Reviewerly/exHarmony huggingface dataset:
Towards Accountability in CRS Generation

It is well known that several cryptographic primitives cannot be achieved without a common reference string (CRS). Those include, for instance, non-interactive zero-knowledge for NP, or maliciously secure computation in fewer than four rounds. The security of those primitives heavily relies upon on the assumption that the trusted authority, who generates the CRS, does not misuse the randomness used in the CRS generation. However, we argue that there is no such thing as an unconditionally trusted authority and every authority must be held accountable for any trust to be well-founded. Indeed, a malicious authority can, for instance, recover private inputs of honest parties given transcripts of the protocols executed with respect to the CRS it has generated. While eliminating trust in the trusted authority may not be entirely feasible, can we at least move towards achieving some notion of accountability? We propose a new notion in which, if the CRS authority releases the private inputs of protocol executions to others, we can then provide a publicly-verifiable proof that certifies that the authority misbehaved. We study the feasibility of this notion in the context of non-interactive zero knowledge and two-round secure two-party computation.
Kalman Filter Estimation and Its Implementation

In this chapter, we use the Kalman filter to estimate the future state of a system. We present the theory, design, simulation, and implementation of the Kalman filter. We use as a case example the estimation of temperature using a Resistance Temperature Detector (RTD), which has not been reported before. After a brief literature review, the theoretical analysis of a Kalman filter is presented along with that of the RTD. The dynamics of the RTD system are analytically derived and identified using Matlab. Then, the design of a time-varying Kalman filter using Matlab is presented. The solution to the Riccati equation is used to estimate the future state. Then, we implement the design using C-code for a microprocessor ATMega328. We show under what conditions the system may be simplified. In our case, we reduced the order of the system to that of a system having a 1st order response, that of an RC system, giving us satisfactory results. Furthermore, we can find two first order systems whose response defines two boundaries inside which the evolution of a second order system remains.
Efficient calculations of NSS-based gaze similarity for time-dependent stimuli

The degree of spatial similarity between the gaze of participants viewing dynamic stimuli such as videos has been previously measured using metrics which are based on the NSS (Normalized Scanpath Saliency). Methods currently used to calculate this metric rely upon a numerical grid, which can be computationally prohibitive for a variety of otherwise useful applications such as Monte Carlo analyses. In the present work we derive a new analytical calculation method for the same metric that yields equal or more accurate results, but with speeds than can be orders of magnitude faster (depending on parameters). Our analytical method scales well with dimensionality, and could also be of use for other applications. The drawback is that it can become very slow if the number of participants in the study is very large or if the gaze sampling rate is high. We provide performance benchmarks for a Fortran implementation of our method, and make available the source code developed.
Mutual Attention Graph Neural Network Based on Joint Representation of Nodes and Reviews for Recommendation

Recently, recommendation system have achieved good results by applying graph neural network to user-item inter-action graph. However, current graph neural network mainly deals with structured data and cannot deal with unstructured re-view text well. Item reviews are a unique way for users to choose to purchase the item. Therefore, combining a user-item interaction graph with related review text will obtain better recommendation performance. At the same time, most of the recommendation methods that have been proposed simply concatenate the representations from different modalities to make predictions. This can-not take advantage of the information from different modalities. To solve these problems, we propose a Mutual Attention graph neural Network (MAN) for personalized recommendation. MAN first extracts user/item node representation on user-item interaction graph through node feature extraction module, and extracts user/item review text representation through review feature ex-traction module. Then a mutual attention module is used to correlate node representation and review text representation, so as to capture the correlation between the node representation and the review text representation during the training process. Experimental results on three real-world datasets show MAN is better than the state-of-the-art personalized recommendation method.
Econometric Modelling and Forecasting Foreign Direct Investment Inflows in Nigeria: ARIMA Model Approach

