Reviewerly/exHarmony huggingface dataset:
Autonomous environment perception and obstacle avoidancetechnologies of Zhurong Mars rover

<p indent="0mm">Mars surface exploration has the characteristics and constraints of harsh and complex environment, lack of prior knowledge, and strict limitation of computing resources. Autonomous environment perception and obstacle avoidance technology is the key to the ability of autonomous exploration, and also one of the key technologies in the field of deep space exploration at home and abroad. In China Tianwen-1 Mars exploration mission, the Zhurong Mars rover, China’s first Mars surface mobile exploration, has realized efficient autonomous environmental perception, obstacle avoidance planning and control under the constraints of limited resources, significantly improved the efficiency of mobile exploration, and the average moving speed is nearly 5 times higher than that of Yutu-2 lunar rover. This paper briefly introduces the sensor configuration, movement mode function and process of Zhurong Mars rover supporting autonomous perception and obstacle avoidance movement, and focuses on the breakthroughs in key technologies of efficient perception and obstacle avoidance planning, including binocular stereo vision efficient perception method based on fast parallel processing, in order to realize the efficiency and safety of autonomous perception and obstacle avoidance movement under severe resource constraints Based on the environmental modeling and obstacle avoidance planning method of trafficability analysis. Finally, the autonomous perception and obstacle avoidance mobile detection of Zhurong Mars rover during the mission is summarized, and the technical development thoughts on further enhancing the autonomous perception and obstacle avoidance ability of Mars rover in the future are put forward in the end.
Linear Hensel Lifting for Zp[x,y] for n Factors with Cubic Cost

We present a new algorithm for performing linear Hensel lifting on bivariate polynomials over the finite field Zp for some prime p. Our algorithm lifts n monic, univariate polynomials to recover the factors of a polynomial A(x,y) in Zp[x,y] which is monic in x, and bounded by degrees dx = deg(A,x) and dy = deg(A,y). Our algorithm improves upon Bernardin's algorithm in [1] and reduces the number of arithmetic operations in Zp from O(n dx^2 dy^2) to O(dx^2 dy + dx dy^2) for p >= dx. Experimental results in C verify that our algorithm compares favorably with Bernardin's for large degree polynomials. Moreover, we've implemented a Quadratic Hensel lifting algorithm in Magma to show that our cubic Linear Hensel lifting algorithm outperforms Magma's Quadratic Hensel lifting for a wide range of input sizes.
Flipping the Script

Information retrieval has traditionally been framed in terms of searching and extracting information from mostly static resources. Interactive information retrieval (IIR) has widened the scope, with interactive dialogues largely playing the role of clarifying (i.e., making explicit, and/or refining) the information search space. Informed by market research practices, we seek to reframe IIR as a process of eliciting novel information from human interlocutors, with a chatbot-inspired virtual agent playing the role of an interviewer. This reframing flips conventional IIR into what we call an inverse information seeking dialogue, wherein the virtual agent recurrently extracts information from human utterances and poses questions intended to elicit related information. In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR.
Role-Play as the Mode of Development of Foreign Language Presentation Skills

The purpose of the article is to consider role-playing in the process of foreign language training at technical universities. The introduced form of the educational process contributes to the formation of foreign language presentation skills in a professional field of a future engineer. A foreign language competence confirms a high competency of a specialist. Role-playing business situations in an educational process are carried out on the basis of an integrated approach where knowledge from the professional field is transferred to the game with the help of a foreign language. Methods of creating typical situations at work in the classroom contribute to the formation of a favorable environment for the development of business qualities and foreign language skills in a professional sphere. In the course of the research a three-stage structure of role-playing, its content in relation to the stated conditions are described. Difficulties that arise during the game and possible methods to overcome them are listed. It is concluded that role-playing related to the professional activities of future engineers motivate them to learn a foreign language and bring the learning process closer to real professional communication. The practical experience gained in the classroom will be a valuable experience for making a successful career in the field of engineering.
Digital didactics: systemic foundations and the image of the future

