Reviewerly/exHarmony huggingface dataset:
A Secure and Fine-Grained Big Data Access Control Scheme for Cloud-Based Services

Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the Internet. Nowadays, access control is one of the most critical problems with cloud computing. Ciphertext-Policy Attribute Based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. This paper proposes an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, it hides the whole attribute (rather than only its values) in the access policies. To assist data decryption, it designs an algorithm called Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. The paper also deals with offline attribute guessing attack. Security analysis and performance evaluation show that this scheme can preserve the privacy from any LSSS access policy without employing much overhead.
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous work has attempted to directly align latent features by the classifier-induced discrepancies. Nevertheless, a common feature space cannot always be learned via this direct feature alignment especially when a large domain gap exists. To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution. In such an indirect way, the distributions over the samples from the two domains will be constructed on a common feature space, i.e., the space of the prior, which promotes better feature alignment. To effectively align the target latent distribution with this prior distribution, we also propose a novel unpaired L1-distance by taking advantage of the formulation of the encoder-decoder. The extensive evaluations on nine benchmark datasets validate the superior knowledge transferability through outperforming state-of-the-art methods and the versatility of the proposed method by improving the existing work significantly.
Oblique didactics: Innovating the ludic experience in kindergarten to promote pedagogical training

The ludic experience through the discovery of body and movement in kindergarten is represented by an experience of fundamental importance for the pedagogical development of children.Also, in this case, it is necessary to research for innovative didactic proposals that motivate the children and lay the foundations for an effective training course.The purpose of this paper is to analyse, with a sample of children aged between 4 and 6 years, oblique-type didactics that can be used to evaluate the training progress obtainable through this innovative approach, and to hypothesize its use in kindergarten.
A Universal Model for Defective Classes Prediction Using Different Object-Oriented Metrics Suites

Recently, research studies were directed to the construction of a universal defect prediction model. Such models are trained using different projects to have enough training data and be generic. One of the main challenges in the construction of a universal model is the different distributions of metrics in various projects. In this study, we aim to build a universal defect prediction model to predict software defective classes. We also aim to validate the Object-Oriented Cognitive Complexity metrics suite (CC metrics) for its association with fault-proneness. Finally, this study aims to compare the prediction performances of the CC metrics and the Chidamber and Kemerer metrics suite (CK metrics), taking into account the effect of preprocessing techniques. A neural network model is constructed using these 2 metrics suites (CK & CC metrics suites). We apply different preprocessing techniques on these metrics to overcome variations in their distributions. The results show that the CK metrics perform well whether a preprocessing is applied or not, while CC metrics’ performance is significantly affected by different preprocessing techniques. The CC metrics always outperform in the recall, while the CK metrics usually outperform in other performance metrics. Normalization preprocessing results in the highest recall values using either of the two metrics suites.
Research and Application of Digital Signature Technology

With the development of science and technology, the spread of images on the Internet is getting faster and faster, which also reduces the cost of infringement and brings immeasurable losses to image creators. In order to solve the problem of digital image infringement, this paper uses encryption algorithm and signature algorithm to design a verifiable message delivery scheme based on timestamp, and applies it to the copyright protection of digital image, designs and implements a copyright protection system based on digital signature and timestamp. Our system provides a new reference solution for the copyright registration protection of digital images, realizes the protection of digital image copyright through a small amount of encryption and decryption operations and a small amount of information transmission. After analysis, our copyright protection system has high computing efficiency and security, the calculation efficiency and advantages are more prominent when transmitting large data information, and it has good application prospects.
A SEIR Model Optimization Using the Differential Evolution

The SEIR is a crucial mathematical model for solving infectious disease prediction and other problems in the field of artificial intelligence. It is used to effectively prevent and control infectious diseases by studying the infectious diseases’ propagation speed, spatial range, transmission route, dynamic mechanism and other issues. In order to improve the prediction of infectious diseases in a certain area, a SEIR model optimization approach based on differential evolution (DE) algorithm is proposed in this paper. In this method, the differential evolution is used to optimization the related variables in the model. The overall prediction of the adjusted and optimized SEIR model algorithm is conformed to the regional development laws. The experimental results show that the SEIR infectious disease model optimized by DE algorithm is accurate and reliable in the analysis of COVID-19 propagation situation, and the model can be used to provide certain theoretical methods and technical support for future outbreak policy formulation.
Game Semantics: Easy as Pi

