Reviewerly/exHarmony huggingface dataset:
A Secure Email Solution Based on Blockchain

Email is one of the most important online communication services between individuals and businesses. The large amount of information that passes through this medium is the object of desire for many attackers who use a whole series of malicious maneuvers to get hold of it. The number of emails circulating in the world per day is growing, going from 319.6 billion in 2021 to more than 361 billion by the end of 2024. Attackers use all kinds of attacks to participate in these communications, perpetuating attacks that range from spam to very sophisticated attacks such as phishing, scams, among others. There are many solutions available in the market and the email security problem continues to grow and there seems to be no solution in sight. We are working on a different approach. First, we have deeply analyzed the state of the art of email in terms of its design, the security problems identified and the proposed solutions. Subsequently, we have identified the security requirements necessary to address the problem and based on them we have designed an architecture based on a Blockchain platform, whose components interact through different protocols to achieve the objectives proposed in our research.
Net Buying Pressure and the Information in Bitcoin Option Trades

How do supply and demand from informed traders drive market prices of bitcoin options? Deribit options tick-level data supports the limits-to-arbitrage hypothesis about market makerâ€™s supply. The main demand-side effects are that at-the-money option prices are largely driven by volatility traders and out-of-the-money options are simultaneously driven by volatility traders and those with proprietary information about the direction of future bitcoin price movements. The demand-side trading results contrast with prior studies on established options markets in the US and Asia, but we also show that Deribit is rapidly evolving into a more efficient channel for aggregating information from informed traders.
Application of Naive Bayesian Classifier Based on Gaussian Distribution in Plant Recognition

This paper presented a kind of plant leaf classification method based on naive Bayesian classifier and leaf shape features. Firstly, estimate the class-conditional probability of the numeric features with the distribution function of Gaussian distribution, then reduce the deviation of probability statistics of the nominal features with Laplace smoothing. In the end, compare and analyse the influence of equal-width binning, equal-frequency binning, MDL discretization method and Gaussian probability estimation method on the classification efficiency. The experimental results on iris data set indicated that the correct classification rate of this method reached 95.3%, and the method was feasible and effective.
A computationally efficient affine-invariant feature for image matching with wide viewing angles

This paper proposes an efficient affine-invariant feature image matching method, especially when there are wide viewing angles. In previous studies, all the AKAZE-based algorithms are built on nonlinear scale-space by using Fast Explicit Diffusion (FED) scheme, which has the advantage of high computational efficiency. But all of them do not have affine invariance and cannot favor the image matching when the image views are too different. Unlike AKAZE features, AFREAK features are extracted from the simulated images obtained by sampling the latitude and longitude angles of an original image, which have good robustness for viewpoint changes. Therefore, we combine the idea of simulating an affine transformation with the construction of a nonlinear scale-space to build an affine-invariant nonlinear scale-space. Then, in order to reduce the redundancy information, we optimize the sampling of longitude angles and simplify the FREAK descriptor. Moreover, a coarse-to-fine matching strategy based on Vector Field Consistency (VFC) and Progressive Sampling Consensus (PROSAC) is developed to further improve the rapidity and correct matching rate. Experimental results on various benchmark image datasets demonstrate that the proposed method is computationally efficient for image matching while has the fully affine invariance.
Research on Intelligent Image Scrambling Transform Encryption Algorithm Based on Big Data Analysis

In order to improve the encryption ability of scrambling transformation of high-resolution knowledge map remote sensing image, a scrambling transformation encryption model of high-resolution knowledge map remote sensing image based on big data analysis and multi feature fusion is proposed. The edge pixel feature analysis model of high-resolution knowledge map remote sensing image is constructed. The edge pixel feature distributed reorganization method is used to realize the pixel structure reorganization. The image is processed by multispectral parameter big data fusion and chaos quantization linear feature analysis. The image encryption key feature analysis is realized by using the combination control method of shallow feature map. The personalized features extracted from image are identified by using intelligent image scrambling transform encryption algorithm, and the multilevel feature analysis model of image scrambling transform encryption is established. The simulation results show that the method has high adaptability and good scrambling performance, and improves the ability of fusion encryption and feature recognition of high-resolution knowledge map remote sensing image.
Adaptive Entity Alignment for Cross-Lingual Knowledge Graph