The article is devoted to the system justification of the e-learning process. The language of cybernetics is used to describe a purposeful process. Using the language of cybernetics allows you to identify all the components of the educational process management system. In the classical case, this is a teacher who has a management goal and achieves this goal. With the advent of computer technology, the process control system becomes more complicated – information and communication technologies are included in it. Based on the cybernetic model of learning as a purposeful process, the evolution of the interaction process in education from classical education in the form of "man-man", then to the form of "man-computer-man" and then to the form of "man-learning environment" is considered. At the same time, the evolution of the didactic content of the educational process is considered. The main conclusions of this study are: the conclusion that there is a fundamental contradiction between the finite possibilities of computer influence as part of the educational process management system and the infinite set of behavioral reactions of the learner. It is shown how this contradiction is resolved at the system level by, on the one hand, simplifying the e-course itself, but at the same time increasing the diversity of the number of courses. It is shown that at this level the development of e-learning and the content of digital didactics can be characterized as "student–teacher - learning algorithm". Further, a forecast is made that an increase in the variety of e-courses will lead to the creation of a personal learning environment, the purpose of which will be marketing: to satisfy the client's training needs as much as possible. It is concluded that digital didactics will be built on the principles of gamification.
Chaos synchronization using q-Chlodowsky operators as uncertainty approximator

In this article, q-Chlodowsky operators are utilized for synchronizing a chaotic master-slave system. The universal approximation property enables q-Chlodowsky operators to approximate uncertainties, which consist of disturbances and unmodeled dynamics. It is verified that uniformly ultimately bounded stability of the tracking/approximation errors can be assured if the q-Chlodowsky operators are utilized as regressors and the polynomial coefficients are tuned by the adaptive laws calculated in the stability analysis. Moreover, it has been assumed that the rate of synchronization error is not at hand and a filter-based strategy has been adopted instead of designing an observer. The Duffing-Holmes oscillator is studied and the results are also compared with three powerful approximation-based and robust control methods. Unlike neural network/fuzzy estimators in which system states are necessary to define the regressor vector, the proposed uncertainty estimator does not require them in the regressor vector. Furthermore, in comparison with radial basis functions neural networks (RBFNNs), we are involved in fewer adaptive coefficients in the regressor vector of the proposed uncertainty estimator. As a result, the tedious and time-consuming procedure of trial and error for tuning the uncertainty estimator is eliminated.
Group Public Key Encryption with Equality Test under Standard Model

Group public key encryption with equality test (G-PKEET) scheme supports group granularity authorization on the equality test. An authorized proxy is able to check whether two ciphertexts belonging to the same group are encrypted from identical plaintext without decrypting them. However, in indistinguishability-based security notion, current existing PKEET and G-PKEET schemes do not allow adversary to invoke equality test as a service. In contrast, under practical circumstance, an adversary is probably able to exploit the equality test service offered by proxy to decipher a ciphertext, leading to unexpected and unwanted privacy leakage. In this paper, we propose a security definition that includes the abovementioned adversary ability. Through extending the functionality of current G-PKEET scheme, we design a concrete scheme that satisfies our new security definition. Furthermore, our G-PKEET scheme is the first G-PKEET scheme whose security properties can be proved under the standard model.
Join Path-Based Data Augmentation for Decision Trees

Machine Learning (ML) applications require high-quality datasets. Automated data augmentation techniques can help increase the richness of training data, thus increasing the ML model accuracy. Existing solutions focus on efficiency and ML model accuracy but do not exploit the richness of dataset relationships. With relational data, the challenge lies in identifying join paths that best augment a feature table to increase the performance of a model. In this paper we propose a two-step, automated data augmentation approach for relational data that involves: (i) enumerating join paths of various lengths given a base table and (ii) ranking the join paths using filter methods for feature selection. We show that our approach can improve prediction accuracy and reduce runtime compared to the baseline approach.
CyberGIS-Cloud: A unified middleware framework for cloud-based geospatial research and education

Interest in cloud-based cyberinfrastructure continues to grow within the geospatial community to tackle contemporary big data challenges. Distributed computing frameworks, deployed over the cloud, provide scalable and low-maintenance solutions to accelerate geospatial research and education. However, for scientists and researchers, the usage of such resources is highly constrained by the steep curve for learning diverse sets of platform-specific tools and APIs. This paper presents CyberGIS-Cloud as a unified middleware to streamline the execution of distributed geospatial workflows over multiple cloud backends with easy-to-use interfaces. CyberGIS-Cloud employs bringing computation-to-data model by abstracting and automating job execution over distributed resources hosted in the cloud environment where the data resides. We present details of CyberGIS-Cloud with support for popular distributed computing frameworks backed by research-oriented JetStream Cloud and commercial Google Cloud Platform.
Gesture Recognition by Self-Supervised Moving Interest Point Completion for CNN-LSTMs

