Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence (AI)-Based Self-Deciding Character Development Application in Two-Dimensional Video Games

Artificial Intelligence (AI) technologies, which have started to be used in almost all areas of life, are now also used in games. Especially for bot systems that play the game automatically in many popular video games recently, it has become possible for these bots to defeat the grandmasters of the game with advanced Deep Learning (DL) methods. The purpose of this study is to develop an AI-based two-dimensional video game in Unity 2D Game Engine, which can decide what the game characters will do within their abilities. In the game application, the two-dimensional game character is controlled by the user. This character moves in a two-dimensional game plane that is created using procedural plane generation algorithms. In addition, statistics such as the number of enemies defeated and the level reached in the game are recorded. The game is based on the Unity2D game engine and is written in C#. In the study, 8 different experiments are performed. In these experiments, the player's success time in the game with different equipment defined for the character is measured. This time ranges from 0.54 seconds to 1.88 seconds. Today, AI algorithms are developed to play computer games better or to design better games, and new games are designed to improve AI algorithms. In this context, it is evaluated that this study will contribute to the literature on the use of AI in computer games.
Integration of the Internet of Things and Blockchain to Promote Collaboration in Smart Cities

This chapter aims to explore the importance of the convergence and collaboration of the internet of things and blockchain in enabling the development of smart cities. It will capture the strong link between the burgeoning advanced technology and China's fast-growing smart cities. In addition, the authors used the CiteSpace bibliometric tool to analyse the core journals related to smart cities, IoT, and blockchain in WoS and CNKI operations and visualise the relationships between the literature as a scientific knowledge map. The research topic in this chapter focuses on the keywords with the highest frequency and determines the research frontier and trend. This chapter will further explore the roles of the government in promoting the integration of the internet of things and blockchain in the development agenda of industrial infrastructure and smart cities through policies (i.e., institutions).
A Critical Review of Educational Games as a Tool for Strengthening Digital Literacy

The digital era is an era where knowledge can be easily obtained anytime and anywhere as long as it is connected to the internet network. To be able to access knowledge requires a special skill called digital literacy skill. Digital literacy skills can be developed in various ways, one of which is through game-based learning. The purpose of this study is to identify the development of the use of games in education that has been carried out by previous researchers related to strengthening digital literacy. The method used to collect data is by a systematic review, which is a research method by collecting and summarizing all the best available research on a particular question, using a transparent protocol that can be replicated to find, evaluate, and synthesize relevant research evidence. The results of the study obtained data from the scopus.com database with the keywords educational games in digital literacy learning as many as 189 articles and 13 articles that met the inclusion criteria for analysis. The use of educational games to strengthen digital literacy has been carried out in several countries with different types of games with the scope of increasing digital literacy skills, Collaboration and creativity, Finding Information, Critical Thinking, and Social Understanding.
Systematic Review of Recommendation Systems for Course Selection

Course recommender systems play an increasingly pivotal role in the educational landscape, driving personalization and informed decision-making for students. However, these systems face significant challenges, including managing a large and dynamic decision space and addressing the cold start problem for new students. This article endeavors to provide a comprehensive review and background to fully understand recent research on course recommender systems and their impact on learning. We present a detailed summary of empirical data supporting the use of these systems in educational strategic planning. We examined case studies conducted over the previous six years (2017–2022), with a focus on 35 key studies selected from 1938 academic papers found using the CADIMA tool. This systematic literature review (SLR) assesses various recommender system methodologies used to suggest course selection tracks, aiming to determine the most effective evidence-based approach.
Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering

Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier.
Future of Medicine in Cognitive Technologies and Automatic Detection via Computational Techniques

