Reviewerly/exHarmony huggingface dataset:
A Study on the Research Security System of the Researcher-Centric

The government is continuously expanding its national R&D investment to actively respond to the advent of the 4th industrial revolution era and to develop the national economy. The R&D structure is likely to be liberalized as the paradigm shifts from the pursuit type R&D to the leading type R&D, and R&D capacity enhancement that focuses on researchers’ creativity is emphasized. Such changes in R&D environment will increase the risk of security accidents such as leakage of research information. In addition, security policy for protection of research result should be the Researcher-Centric Security and security policy should be changed. This study explored transforming the research security system into the Researcher-Centric Security system so that researchers can voluntarily implement necessary security measures in the course of conducting research.
Solving Gossip Problems using Answer Set Programming: An Epistemic Planning Approach

We investigate the use of Answer Set Programming to solve variations of gossip problems, by modeling them as epistemic planning problems.
Vulnerabilities and Excess Gas Consumption Analysis Within Ethereum-Based Smart Contracts for Electricity Market

Blockchain architecture can be used to implement highly decentralized electricity markets. The usage of smart contracts technology allows for fast, seamless, and reliable transaction settlement. Smart contract resistance to vulnerabilities is the key factor for functioning and ensuring the credibility of the power infrastructure. In this paper, an analysis of some specific risks for smart contracting on Ethereum blockchain electricity markets is presented. A test set of 60 smart contracts is assembled and checked for vulnerabilities using three publicly available detection tools. Based on the results the model smart contract is developed and experimental simulations are conducted for estimating gas expenditure of basic operations characteristic to electricity auction smart contracts. The conclusions from the simulations lead to reassuring of a better quality of the smart contract’s code.
Efektivitas Penggunaan Pembelajaran Daring Edmodo Berbantuan Quizstar untuk Meningkatkan Hasil Belajar Mahasiswa

This article aims to determine the effectiveness of using Edmodo-based online learning with the help of Quizstar to improve student learning outcomes for electric lighting installations. The research method used quasi-experimental design. Observation sheets and documentary studies were analyzed in this study. The subject were 43 students that consist of experimental class 21 and the control class 22 students. The research was conducted in six meetings, each class, namely three times meetings. The results showed the value of t count &lt;t table (3.501 &lt;1.997). This means that Ho is rejected so that Ha is accepted or it can be said that online learning using quizstar assisted edmodo is more effective than learning blogs for students majoring in electrical engineering education for semester 3 in the electrical power installation course..&#x0D; Key words: Edmodo, Quizstar, Edmodo-based Online Learning
Relativistic Quantum Information of Anyons

In this paper, a method is developed to investigate the relativistic quantum information of anyons. Anyons are particles with intermediate statistics ranging between Bose-Einstein and Fermi-Dirac statistics, with a parameter α (0 < α < 1) characteristic of this intermediate statistics. A density matrix is also introduced as a combination of the density matrices of bosons and fermions with a continuous parameter, α, that represents the behavior of anyons. This density matrix reduces to bosonic and fermionic density matrices in the limits $\alpha \rightarrow 0$ and $\alpha \rightarrow 1$ , respectively. We compute entanglement entropy, negativity, and coherency for anyons in non-inertial frames as a function of α. We also computed quantum fisher information for these particles. Semions, which are particles with α = 0.5, were found to have minimum quantum fisher information with respect to α than those with other values of fractional parameter.
STRONG COMPLETENESS OF MODAL LOGICS OVER 0-DIMENSIONAL METRIC SPACES

Abstract We prove strong completeness results for some modal logics with the universal modality, with respect to their topological semantics over 0-dimensional dense-in-themselves metric spaces. We also use failure of compactness to show that, for some languages and spaces, no standard modal deductive system is strongly complete.
Cascaded Dimensionality Reduction Method and Its Application in Spectral Classification

Abstract The classification of the high-dimensional spectral is one of the important study domains in the astronomy. However, the curse of dimension problem restrains the performance of the methods to classify the spectral data. In this paper, the cascaded dimensionality reduction, combining with the virtues of the principal component analysis and t-distributed stochastic neighbour embedding, is conducted to improve the performance of classification methods for spectral data. In the cascaded dimensionality reduction, the PCA is employed to pre-reduce dimensions of spectral data for reducing redundant information, under the constraint of preserving the information integrity as far as possible; T-SNE highlights the differences among the samples with different labels, and outputs target results after the dimension reduction. The support vector machine in conjunction with the cascaded dimensionality reduction is applied to classify the spectral data, and its performance is compared with the PCA based SVM and T-SNE based SVM. Experimental results demonstrate that the cascaded dimensionality reduction assists the SVM obtaining better performance than PCA and T-SNE.
Programmable Silicon Photonic Integrated Circuits