Gesture recognition, i.e., classification of videos depicting humans who perform hand gestures, is essential for Human-Computer Interaction. To this end, coupled Convolutional Neural Networks-Long Short-Term Memory architectures (CNN-LSTMs) are employed for fast semantic video analysis, but the typical transfer learning approach of initializing the CNN backbone using pretraining for whole-image classification is not necessarily ideal for spatiotemporal video understanding tasks. This paper investigates self-supervised CNN pretraining for a novel pretext task, relying on spatiotemporal video frame corruption via a set of low-level image/video processing building blocks that jointly force the CNN to learn to complete missing content. This is likely to coincide with visible moving object boundaries, including human body silhouettes. Such a CNN parameter set initialization is then able to augment gesture recognition performance, after retraining for this video classification downstream task, without inducing any runtime overhead during the inference stage. Evaluation on a gesture recognition dataset for autonomous Unmanned Aerial Vehicle (UAV) handling demonstrates the effectiveness of the proposed method, against both traditional ImageNet initialization and a competing self-supervised pretext task-based initialization.
Content Labelling of Hidden Services With Keyword Extraction Using the Graph Decomposition Method

The data investigation of hidden services on the dark web is gaining attention from the research community and law enforcement agencies. However, the anonymity feature of hidden services makes it difficult to index the hidden services for investigation. Therefore, one of the primary focuses of dark web data investigation research is labelling the hidden services so that the labelled services can be classified or indexed further. The methodology deployed in the proposed work is based on keyword extraction using the graph degeneracy method. The proposed work analyzes the text data by extracting keywords from each hidden service document. The accuracy of the proposed method is validated by LDA-based topic modelling approach. The document labelling obtained by the keyword extraction method and LDA model matched with the accuracy of 78. The main intuition behind the keywords extraction method is that central nodes make good keywords. This is because central nodes with high centrality in the GoW of a document correspond to the document's keywords, which are well-understood by humans.
Bayesian Cognitive State Modeling for Adaptive Serious Games

Bayesian modeling of cognitive state is one possible approach to user modeling for use with adaptivity in serious games. Adaptive educational serious games try to keep learners engaged - to keep them in the so called “Flow” channel, i.e., in the right balance between being challenged and entertained. The challenge is to intervene adaptively at the right time. The research question is when to actually adapt, and how to find quantifiable metrics for that. One way to achieve this is to model the users’ cognitive state and to adapt to high or low cognitive load, e.g., to apply dynamic difficulty adjustments. Our user modeling approach is based on Hierarchical Bayesian Models (HBM) which are suitable for drawing conclusions about the learner’s cognitive state inferred from observable variables. An important aspect is that the approach considers activity stream data such as from the Experience API (xAPI) protocol as input to achieve high interoperability and eased applicability. An evaluation with synthetic data for different user group types shows the feasibility of the approach. The model can explain differences between subjects, between subject groups and between different latent variables such as cognitive load or mental working memory capacity.
Principles of the "regulatory guillotine" and methods of computational law used to analyze the requirements for the quality of higher education

Now Russia is undergoing a reform of the control and supervisory activity of the “regulatory guillotine”, which is designed to significantly reduce the number of mandatory requirements in the legislation, leaving only those that are necessary and should be controlled among them. In the presented article, the principles of this reform are applied to the Federal State Educational Standards (FSES). Russian legislation understands the quality of education as compliance with these standards, but their number is extremely large, which previously made it difficult to systematically study them. However, the application of methods of computational jurisprudence made it possible to analyze one of the sections “Conditions for the implementation of the program” for 720 FSES of higher education (specialist’s, bachelor’s, and master's). The computer program generated a “generalized summary” of texts without loss of content (for all federal state educational standards of each level of education). In it, duplicated and similar fragments were combined, differences and variations in wording, noted and visualized. The volume of analysis has decreased by more than 35 times, and an expert assessment of all the requirements of the Federal State Educational Standard became possible. Previously, this method has not been used in computational jurisprudence. As a result, the original tens of thousands of requirements of the Federal State Educational Standard were divided into four categories, three of which are recommended for exclusion (for appropriate reasons), the rest is “mandatory requirements”. These 5 generalized universal requirements are suitable for use in control and supervision activities, and liability can be established for their violation.
Online cloud smart service on student voting, book search and educational server

