Reviewerly/exHarmony huggingface dataset:
Realtime Gray-Box Algorithm Configuration

A solver’s runtime and the quality of the solutions it generates are strongly influenced by its parameter settings. Finding good parameter configurations is a formidable challenge, even for fixed problem instance distributions. However, when the instance distribution can change over time, a once effective configuration may no longer provide adequate performance. Realtime algorithm configuration (RAC) offers assistance in finding high-quality configurations for such distributions by automatically adjusting the configurations it recommends based on instances seen so far. Existing RAC methods treat the solver to be configured as a black box, meaning the solver is given a configuration as input, and it outputs either a solution or runtime as an objective function for the configurator. However, analyzing intermediate output from the solver can enable configurators to avoid wasting time on poorly performing configurations. To this end, we propose a gray-box approach that utilizes intermediate output during evaluation and implement it within the RAC method CPPL. We apply cost sensitive machine learning with pairwise comparisons to determine whether ongoing evaluations can be terminated to free resources. We compare our realtime gray-box configurator to a black-box equivalent on several experimental settings and show that our approach reduces the total solving time in several scenarios.
Bi-convolution Matrix Factorization Algorithm Based on Improved ConvMF

In order to solve the sparsity problem of the traditional matrix factorization algorithm and the problem of the low utilization for review document information, this paper proposes a BiconvMF algorithm based on improved ConvMF. This algorithm uses two parallel convolutional neural networks to extract deep features from the user review set and item review set respectively and fuses these features into the decomposition of the rating matrix, so as to construct the user latent model and the item latent model more accurately. The experimental results show that compared with traditional recommendation algorithms like PMF, ConvMF, and DeepCoNN, the method proposed in this paper has lower prediction error and can achieve a better recommendation effect. Specifically, compared with the previous three algorithms, the prediction errors of the algorithm proposed in this paper are reduced by 45.8%, 16.6%, and 34.9%, respectively.
A Sentiment Analysis Model for Car Review Texts Based on Adversarial Training and Whole Word Mask BERT

In the automotive world, reviews will influence buyer decisions and word of mouth. As an important branch of natural language processing, sentiment analysis provides an effective research method for sentiment analysis of reviews. However, due to the large differences between the review texts in the automotive field and the pre-trained texts, and the large differences in the review texts, when the classification model is directly applied to the car reviews, some indicators of the model will be deteriorated. In order to overcome the above challenges, from the perspective of word vector, use whole-word masks of automotive domain-specific vocabulary and then train with an adversarial strategy. We propose a textual sentiment analysis model for car reviews based on adversarial training and whole-word mask BERT.
Classification and Detection of White Blood Cells using Enhanced YOLOv5 Algorithm

White Blood Cells (WBCs) detection and classification has a significant role in diagnosis application. The count, size, shape, texture, and nucleus of the cells provides the most vital component of the body's immune system. The current study proposes the enhanced novel model of YOLOv5 using deep learning algorithm for small object detection like WBCs for automatic diagnosis application.
Comprehensive Consideration of Mathematical Concepts as a Methodical Technique

&lt;p&gt;The article continues the cycle ([1]-[4], [8]-[11]) methodological developments of the authors. It discusses some problems related to ways to improve the culture of mathematical thinking of mathematics students. The authors rely on the experience of working at the Faculty of Information Technology of MSUPE.&lt;/p&gt;
Distributed Query Processing and Reasoning Over Linked Big Data

The enormous amount of structured and unstructured data on the web and the need to extract and derive useful knowledge from this big data make Semantic Web and Big Data Technology explorations of paramount importance. Open semantic web data created using standard protocols (RDF, RDFS, OWL) consists of billions of records in the form of data collections called “linked data”. With the ever-increasing linked big data on the Web, it is imperative to process this data with powerful and scalable techniques in distributed processing environments such as MapReduce. There are several distributed RDF processing systems, including SemaGrow, FedX, SPLENDID, PigSPARQL, SHARD, SPARQLGX, that are developed over the years. However, there is a need for computational and qualitative comparison of the differences and similarities among these systems. In this paper, we extend a previous comparative analysis to a diverse study with respect to qualitative and quantitative analysis views, through an experimental approach for these distributed RDF systems. We examine each of the selected RDF query systems with respect to the implementation setup, system architecture, underlying framework, and data storage. We use two widely used RDF benchmark datasets, FedBench and LUBM. Furthermore, we evaluate and examine their performances in terms of query execution time, thus, analyzing how those different types of large-scale distributed query engines, support long-running queries over federated data sources and the query processing times for different queries. The results of the experiments in this study show that SemaGrow distributed system performs more efficiently compared to FedX and Splendid, even though in smaller queries the former performs slower.
Practices That Change Teachers’ Beliefs: Use of ICT for the Development of Critical and Creative Thinking at School

