Reviewerly/exHarmony huggingface dataset:
Secret Sharing with Statistical Privacy and Computational Relaxed Non-malleability

Goyal and Kumar (STOC ’18, CRYPTO ’18) initiate the study of non-malleability for secret sharing and proposed the definition of information-theoretical non-malleability for secret sharing. Subsequently, Brian, Faonio, and Venturi (CRYPTO ’19, TCC ’19) proposed computational variants of non-malleability for secret sharing and showed that by focusing on computational non-malleability, it is possible to construct more efficient schemes compared to the existing ones. However, their schemes have a drawback that they do not satisfy statistical privacy. In this paper, we propose a new definition of computational non-malleability for secret sharing in the public parameter model. Although our definition is relaxed compared to the one proposed by Brian et al., it captures a strong security notion called non-malleability against overlap-joint tampering. Then, we show how to transform any secret sharing scheme into the one satisfying our computational non-malleability with small efficiency overhead. This transformation has a nice property that it preserves the statistical privacy of the underlying secret sharing scheme. Thus, through our transformation, we can obtain efficient secret sharing schemes satisfying computational non-malleability and statistical privacy. We achieve this transformation using lossy encryption which satisfies IND-CCA security in the injective mode.
EXPERIMENTAL PEDAGOGICAL ACTIVITY OF THE SCHOOL ON ECOLOGICAL AND PATRIOTIC EDUCATION OF PUPILS

Анализируется опыт воспитательной деятельности школы в рамках реализации экспериментальной программы эколого-патриотического воспитания. Рассмотрены основные подходы и содержание организации воспитательной работы, обозначены личностные показатели результата воспитательной работы. Представлены диагностический результат повышения показателей уровня воспитанности и модель выпускника школы в качестве результата воспитательной деятельности. В качестве основных характеристик деятельности выявлены следующие: экологическая и гражданская ответственность, интерес к этноэкологическим знаниям, отзывчивость, общественная и творческая активность. Рассмотрены результаты участия в конкурсах и олимпиадах в рамках реализации эколого-патриотической программы. The article analyzes the experience of educational activities of the school in the framework of the experimental program of ecological and patriotic education. Based on the state standard and taking into account national characteristics, the educational system of the school is aimed at developing student‘s ecological culture, patriotism, which consists of a responsible attitude to the nature of the native land, to the historical past of the Motherland, and education of civic responsibility. Environmental responsibility and civil responsibility have common tasksprotection of native nature from the harmful influence of people, preservation of monuments of the Fatherland and nature. Ecological culture-the quality of a person, the degree of moral attitude to the world around them, to nature, a sense of responsibility for the ecological state of their country, their small homeland. The experimental program is based on interdisciplinary and systematic approaches that involve: orientation to the development of personal qualities of the student, comprehensive personal development, active involvement in environmental and patriotic activity. The system-activity approach is a modern approach to the organization of the educational process, in which the student’s cognitive activity is accompanied by practical activities, and their own creative experience is acquired. The content of the organization of educational work is revealed, personal indicators of the result of educational work are indicated. To the pedagogical conditions of increase of efficiency of ecological and Patriotic education are: to integrate environmental and patriotic education, the use of ethnocultural means of education, the process of ecological and patriotic education of students involves the use of active forms and methods of educational work. The article presents the diagnostic result of improving the level of education and the model of a school graduate as a result of educational activities. The main characteristics identified are: environmental and civil responsibility, interest in ethno-ecological knowledge, responsiveness, social and creative activity. The results of participation in competitions and Olympiads within the framework of the ecological and patriotic program are considered.
Bo-Tree: An Efficient Search Tree

In today's world, large volumes of data are being generated through various automated data collection tools. Managing and processing of this data is time consuming task which demands an efficient search algorithm. In this paper, an efficient search tree named "BO-Tree", a Balanced "O" structure of the tree is introduced. Nodes in the BO-Tree are organized into various levels and each level is divided into sections for efficient searching of data. A Reference array is created and used to search for data in BO-Tree. Reference array stores a set of minimum and maximum values of each level and its sections along with their corresponding addresses in BO-Tree. BO-Tree is search efficient in all cases of time complexity, with the usage of Reference array, when compared with other existing trees.
Tax-KG: Taxation Big Data Visualization System for Knowledge Graph

