Reviewerly/exHarmony huggingface dataset:
Perspectively Equivariant Keypoint Learning for Omnidirectional Images

Robust keypoint detection on omnidirectional images against large perspective variations, is a key problem in many computer vision tasks. In this paper, we propose a perspectively equivariant keypoint learning framework named OmniKL for addressing this problem. Specifically, the framework is composed of a perspective module and a spherical module, each one including a keypoint detector specific to the type of the input image and a shared descriptor providing uniform description for omnidirectional and perspective images. In these detectors, we propose a differentiable candidate position sorting operation for localizing keypoints, which directly sorts the scores of the candidate positions in a differentiable manner and returns the globally top-K keypoints on the image. This approach does not break the differentiability of the two modules, thus they are end-to-end trainable. Moreover, we design a novel training strategy combining the self-supervised and co-supervised methods to train the framework without any labeled data. Extensive experiments on synthetic and real-world 360° image datasets demonstrate the effectiveness of OmniKL in detecting perspectively equivariant keypoints on omnidirectional images. Our source code are available online at https://github.com/vandeppce/sphkpt.
Learned-Index-Based Semantic Keyword Query on Blockchain

Blockchain has become increasingly popular for data management in recent years. However, the existing blockchain systems lack efficient semantic queries, particularly keyword queries. To address this issue, we propose a learned-index-based semantic keyword query architecture on blockchain. First, our architecture records data semantics information to support semantic keyword queries. Second, we establish the lookup table index for semantic information among blocks and the block-level recursive model index for blocks to improve the query efficiency. We store the lookup table in the extended block headers to maintain the result’s completeness, and we store recursive model indexes off chain to optimize the maintenance efficiency. Third, we propose a verifiable query algorithm based on our proposed architecture to maintain the result’s correctness. Finally, the experimental results show that combining the lookup table and the learned index effectively improves the query efficiency on blockchain.
Architectural profession: Modern creative trends in the university formation

Purpose: The aim of the paper is to determine problems of the educational environment of architectural universities in Russia, formation trends in design solutions based on the analysis of the world experience. Methods: Tendencies in the design of higher educational institutions of the creative orientation are identified for the concept development of design proposal meeting modern requirements. The analysis of tendencies is used to develop the functional zone interconnection. Research findings: The paper describes general tendencies of architectural education, the most common urban planning, volumetric-spatial, functional-planning and architecturalartistic solutions. The main functional zones are considered; the interconnection of functional zones developed for the structure of higher education institution. Based on the identified trends, functional and spatial structures are proposed for creativity-oriented universities. Practical implications: In designing a creativity-oriented university, it is necessary not only to provide a technological process, but also create the environment as an active educational element meeting modern requirements of all participants of the educational process.
A Corpus-Based Study on The Development Of the Javanese Language Among Multilingual Children In East Java

This paper seeks to examine multilingual children’s Javanese across different grades of primary schools with a focus on proficiency, vocabulary, and grammatical development. The data were partially taken from CBLING (Corpus of Bilingual Learners’ Languages), a corpus developed by Universitas Negeri Malang. Using AntConc as a corpus development tool, we measured Javanese proficiency using the Mean Length of Utterances (MLU) method. Our findings on the development of Javanese proficiency and vocabulary growth indicated insignificant improvement across grades. However, the average vocabulary density grows significantly from the first two lower grades to the three higher ones. In terms of grammar, hybrid words showing morphological blendings, such as mastekno or isikno were observed in the participants’ writings. Taken together, our findings are essential for teaching and learning Javanese as a regional home language and generating an important linguistic feature of 21st century multilingual children’s Javanese.&#x0D; Keywords: corpus study, Javanese language, multilingual children, East Java
Neural network approach to scaling analysis of critical phenomena

Determining the universality class of a system exhibiting critical phenomena is one of the central problems in physics. There are several methods to determine this universality class from data. As methods to collapse plots onto scaling functions, polynomial regression, which is less accurate, and Gaussian process regression, which provides high accuracy and flexibility but is computationally expensive, have been proposed. In this paper, we propose a regression method using a neural network. The computational complexity is linear only in the number of data points. We demonstrate the proposed method for the finite-size scaling analysis of critical phenomena in the two-dimensional Ising model and bond percolation problem to confirm the performance. This method efficiently obtains the critical values with accuracy in both cases.
Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning

With the increasing economic growth in developing nations, soil heavy metal pollution has become a growing concern. Monitoring the heavy metal concentration in soil through remote sensing is crucial for safeguarding the ecological environment. However, the current indoor spectral measurement method has limitations, such as the discrete soil sampling space and weak spectral characteristics of soil heavy metals, leading to a poor robustness of remote sensing inversion models. This study presents a novel approach to address these challenges by incorporating a spatial feature of pollution sources and sinks to evaluate the spatial factors affecting pollutant diffusion and concentration. An integrated learning model, combining spatial and spectral information, is developed to estimate heavy metal content in soil using Sentinel-2A satellite data. A total of 235 soil samples were collected in Jiyuan, China, and the effective spectral transformation characteristics of Sentinel-2A data were screened. The impact of spectral characteristics, topographic characteristics, and spatial characteristics on retrieving soil heavy metal lead (Pb) and cadmium (Cd) content were analyzed. The optimal inversion method was determined through various integrated learning models, and the spatial distribution of heavy metals Pb and Cd was mapped. The results indicate that the accuracy of the inversion model was significantly improved by incorporating terrain features and spatial features of pollution sources. The Blending integrated learning method showed a 65.9% and 73.2% reduction in the RMSE of Pb and Cd, respectively, compared to other regression models. With R2 values of 0.9486 and 0.9489 for Pb and Cd, respectively, and a MAPE less than 0.2, the Blending model demonstrated high prediction accuracy.
Intellectual Property Protection for Sports Performances and Sports Moves: An Analysis Under the U.S., EU, and Indian Laws

Fostering innovation and creation in every aspect of life has made intellectual property rights (IPR) more prominent. Sports being an inseparable part of human life, are no exception and have seen massive growth in recent times. However, the same is not adequately addressed as its regulation is governed through piecemeal legislation in India. This article aims to analyze the feasibility of extending protection to the performances and moves of sportspersons under the intellectual property regime. Given the dearth of analytical study on this aspect, the present article will make a significant contribution considering the recent advancements and participation of Indian contestants in various sports, including aesthetic sports, such as gymnastics. With a divide in the scholastic views about extending intellectual property protection to sports performances and moves in special reference to adversarial and aesthetic sports, it is relevant to analyze this challenge to offer concrete solutions for carving a better future for the sports industry and the athletes. Part I of this article introduces the premise of the issue under study along with the interface of sports and intellectual property. Part II of this article deals with issues pertaining to the protection of sports moves and performances under the three most important types of intellectual property: patent, trademark, and copyright. Part III concludes the article with recommendations for the challenges under study.
Moulded RSA and DES (MRDES) Algorithm for Data Security

In the recent days transmission of large amount of data through online is very prominent. Security is necessary while transmitting large amount of data. Since the data may belost or hacked at some point of transmission. Normally there are three important factors interms of security. They are key generation, encryption and decryption. There are two types of crypto system namely symmetric cryptosystem and asymmetric cryptosystem. There are many publicly available cryptosystems. It may lead the intruders to view the original message sent by the sender using all the possible keys. In order to provide secure transmission of data, a novel encryption algorithm is proposed by analyzing all the existing algorithms. The existing Rivest–Shamir–Adleman (RSA) and Data encryption standard (DES) algorithm are moulded together rto produce the proposed MRDES encryption algorithm. The performance of the proposed Moulded RSA and DES is higher than the existing encryption algorithms and provides higher data security.
An Outsourced Multi-authority Attribute-Based Encryption for Privacy Protection with Dynamicity and Audit

Attribute-based Encryption (ABE) realizes a novel and practical many-to-many encryption paradigm, in which the encryptor can appoint someone to decrypt it, and the decryptor does not know who the encryptor is. Therefore, the ABE has better privacy preserving to a certain extent for both participants to a certain degree. The original attribute-based encryption scheme usually has only one trusted authority, and the state of the system is not flexible enough to meet various dynamic needs of users, which become the bottleneck of the system. In addition, the computational overhead of the decryption is not cheap. In order to reduce the cost of decryption, a series of schemes such as encryption/decryption outsourcing have been proposed. However, those solutions simply outsource the process independently without any in-depth investigation into the trustworthiness issues. Hence, we construct an outsourced multi-authority attribute-based encryption with dynamicity and auditing (OMADA-ABE), which makes the system more practical and more flexible. Our solution can support the dynamic changes of the system as well as the auditing of outsourced decryption information to solve the above-mentioned related problems, so as to meet the potential requirements related to the real-world system. What’s more, we prove that the proposed scheme is secure against selective chosen-ciphertext attacks without random oracles, and it also achieves the collusion resistance. Finally, we compare our scheme with related researches and showed our advantage regarding the performance and other aspects.
A Multilevel Secured Mechanism for Data Protection