Programmable photonic circuits offer a new way to explore photonic chips: instead of hardwiring waveguide connections, one programs a mesh of tunable couplers. This requires electronics and software routines to govern the behavior of the chip, making it possible to rapidly develop new photonic applications.
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning

The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses, we introduce variational characterizations that naturally suggest a two-step scheme for their optimization, based on the iterative shift of a probability density and the calculation of a best Gaussian approximation in Kullback–Leibler divergence. Disregarding approximation error in these two steps, the variational characterizations allow us to show a simple monotonicity result for training error along optimization iterates. The two-step optimization schemes for local entropy and heat regularized loss differ only over which argument of the Kullback–Leibler divergence is used to find the best Gaussian approximation. Local entropy corresponds to minimizing over the second argument, and the solution is given by moment matching. This allows replacing traditional backpropagation calculation of gradients by sampling algorithms, opening an avenue for gradient-free, parallelizable training of neural networks. However, our presentation also acknowledges the potential increase in computational cost of naive optimization of regularized costs, thus giving a less optimistic view than existing works of the gains facilitated by loss regularization.
POVMs are equivalent to projections for perfect state exclusion of three pure states in three dimensions

Performing perfect/conclusive quantum state exclusion means to be able to discard with certainty at least one out of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>possible quantum state preparations by performing a measurement of the resulting state. This task of state exclusion has recently been studied at length in \cite{bandyopadhyay2014conclusive}, and it is at the heart of the celebrated PBR thought experiment \cite{pusey2012reality}. When all the preparations correspond to pure states and there are no more of them than their common dimension, it is an open problem whether POVMs give any additional power for this task with respect to projective measurements. This is the case even for the simple case of three states in three dimensions, which is mentioned in \cite{caves2002conditions} as unsuccessfully tackled. In this paper, we give an analytical proof that in this case considering POVMs does indeed not give any additional power with respect to projective measurements. To do so, we first make without loss of generality some assumptions about the structure of an optimal POVM. The justification of these assumptions involves arguments based on convexity, rank and symmetry properties. We show then that any pure states perfectly excluded by such a POVM meet the conditions identified in \cite{caves2002conditions} for perfect exclusion by a projective measurement of three pure states in three dimensions. We also discuss possible generalizations of our work, including an application of Quadratically Constrained Quadratic Programming that might be of special interest.
Average Minimum Distances of periodic point sets

Periodic sets of points model all solid crystalline materials (crystals) by representing atoms as labeled points. Crystal structures are determined in a rigid form and are considered up to rigid motions or isometries. Modern tools of Crystal Structure Prediction output thousands of simulated structures, though only few of them can be really synthesized. The first obstacle is the presence of many near duplicate structures that can not be efficiently recognized on the fly by past tools. To continuously quantify a similarity between periodic sets, their isometry invariants should be continuous under perturbations when all discrete invariants such as symmetry groups can break down. This paper studies the isometry classification problem for periodic sets with the new continuity requirement and introduces the Average Minimum Distances, which form an infinite sequence of continuous isometry invariants. Their asymptotic behaviour for a wide class of sets is explicitly described in terms of a point packing coefficient. All results are illustrated by experiments on large datasets of crystals.
Injecting Power Attacks with Voltage Glitching and Generation of Clock Attacks for Testing Fault Injection Attacks

Fault injection attacks pose serious threat in security of embedded devices since they require less expertise to conduct them. Suitable countermeasures can only be built if there effects are studied and analyzed in detail. This paper presents circuits which produces power fault injection attack (positive, negative, positive/negative) through voltage glitching on cadence 180 nm technology node. Additionally, it presents a VHDL code for generating clock fault (overclocking and underclocking attacks) injection attack. Effect of power and clock fault injection attacks is analyzed on combinational circuits. Techniques presented in this paper can be used as testing platforms for doing analysis of fault injection attacks on different combinational and sequential circuits so that countermeasures can be built in future in order to have attack-resistant devices. The paper emphasizes the perspective from the attacker, rather than the perspective of countermeasure development.
Gesture Planning and Execution for Anchoring between Multi-Embodiment Robots in Decentralized Settings

