Reviewerly/exHarmony huggingface dataset:
The Extended Risk Assessment Form for IT/OT Convergence in IACS Security

Recently, security incidents that caused the shutdown or obstruction of the industrial automation and control systems (IACS) which support social infrastructure have increased rapidly. Many governments and businesses are addressing this urgent issue. IACS consists of two technologies, one is an information equipment based on information technology (IT) such as the Internet and cloud servers, and a control equipment based on operation technology (OT) such as controllers and sensing devices. The assets to be protected in IACS are not only IT information assets, as well as the functions and performance of OT related to human health and social services. For this reason, it has been known that the counter measures based on IT security alone are insufficient and incomplete. As an approach to this problem, IT/OT convergence has attracted attention in the IACS security problem. In order to bridge gaps of technical, human, and system gaps between IT and OT, new methodologies from the view of IT/OT convergence are being studied. This paper would discuss a security risk assessment for analyzing, designing counter measures and evaluating IACS security issues from the perspective of IT/OT convergence. Since the scope of assessment and the items to be examined will expand, we propose the extended risk assessment form that can take them into consideration. By using this form, it is possible to carry out security risk assessment covering IT and OT, safety and security, cyber and physical, as a series of sequences.
Slope: A First-order Approach for Measuring Gradient Obfuscation

Evaluating adversarial robustness is a challenging problem.Many defenses have been shown to provide a false sense of security by unintentionally obfuscating gradients, hindering the optimization process of gradient-based attacks.Such defenses have been subsequently shown to fail against adaptive attacks crafted to circumvent gradient obfuscation.In this work, we present Slope, a metric that detects obfuscated gradients by comparing the expected and the actual increase of the attack loss after one iteration.We show that our metric can detect the presence of obfuscated gradients in many documented cases, providing a useful debugging tool towards improving adversarial robustness evaluations.
Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing

Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image. Offering potentials to reduce expensive manual editing, SIE has attracted much interest recently. However, existing methods can hardly produce accurate editing and even lead to failures in attribute editing when the query sentence is with multiple editable attributes. To cope with this problem, by focusing on enhancing the difference between attributes, this paper proposes a novel model called Contrastive Attention Generative Adversarial Network (CA-GAN), which is inspired from contrastive training. Specifically, we first design a novel contrastive attention module to enlarge the editing difference between random combinations of attributes which are formed during training. We then construct an attribute discriminator to ensure effective editing on each attribute. A series of experiments show that our method can generate very encouraging results in sentence-based image editing with multiple attributes on CUB and COCO dataset. Our code is available at https://github.com/Zlq2021/CA-GAN
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes

Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR). In this paper, we aim to enhance the multilingual ASR performance in two ways, 1)studying the impact of feeding a one-hot vector identifying the language, 2)formulating the task with a meta-learning objective combined with self-supervised learning (SSL). We associate every language with a distinct task manifold and attempt to improve the performance by transferring knowledge across learning processes itself as compared to transferring through final model parameters. We employ this strategy on a dataset comprising of 6 languages for an in-domain ASR task, by minimizing an objective related to expected gradient path length. Experimental results reveal the best pre-training strategy resulting in 3.55% relative reduction in overall WER. A combination of LEAP and SSL yields 3.51% relative reduction in overall WER when using language ID.
Study on the Scientific Interface between Preschool and Primary Education

Preschool education, as a stage of preparation for children's formal entry into curricular education, is an important stage in the socialization of children and a prime period for their intellectual development. Therefore, the effectiveness of preschool education will directly affect the future development of children. Primary education, on the other hand, is the starting stage of compulsory education and the cornerstone of basic education in China, and primary education plays an important role in promoting the overall development of individuals and consolidating and improving the quality of compulsory education. However, at present, there are numerous problems with preschool and primary education in China that need to be addressed urgently. The author takes the current articulation of preschool and primary education as the starting point, analyzes the problems of preschool and primary education through questionnaire research, fieldwork and interviews, and puts forward targeted solutions and suggestions. In an attempt to break the situation of separating preschool education and primary education, ensure that children can smoothly transition from kindergarten to primary school and ensure the healthy physical and mental development of children.
Multimodal Emotion-Cause Pair Extraction in Conversations

