Reviewerly/exHarmony huggingface dataset:
Updatable Linear Map Commitments and Their Applications in Elementary Databases

Linear map commitments allow the prover to commit to a vector, with the ability to prove the image of a linear map acting on the vector. In this paper, we propose linear map commitments with updatable feature and perfectly hiding property. Updatable feature means that the prover can update the commitment more efficiently than recompute the commitment when some of the entries in the committed vector are changed. Perfectly hiding property ensures the commitment reveals no information about the committed vector before opening. Then we present the implementation of our updatable linear map commitment (ULMC) over the 256-bit BN curve recommended in the SM9 standard, which provides around 100-bit security. The implementation shows that our ULMC schemes are efficient enough to support the elementary database constructions that simultaneously permit batching membership test, linear combination test, updatable feature and authenticity. Finally, we show that the ULMC-powered elementary databases are capable of supporting various applications where privacy and trust are the first priority such as exam result management systems, Internet of Things (IoT) management systems and business operations between banks and enterprises.
Real-time AUV Path Planning Algorithm in Complex Marine Environment

To solve the problem of AUV path planning in complex marine environment, this paper proposes a real-time path planning algorithm that can be used under the influence of multiple marine environment elements based on the level set algorithm, combined with the idea of artificial potential field and adaptive rolling window: firstly, Artificial potential field is introduced in the algorithm to vectorize various ocean environment elements such as water depth and construct a level set function related to the environment closely; secondly, in a dynamic ocean environment, by limiting the evolve of the zero level set curve in the adaptive rolling window internally to realize real-time AUV path planning. Based on the real marine environmental data, a three-dimensional marine environmental model is established. The simulation results show that the algorithm proposed in this paper can adjust the path in time according to the dynamic changes of the complex marine environment, and obtain the path trajectory with the best evaluation value.
VRTwitch: Enabling Micro-motions in VR with Radar Sensing

Micro-motions are often difficult to incorporate in Virtual Reality (VR) while macro-motions are a popular interaction method, due to technological limitations with VR tracking methods. In this poster, we introduce VRTwitch, a forearm-mounted wearable device that is able to sense micro hand motions. VRTwitch uses an array of reconfigurable miniaturized radar sensors placed around the hand to capture subtle finger movements for gesture detection towards enhanced interaction in VR space. We created a simple interactive VR shooting game that requires precise finger motion for virtual gun manipulation as a demonstration.
Building upon the Foundations of Branching Space-Times

Abstract This chapter introduces a variety of events that are definable in BST and discusses in which histories these events occur. This gives rise to the concept of the occurrence proposition for events of various kinds. Of particular interest are transitions, defined as pairs of events, one of which is appropriately below the other. Transitions play a crucial role in later chapters. The chapter then discusses the topological aspects of BST, which are picked up again in Chapter 9. It defines a natural topology for BST: the diamond topology, and describes some important facts about it, focusing on the Hausdorff property and local Euclidicity. The chapter also gives an overview of how BST structures may be used to build semantic models for languages with temporal and modal operators.
Treator: a Fast Centralized Cluster Scheduling at Scale Based on B+ Tree and BSP

Centralized cluster scheduling at scale achieves better task scheduling quality but incurs longer scheduling delay than decentralized scheduling, and vice versa. Moreover, scheduling policies may be application-specific, making it hard to employ only one scheduling policy to achieve short scheduling delay as well as high scheduling quality at the same time. This paper proposes a centralized cluster scheduling scheme which achieves short delay as well as high quality, named Treator. The key innovation is the use of two B+ trees and a BSP model in cluster scheduling. One B+ tree is used to maintain the states of cluster resources and the other one stores the resource indexes. The BSP model is leveraged to perform parallel scheduling based on the B+ trees, aiming to make the scheduling delay as short as possible. We implement Treator on Mesos and compare it against the scheduling policies used in Mesos and TPShare. The results show that Treator can significantly reduce the scheduling delays and improve application performance over Mesos and TPShare. In addition, Treator can also reduce the CPU consumption of a Mesos managed cluster by 21%.
Generalized and Multiple-Queries-Oriented Privacy Budget Strategies in Differential Privacy via Convergent Series

For data analysis with differential privacy, an analysis task usually requires multiple queries to complete, and the total budget needs to be divided into different parts and allocated to each query. However, at present, the budget allocation in differential privacy lacks efficient and general allocation strategies, and most of the research tends to adopt an average or exclusive allocation method. In this paper, we propose two series strategies for budget allocation: the geometric series and the Taylor series. We show the different characteristics of the two series and provide a calculation method for selecting the key parameters. To better reflect a user’s preference of noise during the allocation, we explored the relationship between sensitivity and noise in detail, and, based on this, we propose an optimization for the series strategies. Finally, to prevent collusion attacks and improve security, we provide three ideas for protecting the budget sequence. Both the theoretical analysis and experimental results show that our methods can support more queries and achieve higher utility. This shows that our series allocation strategies have a high degree of flexibility which can meet the user’s need and allow them to be better applied to differentially private algorithms to achieve high performance while maintaining the security.
Modeling Multi-Intent Basket Sequences for Next-Basket Recommendation

