Reviewerly/exHarmony huggingface dataset:
Perception of Node-Link Diagrams: The Effect of Layout on the Perception of Graph Properties

The way we choose to draw the networks on the plane (layout) is found to be important for the readability of networks by humans. In this study, we examine how different layouts affect our perception of specific properties of small networks of 16 nodes each. We compare a simple grid layout to the planar and force-directed layouts, which are some of the most well-established layout algorithms. We also introduce an alternative ‘improved’ grid layout, which optimizes the outcome layout in terms of specific aesthetics. When people had to decide whether a network is a tree given a node-link diagram, the layout significantly affected their performance. The same pattern appeared for the detection of the connectedness property. However, when people had to detect two properties at a time, the layout didn’t affect their performance. The results show that the layout we choose for representing a network is crucial for our perception of some of the network’s basic properties. However, when people had to detect more than one property at a time, the chosen layout didn’t seem to significantly affect their performance.
Causality in structural engineering: discovering new knowledge by tying induction and deduction via mapping functions and explainable artificial intelligence

Abstract Causality is the science of cause and effect. It is through causality that explanations can be derived, theories can be formed, and new knowledge can be discovered. This paper presents a modern look into establishing causality within structural engineering systems. In this pursuit, this paper starts with a gentle introduction to causality. Then, this paper pivots to contrast commonly adopted methods for inferring causes and effects, i.e., induction (empiricism) and deduction (rationalism), and outlines how these methods continue to shape our structural engineering philosophy and, by extension, our domain. The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws (i.e., mapping functions ) through explainable artificial intelligence (XAI) capable of describing new knowledge pertaining to structural engineering phenomena. The proposed approach and criteria are then examined via a case study.
Semantic Protocol and Resource Description Framework Query Language: A Comprehensive Review

This review presents various perspectives on converting user keywords into a formal query. Without understanding the dataset’s underlying structure, how can a user input a text-based query and then convert this text into semantic protocol and resource description framework query language (SPARQL) that deals with the resource description framework (RDF) knowledge base? The user may not know the structure and syntax of SPARQL, a formal query language and a sophisticated tool for the semantic web (SEW) and its vast and growing collection of interconnected open data repositories. As a result, this study examines various strategies for turning natural language into formal queries, their workings, and their results. In an Internet search engine from a single query, such as on Google, numerous matching documents are returned, with several related to the inquiry while others are not. Since a considerable percentage of the information retrieved is likely unrelated, sophisticated information retrieval systems based on SEW technologies, such as RDF and web ontology language (OWL), can help end users organize vast amounts of data to address this issue. This study reviews this research field and discusses two different approaches to show how users with no knowledge of the syntax of semantic web technologies deal with queries.
Splintering and centralizing platform governance: how Facebook adapted its content moderation practices to the political and legal contexts in the United States, Germany, and South Korea

The proliferation of hate speech and disinformation on social media has prompted democratic countries around the world to discuss adequate regulations to limit the power exerted by platforms over national politics. As a result, the once ostensibly uniform content moderation practices of social media companies are becoming increasingly territorialized, and the governance of online political speech is constantly negotiated between global social media platforms and national governments. To comprehend the evolving landscape of online political speech governance, this paper scrutinizes how Facebook has adapted its content moderation practices to the political and legal contexts of three democratic nations: the United States, Germany, and South Korea. We assessed national laws and governmental documents to explain the regulatory landscapes of the three countries, and used VPNs and corporate PR materials to see how Facebook’s platform design and public communication diverge by location. The findings suggest that the seemingly ‘splintering’ regulatory frameworks still have a ‘centralizing’ effect: Facebook formally complies with national laws, but its platform interface and communication activities steer users away from the local systems and towards its centralized operations. We discuss future implications for the regulation of online political speech in democratic nations.
Relational data watermarking resilience to brute force attacks in untrusted environments

Watermarking techniques for relational data that aim at robustness face the challenge of guaranteeing watermark persistence despite aggressive attacks. Nevertheless, attackers cannot compromise watermark detection to their will since they must ensure the usability of the stolen data. Attacks that constitute a potential master-key for false ownership claims focus on leaking or compromising values used for watermark synchronization. For instance, brute-force attacks attempt to guess the values of parameters used for watermark embedding. The chances for brute-force attacks of successfully performing illegal watermark detection increases in untrusted environments where the leaks of parameters are common. In this paper, (i) we show how brute-force attacks succeed even if not all watermark detection parameters' values are correctly guessed, (ii) we define the role played by each parameter in robustness, and (iii) we formalize a method for improving the resilience of existing watermarking techniques in scenarios lacking trust. According to the experimental results, watermarking approaches to which our proposal has been applied benefit in terms of robustness and security requirements, extending the deployment opportunities for other techniques.
A novel identity-based multi-signature scheme over NTRU lattices