Today the cloud computing plays an active role in the form cloud services to the people. All cloud services are very useful and interesting for the society. The proposed work provides three different cloud services such as Student Voting, Book Search and Educational Server. The Student Voting cloud service is to develop an efficient voting system for students to cast their vote through the unique user ID provided to them. A specific admin key is also created to provide exclusive access to the voting progress, where the votes obtained by each candidate can be seen. The proposed Student Online Voting service is really useful for Student Election with high accuracy and quick time management. The proposed Book Search service is to develop the best search algorithm for searching the books in the library. It works on the principle of divide and conquer rule and binary search. The proposed Educational Server is consisting of Subscription/Account creation in a Server from a client, Username/password creation, Share files among Class Students with/without encryption to a Server and Chat using Sockets program. The proposed online cloud Services are provided very useful service for the education field. It Provide a simple platform to access information and services for all clients. It also improves the online communications ability for faculty and students. The result of the proposed online cloud services to enhance the learning methodology of students, so they would eagerly involve themselves.
Efficient Compiler to Covert Security with Public Verifiability for Honest Majority MPC

We present a novel compiler for transforming arbitrary, passively secure MPC protocols into efficient protocols with covert security and public verifiability in the honest majority setting. Our compiler works for protocols with any number of parties $$> 2$$ and treats the passively secure protocol in a black-box manner. In multi-party computation (MPC), covert security provides an attractive trade-off between the security of actively secure protocols and the efficiency of passively secure protocols. In this security notion, honest parties are only required to detect an active attack with some constant probability, referred to as the deterrence rate. Extending covert security with public verifiability additionally ensures that any party, even an external one not participating in the protocol, is able to identify the cheaters if an active attack has been detected. Recently, Faust et al. (EUROCRYPT 2021) and Scholl et al. (Pre-print 2021) introduced similar covert security compilers based on computationally expensive time-lock puzzles. At the cost of requiring an honest majority, our work avoids the use of time-lock puzzles completely. Instead, we adopt a much more efficient publicly verifiable secret sharing scheme to achieve a similar functionality. This obviates the need for a trusted setup and a general-purpose actively secure MPC protocol. We show that our computation and communication costs are orders of magnitude lower while achieving the same deterrence rate.
Securing Personal Data in a Video Identification System

The increasing use of video surveillance systems raises security concerns regarding the processing of personal data. The security of personal data must be ensured throughout the process of acquisition, transport, storage, processing and discretionary access. In this paper we propose a solution that provides protection for personal data carried by, or stored in a video identification system. Using simple cryptographic methods, the proposed system architecture manages to ensure confidentiality, integrity and authenticity, but also controlled access to personal data. Furthermore, we demonstrate the possibility of using a inexpensive solution with limited resources to secure the video stream in transit. The presented solution is suitable for real-time video communication systems and can ensure the requirements imposed by the GDPR regulations regarding the protection of personal data.
RepAr-Net: Re-Parameterized Encoders and Attentive Feature Arsenals for Fast Video Denoising

Real-time video denoising finds applications in several fields like mobile robotics, satellite television, and surveillance systems. Traditional denoising approaches are more common in such systems than their deep learning-based counterparts despite their inferior performance. The large size and heavy computational requirements of neural network-based denoising models pose a serious impediment to their deployment in real-time applications. In this paper, we propose RepAr-Net, a simple yet efficient architecture for fast video de noising. We propose to use temporally separable encoders to generate feature maps called arsenals that can be cached for reuse. We also incorporate re-parameterizable blocks that improve the representative power of the network without affecting the run-time. We benchmark our model on the Set-8 and 2017 DAVIS-Test datasets. Our model achieves state-of-the-art results with up to 29.62% improvement in PSNR and a 50% decrease in run times over existing methods. Our codes are open-sourced at: github.com/spider-tronix/RepAr-Net.
Unsupervised Depth Completion and Denoising for RGB-D Sensors

Depth information is considered valuable as it describes geometric structures, which benefits various robotic tasks. However, the depth acquired by RGB-D sensors still suffers from two deficiencies, i.e., incompletion and noises. Previous methods complete depth by exploring hand-tuned models or raising surface assumptions, while nowadays, deep approaches intend to solve this problem with rendered image pairs. For depth denoising, as a consequence of different sensor mechanisms, most methods can only work under specific devices. With existing methods, three challenges emerge: the onerous training set collecting process, the mismatch between existing models and present RGB-D sensors, and the non-real-time computation. In this paper, we first state depth completion and denoising are inherently different and without the need to collect or render complete and noiseless ground truths. We address all mentioned challenges with two separate un-supervised learning procedures. The completion network takes color and incomplete depth as input and predicts values to the unobserved area, which combines prior knowledge and color-depth correlations. The denoising step exploits image sequences to construct noise models in a self-supervised manner with the ability to cater to different sensors. Experimental comparisons and ablation studies demonstrate that even without human-labeled ground truths, the proposed method could produce better completion results and also reduce noises in real-time.