Entity alignment is a key step in knowledge graph (KG) fusion, which aims to match the same entity from different KGs. Currently, embedding-based entity alignment is the mainstream. It embeds entities into low-dimensional vectors and transfers entities to the same vector space. However, the precision of entity alignment depends largely on the number of alignment seeds. Manually labelling alignment seeds is very expensive and inefficient. To address this problem, this paper proposes a novel adaptive entity alignment approach for cross-lingual KGs, namely AdaptiveEA. This approach adopts a new joint KG embedding network to accurately learn the embeddings of entities from both structural semantics and relational semantics. Then, it employs an adaptive entity alignment strategy to iteratively capture new aligned entity pairs that meet a dynamic alignment threshold. Then the new captured aligned entity pairs are utilized to expand alignment seeds to further guide the next embedding training. Experimental results on public datasets show that the proposed approach can achieve much better precision than the latest state-of-the-art models in a small number of labeled alignment seeds.
Fast Stereo Matching Method Based on Two-step AD-Census Fusion

With the development of industrial information technology, an online industrial quality inspection technology which can make the computer quickly and efficiently obtain the 3D information of the industrial production line environment is particularly important. With the development of computer technology and the extensive application of stereo matching technology, more and more industrial inspection systems have adopted online monitoring programs such as binocular stereo vision. Due to the shortage of the traditional stereo matching algorithm, such as complicated process and demanding hardware conditions, a fast AD-Census stereo matching algorithm was proposed in this paper, combining the idea of image pyramid, aiming at the characteristics of large resolution and high demand on algorithm efficiency of industrial online inspection. In this paper, AD algorithm is simple and fast, Census algorithm is insensitive to illumination difference and has high robustness to edge information and makes full use of the idea that multi-scale pyramid pays attention to the main trend, which can achieve a higher efficiency of stereo matching algorithm without too much loss of accuracy.
Prototype-Driven Innovation: Propositions Based on Challenges and Opportunities

Existing new product development (NPD) methods are specification driven, where product specifications are typically made before prototyping starts. The existing method does not allow enough space for modification because of the association of sunk cost. Hence, the prototype-driven specification would enhance new product development with better prototype experience. But the prototype-driven approach does not have structured tools and methods to foster. Hence, it is very difficult to introduce and practice in design education. Systematic literature review had been done to identify the drivers, principles or guidelines and support tools in prototyping and prototype-driven new product development process. A structured and sequential process is followed to illustrate the initial reference model and impact model to identify the means that may foster the emergence of prototype-driven new product development from design education perspective. This state-of-the-art review also describes, discusses and connects the existing research to articulate propositions, which would help the future research in prototype-driven innovation.
Concept Word Extraction for Bilingual Ontology Construction in Unstructured Text Environment

Aiming at the unsatisfactory efficiency of concept word extraction from unstructured text for domain ontology construction, this work first uses a combined statistic to judge the correctness of the concept word boundary determined by the word segmentation, and corrects the wrong segmentation position, thereby strengthening the structural integrity of the segmented candidate concept words. On this basis, the improved methods and various resource libraries are used to adjust the weight of concept words, and the main purpose is to strengthen the correlation between the weight and its domain attributes of concept words. We conducted experiments and comparisons on English-Chinese bilingual corpus, and found that the method of strengthening the structural integrity of concept words and the method of dynamically adjusting the weight of concept words proposed in this paper both brought a certain improvement in the efficiency of concept word extraction.
Simulating permissionless blockchains: Distribution fitting, tool support, and a case study

Summary Discrete‐event simulation is an important tool for the analysis of permissionless blockchain systems. Simulation allows one to evaluate blockchains for scenarios that cannot be mimicked easily on the life system. To simulate blockchain systems as close to reality as possible a realistic characterization of the probability distributions of various variables is required. In this paper, we obtain frequency distributions for Ethereum smart contract transactions with respect to gas limit, used gas, gas price, and CPU time. The distribution fitting utilizes Gaussian mixture models, correlation analysis, and ensemble regression techniques, and we demonstrate the appropriateness of each of these methods. We use publicly available Ethereum smart contract data, supplemented with experimental data for over 300,000 smart contracts obtained on a test bed. In addition, we extend the BlockSim simulation tool to support the required distributions and use the simulator for a case study in optimizing miner returns through transaction selection strategies in Ethereum under future scenarios. The outcomes not only demonstrate the impact of selection strategies on miner return but also demonstrate the sensitivity of the outcomes to chosen distributions and therefore illustrate the importance of using realistic distributions.
Fast and Secure Key Generation for Low Rank Parity Check Codes Cryptosystems