Although large scale knowledge graph (KG) has drawn lots of attentions in academia and industry, it's still quite difficult for ordinary users to access KG directly because of the complex structure and query process of KG. Based on the anonymous corporate taxation information related big data, we develop a corporate taxation big data visualization system to help user navigate KG more easily. Through the system, users could get a visualized overview of tax big data and solve specific query issues which traditional relational database cannot complete. We simplify the build processes of system, expand capacity of node to billions and improve the query efficiency greatly. Moreover, the system and method of knowledge graph construction can be applied for other majors easily.
Soft Measurement of Drum Boiler Water Level Based on Deep Belief Network

Drum is an important part of thermal power unit, its water level control is directly related to the performance and safe operation of the unit. There are still many deficiencies in the existing water level control methods, and the control performance needs to be further improved. To solve these problems, a soft sensing method based on deep belief network is proposed to control the boiler drum water level. Firstly, by analyzing the key conditions that can affect the water level, the auxiliary variables related to the controlled object are selected. Then, deep belief networks are trained by using historical operating data. Finally, the BP neural network is compared with the proposed method. Experimental results show that the prediction accuracy of the proposed method is significantly higher than that of the comparison method, and the proposed method has the ability to predict the false water level in advance.
Research on acrostic poem generation based on handwritten Chinese character recognition and machine learning

In this paper, we study the automatic generation of acrostic poems based on handwritten Chinese character recognition and machine learning. First, AlexNet network and ResNet50 network are used to train the handwritten Chinese character data set respectively. Select ResNet50 which recognition accuracy is 82.56% as the input module of poem generation model. After this, the GPT-2 model is used to train 26,000 five-character poems in order to build an acrostic poem generation model. Finally, combine these two parts. If we input four images of handwritten Chinese characters, the system can automatically generate a fluent acrostic poem headed by these four characters after character recognition. Experiment results verify the effectiveness of the method.
OSN: Onion-ring support neighbors for correspondence selection

Correspondence Selection (CS) identifies the reliable correspondences from the putative ones. It provides fundamental cues for many computer vision tasks, therefore the performance and efficiency of CS are pivotal. Previous methods are either accurate but time-consuming process or efficient but ambiguous. It is difficult to strike a good balance between performance and efficiency. In this paper, we propose the Onion-ring Support Neighbors (OSN) method for CS. OSN can fully exhaust the coherency information of neighbors to identify all reliable correspondences but it only requires the O(n) time and space complexity. To our best knowledge, we are the first to prove that the performance of CS can be improved by considering information from more neighbors. To this end, onion-ring shaped support neighbors are introduced, whereby the directional relationships can be eliminated from the central region to neighbor regions, and rotation invariance in putative correspondences can be retained. To efficiently identify the reliable correspondences, we present the Fixed Correspondence algorithm. This algorithm can reduce the search space of putative correspondences within constant time, which contributes to a O(n) time complexity of the overall OSN process. Extensive experiments over widely used datasets show that OSN achieves state-of-the-art performance in F1-measure and Pose estimation.
Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning

This study develops a robot mobility policy based on deep reinforcement learning. Since traditional methods of conventional robotic navigation depend on accurate map reproduction as well as require high-end sensors, learning-based methods are positive trends, especially deep reinforcement learning. The problem is modeled in the form of a Markov Decision Process (MDP) with the agent being a mobile robot. Its state of view is obtained by the input sensors such as laser findings or cameras and the purpose is navigating to the goal without any collision. There have been many deep learning methods that solve this problem. However, in order to bring robots to market, low-cost mass production is also an issue that needs to be addressed. Therefore, this work attempts to construct a pseudo laser findings system based on direct depth matrix prediction from a single camera image while still retaining stable performances. Experiment results show that they are directly comparable with others using high-priced sensors.
A Code Similarity Detection Algorithm Based on Maximum Common Subtree Optimization