&lt;p&gt;This article examines the change in teachers' beliefs regarding new educational outcomes and new forms of using digital tools. For six months, 18 teachers developed and conducted lessons that form students' critical and creative thinking. At the same time, ICT tools were used not only by the teacher, but, most importantly, by students themselves. The intervention took place within the framework of the &amp;lsquo;action research&amp;rsquo; approach, where the author acted as an organizer and a facilitator of the process. All teachers received preliminary training and were involved in the action research. As a result, it was revealed that the change of beliefs occurs during the transformation of practice, considering the fact that teachers were in exploratory, reflexive position regarding their own activities. That is, the condition for changing teachers' perceptions was a continuous personal experience of using, testing new tools in a professional context and reflecting on new practices.&lt;/p&gt;
Research on intelligent path planning technology of logistics robots based on Giraph architecture

In the development of modern e-commerce, the logistics robot is an important part of the intelligent warehouse. Especially in large-scale warehouses, the operation efficiency of logistics robots largely depends on the processing ability of patrol efficiency. In order to solve the problems of difficult path planning and complex operation in the logistics robot operating system, based on the analysis of the traditional path algorithm, an improved logistics robot path planning algorithm based on graph architecture, CAA*path planning algorithm, is proposed and used to solve the conflict-free path planning problem of the logistics robot in the intelligent logistics warehouse. The improved CAA*path planning algorithm is compared with the traditional a*algorithm, ant colony algorithm, simulated annealing algorithm, ant colony optimisation algorithm and genetic algorithm. The simulation analysis shows that the improved CAA*path planning algorithm is more efficient in time operation. Under the same number of tasks and the same map specification, the improved CAA*path planning algorithm takes less time and runs more efficiently.
SecDT: Privacy-Preserving Outsourced Decision Tree Classification Without Polynomial Forms in Edge-Cloud Computing

In the era of cloud computing with security, how to outsource evaluation services to a cloud server but preserve the model privacy is an important issue. In this paper, we study how to perform decision tree evaluation on the cloud server while achieving privacy preservation with support by edges. Existing research mainly focuses on treating the decision tree model as a polynomial form and using homomorphic encryption to ensure security, and both of them are reckoned with computational overhead. However, due to the environment that devices are getting more but smaller while the network is getting faster (like IoT or 5G), there should be some more suitable solutions to solve this problem. Therefore, we aim for constructing an outsourced decision tree classification with lightweight cryptographic tools to preserve data privacy. The main technique is to build secret sharing-based protocols for the model owner, service user, and cloud server. We trade off the communication rounds with the computational cost to reduce the overhead of the cloud server and users. We conduct some experiments with real-world datasets, which show that our scheme has desirable utility and efficiency.
REALITIES OF INNOVATION IN THE DEVELOPMENT OF HIGHER PEDAGOGICAL EDUCATION, OR WHEN THE INTERNET DISAPPEARS

The article attempts to explore and highlight the peculiarities of innovative development of higher pedagogical education in Ukraine at the present stage, to outline the possible “modernization” risks of such progress. The relevance of the chosen topic is due, on the one hand, to the fact that innovation in education is a requirement of the time, on the other hand, the sphere of innovation is extremely complex and multifaceted, so it is quite difficult to study and predict. The following theoretical methods were used for the study: systematic analysis of researches of domestic scientists on the problems of innovative development of society and education, meta-analysis of applied research; structural and logical analysis of the studied processes. Five main featuresof the development of higher pedagogical education and related “modernization risks” were outlined the first two key features of the innovative development of higher education in Ukraine: formalization and regulation of innovative activities in the field of education («top-down» position) and the rapid accumulation of scattered empirical data on certain aspects of innovation in education; the third feature of innovative development of higher pedagogical education in Ukraine is the dominance of empirical findings on certain innovative aspects with low productivity of innovative solutions. Higher education in the information society really needs innovations and can no longer be imagined without digital technologies, but, in our opinion, there is a phenomenon of certain identification and sometimes substitution of the concepts of «innovation» and «digitalization». This is the fourth feature of the innovative development of higher pedagogical education in Ukraine. The last (fifth) feature of the innovative development of higher pedagogical education that we have identified is closely related to the concept of «digital divide»
2.5D Mapping, Pathfinding and Path Following For Navigation Of A Differential Drive Robot In Uneven Terrain

Safe navigation in uneven terrains is an important problem in robotic research. In this paper we propose a 2.5D navigation system which consists of elevation map building, path planning and local path following with obstacle avoidance. For local path following we use Model Predictive Path Integral (MPPI) control method. We propose novel cost-functions for MPPI in order to adapt it to elevation maps and motion through unevenness. We evaluate our system on multiple synthetic tests and in a simulated environment with different types of obstacles and rough surfaces.
Virtual Reality and Augmented Reality Applied to E-Commerce: A Literature Review