Multi-signature is an important technology to compress multiple signatures on the common message into a compact one, thereby reducing the consumption of storage space and transmission bandwidth. Such a cryptographic primitive is widely used in financial applications such as blockchain which requires multiple keys to authorize a transaction. With the advent of quantum computers, traditional multi-signature schemes may no longer be secure as their underlying security assumptions (e.g. RSA or discrete logarithm problems) may not hold anymore. In this paper, we propose a novel identity-based multi-signature (IBMS) scheme over NTRU lattices, which is secure against the attacks of quantum computers. To the best of our knowledge, it is the first lattice-based IBMS scheme. We show that our scheme is provably secure in the random oracle model based on the ring version of the short integer solution assumption (Ring-SIS). Compared with the closely related works in the literature, our scheme enables the signer to select its system identity (such as email address, physical IP address, and etc.) as the public key, which effectively alleviates the certificate management problem in the PKI setting, and takes the advantage of discrete Gaussian distribution instead of uniform distribution to generate secret signing keys and multi-signatures. Besides, our scheme does not require all the system users to setup a trusted common string, which further simplifies the deployment of our scheme in practice.
Local Learning-based Multi-task Clustering

Clustering plays an essential role in machine learning and data mining. Many real-world datasets for clustering are often different but related in the big data era. Recent research suggests that the individual performance of each learning task could be significantly improved by appropriately transferring knowledge among the related tasks. However, traditional clustering methods, which is limited to a single task, often ignore the correlation between multiple clustering tasks. Multi-task clustering (MTC) has attracted widespread interest recently by attempting to mine sufficient knowledge within multiple related tasks. Existing MTC methods still have the following limitations: 1) the intrinsic geometry of data is seldom considered; 2) the discriminative low-dimensional representation of data is not well explored; 3) the cluster structure of data is neglected in the process of clustering. In order to tackle the above issues, we propose a novel end-to-end Local Learning-based Multi-task Clustering (LLMC) method, which can simultaneously explore discriminative information in a low-dimensional subspace and expose the clustering results for multiple tasks. In particular, the proposed LLMC method can effectively integrate transfer learning, subspace learning, local manifold learning, and clustering. Specifically, a joint projection of heterogeneous features is introduced to control the number of features shared by all the tasks to transfer knowledge among tasks. An efficient iterative algorithm is designed to optimize the objective and is theoretically guaranteed to be convergent. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art single-task and multi-task clustering methods.
Multispectral interaction convolutional neural network for pedestrian detection

Fusion of multispectral data in object detection is inevitable in order to cover various environments. However, there is still insufficient research on how to fuse information between two-stream multispectral networks. This paper proposes a novel multispectral interaction convolutional neural network (MICNN) for fusing information between multispectral networks. Unlike existing fusion methods that do not have interactions between multispectral networks, the proposed MICNN reflects information from each multispectral network in the training process. The MICNN is a simple way of forcing interactions by exchanging weights of feature maps between multispectral networks. It does not require any parameters to learn, and does not need additional computations or modifications to the network structure . We verified the effectiveness of the MICNN with the KAIST multispectral pedestrian dataset and YU far-infrared (FIR) pedestrian dataset. • The limitations in fusion of multispectral networks are proved. • MICNN model for multispectral pedestrian detection is proposed. • Forcing interaction between multispectral networks is accomplished. • Enhanced results in from both YU and KAIST datasets are verified.
Implementation of Blockchain for Digital Document Data Collection Website

The progress in technology and the digital paradigm forces a new standard, such as switching from physical to digital documents. Digital documents are implemented in various ways like certificates, certificates of merit, even diplomas. However, those things are vulnerable to counterfeit, and the integrity compromise of its content will be challenging to detect. One of the solutions is increasing transparency for the document data to ensure that no single point of failure exists. Blockchain has the potential to solve the problem. Blockchain has the transparency aspect to it. Therefore, it can be used to maintain the validity of document data. Because in blockchain, only read and write operations could be done, nothing could compromise the integrity of the data. This paper implemented a document validation system with Ethereum blockchain and Rinkeby Test Network as blockchain network. This blockchain application can do input, retrieve, update, and delete with 100% black-box testing and 100% white box testing. Performance testing for all transactions has the fastest time, 6.04 seconds, and slowest time, 93.32 seconds.
Swin Transformer with Local Aggregation