The code similarity detection is different from the traditional text duplication checking. The former has a lot of the same syntax content in the code. There are two code duplication detection algorithms. One is realized by extracting and counting characteristic attributes, which can result in a lack of the logical relationship between code structures. The other is realized by abstracting code into a string, tree structure or graph structure, which can lead to a lack of codes' semantic features. To rectify these deficiencies, an optimization algorithm based on maximum common subtree is proposed. First of all, the structural information based on the largest common subtree is extracted to calculate the structural similarity of codes. After this, the semantic information based on the longest common subsequence is extracted to calculate the semantic similarity of codes. Finally, the semantic similarity and structural similarity are assigned different weights using TF-IDF algorithm. Experimental results show that, the optimization for code similarity detection based on the maximum common subtree is able to reduce the non-plagiarism similarity of codes and keeps more feature information than the traditional code duplication checking algorithm.
A method of English-Chinese language recognition and its application in oral English learning system

In this paper, Gammatone Frequency Cepstrum Coefficients (GFCC) and Shifted Delta Cepstra (SDC) hybrid model were used to extract the speech feature parameters, and Gaussian Mixture Model-Universal Background Model (GMM-UBM) was used for language recognition. Taking oral English speeches of Chinese students as a corpus, we developed an English-Chinese language recognition module and applied it to the oral English learning system. The module could identify whether the student's answer language was English, which could increase the intelligence of the learning system. The experiment results showed the language recognition system introduced in this paper had a higher recognition accuracy and enhanced the function of the learning system.
SVHF: A Lightweight Stream Cipher Based on Feistel and OFB

For the demand of the resource constrained mobile terminal to the lightweight cipher, this paper presents a new lightweight stream cipher, which is based on the Feistel and OFB mode called SVHF. Similar to many other lightweight stream ciphers, the key-stream size of SVHF is 128-bit while the IV is specified to be 128 bits. Our security evaluation shows that SVHF can achieve enough security margin against known attacks, such as differential cryptanalysis, linear cryptanalysis and impossible differential cryptanalysis. Furthermore, SVHF can be implemented efficiently not only in hardware environments but also in software plat-forms. Hardware implementation of SVHF requires about 1632GE, which is comparable with the 2194 GE hardware implementation of WG-7. The software implementation of SVHF on 8-bit microcontroller is about 50.82 Mb/s, and its efficiency is 4 times as much as that of WG-7 in RFID environment.
Do We Need Depth in State-Of-The-Art Face Authentication?

Some face recognition methods are designed to utilize geometric information extracted from depth sensors to overcome the weaknesses of single-image based recognition technologies. However, the accurate acquisition of the depth profile is an expensive and challenging process. Here, we introduce a novel method that learns to recognize faces from stereo camera systems without the need to explicitly compute the facial surface or depth map. The raw face stereo images along with the location in the image from which the face is extracted allow the proposed CNN to improve the recognition task while avoiding the need to explicitly handle the geometric structure of the face. This way, we keep the simplicity and cost efficiency of identity authentication from a single image, while enjoying the benefits of geometric data without explicitly reconstructing it. We demonstrate that the suggested method outperforms both existing single-image and explicit depth based methods on largescale benchmarks, and even capable of recognize spoofing attacks. We also provide an ablation study that shows that the suggested method uses the face locations in the left and right images to encode informative features that improve the overall performance.
Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection

Model-based approaches have been widely utilized to investigate multi-dimensional positive features for the purpose of gaining beneficial knowledge. In fact, feature selection is a critical and challenging task when modeling data that are represented in high-dimensional features spaces, which is due to the fact that some of these features maybe irrelevant to the learning process. As a result, a statistical mixture model, which is capable of dealing with multi-dimensional vectors, is developed to tackle the issue of clustering positive vectors. Simultaneously, we take into consideration the irrelevant features that could compromise the proposed model accuracy. The maximum likelihood (ML) method is performed via expectation maximization (EM), and employed for estimating the parameters of the proposed model. Furthermore, experiments are conducted using real-life applications, which include texture, shape and scene images to investigate the performance of our approach.
Incremental Automatic Vehicle Control Algorithm Based on Fast Pursuit Point Estimation