Game semantics has proven to be a robust method to give compositional semantics for a variety of higher-order programming languages. However, due to the complexity of most game models, game semantics has remained unapproachable for non-experts. In this paper, we aim at making game semantics more accessible by viewing it as a syntactic translation into a session typed pi-calculus, referred to as metalanguage, followed by a semantics interpretation of the metalanguage into a particular game model. The syntactic translation can be defined for a wide range of programming languages without knowledge of the particular game model used. Simple reasoning on the model (soundness, and adequacy) can be done at the level of the metalanguage, escaping tedious technical proofs usually found in game semantics. We call this methodology programming game semantics. We design a metalanguage (PiDiLL) inspired from Differential Linear Logic (DiLL), which is concise but expressive enough to support features required by concurrent game semantics. We then demonstrate our methodology by yielding the first causal, non-angelic and interactive game model of CML, a higher-order call-by-value language with shared memory concurrency. We translate CML into PiDiLL and show that the translation is adequate. We give a causal and non-angelic game semantics model using event structures, which supports a simple semantics interpretation of PiDiLL. Combining both of these results, we obtain the first interactive model of a concurrent language of this expressivity which is adequate with respect to the standard weak bisimulation, and fully abstract for the contextual equivalence on second-order terms. We have implemented a prototype which can explore the generated causal object from a subset of OCaml.
Digital transformations in educational process: challenge of time

This article is dedicated to examination of transformations in educational process during pandemic. Relevance of the selected topic is substantiated by the current cultural realities: contagion of coronavirus infection, quarantine, imposed restrictions, and thus, the need for restructuring communication channels in sociocultural reality, namely with regards to educational space. The goal consists in the analysis of new forms of communication, organizational education technologies that appeared in media space under the indicated circumstances. Characteristics is given to distance learning and communications means for its implementation. The author describes synchronous, asynchronous, and mixed learning techniques, as well as their substantive aspects. Such type of distance learning as online courses is being discussed. Particular attention is turned to the peculiarities of final academic assessment in the context of quarantine or self-isolation. In the conditions caused by pandemic, there emerges a need for more active usage of media space for arranging social, economic, and educational processes. The sphere of education, which is also related to health care of young generation, undergoes transformation taking into account the relevant sociocultural realities caused by pandemic. Such previously extracurricular forms of learning as synchronous, asynchronous, mixed, and online courses enjoy popularity, and even become essential. Final academic assessment in the conditions of imposed restrictions and distance learning acquires twofold meanings: on the one hand, there is a risk for distortion of perception of information in the course of graduate paper presentation, while on the other hand it forms a more comfortable psychological environment for the student due to being in a familiar environment. The newly established situation for culture and education in particular is the prime subject for researchers. &amp;nbsp;
HIGH PERFORMANCE WINDOW SYSTEM APPLIED DYNAMIC INSULATION TECHNIQUE

An airflow window is a system to improve solar blocking performance of windows. It is a practically beneficial system, however, still has a potential to improve the performance by controlling the air flow in the cavity between the glazing. In this development, we apply a Dynamic Insulation technique into the cavity of the airflow window. CFD analyses were carried out to measure the thermal performance. The results show that declines in the solar heat gain coefficient as well as the surface temperature of the inner glass were observed. The effects improve the radiant environment at perimeter zone in office buildings.
HCC: 100 Gbps AES-GCM Encrypted Inline DMA Transfers Between SGX Enclave and FPGA Accelerator

This paper describes a Heterogeneous Confidential Computing (HCC) system composed of a CPU Trusted Computing Environment and a hardware accelerator. We implement two AES-GCM hardware engines with high-bandwidth and low-latency that are designed for end-to-end encryption of DMA transfers. Our solution minimizes changes to the hardware platform and to the application and SW stack. We prototyped and report the performance of protected image classification with proposed encrypted-DMA on an Intel Arria-10 FPGA.
Path Planning of UAV based on Fluid Computing via Accelerated Method

This paper presents our study on the method for planning the path for low-flying unmanned aerial vehicle (UAV) in complex terrain based on the theory of fluid flow.First, the 2D terrain map is generated using hill algorithm.Then the fluid field distribution is computed using fluid mechanics to establish streamlines in the field.These streamlines are then used as flight paths for UAV.In fluid mechanics, Laplace's equation is used as the controlling equation of the potential flow.The solutions of Laplace's equation can be solved using finite difference method.Existing methods are slow in computing the solutions for generating UAVs flight path.Hence, they are not suitable for real-time processing.In this paper, an efficient iterative method namely Two-Parameter Overrelaxation (TOR) method that employ an acceleration parameter is proposed.The efficiency of this approach is shown by comparing its performance with the previous methods.It was shown that the proposed TOR method outperformed the previous methods.
Research on Kinect Calibration and Depth Error Compensation Based on BP Neural Network