Despite the many advantages of Convolutional Neural Networks (CNN), their perceptual fields are usually small and not conducive to capturing global features. In contrast, Transformer is able to capture long-range dependencies and obtain global information of an image with self-attention. For combining the advantages of CNN and Transformer, we propose to integrate the Local Aggregation module to the structure of Swin Transformer. The Local Aggregation module includes lightweight Depthwise Convolution and Pointwise Convolution, and it can locally capture the information of feature map at stages of Swin Transformer. Our experiments demonstrate that accuracy can be improved with such an integrated model. On the Cifar-10 dataset, the Top-1 accuracy reaches 87.74%, which is 3.32% higher than Swin, and the Top-5 accuracy reaches 99.54%; on the Mini-ImageNet dataset, the Top-1 accuracy reaches 79.1%, which is 7.68% higher than Swin, and the Top-5 accuracy reaches 94.02%, which is 3.25% higher than Swin 3.25%.
Face Detection and Tracking Based on Neural Network

Face detection and tracking technology is used in transportation, security, military fields. In view of the traditional face detection and tracking technology is easy to be affected by light, which leads to low detection accuracy, this paper uses Retinaface and Camshift algorithm to face detection, and realizes real time face detection and tracking by P control steering gear in PID control. Through tests in different environments, the detection accuracy of the Retinaface algorithm and the Camshift algorithm is above 99%. The camera is rotated through P to ensure that the face can be captured by the camera, and the camera response time can reach 0.1s.
The Effect of Anthropomorphic Design Cues on Increasing Chatbot Empathy

Empathy is the process of understanding and reacting to other people emotional expressions that involve cognitive and affective aspects. Empathy in education is very important, because service experiences in student academic services that are emotional and generate student affective responses have an influence on student motivation and academic achievement. Recent advances in artificial intelligence (AI) allow computers to gain the ability to express empathy by analyzing and reacting to user emotional expressions via chatbot conversational agents. Chatbots with human-like cues such as their language style can also affect user emotions. In order for chatbots to have human-like cues, it is necessary to apply the concepts in Human Computer Interaction (HCI) to chatbots. This research will apply one of the concepts in HCI, called Anthropomorphic Design Cues (ADC). Anthropomorphic is an attribution of human characteristics and behavior that is applied to non-human entities such as animals, plants, or technological devices like chatbot. This study will apply two designs from ADC on chatbots, verbal design and non-verbal design. This study will compare the level of empathy between two chatbots with ADC and without ADC. The results showed that based on the results of statistical tests using the Mann Whitney U Test ADC had a significant p-value of 0.000<0.05 on chatbot empathy, so it can be concluded that ADC had a significantly positive effect on increasing chatbot empathy.
A Two-Stage NER Method for Online-Sale Comments

In this paper, we introduce a two-stage NER method to recognize the entities in online-sale comments. Firstly, we pick out the NN-labeled words from the pre-labeled dataset to generate the topic-based dictionaries, and in turn, these dictionaries are exploited to identify topic-related entities of each comment. Secondly, [−1, 1] windows are applied to identify the adjectives around the topic-related entities which we recognize in step 1. After the above two steps, we use these semantic features to construct the comment-by-topic matrix for further clustering. We then have an exploratory data analysis of our dataset.
Using a Professional Skills Framework to Support the Assessment of Dispositions in IT Education

The IT2017 ACM/IEEE Curriculum Guidelines for Baccalaureate Degree Programs introduced a significantly new way of framing computing education curricula. Instead of focusing on how to structure the Information Technology body of knowledge, the IT2017 report centered its curricular recommendations on the competencies that IT programs are expected to develop in their graduates. A major contribution of the report is a model of IT competency that identifies three interrelated components: content knowledge, skills, and dispositions, where dispositions represent personal qualities desirable in the workplace.
Magic shapes for SHACL validation