Image-based autonomous driving control is one of the important research directions in the field of autonomous driving. Most of the existing image-based control algorithms use end-to-end mapping from image to vehicle control amount, which is not explanatory enough, and the control amount is not intuitive enough to effectively implement human-machine collaborative control and incremental learning of models. This paper proposes an incremental learning algorithm for driving vehicle control based on fast pursuit point estimation. We establish a model to calculate the mapping of image to the pursuit point, and then get the actual control amount of the vehicle throttle value and front-wheel rotation angle value by the pursuit point. Combining the features of pursuit point which can be observed intuitively and has obvious physical meaning, we propose an incremental model updating method based on man-machine collaborative control, which can incrementally improve the model performance in the actual driving process of vehicles. Finally, the experiment of automatic control is carried out on the Carla simulation platform. The experimental results show that the algorithm can incrementally improve the performance of the automatic control model, with the average calculation speed over 50fps. The autonomous driving system realizes automatic cruise in the real campus environment.
A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions’ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human–machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system’s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.
Comparative Study of Visual Question Answering Algorithms

Visual Question Answering (VQA) is a recent task that challenges algorithms to reason about the visual content of an image to be able to answer a natural language question. In this study, we compare the performance of state of the art VQA algorithms on different VQA benchmarks. Each benchmark is more effective at testing VQA algorithms on different levels. Some datasets challenge the algorithms to perform complex reasoning steps to arrive to an answer. Other datasets might challenge algorithms to retrieve external world knowledge to answer the posed questions. We categorize the algorithms by their main contributions into 4 categories. Firstly, the joint embedding approach which focuses on how to map the visual and textual data into a common embedding space. Secondly, attention based methods which focuses on relevant parts of the image or the question. Thirdly, compositional models which deal with composing a model from smaller modules. Finally, we introduce external-knowledge based algorithms which need external sources to be able to retrieve facts necessary to answer a question when those facts may not be present in the scene nor in the whole training data set. We also mention other algorithms that don't specifically belong to the aforementioned categories, but offers performance competitive with the state of the art.
Education Program to Produce Electric Vehicles Driven by Renewable Energy

In the School of Engineering at Tokyo University of Technology, we are promoting the “Education program to produce electric vehicles (EVs) driven by renewable energy” with the aim of enabling students to learn practical science through manufacturing. In this project, students in the School of Engineering produce EVs through extracurricular activities. In the last three years, we have participated in three off-campus races for energy conservation with small carts, and produced the first large-scale EV in cooperation with three departments in the School of Engineering. The following measures have been taken to maintain the motivation of the students : leader meetings to unify the goals of teachers and students, coordination between teachers and leaders, participation in off-campus competitions, and social gatherings that are fun for the participants.
Exploring interdisciplinary nature of postgraduate research in the field of Computing using Text mining: a case study

The aim of this article is to determine whether a compartmentalized curriculum at undergraduate lead to similar silos appearing in postgraduate research outputs. For this, data in the form of subject contents of undergraduate studies were obtained from the prospectus of various academic departments in the faculty. Additionally, data in the form of abstracts of research articles published by the postgraduate researchers in these departments were obtained. A total of 118 articles published between January 2016 and May 2020 was extracted from Scopus database. K-means algorithm was used on the corpus consisting of abstract dataset to obtain the clusters. Topic modelling using Latent Dirichlet Allocation (LDA) was then applied to obtain the main topics of the clusters. It was observed that three clusters were adequate in explaining a high percentage of variance in the data and there exists a substantial overlap in the main topics of the three clusters.
Research on Evaluation Index of Airborne Ammunition Support Equipment Optimization

Through organizing the equipping and usage of current airborne ammunition support equipment, this paper analyzed the corresponding support efforts and determined the procedure and equipment list of single equipment based on present support mission, obtained the sequence diagram of single equipment support by adopting the IDEF3 approach, analyzed the influential factors of airborne ammunition support equipment optimization via the DEMATEL methodology, and established the evaluation index system of airborne ammunition support equipment optimization.