This study examined econometric modelling and forecasting foreign direct investment inflows in Nigeria over the next decade using Box-Jenkins ARIMA model approach. The scope of the study is from 1970 to 2020. The correlogram show that the net foreign direct investment inflow in Nigeria is integrated of the first order. Based on the number of significant coefficients, highest adjusted R-squared, lowest volatility and the lowest SBIC and the AIC, the study estimated and presents the ARIMA (1, 1, 3) model. The diagnostic test also shows that the estimated model is not only consistent but good for forecasting the net foreign direct investment inflows in Nigeria and it also explains the dynamics around it. The result of the study shows that net foreign direct investment inflows in Nigeria are likely for exhibit very slow upward trend between 2.80 billion USD and 3.26 billion USD in the next decade which is not significantly different from values of FDI inflows in Nigeria in the recent years. The study also provide policy recommendations so as to assist policy makers and the Nigerian government on better ways to accelerate and maintain higher level of net foreign direct investment inflows in Nigeria.
Research on Physical Education Distance Education under the Background of "Internet +"

The development of network technology provides technical support for the development of sports network courses, and the introduction of policies provides institutional guarantee for the development of sports network courses in colleges and universities in China. The establishment of the network teaching platform for college physical education is the need of the present era and the inevitable trend of the development of college physical education. Only in this way can the physical education class be optimized, the students' interest in learning be promoted and their enthusiasm for learning be aroused under the background of the "Internet +" era. Under this background, it is very feasible to combine the Internet with distance education to build a "Internet + distance education" system and apply it widely to the education and teaching of college physical education courses. This paper mainly expounds the significance of distance education of physical education in colleges and universities under the background of "Internet +" era and some problems still existing in physical education teaching. At the same time, it further explores the distance education of physical education, hoping to provide reference for relevant workers.
Implementation of Blockchain-Based Security and Privacy in Energy Management

Digital infrastructure can be used to help communities to meet their energy needs by exploiting the community-based power generation network. The present scenario of power generation can be seen in industries, educational establishments, and other organizations, where these installations are using industrial solar panels in their buildings. The ability to control access of energy with two or more resources has not been convinced by authentication. Commonly, the implementation of an individual solar panel system in each resource is very sensitive and expensive. Hence, it is very important to provide accuracy of the grid environment for social communities, so that they can be benefited with minimal cost for accessing the shared distributed energy and authenticating the access control. Each energy resource is authenticated by the consensus mechanism using the proof of work once connected to the first block as a genesis block. Such energy resource supports web chained database architectures and links to encrypt the large volumes of energy-driven data with blockchain-based distributed cloud storage design. This chapter aims to examine the significance of blockchain technology in accessing its role to date in enhancing energy protection and performance based on its technological advantages. The blockchain technology will be implemented in energy management to secure and understand the consumption and utilization of energy levels to reduce the overburden and waste. In addition, a case study based on energy resource management in educational institutions is provided to demonstrate how well implementation of blockchain technology can be offered alternatives for community-oriented energy security to increase the quality of service.
Algorithm Research on Improving Halo Defects Based on Guided Filtering

In this paper, the improvement of guided filtering on halo defects in spatial color gamut mapping is proposed. The halo phenomenon is the main defect of the spatial color gamut mapping algorithm. This paper analyzes the principle and theoretical calculation method of guided filtering and proposes a new algorithm to improve the halo defect based on guided filtering. The mapping experiment of the new algorithm is carried out, and compared with the HPMinDE algorithm, the secondary algorithm in the paper. The comprehensive evaluation is carried out from three aspects of image color difference value, structural similarity, and algorithm running time. The results show that the new algorithm proposed in this paper has smaller color difference, larger structural similarity index and is closer to the original image, which verifies the feasibility and advantages of this algorithm. The research results of this paper have positive significance in the field of spatial color gamut mapping, and also have certain application value in the fields of edge detection, tone mapping, non-photorealistic rendering, and so on.
Random Grid-Based Visual Cryptography for Grayscale and Colour Images on a Many-Core System

Raviraja Holla, M. Pais, Alwyn R.The traditional visual cryptography (VC) is a technique used to encrypt the secret images in several shares with minimum computation so that there will be no processing for decryption. A conventional random grid-based VC is a different cryptosystem that generates an encoded grid one pixel at a time based on the pixel in the original image by using a single master grid. These encrypted grids are of the same size as the original image, unlike traditional VC. No other matrices are required to generate these grids. Despite its simplicity and flexibility, it is inefficient for the real-time applications due to its reduced efficiency in the large image sizes. Thus, it is necessary to exploit the currently prevalent many-core computing power to elevate this cryptosystem for achieving a better efficiency. This paper proposes a novel (2, 2) random grid-based VC that exploits a many-core system’s computational capability applied to grayscale and colour images. For more efficiency, this approach uses Compute Unified Device Architecture (CUDA) constant memory. This approach finds significance from the perspective of efficiency demand and the rapid growth in many-core computing. Experimental results proved that, the proposed method on a many-core system outperforms the normal random grid-based VC with an improved speedup.
WikiMorph: Learning to Decompose Words into Morphological Structures