A key prerequisite for the successful adoption of the Shapes Constraint Language (SHACL)---the W3C standardized constraint language for RDF graphs---is the availability of automated tools that efficiently validate targeted constraints (known as shapes graphs ) over possibly very large RDF graphs. There are already significant efforts to produce optimized engines for SHACL validation, but they focus on restricted fragments of SHACL. For unrestricted SHACL, that is SHACL with unrestricted recursion and negation, there is no validator beyond a proof-of-concept prototype, and existing techniques are inherently incompatible with the goal-driven approaches being pursued by existing validators. Instead they require a global computation on the entire data graph that is not only computationally very costly, but also brittle, and can easily result in validation failures due to conflicts that are irrelevant to the validation targets. To address these challenges, we present a 'magic' transformation---based on Magic Sets as known from Logic Programming---that transforms a SHACL shapes graph S into a new shapes graph S' whose validation considers only the relevant neighbourhood of the targeted nodes. The new S' is equivalent to S whenever there are no conflicts between the constraints and the data, and in case the validation of S fails due to conflicts that are irrelevant to the target, S' may still admit a lazy, target-oriented validation. We implement the algorithm and run preliminary experiments, showing our approach can be a stepping stone towards validators for full SHACL, and that it can significantly improve the performance of the only prototype validator that currently supports full recursion and negation.
Advancements in Blockchain Technology With the Use of Quantum Blockchain and Non-Fungible Tokens

Blockchain is a new but quickly growing technology in the world, which was developed by a pseudonymous Satoshi Nakamoto in 2009 as the cryptocurrency Bitcoin. Blockchain was un-hackable but now, due to use of quantum computers, it is possible to tamper with blockchain. As a counter to this, the researchers have come up with quantum blockchain using the principles of quantum cryptography. Today we see that the technology has given birth to many new technologies as well. One of its examples is non-fungible tokens (NFTs). These are a new sort of blockchain-based token that is unique and indivisible. They were first created in 2014. These are blockchain-based virtual assets. Since early 2021, the phenomena and its marketplaces have increased dramatically.
Design of a Blockchain-Powered Biometric Template Security Framework Using Augmented Sharding

Biometric templates must be secured with traceability, immutability, and high-trust capabilities. A variety of system models are proposed by researchers, most of which either utilize blockchains or machine learning for improved security and quality of service (QoS). The augmented sharding model is designed using light weight incremental learning framework, which assists in shard formation and management. Performance evaluation of the proposed model indicates that it is able to achieve high accuracy attack mitigation, along with low block mining delay and high throughput. This performance is compared with various state-of-the-art methods and an improvement of 10% in terms of delay and 14% in terms of throughput is achieved. Further, an attack detection accuracy of 99.3% is obtained for sybil, masquerading, and man in the middle (MITM) attacks. This text further recommends improvement areas which can be further researched for enhancing security and QoS performance of the proposed model.
A Scrutiny Review of CPS 4.0-Based Blockchain With Quantum Resistance

Data collection mechanisms have become effectively advanced by leveraging the internet of things and cyber physical systems. The sensors are heavily developed with intricate details to capture data in varied forms which can be stored and used as an information base for knowledge extraction using analytics and statistical prognostication in artificial intelligence sub-branches. Storing this data with a different approach that ensures stringent security measures is done using blockchain. The loopholes that compromise the security of blockchain are quantum computing for which quantum resistant blockchain ideas are discussed. This chapter finally sheds some light on the effective approach to implement the CPS 4.0-based blockchain mechanism with detailed scrutiny.
Readability Evaluation of Books in Chinese as a Foreign Language Using the Machine Learning Algorithm

The arrival of the boom of teaching Chinese as a foreign language (TCFL) and personalized learning has led to a sharp increase in the demand for the Chinese language reading material. There are numerous reading materials available in Chinese for foreign students and learners to read and evaluate. The high-quality TCFL reading materials with reasonable arrangement can provide convenience for learners with different reading comprehension, interpretation abilities, and levels to master a language more quickly. Therefore, this study carries out an automatic readability assessment of books in Chinese as a foreign language. This paper comprehensively considers the factors affecting the difficulty of reading materials from the perspective of Chinese ontology based on the existing readability assessment research. Using natural language processing and a database management system to extract the features of books in Chinese as a foreign language, the text readability is evaluated with a statistical machine learning algorithm. The model is optimized by feature selection and sorting feature selection technology. The packaging feature selection technology is introduced to optimize the algorithm performance. The feature sets and each independent feature in the three dimensions of word meaning, part of speech, and discourse were optimized by the machine learning regression model based on certain evaluation indexes. Moreover, this work examined that the regression model is effective at identifying and recommending simpler textbooks for learning with difficult foreign language materials. For high-proficient learners, this approach significantly improves performance and measurement efficiency of reading books.