Recommendation systems have a preponderance in assisting customers to save time by suggesting relevant options. With this convenience, a customer may purchase multiple items in a browsing session, referred to as an item basket. The notion of basket manifests his underlying preference of multiple implicit intentions, which becomes more sophisticated once considering the basket sequence of his chronological intersession list. With the objective of modeling basket sequences, most of previous methods hypothesize a homogeneous intention in each basket. The exploitation on multi-intent basket sequences for the recommendation task becomes an emerging demand. In this work, we present a novel framework named MIBS to model multi-intent basket sequences to recommend next basket of relevant items. Given a user's basket sequence, each basket is encoded via aggregating the item-item correlation matrix with a latent intent parameter matrix to generate the respective basket representation. This representation is later fed into a LSTM layer to infer the sequential encoding, which is also combined with the correlation matrix and the multi-intent matrix to produce item scores. The top-K items with the highest scores are employed to form the next-basket suggestion. Comprehensive experiments on three publicly-available datasets demonstrate the superiority of MIBS compared against state-of-the-art baselines for the next-basket recommendation task.
Student’s Perception of Higher Education in Technical Universities and Its Connection With Subsequent Professional Development (MSTU, MADI and MIREA - Comparative Analysis) (Continuation Part 3)

This article is a continuation of the previous publication devoted to the presentation of the results of a comparative study of students of technical universities: University of Bauman, MADI and MIREA. These results were obtained using a sociological questionnaire with closed-type questions. The questionnaire contained nine main thematic blocks: 1. Professional choice; 2. Profession; 3. Study and education; 4. Educating; 5. Professional and personal competence; 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. This article is primarily devoted to the further presentation of the results of a comparative study of students of technical universities: University of Bauman, MADI and MIREA. In it, we will present the data obtained when using two blocks sociological questionnaire: 4. Educating; 5. Professional and personal competence. At the same time, we also want to supplement them with the results of our previous research of students – first of all students of MSTU. In the last article, we outlined the most common trends found in all MSTU students when they were examined using the questionnaire “Studying at university in the perception of a student” and “Career forecast after graduation”. In this article, we will consider the specific features found in students studying at various faculties of the Bauman Moscow State Technical University.
Towards the Development of a Task Oriented Robotic Assistance in Vertical Farming

This paper focuses at the planning of a robotic assistive system in a modular vertical farm facility. Smart vertical farms are designed particularly for indoor systems with artificial environment control covering a small area of land but utilizing dimensions through vertical height. A layout of a proposed vertical farm is presented with a 4-Degrees of Freedom (DoF) robotic manipulator to cover three major tasks. The Task Space Locations (TSLs) are identified based upon the transplantation of the seedlings from the seedling tray into the pots of the hydroponic system. The prescribed tasks are simulated for a 4-DoF Kinova manipulator within the design vertical farm environment using Robot Operating System (ROS). It is also planned to develop a work cell for experimental validation of the planned assistance.
The application of big data real-time stream processing technology in earthquake rapid report

This paper briefly introduced SeisFLINK, a concurrent big data real-time stream processing platform established based on Apache Kafka and Apache Flink according to the actual requirements of earthquake rapid report. By comparing the actual earthquake monitoring data with the monitoring data of SeisComP3 and CENC, this paper summarized the the advantages of earthquake monitoring and rapid report of this system.
PRINCIPLES OF BUILDING A MODEL FOR TRAINING CADETS

The article outlines the following principles for building a model of a responsible approach to teaching cadets: – diversity - direction of the complexity of the program materials for cadets for mastering by adapting to different levels; – differential (difference) - a grouping of cadets based on a mixed differential (according to the level of knowledge, degree, motivation, interests, demand and skills); – subjectivity - the uniqueness and individuality of each student. acceptance of differences, uniqueness, the right to choose and the existence of a unique vision system; The importance of recognizing the student as a key figure in the entire educational process, as a guiding personality-oriented learning model, is stated: – development of the trainee as a person, expansion of experience as a person; – building the maximum means of pedagogical influence on the experience of cognition and communication; And different types of learning process: – synchronization of the public; – individual (individual) experience; – self-government as a path to professional growth, etc.,
Quantitative analysis of Sesotho sa Leboa part-of-speech taggers