This paper presents WikiMorph, a tool that automatically breaks down words into morphemes, etymological compounds (morphemes from root languages), and generates contextual definitions for each component. It comes in two flavors: a dataset and a deep-learning-based model. The dataset was extracted from Wiktionary and contains over 450k entries. We then used this dataset to train a GPT-2 model to generalize and decompose any word into morphemes and their definitions. We find that the model accurately generates complex breakdowns when given a high-quality initial definition.
Research on Reinforcement Learning Algorithm for Path Planning of Multiple Mobile Robots

Abstract Reinforcement learning algorithm can better accelerate the alternately between the robot and the environment, accelerate the robot through trial and error iterative reinforcement learning process, and automatically carry out state assessment, so as to realize the gradual perception and learning of the environment, so it has important research value. Based on this, this paper first analyses the path planning of multiple mobile robots, then studies the model and function of reinforcement learning algorithm, and finally gives the path planning of multiple mobile robots based on reinforcement learning algorithm.
Implementation of Large Instrument Sharing Platform Based on District, University and Enterprise

Abstract Large-scale instruments sharing platform has been completed based on using Java language to program, and using spring Boot framework technology for building the background project, and using MySQL database to store the data, and using Vue to design the front-page, which will been shared and used in District, University and Enterprise. The test system of eclipse is used for case test to realize the sharing function of large-scale instruments. Through this platform, the instruments’ information and leasing of large-scale instruments can be understood in real time, and the status information, online use records, equipment using experience, and online booking, comments and customer service consultation can also be carried out on the platform, so as to make rational and efficient use of large-scale instruments, reduce the using waste of large-scale instruments, and be more conducive to the sustainable development of economy, science and technology and expand the scope of resource sharing.
On cyclic algebraic-geometry codes

In this paper we initiate the study of cyclic algebraic geometry codes. We give conditions to construct cyclic algebraic geometry codes in the context of algebraic function fields over a finite field by using their group of automorphisms. We prove that cyclic algebraic geometry codes constructed in this way are closely related to cyclic extensions. We also give a detailed study of the monomial equivalence of cyclic algebraic geometry codes constructed with our method in the case of a rational function field.
Arbitrary Perspective Crowd Counting via Local to Global Algorithm

Crowd counting is getting more and more attention. More and more collective activities, such as the Olympics Games and the World Expo, are also important to control the crowd number. In this paper, we address the problem of crowd counting in the crowded scene. Our model accurately estimated the count of people in the crowded scene. Firstly, we proposed a novel and simple convolutional neural network, called Global Counting CNN (GCCNN). The GCCNN can learn a mapping, transforms the appearance of image patches to estimated density maps. Secondly, The Local to Global counting CNN (LGCCNN), calculating the density map from local to global. Stiching the local patches constrains the final density map of the larger area, which make up for the difference values in the perspective map. In general, it makes the final density map more accurate. The dataset we used is a set of public dataset, which are WorldExpo’10 dataset, Shanghaitech dataset, the UCF_CC_50 dataset and the UCSD dataset. The experiments have proved our method achieves the state-of-the-art result over other algorithms.
Collaborative Foreign Language Teaching Mode Based on Multimedia Network Technology

Multimedia teaching combines sound, text and image organically by means of modern science and technology, enriches teaching means, and plays a great role in stimulating students' interest in learning and expanding their knowledge. The evaluation of Multimedia English teaching effect has become an important research topic in College English Teaching in the new era. It can guide teachers how to make full use of the advantages of new technology in English teaching, and further expand the classroom teaching function. This paper proposes a collaborative multimedia teaching model, which has wide applicability. The experimental results show that this method is more efficient than traditional multimedia teaching in English teaching. This method has a certain reference value for the research of College English teaching evaluation in the new era.
Visualising Scientific Topic Evolution

The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.
Design and Development of Human-Computer Interaction through Judicial Expert System for Violence against Female