In order to improve the depth measurement accuracy of Kinect and reduce the depth error, we applied BP neural network to the calibration of Kinect depth camera in the research. The corner information of the target image is extracted as the training set, and the dual neural network is used to calibrate the data information in different directions according to the imaging rules of pixels. The results of neural network training prediction are used to improve the calibration accuracy further. Then, based on the improvement of calibration accuracy, error analysis, and error compensation are performed for the situation where the depth error in Kinect measurement is significant. An error compensation model is established by using the error symmetry of the Kinect depth measurement, and the depth information is compensated by the optical path difference, which reduces the order of parameters and improves the measurement accuracy. Experimental results show that this method can effectively improve the calibration and depth measurement accuracy of Kinect cameras.
A New General Method of Searching for Cubes in Cube Attacks

Cube attack, proposed by Dinur and Shamir at EUROCRYPT 2009, is one of general and powerful cryptanalytic techniques against symmetric-key cryptosystems. However, it is quite time consuming to search for large cubes using the existing techniques, e.g., random walk, and practically infeasible to execute the cube attack when the size of cube exceeds an experimental range, e.g., 50. Thus, how to find favorite cubes is still an intractable problem. In this paper, a new general method of searching for cubes in cube attacks, called iterative walk, is proposed. Iterative walk takes the technique numeric mapping proposed at CRYPTO 2017 as a tool, which is used to test cubes and find out the best cubes among them. This new method consists of two concrete techniques, called incremental iterative walk and decremental iterative walk, respectively. Both of them split the process of searching for cubes with large size into several iterative processes, each of which aims at searching for a ‘best’ set of input variables with small size. After each iterative process, the input variables in the obtained ‘best’ set are added to (or dropped from) the cube in incremental (or decremental) iterative walk. As illustrations, we apply it to the authenticated encryption cipher ACORN v3, which was selected as one of seven finalists of CAESAR competition. Some new distinguishing attacks on round reduced variants of ACORN v3 are obtained.
Adaptive-Secure Identity-Based Inner-Product Functional Encryption and Its Leakage-Resilience

There are lots of applications of inner-product functional encryption (IPFE). In this paper, we consider two important extensions of it. One is to enhance IPFE with access control such that only users with a pre-defined identity are allowed to compute the inner product, referred as identity-based inner-product functional encryption (IBIPFE). We formalize the definition of IBIPFE, and propose the first adaptive-secure IBIPFE scheme from Decisional Bilinear Diffie-Hellman (DBDH) assumption. In an IBIPFE scheme, the ciphertext is related to a vector $$\varvec{x}$$ and a new parameter, identity $$\mathrm {ID}$$ . Each secret key is also related to a vector $$\varvec{y}$$ and an identity $$\mathrm {ID}'$$ . The decryption algorithm will output the inner-product value $$\langle \varvec{x},\varvec{y} \rangle $$ only if $$\mathrm {ID}=\mathrm {ID}'$$ . The other extension is to make IBIPFE leakage resilient. We consider the bounded-retrieval model (BRM) in which an adversary can learn at most l bits information from each secret key. Here, l is the leakage bound determined by some external parameters, and it can be set arbitrarily large. After giving the security definition of leakage-resilient IBIPFE, we extend our IBIPFE scheme into a leakage-resilient IBIPFE scheme in the BRM by hash proof system (HPS).
Common Factors in Fraction-Free Matrix Decompositions

We consider LU and QR matrix decompositions using exact computations. We show that fraction-free Gauss--Bareiss reduction leads to triangular matrices having a non-trivial number of common row factors. We identify two types of common factors: systematic and statistical. Systematic factors depend on the reduction process, independent of the data, while statistical factors depend on the specific data. We relate the existence of row factors in the LU decomposition to factors appearing in the Smith--Jacobson normal form of the matrix. For statistical factors, we identify some of the mechanisms that create them and give estimates of the frequency of their occurrence. Similar observations apply to the common factors in a fraction-free QR decomposition. Our conclusions are tested experimentally.
USING CLOZE PASSAGES TO ESTIMATE READABILITY FOR RUSSIAN UNIVERSITY STUDENTS: PRELIMINARY STUDY