Emotion cause analysis has received considerable attention in recent years. Previous studies primarily focused on emotion cause extraction from texts in news articles or microblogs. It is also interesting to discover emotions and their causes in conversations. As conversation in its natural form is multimodal, a large number of studies have been carried out on multimodal emotion recognition in conversations, but there is still a lack of work on multimodal emotion cause analysis. In this work, we introduce a new task named Multimodal Emotion-Cause Pair Extraction in Conversations, aiming to jointly extract emotions and their associated causes from conversations reflected in multiple modalities (text, audio and video). We accordingly construct a multimodal conversational emotion cause dataset, Emotion-Cause-in-Friends, which contains 9,272 multimodal emotion-cause pairs annotated on 13,509 utterances in the sitcom Friends. We finally benchmark the task by establishing a baseline system that incorporates multimodal features for emotion-cause pair extraction. Preliminary experimental results demonstrate the potential of multimodal information fusion for discovering both emotions and causes in conversations.
Comparative Analysis of Mobile Learning Trends of Physical Education Teaching Students: Republic of Kosovo and Russia Example

The aim of this research is to conduct a comparative analysis of the mobile learning trends of students of the physical education teaching department studying in The Republic of Kosovo and Russia. In this research, causal comparative research method was used. The study group consists of 40 students studying physical education teaching at various universities in Kosovo and Russia in the 2020-2021 academic year. A total of 80 students participated in the study. The research data were collected by the semi-structured interview form developed by the researcher and the data was analyzed by content analysis method. The research results reveal that students studying in both countries have a high mobile learning tendency. In addition, students from both countries have similarly defined the advantages and disadvantages of mobile device use in education.
성적 자료를 활용한 모의 토익 시험의 유용성 검증

This study seeks to analyze the construct validity of the mock TOEIC test by utilizing corpus analysis indicators. A commercial corpus program extracted key indicators related to vocabulary and syntax on a mock TOEIC test. A correlation analysis examined the relationship between these indicators and test results. It was possible to observe the causal relationship between corpus indices and variations in test scores when the test was taken two times. The regression analysis showed that the usefulness of the mock TOEIC test was lowered if the coefficient values of the corpus factors that had a significant effect on the change in grades in the first and second tests did not show a constant pattern in one direction (positive or negative). The results demonstrate that each indicator is complex and their influence on test grades varies depending on test types. Overall, most corpus indices used in this study showed poor predictive power of changes in test scores, and it was revealed that the mock TOEIC test is an evaluation tool lacking in authenticity. This paper contributes to the academic literature by demonstrating the possibility of using a corpus index as a usefulness verification tool. Usefulness verification can be established by examining the causal relationship between the repeated actual scores of the mock TOEIC test and the corpus index.
The role of intellectual property law in the regulation of micro and small and medium enterprises

A lot of businesses start with humble beginnings and grow into big corporations, therefore how it starts matters. The hallmark of a good business is its product and the way to distinguish one product from another is through the use of trademarks and its registered name. While trademarks consist of signs as it is used to indicate a connection in the course of trade between the goods and some person having the right either as proprietor or as a registered user to use the mark, a business name is simply an alphabetic name used for the identification of business registered to become a legal entity. The crux of this paper is to examine the legal framework for the regulation of business names and trademarks in Nigeria. The paper adopts a doctrinal research methodology. It analyses the various forms of business unit in Nigeria by examining the legal implications. Thus, the article presents an introduction to the general structure of Micro and Small and Medium (MSMEs), business names and trademarks law. This is followed by offering an analysis that focus on the MSMEs and what they need to know to help boost the total outlook and of the business. The paper however concludes that MSMEs need to know the laws and regulations that guide the setting up of their business as trademarks are important assets of any business and their use must be done in relation to other law and regulation.
A Deep Learning Approach for Active S-box Prediction of Lightweight Generalized Feistel Block Ciphers

