ontology, scientific and organisational challenges for its development as well as an overview of advanced and promising methods and technologies that may create value for a future Lessons Learned ecosystem.
Online Model Generation for Scalable Predictive Process Monitoring

Predictive process monitoring techniques are intended to forecast outcomes of running process instances. This is achieved through the use of predictive models inferred from past event logs. However, the use of such procedures in scenarios where there is an initial lack of previous data or concept drift can be especially challenging. To overcome those limitations, this paper is focused on enabling online model generation, addressing problems such as uncertainty about process completeness. Further, a scalable streaming system based on Apache Flink platform is built and applied on an incident management system dataset in order to assess prediction performance. The results presented show the capacity of these techniques to support predictive process monitoring.
The role of AI in digital contact tracing

With a pandemic widespread and a worldwide lockdown, causing an unprecedented economic crisis, contact tracing is identified by WHO, CDC, and many other government health institutions as a must for pandemic control. The EU Commission, for instance, recommends that contact tracing is needed for people to return to hotels and camping sites. In the USA, several states have made contact tracing a prerequisite for reopening, including California, Pennsylvania, and Virginia, to name a few. Motivated by the limitations of human contact tracing, several governments have partnered with the IT industry such as telecommunication companies and tech giants such as Apple and Google to deploy digital contact-tracing solutions. The result is several approaches and hundreds of mobile contact-tracing apps available on different app stores. In this chapter, we will depict the backbone of digital contract-tracing solutions. We will show that despite the variety of proposals and applications, existing contact-tracing solutions revolve around two main approaches: (1) proximity-based contact tracing using Bluetooth and (2) location-based contact tracing, which relies on localization technologies such as global positioning system, call data record, and WiFi. We will also discuss the advantages and limitations of each approach, their privacy implications, and finally the role of artificial intelligence in improving their efficiency.
Design and Implementation of Student Management System Based on Internet Big Data

Abstract Due to fast development and wide popularization of information technology, the traditional teaching ideas, teaching methods, teaching processes and teaching organizations have changed, which has caused fundamental changes in teaching mode and education system. The student management system in view of Web technology is the main carrier of information administration in universities. This paper puts forward a solution to the self-management system of universities based on big data mobile internet. This system is related to self-learning, self-management, and using multiple mobile internet applications to help students use the self-management system, in order to improve students’ ardour and initiative take part in teaching activities; The learning process management and curriculum evaluation management have been established, and the teaching quality has been comprehensively considered from multiple dimensions, so that the training have a gift for universities has been effectively guaranteed.
Stock Direction Prediction Using Sentiment Analysis of News Articles

It is widely acknowledged that stock price prediction is a job full of challenges due to the highly unpredictable existence of financial markets. Many market participants or analysts, however, attempt to predict stock prices using different mathematical, econometric, or even neural network models in order to make money or understand the nature of the equity market. In the past few years, a lot of models based on deep learning have been gaining popularity for predicting the volatility of the stock market prices. In this paper, the outcomes of many classical deep learning models such as LSTMs, GRUs, CNNs, and their several common variants are contrasted with two distinct stock price prediction targets: absolute stock price and volatility. The aim of the comparative study is to find out which model is the best fit for stock market prediction. We also attempt to research the relationship between news and stock trends, believing that news stories have an impact on the stock market by incorporating sentiment analysis into our model. Our methodology was to scrape news articles of a particular stock and use the corpus gathered to generate a sentiment score which is further used as an input to the model.