This paper focuses on Human-Computer interaction by designing and implementing for Crimes against Women Judicial Expert System (CAWJES). This CAWJES imitates the role of a lawyer or guidance counselor of NGO to offer a virtual mentor. Lawyer or legal advisers will at all times use this system to determine the crime happened on the women. However, CAWJES designed to change this manual system into computerized system. The CAWJES is rule-based expert system. Development of CAWJES carried out by using expert system development life cycle. This paper focuses on Human-Computer Interaction (HCI) applied in CAWJES.
Spherical Harmonics, Splines, and Wavelets

This contribution substantially represents a geodetically relevant collection of particularly valuable material in the diverse approximation areas involving spherical harmonics, splines, and wavelets, thereby establishing a consistent and unified setup. The goal of the work is to preferably convince members from geodesy that spherically oriented approximation provides a rich mathematical cornucopia that has much to offer to a large palette of applications. Geomathematically it reflects both the approximate shape of the Earth’s surface and the typical satellite geometry of a low Earth orbiter (LEO). Our essential interest is in reconstruction and decomposition characteristics corresponding to different types of data on spheres and various observables naturally occurring in geodetic context, when efficient and economic numerical realizations are required. Another objective is to provide an addition to the library of any individual interested in geodetically reflected local as well as global spherical approximation theory.
Privacy Issues in Contact Tracing Applications

As the spread of coronavirus disease 2019 (COVID-19) peaked worldwide, many countries developed contact tracing applications (CTAs) in an attempt to control the epidemic. However, CTAs collect users’ personal information and track their locations or contacts, which raises privacy concerns about users’ data. In this study, we seek to ascertain whether trust is the factor that most assuages users’ privacy concerns about these applications. We compare twelve CTAs from various countries, and analyze the factors regarding users’ trust and privacy concerns about these applications. Then we develop a questionnaire to verify if trust helps reduce users’ privacy concerns about CTAs.
Building a Classifier of Behavior Patterns for Semantic Search Based on Cuckoo Search Meta-heuristics

The paper deals with the problems of extracting and classifying the thematic categories representing user preferences to increase the pertinence of semantic search in the knowledge-based information systems. The authors propose building the classifier of behavioral user profiles by analyzing the information requirements, implicit preferences, and knowledge resources interaction. The method is based on the probabilistic algorithm for Expectation-Maximization clustering of user characteristics and distributed knowledge resources to generate the structure of the classifier input parameters. Optimization is implemented by the mechanism of selecting informative features based on revealing user latent interests and the resource ability to satisfy the users. To reduce the search space, the paper proposes a bio-inspired algorithm based on the cuckoo search. The algorithm is compared with fuzzy c-means algorithm on Machine Learning Repository of the University of California, Irvine. The results show the effectiveness and prospects of the proposed method in the semantic search systems.
KORELASI ANTARA MOTIVASI BELAJAR DAN TINGKAT EKONOMI ABAD 21

The economic level of the family has an important role in the development of children. With sufficient economic conditions, children will get wider opportunities to develop themselves and pursue the level of education they want. Parents who have sufficient economic conditions certainly have the opportunity to devote sufficient attention to their children, especially in terms of learning. In conclusion, using a significant level of 5%, then this hypothesis is accepted. That is, there is an influence between the family economy level on student learning motivation. To find out the high and low correlation of the influence between the family economic level on student learning motivation, we can see in the conservative size table. Based on the conservative size, the value of rxy = 0.345 is in the range of 0.200 - 0.400 with a low interpretation. From the results of analysts and hypotheses we can know that there is an influence between the family economy level on student learning motivation. While the magnitude of the influence between family economic levels on low learning motivation.
Checking the identity of the state of qubits using the CSWAP operator

Currently, quantum computing and quantum computers are beginning to actively penetrate into an increasing number of areas and begin to go beyond the scope of only scientific theoretical activity. Therefore, now it is very timely to study in detail the elements of quantum programming, which, quite possibly, in the near future, can be actively used, including in industrial automation. In this work, we will get acquainted with such an element of quantum programming as CSWAP, analyze the logic of its operation on the classical scheme for checking two qubits for equality, and also build and test a circuit that allows checking for equality of two qubits that are in a state of superposition.