Among the candidates for NIST's post-quantum cryptography standardization project, cryptosystems that rely on Low Rank Parity Check (LRPC) codes have interesting properties, such as a low public key size. However, the key generation phase for these cryptosystems is computationally expensive when done in constant-time, which is a security requirement on the standardization project, making it almost unusable for ephemeral key generation. We present a new constant-time algorithm for key generation on LRPC code-based cryptosystems, that divides the computational costs by four when compared to previous work over ROLLO, one of the NIST candidates. Our improvement consists in changing the way objects of a quotient ring are represented. By switching from a canonical basis to an optimal normal basis, we enable the full potential of the Itoh-Tsuiji algorithm for field inversion.
Kuran Kursu Öğreticilerinin Dijital Okuryazarlık Seviyesinin İncelenmesi

The phenomenon of digitalization, which manifests itself in almost every field of daily life, strongly affects education processes, and traditional education methods and techniques are replaced by digital technologies. The rapid digitalization experienced today has brought the concept of digital literacy. In its most general definition, digital literacy refers to the ability to find, understand, analyze, produce and share information through network devices such as smartphones, tablets, laptops and desktop computers. Digital literacy also includes knowing the unique legal rules and ethical norms of online environments, production and sharing patterns. Digitalization seems inevitable in religious education and Quran Courses as in all fields of education. This research aims to examine the digital literacy levels of Qur'an Course Instructors. The sample consists of 351 Qur'an Course Instructors working in Sakarya and Düzce provinces. For collecting the data, a questionnaire prepared by the researcher to determine the demographic variables of the participants and the Digital Literacy Evaluation Scale developed to measure the digital literacy level of the participants were used. The scale consists of “awareness, contextual use, secure participation, digital identity management and basic tool and media knowledge” sub-dimensions. The findings show that the digital literacy level of Qur'an Course Instructors differs according to the type of course they work. It has been observed that the digital literacy level of those who need training on the use of digital technologies is significantly lower. It is thought that providing basic digital literacy training to Qur'an Course Instructors will increase the quality of service
Evaluation Framework for Performance Limitation of Autonomous Systems Under Sensor Attack

Autonomous systems such as self-driving cars rely on sensors to perceive the surrounding world. Measures must be taken against attacks on sensors, which have been a hot topic in the last few years. For that goal one must first evaluate how sensor attacks affect the system, i.e. which part or whole of the system will fail if some of the built-in sensors are compromised, or will keep safe, etc. Among the relevant safety standards, ISO/PAS 21448 addresses the safety of road vehicles taking into account the performance limitations of sensors, but leaves security aspects out of scope. On the other hand, ISO/SAE 21434 addresses the security perspective during the development process of vehicular systems, but not specific threats such as sensor attacks. As a result the safety of autonomous systems under sensor attack is yet to be addressed. In this paper we propose a framework that combines safety analysis for scenario identification, and scenario-based simulation with sensor attack models embedded. Given an autonomous system model, we identify hazard scenarios caused by sensor attacks, and evaluate the performance limitations in the scenarios. We report on a prototype simulator for autonomous vehicles with radar, cameras and LiDAR along with attack models against the sensors. Our experiments show that our framework can evaluate how the system safety changes as parameters of the attacks and the sensors vary.
Sensitivity of camera’s height based on coordinate variation of monocular vision ranging model

Using computer vision technology to perceive the distance of obstacles is an important technology in intelligent driving decision-making. Monocular vision ranging technology has a wide application range and reduces the requirement of camera quality, so it is a hot research direction in the field of visual ranging. In practical application, the change of the camera’s external parameters will have a certain impact on the ranging accuracy. This paper will study the sensitivity of the monocular vision ranging model based on the coordinate change to the camera height. We propose a conjecture that the product of the model’s formula accuracy and the height measurement accuracy is equal to the final ranging accuracy of the model. This conjecture will be preliminarily verified through experiments in this paper, and an error correction mechanism is proposed to reduce the impact of camera height changes on ranging accuracy.
A Research on the Teaching Process of Basic Blended Model Computer Course Based on the Concept of OBE

Outcomes Based Education (OBE) is an educational practice in which its teaching process is designed to promote students' competences. Based on OBE principles, the purpose of this research is seek to improve learning efficiency of students who attend college general computer programme. Attempts were made to define the orientation and objectives of the programme through the mixed learning methods. Research methods were compared to redesign the teaching process in this study by means of collecting data from online platforms. All data of dynamic learning online was analyzed to prove the effectiveness after the traditional teaching methods were modified based on the study of this research.
Collaborative Development of Outing Assistants for People with Dementia: a Case Study on a Co-design Approach