With the advancements in technologies, there is an urgent need to secure the data. For secured mechanisms, two processes are combined, i.e. cryptography and steganography. This is stated as a multilevel secured mechanism as data is secured at various levels. This paper focuses on developing a multilevel secured mechanism for data communication. The proposed work encrypted the data using key-dependent encryption process making it robust and secure. Then the data is compressed using Huffman compression to achieve high embedding capacity for large-size data. The resultant data is then embedded into the edges of the image randomly using LSB steganography. To reveal the efficacy of the proposed methodology, it is compared with the existing renowned methods on the basis of various parameters using MATLAB R2014a. It is asserted from the Experimental results that the proposed model shows the best results in BER, UIQI, SSIM and visually as compared to other latest techniques available in the literature. Our model shows comparable results in terms of PSNR and MSE.
Modulation Recognition Based on BP Neural Network

In the real wireless communication applications, there are always existing multiple signal modulation schemes. To complete the demodulation of modulated signals, it is necessary to understand the signal modulation method, so it is important for the signal receiver to have the ability to automatically identify the signal modulation schemes. In this paper, for six common digital modulation schemes, each modulation scheme modulates one hundred signals respectively, then extracts five feature parameters from each signal, and finally classifies them using back propagation (BP) neural network. The experimental results illustrate that the recognition accuracy can reach more than 90% when SNR > 10 dB. The model structure is simple and practical, and it can meet the requirements of automatic recognition of modulation schemes.
Introduction of ICT subject in Zanzibar primary education: Challenges and opportunities

This study was intended to examine the challenges and opportunities of ICT subject teachers in teaching ICT subjects to primary school students. The study was meant to answer the following questions: What are the challenges of implementing ICT in education policy and in introducing ICT subjects in Zanzibar's primary schools? What opportunities arise in the introduction of ICT subjects in Zanzibar's primary schools? This quantitative study used questionnaires to collect data from 73 primary school ICT teachers who were selected from 44 schools. Four schools were randomly selected from each of the 11 districts of Zanzibar, Tanzania. Quantitative data was analyzed through descriptive analysis whereby percentages and statistical measures were employed. The study found that 80% of the teachers had positive perceptions of the importance of ICT subjects in primary school. The results showed that challenges were based on the insufficiencies of ICT facilities, teachers' knowledge, and technological support in most primary schools. However, the study revealed that the positive attitudes of the teachers engendered the opportunities for the introduction of ICT subjects in Zanzibar's primary schools. This study recommended effective implementation of ICT subjects in schools through employing qualified ICT graduates. In addition, teachers should be well-equipped with ICT teaching methodologies and ICT practical skills. Also, the Ministry of Education should cover the existing gap of knowledgeable teachers in the ICT field as well as equip ICT facilities in schools.
On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues

Multilabel data comprise instances associated with multiple binary target variables. The main learning task from such data is multilabel classification, where the goal is to output a bipartition of the target variables into relevant and irrelevant ones for a given instance. Other tasks involve ranking the target variables from the most to the least relevant one or even outputting a full joint distribution for every possible assignment of values to the binary targets.
The Effects of Interaction Mode and Individual Differences on Usability and User Experience of Mobile Augmented Reality Navigation

In the context of the rapid development of navigation technology and the deepening of users’ diversified needs, as an emerging public service, mobile AR (Augmented Reality) navigation is supposed to focus on human-computer interaction and user experience. To extract the influencing factors of efficient service of mobile AR navigation, we constructed an experimental method for the usability of mobile AR navigation and the users’ emotional experience based on behavior-emotion analysis. In this study, the user types were divided according to the differences in Mental Cutting Ability and Gender. We explored the effects of Interaction Mode, Mental Cutting Ability, and Gender on the usability of mobile AR navigation and the users’ PAD (Please-Arousal-Dominance) three-dimensional emotion through the objective performance and subjective scoring of users when completing AR navigation tasks. The results showed that the Interaction Mode and Mental Cutting Ability had significant effects on the usability of mobile AR navigation and the users’ emotional experience; the Ease of Learning, Ease of Use in usability indicators, and the Arousal experience of three-dimensional emotion were significantly affected by Gender. Based on the experimental results, we excavated the mechanism of effects between various factors, extracted the behavioral and emotional trends of different types of users, broadened the research scope of mobile AR navigation-related fields, and finally summarized the design strategies from the perspective of human-robot-environment.
A Distributed and Decentralized Certificateless Framework for Reliable Shared Data Auditing for FOG-CPS Networks