One of the main security requirements for symmetric-key block ciphers is resistance against differential cryptanalysis. This is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical solvers that incur high computational costs. These costs increase exponentially with respect to block cipher size and rounds, quickly becoming inhibitive. Conventional S-box enumeration methods also require niche cryptographic knowledge to perform. In this paper, we overcome these problems by proposing a data-driven approach using deep neural networks to predict the number of active S-boxes. Our approach trades off exactness for real-time efficiency as the bulk of computational work is brought over to pre-processing (training). Active S-box prediction is framed as a regression task whereby neural networks are trained using features such as input and output differences, number of rounds, and permutation pattern. We first investigate the feasibility of the proposed approach by applying it on a reduced (4-branch) generalized Feistel structure (GFS) cipher. Apart from optimizing a neural network architecture for the task, we also explore the impact of each feature and its representation on prediction error. We then extend the idea to 64-bit GFS ciphers by first training neural networks using data from five different ciphers before using them to predict the number of active S-boxes for TWINE, a lightweight block cipher. The best performing model achieved the lowest root mean square error of 1.62 and R2 of 0.87, depicting the feasibility of the proposed approach.
Fourier Optic Convolutional Neural Network

Here we report a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel showing an MNIST and CIFAR-10 accuracy of 96% and 54%, respectively.
State Estimation under Outliers by the Maximum Correntropy Extended Kalman Filter

In this study, we treat the problem of state estimation under measurement outliers for nonlinear systems. The Kalman filter (KF) is optimal under linear systems with Gaussian noise but will deteriorate seriously if outliers such as impulse noises are involved. In this work, we propose a nonlinear system state estimator based on the extended Kalman filter (EKF) with the maximum correntropy criterion (MCC), and improve the MCC with a fixed-point iteration. With the novel MCC as the weight of Kalman gain, the EKF can counteract the influence of outliers during estimation. A series of simulation results shows that the maximum correntropy extended Kalman filter (MCEKF) has much better performance than the traditional EKF under outliers.
The similarity index of mathematical and other scientific publications with equations and formulas and the problem of self-plagiarism identification

The problems of estimating the similarity index of inhomogeneous scientific publications containing equations and formulas are discussed for the first time. It is shown that the presence of equations and formulas (as well as figures, drawings, and tables) is a complicating factor that significantly complicates the study of such texts. It has been proved that the method for determining the similarity index of publications, based on taking into account individual mathematical symbols and parts of equations and formulas, is ineffective and can lead to erroneous and even completely absurd conclusions. Possibilities of the most popular software systems Antiplagiat and iThenticate, currently used in scientific journals, are investigated for detecting plagiarism and self-plagiarism. The results of processing by the iThenticate system of specific examples and specific test problems containing equations and formulas are presented. It has been established that this software system, when analyzing heterogeneous texts, is often unable to distinguish self-plagiarism from pseudo-self-plagiarism, seeming real (but false and imaginary) self-plagiarism. A model complex situation is considered, in which the identification of self-plagiarism requires the involvement of highly qualified specialists of a narrow profile. Various ways to improve the work of software systems for comparing inhomogeneous texts are proposed. This article will be useful to researchers and university teachers in physics, mathematics, and engineering, programmers dealing with problems in image recognition and research topics of digital image processing, as well as a wide range of readers who are interested in issues of plagiarism and self-plagiarism.
안전보건교육의 실효성 제고 방안에 관한 연구 : VR·AR기반 체험교육 중심으로

Activities to prevent industrial accidents can be divided into management of disaster risk factors of production facilities, disaster prevention systems and procedures, and human factors management. Human factor management is the last and most effective means to minimize disaster incidence and loss costs. The key strategy for this is safety and health education. However, formal or fictitious education is still being carried out in the industrial field. In addition, it is true that the application of practical use is insufficient due to the simple theoretical education based on knowledge transfer. In order to change the safety consciousness of employers and workers, we think that the paradigm of education should be changed from the traditional text and video-oriented lecture education method to experience-oriented education. The purpose of this study is to measure and analyze the effectiveness of safety education through education contents using VR(Virtual Reality) and AR(Augmented Reality) technology in keeping with the changing times of the 4th Industrial Revolution, And to suggest the direction of future safety and health education and content development trend.
Characterizing Children's Motion Qualities: Implications for the Design of Motion Applications for Children