People with dementia (PwD) have been conducting educational activities to live well with dementia. Appropriate CHI design is needed to support the lives of PwD. However, current prototypical systems are mostly designed for needs on the surface and do not approach hidden needs. To develop an system that PwD wants to use on a daily basis, we conducted a co-production practice with PwD focussing on outing their home. We collaborate with a group of early-onset dementia who wanted their opinions to be reflected in ICT. A participatory design workshop was undertaken to discuss the development of outing assistants. As a result, it was found that they did not want the system to be responsible for intervening in their disabilities, but to positively encourage their activities. To conduct a case study for outing assists, we focused on a person with dementia's activity to verify whether it contributes to her life. The system was implemented as a voice and visual interaction system, designed to encourage a positive relationship between the supporter and her. Our results indicate that the system design to support PwD should produce positive interaction and should be adapted to the individual context.
Ensembled Approach for Text Summarization

There is a tremendous amount of data which is present online, and to extract the useful content is a challenging task. The solution is made possible by the introduction of text summarization. In this paper, an ensembled approach for text summarization is proposed, in which the robustness of extractive summarization and abstractive summarization is combined to make the most sense out of the raw data. Extractive text summarization is implemented by using RNN model based on LSTM architecture. The output generated by this model is used as input for the abstractive summarization. Pointer Generator Network is used for implementation of abstractive text summarization. The standard CNN/daily mail dataset is used for experimental purpose. The results are evaluated using ROUGE scores.
LDSQP: Scalable and Efficient Log Data Storage and Query Processing Scheme for Cloud Data Centers

Cloud data centers are one of the most important information infrastructures for most real-world applications involving information technology, and serves millions of user requests daily. To provide various services with Quality-of-Service (QoS) guarantees to users, such as low network latency and fast response time, it is vital that cloud data centers should (1) manage its resources efficiently, and (2) avoid resource overprovisioning in situation such as virtual machine allocation. To achieve these goals, cloud data centers usually employ modern techniques from artificial intelligence or machine learning community to analyze status information of cloud data center, so as to find actionable strategies. In this paper, we propose LDSQP, an efficient scheme for storing and querying status information in cloud data centers, i.e., log data that contains enriched information collected from servers, virtual machines, and containers in cloud data centers. We design scalable cluster structure and row key format for log data record in data storage, and we also propose a compact and efficient query expression to capture user's data requirement. We introduce an efficient query processing algorithm that can minimize the number of table partitions to scan. Experiments on a real cluster show that our proposed LDSQP is efficient in log data management.
Virtual shakuhachi with dai-shihan Michael Chikuzen Gould

Virtual learning environments have become commonplace in the midst of COVID-19. Although it was not unheard of, taking Skype lessons on a musical instrument was considered to be unusual ten-plus years ago. This article will briefly discuss the ways in which the pandemic has affected those who study shakuhachi online with dai-shihan (grandmaster) Michael Chikuzen Gould. While individuals and institutions continue to figure out how to navigate this new cyber context, virtual shakuhachi lessons with Sensei Gould continue as scheduled and remain unaffected as in-person workshops and intensives are cancelled until further notice.
Mixed-protocol multi-party computation framework towards complex computation tasks with malicious security

With the rapid development of secure multi-party computation (MPC) over past decades, applications of MPC has been moving from completing simple computation tasks (e.g., private set intersection) to complex computation tasks (e.g., privacy-preserving machine learning). This is an inevitable trend when more strict privacy protection requirements face more complex and large-scale computation such as big data analytics being applied in many fields. Although the complex computation tasks are not easy to be evaluated with one type of MPC protocols from beginning to the end, it can be more efficiently evaluated by decomposing the complex task into many simple sub-tasks and evaluating each of them with the proper type of MPC protocol in sequence. Therefore, we propose a mixed-protocol MPC framework towards complex computation tasks with malicious security in this work. In particular, we utilize the homomorphic commitment technique to construct six types of share conversion protocols in the malicious model. Then, we construct the maliciously secure mixed-protocol MPC framework based on these share conversion protocols. This is the first maliciously secure mixed-protocol MPC framework relying on the standard model, providing a higher security guarantee than all the previous works in the literature. Also, this is the first general mixed-protocol MPC framework for n parties in the malicious model, in comparison to previous works that either only support fixed number of parties in the malicious model, or only handle limited types of share conversions. Furthermore, we provide the theoretical analysis of the computation and communication costs for the six types of share conversion protocols, as an important reference for future developers, who intend to implement some complex computation task by following this mixed-protocol MPC framework with malicious security.