In robotics, competitiveness and cost effectiveness have compelled manufacturers to make specialized and less expensive robots. These specialized robots will eventually become part of the multi-robot system and will need to coordinate with each other to handle cooperative tasks. In a multi-robot system, diversity and heterogeneity of robotic agents facilitates capabilities, performance, autonomy, ease of use, and cost-effectiveness. Individually, these heterogeneous robots have limitations in communication, sensing and sharing knowledge, making cooperation and coordination among other robots quite challenging. The problem of collaboration can be easily solved when the internal representations can be communicated between the agents. However for heterogeneous robots, the available sensors are likely to differ, making it impossible to share the internal representations directly. In such a multi-robot system, using shared symbols for objects in the environment is a pre-requisite for collaboration. Sharing symbols requires both that each agent has anchored a symbol with an internal, sensor level representation, as well as the fact that these symbols match between the agents. Humans use gestures, such as pointing, extensively in order to anchor linguistic expressions to objects in the physical world. Similarly, gestures can be valuable in decentralized robotic systems, allowing communication between agents and the transfer of symbolic meanings. Pointing gestures are especially valuable in crowded scenes where multiple possible matches are present. However, pointing in crowded scenes can itself remain ambiguous if the pointing direction is not carefully chosen. We propose the use of pointing gestures to align symbols between a heterogeneous group of robots. In this thesis a probabilistic model for pointing and gesture detection accuracy is proposed. The model allows for the planning of optimal pointing actions by minimizing the probability of pointing errors resulting from ambiguities and limited accuracy. We also describe how to measure the accuracy of an agent's pointing gesture and to calibrate the model for that agent. Experimental results suggest that the proposed model captures the qualitative behaviour of pointing success well. We then describe a planning framework that minimizes the required effort for anchoring representations across robots. The framework allows planning for both the gesturing and observing agents in a decentralized fashion. It considers both implicit sources of failure such as ambiguous pointing as well as costs required by actions. Simulation experiments show that the resulting planning problem has a complex solution structure with multiple local minima. Furthermore, a real-life heterogeneous two-robot system demonstrates the practical viability of the approach.
A Privacy Preserving Data Collecting Scheme in Vanet

As an important application of Vanet (Vehicle Ad hoc network), the Vehicle Crowd Sense (VCS) uses intelligent vehicles as sensing nodes to collect road information. In VCS, vehicle information is shared, which necessitates privacy protection. How to balance information sharing and privacy protection is a great challenge. Identity privacy protection is the basic requirement of VCS. In many schemes, pseudonyms are used to protect the vehicle’s identity privacy. However, it is inconvenient to update and manage pseudonyms. Specifically, in our scheme, the vehicle’s identity privacy is protected without pseudonyms. Furthermore, our scheme supports reputation privacy protection. Finally, security analysis shows that our scheme satisfies the requirement of privacy preserving, and the experiment and performance analysis show our scheme is efficient on computation and communication.
Local Centroid Distance Constrained Representation-Based K-Nearest Neighbor Classifier

The K-Nearest Neighbor (KNN) algorithm is widely studied for its simplicity and ease of understanding. However, classical KNN follows the principle of simple majority voting and treats each neighbor equally. In fact, the degree of importance for each neighbor is different, so the principle of simple majority voting would reduce the accuracy of classification without being improved of it. To overcome this problem, we propose a local centroid distance constrained representation-based KNN classifier (LCDR-KNN). K nearest training samples are selected from each class to form a sample subset, the distance from the centroid of the k nearest neighbors of each class to the unknown sample is served as the constraint. The representation coefficient of each neighbor in this subset is obtained with the method of Collaborative representation(CR), and it is used as the weight of neighbor voting. Compared with the conventional or state-of-the-art algorithms, the proposed algorithm have great advantages on condition of the different k values and the optimal k value.
Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning

The availability of various large-scale datasets benefits the advancement of deep learning. These datasets are often crowdsourced from individual users and contain private information like gender, age, etc. Due to rich private information embedded in the raw data, users raise the concerns on privacy leakage from the shared data. Such privacy concerns will hinder the generation or use of crowdsourcing datasets and lead to hunger of training data for new deep learning applications. In this work, we present TAP, a task-agnostic privacy-preserving representation learning framework to protect data privacy with anonymized intermediate representation. The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks. We adopt the federated learning paradigm to train the feature extractor, such that learning the extractor is also performed in a privacy-respecting fashion. We extensively evaluate TAP and compare it with existing methods using two image datasets and one text dataset. Our results show that TAP can offer a good privacy-utility tradeoff.
A Novel Feature Engineering Approach for Twitter-Based Text Sentiment Analysis

With the increasing availability of handheld devices and greater affordability of mobile data, social media has become an inseparable part of the daily life of most of the society. Free availability, diversity, and massiveness of this data have inspired many research work to use it for extracting various insights about a variety of subjects. Text sentiment analysis is the process of mining opinion polarity from a given document in natural language. Social media data is no exception to extract sentiments which could be used for a variety of tasks right from opinion mining to recommendations. In this work, Twitter has been chosen as the source of data corpus required due to the summarized content, ease of availability, and humongous reach among all classes of the society. This work uses traditional machine learning approaches for solving the problem of text sentiment analysis and proposes a novel feature engineering approach for merging the text-based and non-textual features of the dataset by including the predicted output lists also in the final feature set. The result analysis shows significant improvement in the performance of the machine learning model on the test dataset using the proposed approach.
Scientific Potential of Teachers: Mechanisms for Identification and Improvement

Monitoring the levels of scientific potential of teachers is due to the need to improve the methodology for determining compliance with the position and the requirements of the first and highest qualification categories in accordance with the new procedure for certification of teachers.&#x0D; On this basis, there are opportunities to develop mechanisms for increasing scientific potential in order to introduce it functionally into the structure of professional pedagogical activity of teachers in accordance with the job description and the requirements of Federal state educational standards to improve the effectiveness of educational activities.&#x0D; When assessing the levels of scientific potential of teachers, along with other parameters, it is advisable to take into account the influence of age, educational level and retraining of specialists. It is necessary to take into account the level of teaching experience and work experience in the subject taught.
IWannaPlay - an eye-tracking based AI tutoring chinese chess system

In this poster, we present IWannaPlay, an eye-tracking based Artificial Intelligence (AI) tutoring Chinese chess system. Through ordinary webcams, the IWannaPlay system captures the player's sight and facial expressions to acquire his/her gaze points and emotional status. Combining this information with the game situation, the system intelligently provides AI suggestions with visualizations. With the guidance of the AI, the player can strategize more thoroughly and thus make better decisions, achieving a better training outcome. When playing Chinese chess on IWannaPlay, the system interface displays a real-time game analysis and warns against potentially dangerous pieces within the game. Upon detection of the user's anxiety or after a period of long contemplation, the system intelligently provides graphic visualizations of dynamic multi-step strategies to assist the user in making decisions. We conducted experiments to verify the usability of our system, the user feedback demonstrated that IWannaPlay does provide effective guidance to players.
PLM Migration in the Era of Big Data and IoT: Analysis of Information System and Data Topology

As product lifecycle management is starting to be a standard information system in most companies, a data manipulation issue starts to emerge. Indeed, the information contained in a system often needs to be accessible or even transferred to another one to exploit a new software’s functionalities. With the Big Data era, new methods of analysis start to emerge, and the graph visualisation of data enables a better human understanding of the data itself. We propose to address the PLM migration issues with a new approach based on analysis of information system and data topology. The data is passed onto a graph and bundles are made thanks to clustering algorithms; this bundling enables a better understanding of the data and ease migration. However, PLM has links between links which complicate the transition to graphs. Finally, the described use-case proves that data bundling eases the data migration and prevents some usual pitfalls and delays.
A Guests Managing System with Lattice-Based Verifier-Local Revocation Group Signature Scheme with Time-Bound Keys

Perera, Maharage Nisansala Sevwandi Koshiba, TakeshiIn this paper, we propose a visitors managing system using group signature schemes. The paper focuses on situations that guests who are residing in a hotel are like to keep their activities such as accessing hotel amenities anonymously. Moreover, guests are generally staying in a hotel for a short period. Thus, they should be managed with a proper revocation mechanism. This paper proposes a system using a group signature scheme with Verifier-local revocation and time-bound keys.