The advancement in medicine and healthcare system has prompted a greater focus on platform development, both at software and hardware levels. The fundamental technology consists of systems for communication established between the sensory nodes and the controlling unit, and the algorithms that process the collected data for interpretation of the result. The advent of artificial intelligence (AI) technology which includes machine learning (ML), deep learning (DL), neural networks (NN), and fuzzy logic changed the approach of healthcare system. These technologies enable healthcare in processing health records, diagnosis of the disease, and therapeutic procedures. This chapter investigates the current trend of computational techniques used in healthcare as well as the future pathway of such technologies. The impact of machine and deep learning procedures in clinical care is briefed. The advanced computational intelligence techniques used in applications such as clinical imaging, e-health records, and genomics are also investigated. This chapter concludes with a brief study of cases on the application of data mining in processing the brain tumor health record and the implementation of AI in automatic stress detection through the data recorded by wearable sensor devices.
Short Text Classification Based on Hierarchical Heterogeneous Graph and LDA Fusion

The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical heterogeneous graph with latent Dirichlet allocation (LDA) fusion. Our method first models the short text dataset as a hierarchical heterogeneous graph, which incorporates more syntactic and semantic information through a word graph, parts-of-speech (POS) tag graph, and entity graph. We then connected the representation of these three feature maps to derive a comprehensive feature vector for the text. Finally, we used the LDA topic model to adjust the feature weight, enhancing the effectiveness of short text extension. Our experiments demonstrated that our proposed approach has a promising performance in English short text classification, while in Chinese short text classification, although slightly inferior to the LDA + TF-IDF method, it still achieved promising results.
Camouflaged objects detection network via contradiction detection and feature aggregation

Camouflaged Object Detection(COD) aims to segment objects with a similar appearance to the background. There are some problems in existing algorithms, such as blurred edges and incomplete detection. To address the above issues, we propose a novel COD framework termed CFNet. Our network consists of the Contradiction Area Detection Module(CADM) and Feature Aggregation Module(FAM). In the CADM, we propose an improved receptive field mechanism, which utilizes max pooling operation and convolution block to highlight the contradictory areas and refine the edge of the hidden object. Besides, the FAM is designed to connect two adjacent layers via attention and anti-attention strategy, leading to cross-layer feature enhancement and information fusion. More specifically, the self-attention mechanism helps supplement semantic information, and the anti-attention mechanism contributes to removing redundant information. Extensive experiments conducted on the four public COD datasets show the comparable performance of the proposed CFNet with SOTAs, and the ablation experiments demonstrate the effectiveness of the proposed modules.
Image cognition using contour curvature statistics

Drawing on elementary invariance principles, we propose that a statistical geometric object, the probability distribution of the normalized contour curvatures (NCC) in the intensity field of a planar image has the potential to categorize objects. We show that NCC is sufficient for discriminating between cognitive categories such as animacy, size and type, and demonstrate the robustness of this metric to variation in illumination and viewpoint, consistent with psychological experiments. A generative model for producing artificial images with the observed NCC distributions highlights the key features that our metric captures, and those that it does not. More broadly, our study points to the need for statistical geometric approaches to cognition that build in both the statistics and the natural invariances of the visual world.
GT-NRSM: efficient and scalable sharding consensus mechanism for consortium blockchain

Blockchain is an innovative application of distributed storage, consensus mechanism, cryptographic algorithm, and other computer technologies. As the underlying architecture of blockchain, consensus is the key to realizing service-oriented applications of blockchain in terms of its security, efficiency, and scalability optimization. In some high-complexity consensus, such as Practical Byzantine Fault Tolerance (PBFT), throughput is severely reduced as the number of nodes increases. Even in low-complexity algorithms such as Raft, the load on the leader is severely affected as the network size increases, negatively affecting consensus efficiency. To solve these problems, we propose a node reliable shard model based on guarantee tree that achieves high scalability while maintaining a certain degree of decentralization and security based on consortium blockchain. Firstly, we create a guarantee mechanism to represent the trust relationship between nodes, and then, we design a reliable node selection strategy based on the guarantee mechanism to evaluate the node guarantee results and consensus behavior, determine the node trust status, and identify malicious nodes and choose a list of trusted leaders. Secondly, we propose a Dual-Leaders supervision mechanism, in which the deputy senses the heartbeat of leader while the deputy activity is detected by consensus nodes. Finally, we use a guarantee mechanism and reliable node selection strategy to design a network partitioning method to achieve high concurrent consensus for multiple partitions and significantly improve consensus efficiency. Subsequent experiments show that the throughput of the proposed algorithm improves by 48% over Raft and is significantly greater than PBFT, which has better throughput but lower consensus latency.
Copyright protection for image classification models using pseudo-holographic watermarks