This study reported in this article conducts a quantitative analysis of the Sesotho sa Leboa National Centre for Human Language Technology (NCHLT) part-of-speech annotated data set, and compares the quality of the NCHLT and CTexT part-of-speech taggers based on the data set. The two taggers were developed as part of the NCHLT Text project and are both based on Taljard et al.’s fine-grained tagset, aligned to the morphological structure of Sesotho sa Leboa. A gold standard data set of 7 153 tokens is utilised for comparison and evaluation of the overall performance of the two part-of-speech taggers and to perform fine-grained error analysis. We find that the NCHLT and CTexT taggers obtain 88.40% and 94.18% accuracy respectively and describe the linguistic nature of the most frequent errors observed in the two taggers.
Earthquake Disaster Mitigation Based on Peak Ground Acceleration with Multi-Sensor System

Indonesia is a country at the meeting point of tectonic plates, so it will likely experience the natural phenomenon of earthquakes. Earthquakes are natural events that humans cannot predict and cause fatal impacts, such as the number of fatalities and damage to buildings that cause a lot of material losses. This system was created to reduce the secondary effect of natural disasters. With the existence of an earthquake mitigation system in the form of an alarm warning and auto cut-off system, it is hoped that this system can minimize damage, loss of life and increase the level of earthquake alertness. Along with the development of technology, one of the main objectives of this research is to make people aware of evacuating immediately after the alarm and cut off some crucial flows such as electricity, gas, and others. This system utilizes multi-sensor technology, the Internet of Things, and Artificial Intelligence. This system implements 2 algorithms, such as K-Nearest Neighbor (KNN) and Logistic Regression. In KNN with 3 active sensors, the resulting accuracy is 94%. While Logistics Regression with 10 active sensors, the resulting accuracy is 83%.
CYBERSPACE AS A SPACE: ANALYSIS OF METAPHORICAL STATEMENTS OF UNIVERSITY STUDENTS

Over the last thirty years, technology has created a new space (cyberspace) where people meet each other, seek information, or simply try to navigate through. However, there is no consensus in research on the character of cyberspaces and the extent to which they are real. In the first systematic empirical research of this nature, the study found an answer to this question through a survey of metaphorical accounts of university students in Information Studies, and Librarianship (N=102) collected over three years (2019-2021). Cyberspace is a real space in students' experiences, language, and thought structures. A space that allows movement, orientation, and search to be related with one another. An environment in which cognition, learning, and knowledge are structuring activities. Learning and cognition in this space occur differently than in the physical environment, which poses a challenge for developing specific didactic practices and social programs for students. Students perceive cyberspace as linked to the need to acquire new epistemic tools to help them overcome the crisis of knowledge they experience through this space. Keywords: cyberspace, didactic practices, information literacy, metaphors, pragmatism, tacit knowledge, on life
A NOVEL IMAGE PROCESSING ALGORITHM TO AUTOMATICALLY EXTRACT AND CLASSIFY LIVER BEARING TUMORS USING PATTERN RECOGNITION AND FUZZY TRANSFORM

This study presents a novel liver disease classification method by applying pattern recognition technique to automatically segmented liver from the images of computed tomographic (CT) scans. The methodology comprises of disease classification by the extraction of textural features from focal liver region bearing tumors. Two types of liver textures are investigated in this study for classification accuracy judgement. First, original liver texture is considered for feature extraction. Second, liver is used for feature extraction. The CT image dataset comprises 308 liver samples with 193 samples of malignant tumor and 115 samples of benign tumor. The entire liver tissue bearing tumor is segmented from the CT image automatically in the pre-processing stage using fuzzy transformation function and morphological processing. Four sets of textural feature matrices are applied to the liver for feature extraction. Gray level co-occurrence matrix (GLCM), standard deviation gray level co-occurrence matrix (SDGLCM), seven-moment matrix (7MM) and seven-moment gray level co-occurrence matrix (7MGLCM) are the combinational feature matrices applied to classify the liver as malignant or benign using support vector machines (SVMs). The best classification accuracy is achieved for original liver texture by 7MGLCM, which is 97% with AUC = 0.99 for training dataset and 97.8% with AUC = 1 for test dataset.
Memory Aid Application Based on Memory Palace Theory

Since 1936, the Mind Palace memory theory has gradually been known to people. As more people understand and use this memory method, Mind Palace has gradually become a mature memory theory. The memory aid application proposed in this article uses Mind Palace Memory theory helps users build their own Mind Palace in the application to achieve the purpose of allowing users to keep their own memory palace in electronic products. At the same time, users can use application-assisted memory palaces to help themselves with memory activities.
Knowledge and Skills in the System of Training Scientific-Pedagogical Personnel in the USA Postgraduate Studies