The goal of this paper is to understand differences between children's and adults’ motions in order to improve future motion recognition algorithms for children. Motion-based applications are becoming increasingly popular among children (e.g., games). These applications often rely on accurate recognition of users’ motions to create meaningful interactive experiences. Motion recognition systems are usually trained on adults’ motions. However, prior work has shown that children move differently from adults. Therefore, these systems will likely perform poorly on children's motions, negatively impacting their interactive experiences. Although prior work has established that there are perceivable differences between child and adult motion, these differences are yet to be quantified. If we can quantify these differences, then we can gain new insights about how children perform motions (i.e., their motion qualities). We present 24 articulation features (11 of which we newly developed) that describe motions quantitatively; we then evaluate them on a subset of child and adult motions from the publicly available Kinder-Gator dataset to reveal differences; motions in this dataset are represented as postures, each of which is defined by 3D positions of 20 joints tracked by a Kinect at a specific time instance. Our results showed that children perform motions that are quantifiably faster, more intense, less smooth, and less coordinated as compared to adults. Based on our results, we propose guidelines for improving motion recognition algorithms and designing motion applications for children.
Beating the classical phase precision limit using a quantum neuromorphic platform

Phase measurement constitutes a key task in many fields of science, both in the classical and quantum regime. The higher precision of such measurement offers significant advances, and can also be utilised to achieve finer estimates for quantities such as distance, the gravitational constant, electromagnetic field amplitude, etc. Here we theoretically model the use of a quantum network, composed of a randomly coupled set of two-level systems, as a processing device for phase measurement. An incoming resource state carrying the phase information interacts with the quantum network, whose emission is trained to produce a desired output signal. We demonstrate phase precision scaling following the standard quantum limit, the Heisenberg limit, and beyond. This can be achieved using quantum resource states such as NOON states or other entangled states, however, we also find that classically correlated mixtures of states are alone sufficient, provided that they exhibit quantum coherence. Our proposed setup does not require conditional measurements, and is compatible with many different types of coupling between the quantum network and the phase encoding state, hence making it attractive to a wide range of possible physical implementations.
On Explicit Constructions of Extremely Depth Robust Graphs.

A directed acyclic graph $G=(V,E)$ is said to be $(e,d)$-depth robust if for every subset $S \subseteq V$ of $|S| \leq e$ nodes the graph $G-S$ still contains a directed path of length $d$. If the graph is $(e,d)$-depth-robust for any $e,d$ such that $e+d \leq (1-\epsilon)|V|$ then the graph is said to be $\epsilon$-extreme depth-robust. In the field of cryptography, (extremely) depth-robust graphs with low indegree have found numerous applications including the design of side-channel resistant Memory-Hard Functions, Proofs of Space and Replication, and in the design of Computationally Relaxed Locally Correctable Codes. In these applications, it is desirable to ensure the graphs are locally navigable, i.e., there is an efficient algorithm $\mathsf{GetParents}$ running in time $\mathrm{polylog} |V|$ which takes as input a node $v \in V$ and returns the set of $v$'s parents. We give the first explicit construction of locally navigable $\epsilon$-extreme depth-robust graphs with indegree $O(\log |V|)$. Previous constructions of $\epsilon$-extreme depth-robust graphs either had indegree $\tilde{\omega}(\log^2 |V|)$ or were not explicit.
On algebraic structure of the Reed-Muller codes

It is known that the Reed-Muller codes over a prime field may be described as the radical powers of a modular group algebra. In this paper, we give a new proof of the same result in a quotient of a polynomial ring. Special elements in a prime field are studied. An interpolation polynomial is introduced in order to characterize the coefficients of the Jennings polynomials.
Observing a group to infer individual characteristics.

In the study of collective motion, it is common practice to collect movement information at the level of the group to infer the characteristics of the individual agents and their interactions. However, it is not clear whether one can always correctly infer individual characteristics from movement data of the collective. We investigate this question in the context of a composite crowd with two groups of agents, each with its own desired direction of motion. A simple observer attempts to classify an agent into its group based on its movement information. However, collective effects such as collisions, entrainment of agents, formation of lanes and clusters, etc. render the classification problem non-trivial, and lead to misclassifications. Based on our understanding of these effects, we propose a new observer algorithm that infers, based only on observed movement information, how the local neighborhood aids or hinders agent movement. Unlike a traditional supervised learning approach, this algorithm is based on physical insights and scaling arguments, and does not rely on training-data. This new observer improves classification performance and is able to differentiate agents belonging to different groups even when their motion is identical. Data-agnostic approaches like this have relevance to a large class of real-world problems where clean, labeled data is difficult to obtain, and is a step towards hybrid approaches that integrate both data and domain knowledge.