With the growing number of solutions based on deep learning methods, there is a need to protect pretrained models against unauthorized distribution. For deep model watermarking, one of the most important criteria is to maintain the accuracy of predictions after embedding the protective information. In this paper, we propose a black-box watermarking method based on fine-tuning image classification models on a watermarking dataset, which is synthesized by superimposing pseudo-holograms on images of the original dataset. The proposed method allows to preserve the initial quality of classification, in addition, a series of experiments for five different models showed the invariance of the method to the architecture of a deep neural network. The conducted simulation of the most common attacks on watermarked models shows that adversarial attempts to completely remove the watermark are improbable without significant loss of model accuracy. Additionally, experimental results contain the selection of parameters, such as the number of triggers and original images in watermarking dataset, allowing to increase method efficiency.
The translator's visibility: A stylistic perspective

Abstract No agreement has yet been reached as to whether translators are visible or not in translation. To answer this question, we first chose Lin Yutang and James Legge as cases, supplemented their translations with their originals, and then applied Principal Component Analysis along with N- MFW metrics to the texts for their stylistic features. In our cases, we found that (1) for each translator, the styles of their originals and translations vary; (2) for both translators, the styles of their translations converge for the same source text; (3) the two translators preserve only part of their stylistic fingerprints in their translations, suggesting the existence of a powerful compensation mechanism. Our findings bring new insights into the translator's style, help settle the (in)visibility dispute, and illustrate effective metrics for developing translator identification tools.
Simple querying service for OpenAPI descriptions with semantic extensions

We introduce OpenAPI Query Language 2 (OAQL2), an SQL-like language for querying OpenAPI documents. OpenAPI is a standard format for the description of REST services, based on JSON (or YAML). A Web service capable of executing OAQL2 queries is implemented. The service stores metadata for each OpenAPI document and executes the queries on metadata to avoid the overhead of expensive join operations. It builds indexes to speed up queries, handles composite schema objects, and uses reasoning to support searching on semantic models referred to by OpenAPI properties. The run-time performance of OAQL2 has been assessed experimentally on a large database of 10,000 real service descriptions downloaded from SwaggerHub. Compared to the system implemented in our previous work, OAQL2 is proven to be faster and more complete in terms of syntax and compatibility with OpenAPI. To the best of our knowledge, there is nothing similar to SQL for OpenAPI. All other query methods are not designed for OpenAPI and require the user to be familiar with the peculiarities of OpenAPI syntax. This results in long complicated queries.
Discriminating the Phase of a Coherent Tone with a Flux-Switchable Superconducting Circuit

We propose a new phase detection technique based on a flux-switchable superconducting circuit, the Josephson digital phase detector (JDPD), which is capable of discriminating between two phase values of a coherent input tone. When properly excited by an external flux, the JDPD is able to switch from a single-minimum to a double-minima potential and, consequently, relax in one of the two stable configurations depending on the phase sign of the input tone. The result of this operation is digitally encoded in the occupation probability of a phase particle in either of the two JDPD wells. In this work, we demonstrate the working principle of the JDPD up to a frequency of 400 MHz with a remarkable agreement with theoretical expectations. As a future scenario, we discuss the implementation of this technique to superconducting qubit readout. We also examine the JDPD compatibility with the single-flux-quantum architecture, employed to fast-drive and measure the device state.
Social media for academic libraries