The paper aims to reveal the specificity of knowledge and skills in the system of training scientific-pedagogical personnel in the USA postgraduate studies. The article clarifies the role and functional responsibilities of faculty-developers as the leading coordinators and helpers who provide professional support to scientific-pedagogical personnel in the USA postgraduate studies. On the basis of research of authentic literature in the foreign language, the authors have defined and characterized the basic knowledge and skills which scientific-pedagogical personnel have to acquire during their postgraduate studies. The scientific originality of the research lies in the fact that it has introduced new and original English materials which expand and complement scientific ideas about the training system of postgraduate students abroad into scientific circulation for the first time. The authors have identified the directions of coordination work to improve the educational and research activity of postgraduate students; revealed the knowledge and skills necessary for postgraduate students to successfully complete their studies in the USA; introduced recommendations for optimization of postgraduate students’ learning process in terms of its content.
Deep recursive multi-scale residual network for single image super-resolution reconstruction

Due to weather, camera technology, compression transmission, etc., people are not always able to obtain highresolution images that meet the needs of specific scenes. A deep recursive multi-scale residual network (DRMRN) for single image super-resolution is possessed to solve this problem. The proposed multi-scale residual block is based on the basic residual unit and different branches consist of convolution kernels of different sizes. Adaptive image feature extraction is beneficial to fully extract and utilize LR image features. In addition, our network has both local residual learning and global residual learning, which reduces the difficulty of training and solves the problem of gradient disappearance and gradient explosion in the training process. Compared with other models, the proposed model improves the reconstruction effect, and the reconstruction results have higher PSNR and SSIM values.
ASPIDA: A client-oriented platform for assessing websites security practices adoption and reward

The adoption of good security practices for the e-commerce business is a challenging problem. The enforcement of a strict legal framework is not always effective. On the other hand, simple recommendations and raising awareness actions are not sufficient to prioritize the necessity of secure e-commerce websites. Our approach is a combination of self-assessment, self-improvement and self-regulation incentivization activities, implemented in the observAtory for Security and PrIvacy DAta (ASPIDA) system. To minimize security and privacy issues in commercial websites and e-shops, we propose a hybrid, multi-dimensional methodology to gather, monitor and analyze various indicators and metrics, into an integrated system. Although several interesting efforts have been developed for monitoring compliance with basic cybersecurity principles, their implementation is limited without any stimulus for the participants. ASPIDA automatically analyzes a set of technical security and privacy characteristics of commercial websites and correlates the output with the results of questionnaires answered by the respective owners, web-developers and webadministrators. Positive evaluation in the automatic testing and participation in the e-commerce cybersecurity survey generates a digital badge of good practices for the specific website. The digital badge is a premium for informed e-buyers that are aware of the impact of recent catastrophic security incidents and the importance of personal data.
Combination AHP and TOPSIS methods optimizes performance of decision support system for the recipients family hope program in Huta Limbong Padang Sidempuan

The provision of assistance to the Family Hope Program is often considered not right on target. Often there are many errors caused by the help system which is done manually and the criteria used in this application are very good. Therefore, a decision-making system is needed to make it easier to choose the right recipients of the Family Hope program. In this study, the author contains a system that discusses the selection of recipients of the Family Hope Program in Huta Limbong Village, Padang Sidempuan Southeast District, using a combination of the AHP and TOPSIS methods. The criteria are floor area, house status, wall type, floor type, drinking water source, roof type, latrine facilities, lighting source, last place of defecation and cooking fuel. With the application of the AHP-TOPSIS combination in this decision support system, the correct order of names for the prospective recipients of the family of hope program has been obtained. The performance of this combination is also optimized which can be seen from the accuracy of the AHP-TOPSIS combination of 80.36%, and if the AHP alone is 75% and TOPSIS is 76.78%.
A Collaborative Weightless Neural Network

In the last few years, the demand for cloud-based high-performance systems has gone hand in hand with the field of Machine Learning (ML), as most deep learning models are increasingly requiring more powerful machines to efficiently handle soaring amounts of data on a daily basis. Although this seems like a good distributed solution, it actually leads to centralizing training and inference in commercially available data centers. Thus, this work presents a collaborative architecture that uses Weightless Neural Networks (WNNs) to enable concurrent training/inference in modern container-based cloud systems, such as Kubernetes and Docker Swarm. A WNN is a class of Artificial Neural Networks (ANNs) that uses a set of Random Access Memories (RAMs) to map training and classification information. Performance results show that our collaborative architecture can efficiently learn and classify well-know datasets in about 2 times faster than traditional architectures, while also being able to scale to many concurrent clients.