FOG computing enhances communication efficiency by processing data at the network’s edge. In many critical infrastructure (CI) networks, user entities outsource data to the FOG server, but the reliability of the data is a concern. To avoid incorrect decision-making due to corrupt data, a data auditing process is mandatory. Further, a group of user entities in a CI network generate data onto a common file and outsource the file to the FOG server. Auditing such shared group data creates additional complexities. Existing shared group data auditing schemes using public key infrastructure (PKI) or identity-based cryptography (IBC) face challenges such as certificate management or key escrow problems respectively. While, certificateless cryptography (CLC) offers better security and efficiency, but many CLC-based shared group data auditing schemes lack protection against key generation center compromise. The key generation process in many of the existing shared group data auditing schemes is centralized and does not support flexible user entity admission. This paper proposes a distributed and decentralized cryptography approach to enhance the security of key generation for group members. The scheme offers flexible user admission, allowing for full or semi-membership. The proposed scheme provides efficient and secure shared group data auditing against public key replacement, metadata forgery, and integrity attacks, as shown by performance evaluation and security analysis.
Verifiable Homomorphic Secret Sharing for Machine Learning Classifiers

When using machine learning classifiers to classify data in cloud computing, it is crucial to maintain data privacy and ensure the correctness of classification results. To address these security concerns, we propose a new verifiable homomorphic secret sharing (VHSS) scheme. Our approach involves distributing the task of executing a polynomial form of the machine learning classifier among two servers who produce partial results on encrypted data. Each server cannot obtain any data information, and the classification result can be reconstructed and verified using a verification key in conjunction with the two partial results. Compared to previous VHSS schemes, our scheme can compute the degree of polynomials as high as the polynomials in the system security parameters while performing comparably to homomorphic secret sharing (HSS) schemes. We implement our proposed scheme and demonstrate its application to decision trees (a type of machine learning classifier). Our experiments show that our scheme is twice as fast as previous VHSS schemes when evaluating decision trees with depths ranging from 2–14.
Exploring the Viability of Bypassing the Image Signal Processor for CNN-Based Object Detection in Autonomous Vehicles

In the field of autonomous driving, cameras are crucial sensors for providing information about a vehicle’s environment. Image quality refers to a camera system’s ability to capture, process, and display signals to form an image. Historically, “good quality” in this context refers to images that have been processed by an Image Signal Processor (ISP) designed with the goal of providing the optimal experience for human consumption. However, image quality perceived by humans may not always result in optimal conditions for computer vision. In the context of human consumption, image quality is well documented and understood. Image quality for computer vision applications, such as those in the autonomous vehicle industry, requires more research. Fully autonomous vehicles inevitably encounter constraints concerning data storage, transmission speed, and energy consumption. This is a result of enormous amounts of data being generated by the vehicle from suites made up of multiple different sensors. We propose a potential optimization along the computer vision pipeline, by completely bypassing the ISP block for a class of applications. We demonstrate that doing so has a negligible impact on the performance of Convolutional Neural Network (CNN) object detectors. The results also highlight the benefits of using raw pre-ISP data, in the context of computation and energy savings achieved by removing the ISP.
Image denoising method based on primal sketch correction and matrix filling

This paper only aims to develop a SAR denoising algorithm based on Primal Sketch classification and SVD domain to improve MMSE estimation. The main contents of this paper are as follows: 1) In Primal Sketch algorithm, dual-domain contrast enhancement method is used to improve the energy image. 2) SAR images are divided into edge class and non-edge class by Primal Sketch algorithm. Then the NLSVD decomposition of the two kinds of pixels is carried out, and the singular value matrix is estimated by the minimum mean square error criterion containing the contracted silver. The estimated value of the edge class and the non-edge class are obtained by the inverse transformation. 3) The edge coefficient is calculated, and Butterworth fusion method is used to fuse the edge class and non-edge class boundary to get the denoising result. Our results show that this method can effectively remove speckled noise in SAR images, and keep the edge and point target information well.