AbstractAbstractSocial media platforms are the primary communication mechanism among students on college campuses in the United States. Libraries using social media channels to promote services and connect with their communities may wish to expand their social media presence by creating innovative content to support their teaching initiatives. YouTube is a powerful and popular social media channel libraries can use to post a range of content to enhance their presence in their community. This column provides an overview of current college students' use of social media, highlights some advantages of YouTube, and provides some best practices libraries can follow with social media initiatives.Keywords: Library instructionreference servicesinformation servicesinformation seeking Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsEleanor Layo FreedEleanor Layo Freed, MLIS, MSSAS has experience in Film, UX, LLM, Fine Arts, and youth-bound interaction objects, and currently focuses on development of social communication imaginaries.
Towards automated verification of Bitcoin-based decentralised applications

The Bitcoin language SCRIPT has undergone several technically non-trivial updates, still striving from security and minimal risk exposure. Up-to-date, formal verification is of strong interest for script programs that validate the correctness of the Bitcoin decentralised ledger, and allow more and more sophisticated protocols and decentralised applications to be implemented on top of Bitcoin transactions. We propose ScriFy, a comprehensive framework for the verification of the current SCRIPT language: a symbolic semantics and execution model, a model checker, and a modular (dockered), open-source verifier. Given the SCRIPT code that locks a Bitcoin transaction, ScriFy returns the minimal information needed to successfully execute it and authorise the transaction. Distinguishably, ScriFy features both recently added SCRIPT operators and an enhanced analysis, which considers prior information in the ledger. The framework is proved correct and validated through significant examples.
Expressive and Systematic Risk Assessments with Instance-Centric Threat Models

A threat modeling exercise involves systematically assessing the likelihood and potential impact of diverse threat scenarios. As threat modeling approaches and tools act at the level of a software architecture or design (e.g., a data flow diagram), they consider threat scenarios at the level of classes or types of system elements. More fine-grained analyses in terms of concrete instances of these elements are typically not conducted explicitly nor rigorously. This hinders (i) expressiveness, as threats that require articulation at the level of instances can not be expressed nor managed properly, and (ii) systematic risk calculation, as risk cannot be expressed and estimated with respect to instance-level properties.
Challenges in Large-Scale Agile Software Development Projects

Agile has shown its success in small and medium-sized projects. It is becoming increasingly popular in large-scale software projects. However, agile ride in large-scale software development projects is not smooth. It is essential to understand the barriers hindering agile from reaping its benefits in the large-scale software development context. We performed a case study to investigate large-scale agile challenges with two Swedish software companies. We conducted eleven semi-structured interviews with two teams from both case companies. We used thematic analysis and identified 13 challenges in agile at a large-scale. The prominent challenges are a lack of testing strategies, chaos in sprint execution and deadlines, ignoring coding standards, and requirements scoping. By being aware of these challenges, projects in similar contexts may better synchronize their activities and reap the benefits of agile at large-scale. We discuss these challenges in detail and offer a research direction.
Privacy-Preserving and Traceable Blockchain-Based Charging Payment Scheme for Electric Vehicles

With the rapid development of the global clean energy industry, the electric vehicle (EV) has gradually replaced the traditional fuel vehicle as a promising modern transportation tool due to its environmental friendliness and competitive prices. Its widespread adoption has led to a substantial increase in demand for subsidiary facilities such as charging piles and stations. Despite the convenience and accessibility offered by charging services, the privacy of EV users may be compromised in the process, as malicious attackers can infer their true identities and consumption habits from service orders. Existing studies introduce blockchain technology into vehicle charging payment scenarios to protect the privacy of EV users. Unfortunately, it cannot achieve the desired full anonymity and may even hinder electricity regulators’ investigation of suspicious transactions and entities. Therefore, designing a vehicle charging payment scheme that simultaneously supports privacy protection and traceability is a challenge. To address these concerns, this paper proposes a privacy-preserving and traceable blockchain-based charging payment (PTB-CP) scheme for EVs. It can protect users’ identity privacy and transaction unlinkability and track abnormal transactions or entities in exceptional circumstances. We conduct a comprehensive analysis of PTB-CP from both theoretical and experimental aspects. The analysis results demonstrate that our scheme can realize privacy protection, reliability & authentication, traceability, scalability, and high efficiency.