This preliminary study examines the relationships between each of six first language (L1) readability indexes and the cloze passage mean performances of Russian EFL students. The cloze passages were created by randomly selecting 50 text passages from an American public library and deleting every 12th word in each passage to create a 30-item cloze procedure. The participants were 5170 EFL students from 38 universities in theRussian Federation. Each student was randomly assigned to take one of the 30-item cloze passages. The L1 readability indexes calculated for each of the 50 passages were the Flesch, Flesch-Kincaid, Fry, Gunning, Fog, and modified Gunning-Fog indexes. The preliminary results indicate that the L1 readability indexes were moderately to highly correlated with each other, but only somewhat correlated with the mean performances of Russian university students on cloze versions of those same passages. These results are discussed in terms of why the L1 readability indexes are moderately to highly correlated with each other but only somewhat correlated to the Russian EFL means. The authors also explain what they are planning in terms of further linguistic analyses (e.g., of variables like average word length, percent of function words, number of syllables per sentence, number of words per paragraph, frequencies of words in the passages, and so forth) and statistical analyses (including at least factor analysis, multiple regression analyses, and structural equation modeling) of these data.
Semi Fragile Audio Crypto-Watermarking based on Sparse Sampling with Partially Decomposed Haar Matrix Structure

In the recent era the growth of technology is tremendous and at the same time, the misuse of technology is also increasing with an equal scale. Thus the owners have to protect the multimedia data from the malicious and piracy. This has led the researchers to the new era of cryptography and watermarking. In the traditional security algorithm for the audio, the algorithm is implemented on the digital data after the traditional analog to digital conversion. But in this article, we propose the crypto – watermarking algorithm based on sparse sampling to be implemented during the analog to digital conversion process only. The watermark is generated by exploiting the structure of HAAR transform. The performance of the algorithm is tested on various audio signals and the obtained SNR is greater than 30dB and the algorithm results in good robustness against various signal attacks such as echo addition, noise addition, reverberation etc.
Interpretations of Support Among Arguments

The theory of formal argumentation distinguishes and unifies various notions of attack, support and preference among arguments, and principles are used to classify the semantics of various kinds of argumentation frameworks. In this paper, we consider the case in which we know that an argument is supporting another one, but we do not know yet which kind of support it is. Most common in the literature is to classify support as deductive, necessary, or evidentiary. Alternatively, support is characterized using principles. We discuss the interpretation of support using a legal divorce action. Technical results and proofs can be found in an accompanying technical report.
Theoretical model of children’s ecological and artistic education system in the context of ethno-cultural traditions

When studying the integration issues in education, there is a global trend of the last two decades aimed at strengthening the interdisciplinary connection between natural-science and socio-humanitarian components of education. Among the scientific community it is noted that the opposition of designated knowledge areas has a negative impact on the intradisciplinary development which leads to a slowdown in innovative processes at all stages of an individual’s education. Considering the possible ways to solve the problem, we turned to integrated ecological and artistic educational activities: the article gives the definition and reveals the content; it also traces the connection with the folk culture, specifically the traditions of nature management and artistic creativity. Searching for an effective solution aimed at achieving the goals of integrated educational activities, the author turns to the pedagogical modelling method the application result of which are: systematized and consistently stated theoretical foundations of children’s ecological and artistic education in the context of ethno-cultural traditions; justification of its targets, approaches and principles, content, forms, methods, means; possible conditions for ensuring educational activities, evaluation of its efficiency and the expected result. It is noted that the developed theoretical model has, in addition to integrity and consistency, its practical significance which shall have a further effect on its practicality.
Privacy-enabled system based on Elliptic Curve Cryptography to reduce risks of contagion in pandemics

The increase in the frequency of pandemic crises in recent years, with the current example of COVID-19, leads to the need of having technological tools that help to control the spread of these diseases in the first instance, and to avoid the generation of new outbreaks in the phases of back to normality. The main issue in these tools is the how to make their effectiveness compatible with privacy. In this work we present a system enabling secure distribution of people in closed environments such as public transport, restaurants or cinemas while maintaining privacy of health data. The system is based on Elliptic Curve Cryptography and it has been implemented on low-cost FPGA devices.