The massification of technologies, the implementation of 5G and the Internet of Things (IoT), allow implementing systems that contain virtual or augmented reality or implementation of both. In this sense, the covid 19 pandemic in the last years, has also affected people's behavior and leaned to shop without leaving their homes. VR, RA, and/or MR techniques are currently widely used for medicine, education, and entertainment, among others. In this study, we combine both elements to analyze the literature on e-commerce and the use of VR, AR, and/or RM. Searching and analyzing recent scientific articles were defined, and virtual reality is the most used, followed by the mixture of RV and RA, the above to improve the shopping experience, providing the customer with a more authentic and immersive environment. In future works, we will expect to expand this study, including how to evaluate the shopping experience and relate it to the customer experience.
An Approach to Model Haptic Awareness in Groupware Systems

Awareness is of paramount importance to effective collaboration. Groupware systems have traditionally implemented awareness in the form of visual cues. With the advent of alternatives user interface strategies, such as haptic feedback, new ways of implementing awareness are available. The lack of documented experience on the design of effective, haptic awareness mechanisms makes it important to count on flexible prototyping and evaluation tools that can support and shorten an exploratory design process. We argue that design of awareness features should consider modalities from the start. To support the discussion, we present a modeling language and supporting tools to express haptic awareness features, and we show how the language can be used in the context of model driven development of groupware.
Multi-user Security of the Sum of Truncated Random Permutations

For several decades, constructing pseudorandom functions from pseudorandom permutations, so-called Luby-Rackoff backward construction, has been a popular cryptographic problem. Two methods are well-known and comprehensively studied for this problem: summing two random permutations and truncating partial bits of the output from a random permutation. In this paper, by combining both summation and truncation, we propose new Luby-Rackoff backward constructions, dubbed $$\textsf{SaT 1}$$ and $$\textsf{SaT 2}$$ , respectively. $$\textsf{SaT 2}$$ is obtained by partially truncating output bits from the sum of two independent random permutations, and $$\textsf{SaT 1}$$ is its single permutation-based variant using domain separation. The distinguishing advantage against $$\textsf{SaT 1}$$ and $$\textsf{SaT 2}$$ is upper bounded by $$O({\sqrt{\mu q_{\max }}}/{2^{n-0.5m}})$$ and $$O({\sqrt{\mu }q_{\max }^{1.5}}/{2^{2n-0.5m}})$$ , respectively, in the multi-user setting, where n is the size of the underlying permutation, m is the output size of the construction, $$\mu $$ is the number of users, and $$q_{\max }$$ is the maximum number of queries per user. We also prove the distinguishing advantage against a variant of $$\mathsf {XORP[3]}$$ (studied by Bhattacharya and Nandi at Asiacrypt 2021) using independent permutations, dubbed $$\mathsf {SoP3\hbox {-} 2}$$ , is upper bounded by $$O({\sqrt{\mu } q_{\max }^2}/{2^{2.5n}})$$ . In the multi-user setting with $$\mu = O(2^{n-m})$$ , a truncated random permutation provides only the birthday bound security, while $$\textsf{SaT 1}$$ and $$\textsf{SaT 2}$$ are fully secure, i.e., allowing $$O(2^n)$$ queries for each user. It is the same security level as $$\mathsf {XORP[3]}$$ using three permutation calls, while $$\textsf{SaT 1}$$ and $$\textsf{SaT 2}$$ need only two permutation calls.
A Modular Approach to the Incompressibility of Block-Cipher-Based AEADs

Incompressibility is one of the most fundamental security goals in white-box cryptography. Given recent advances in the design of efficient and incompressible block ciphers such as SPACE, SPNbox and WhiteBlock, we demonstrate the feasibility of reducing incompressible AEAD modes to incompressible block ciphers. We first observe that several existing AEAD modes of operation, including CCM, GCM(-SIV), and OCB, would be all insecure against white-box adversaries even when used with an incompressble block cipher. This motivates us to revisit and formalize incompressibility-based security definitions for AEAD schemes and for block ciphers, so that we become able to design modes and reduce their security to that of the underlying ciphers. Our new security notion for AEAD, which we name whPRI, is an extension of the pseudo-random injection security in the black-box setting. Similar security notions are also defined for other cryptosystems such as privacy-only encryption schemes. We emphasize that whPRI ensures quite strong authenticity against white-box adversaries: existential unforgeability beyond leakage. This contrasts sharply with previous notions which have ensured either no authenticity or only universal unforgeability. For the underlying ciphers we introduce a new notion of whPRP, which extends that of PRP in the black-box setting. Interestingly, our incompressibility reductions follow from a variant of public indifferentiability. In particular, we show that a practical whPRI-secure AEAD mode can be built from a whPRP-secure block cipher: We present a SIV-like composition of the sponge construction (utilizing a block cipher as its underlying primitive) with the counter mode and prove that such a construction is (in the variant sense) public indifferentiable from a random injection. To instantiate such an AEAD scheme, we propose a 256-bit variant of SPACE, based on our conjecture that SPACE should be a whPRP-secure cipher.
Blockchain Traceability Platform Based on Green Agricultural Products

This paper designs and completes a blockchain traceability platform based on green agricultural products. Using item coding technology, cryptography technology, blockchain, big data and other new generation information technologies, green agricultural products (especially Ulanqab potatoes, oats and other geographical indication products) from growth to harvest to circulation throughout the life cycle. The key data can be trusted and stored in the blockchain, providing consumers with real-time agricultural product traceability query services, and providing agricultural and animal husbandry departments with effective agricultural product quality and safety supervision and management mechanisms and means. With the help of QR code as the mobile Internet entrance, link online and offline, design O2O e-commerce transaction system, form a closed loop of planting, traceability and sales, digitally control the quality of authentic agricultural products, and empower the brand value of agricultural products. Realize the whole chain supervision of green products from planting source to circulation through a perfect traceability system, improve product safety and quality, enhance the market's popularity and recognition of agricultural products in the region, form effective protection of agricultural product geographical indication products, and achieve agricultural economic development “Increase quality and efficiency”, and then promote the construction of smart agricultural big data projects.
A Transformer-Based Approach for Choosing Actions in Social Robotics

The growing deployment of social robots requires the ability to adapt to the dynamic changes occurring in the real environments. These reactive behaviors, however, are often incapable of reasoning and predicting the effects of their actions in the next future. Therefore, they must be accompanied by forms of deliberative semantic/causal reasoning. The combination of the reactive and deliberative forms of reasoning, which resembles the dual process theory, raises the problem of entrusting tasks to the corresponding modules. Just as happens in biological systems, the tendency to assign activities, as much as possible, towards the lower abstraction layers, equips the systems with more responsive capabilities at the cost of making the reactive layers more difficult to implement. In this document, we will introduce an architecture that, inspired by the classic three-tier architecture, combines slow and fast forms of reasoning, allowing social robots to achieve complex and dynamic behaviors. Since entrusting tasks to the more reactive components complicates their implementation (e.g., it requires the definition of formal rules which may not adequately generalize to unforeseen scenarios), we aim to reduce the technicalities and, consequently, to facilitate to the developers the implementation of the reactive behaviors. By relying on recent achievements in natural language translation, we will describe our recent efforts to adopt Transformer-based architectures, allowing the replacement of formal rules with easier to write “stories”, defined through sequences of perceived events and actions, entrusting the system with the task of learning behaviors by generalizing from them.
Implementation of Interactive Learning in Linear Algebra Courses using HTML

Education or learning is essentially a process of optimizing a child's potential towards the achievement of certain abilities as a standard for learning outcomes, by the task of growth and development which is reflected in the selection of life skills. Generating motivation regarding the learning material will increase the interest of learners in participating in learning. Giving attention to learning can be through learning media. Learning media is very developed and continues to increase due to the use of computer information technology. The purpose of this project was to examine how HTML may be used to enable interactive learning in Linear Algebra courses. The experimental research method employed in this study was the One Group Pre-test Post-test Design. Data analysis in this study used the Wilcoxon test because the data were not normally distributed. The results indicate that student scores significantly improved between the pre-test and post-test. Interactive media affect online learning today, so it is hoped that teachers/lecturers will use interactive media more in learning.
The use of MOOCs in higher education

The article studies the main theoretical and practical provisions on the structural application of MOOCs in higher education. The main barriers towards the use of MOOCs in Russian universities are considered as well.
Advanced audio coding steganography algorithm with distortion minimization model based on audio beat

Currently, most advanced audio coding (AAC) steganography methods are content-non-adaptive without considering the characteristics of audio, and there are several limitations in imperceptibility and steganalysis. In this paper, we use an audio feature beat as the anchor point to identify the cover elements, group the quantized modified discrete cosine transform (QMDCT) coefficients in the small value area, and finally use the syndrome-trellis codes (STCs) framework for content-adaptive embedding to obtain the minimum distortion. In the STCs framework, we comprehensively consider auditory and data distortions. Experimental results demonstrate that the proposed steganography algorithm has a 10% improvement over the compared algorithms in terms of imperceptibility and steganalysis, and it can accurately extract secret information in face of frame loss and misalignment.