Reviewerly/exHarmony huggingface dataset:
Quantum Computing, Digital Constitutionalism, and the Right to Encryption: Perspectives from Brazil

This article examines how the debates on a right to encryption, understood within the framework of digital constitutionalism, may be impacted by the development of quantum computing. An important question is how to ensure that fundamental rights and freedoms in the digital environment are adequately preserved, especially considering that the development of quantum capabilities is likely to occur in a disparate manner between developed and developing countries. For this reason, the article brings as an example the case of Brazil, a country that has a significant history of discussing digital rights and in which the issue of encryption is currently in debate before the Supreme Court. The paper is structured in three main parts, beginning with an overview of the discussions on the idea of a right to encryption within digital constitutionalism initiatives, particularly in Brazil. Next, the article examines how the development of quantum technologies may impact encryption, analyzing both technical and geopolitical repercussions of the race for quantum supremacy. Finally, it assesses the potential impacts of quantum computing on the enjoyment of fundamental rights in the digital environment and examines three different approaches: the development of post-quantum cryptography standards, the adjustment of domestic policies and further development of flexible legal and regulatory strategies, and global cooperation through binding and non-binding legal instruments. To conclude, the paper assesses the specific challenges faced by developing countries, such as Brazil, in connecting the debate on fundamental rights with the new technical and legal issues raised by emerging technologies.
A Survey of Lightweight Image Encryption for IoT

IoT networks serve as a way for various devices interconnected over the internet to exchange data with each other and with other services. Most smartphones, laptops, and other communication devices are connected to the cloud today, making data accessible to everyone. There are many applications for IoT, from smart IoT applications to industrial products. Encryption is one of the best ways to make IoT networks secure since so much data is being transferred. A lightweight block cipher is one of the most sophisticated means for overcoming the security problems inherent to IoT networks. Because of the limited resources available to nodes, classical cryptography methods are costly and inefficient. In this paper, we have compared the systems, we have found that these modifications were made to the original AES algorithm, while the original algorithm security remains robust, the modified AES algorithm remains lightweight and faster, providing more satisfaction for embedding in IoT devices and sensors that consume little power. Furthermore, this algorithm enhanced the AES-ECC hybrid encryption system, which has good flexibility and versatility, and optimized the design of the ECC function according to the characteristics of wireless sensor networks. Using Salsa20/12 stream cipher, the texture images can be encrypted using bit masking and permutation procedures and as part of a new scheme for encrypting 3D objects, which complements the existing methods for 3D object encryption. With PLIE implemented in Python, the encryption time was approximately 50% faster than that of AES using the throughput increase, faster encryption time, and minimal complexity.
Stylometric features in librarian’s responses to user queries: implications for user interaction in digital information services

Purpose The purpose of this study is to explore linguistic stylometric patterns encompassing lexical, syntactic, structural, sentiment and politeness features that are found in librarians’ responses to user queries. Design/methodology/approach A total of 462 online texts/transcripts comprising answers of librarians to users’ questions drawn from the Internet Public Library were examined. A Principal Component Analysis, which is a data reduction technique, was conducted on the texts and transcripts. Data analysis illustrates the three principal components that predominantly occur in librarians’ answers: stylometric richness, stylometric brevity and interpersonal support. Findings The results of the study have important implications in digital information services because stylometric features such as lexical richness, structural clarity and interpersonal support may interplay with the degree of complexity of user queries, the (a)synchronous communication mode, application of information service guideline and manuals and overall characteristics and quality of a given digital information service. Such interplay may bring forth a direct impact on user perceptions and satisfaction regarding interaction with librarians and the information service received through the computer-mediated communication channel. Originality/value To the best of the authors’ knowledge, the stylometric features encompassing lexical, syntactic, structural, sentiment and politeness using Principal Component Analysis have not been explored in digital information/reference services. Thus, there is an emergent need to explore more fully how linguistic stylometric features interplay with the types of user queries, the asynchronous online communication mode, application of information service guidelines and the quality of a particular digital information service.
Blind key-generation attribute-based encryption for general predicates

Abstract Attribute-based encryption (ABE) is a form of public-key encryption that allows fine-grained access control on encrypted data. Blind key-generation (BKG) attribute-based encryption (Rial, Designs, Codes and Cryptography 2016 ) is a variant in which the master authority issues secret keys without learning any information about the attributes associated to them. This extra functionality makes it an appealing building block for several applications. In this work, we extend the generic framework of ABE based on pair encodings (Attrapadung, Eurocrypt 2014 ) to support blind key-generation. In particular, we define two new notions of pair encodings that we coin BKG-compatible and algebraic pair encoding. We show that every encoding satisfies the former without loss of generality, whereas the latter is satisfied by all existing pair encodings from the literature. We then show how to enhance any ABE based on a BKG-compatible pair encoding to achieve honest-but-curious blind key-generation. In the case of algebraic encodings, our protocol admits a very efficient version, secure against malicious parties. The main advantage of our work is generality. Our protocol is designed over the recent and most advanced modular frameworks of ABE that can handle a rich variety of predicates.
Hands-on PLC Training Approach for IT Students Using Virtual Reality

The Automation 4.0 needs the integration of Information Technology and the classic automation technology. Most of the IT students lack of the automation technology part where hardware plays an important role. To overcome this, we constructed an approach to create a hands-on PLC training for IT students using Virtual Reality (VR) technology. The pedagogical concept of ADDIE model was implemented in the phase of instructional design. The 3D models of PLCnext learning kit was created using Blender. For interaction purpose, the Unity engine is taken into account. The proposed virtual training system was evaluated in a closed settings. The System Usability Scale (SUS) score suggests that the virtual training system is a good application (68.125).
A radical theory for graphs that do not admit loops

Abstract There is a well-developed theory of connectednesses and disconnectednesses (= radical theory) for the category of graphs that admit loops. Here it is shown that such a theory for the category of graphs that do not allow loops degenerates to the trivial case for all the Hoehnke radicals, but there are non-trivial connectednesses (KA-radical classes) and disconnectednesses (KA-semisimple classes). Moreover, the connectednesses and disconnectednesses always come as complementary pairs.
Particle Swarm Optimization in Small Case Bases for Software Effort Estimation

To facilitate research in the field of user story effort estimation (USEE), this work analyzes the applicability of case-based reasoning to effort estimation of user stories. The analysis uses real-world user stories of a software development project of the Capgemini Group. The focus of the analysis is the development of an effort estimation tool that could generate accurate effort estimates serving the purposes of project planning and control. In addition to a classical structural CBR approach, the paper applies the weights optimization method Particle Swarm Optimization to small case bases. The highly accurate effort estimates resulting from a couple of experiments on real data show that estimation by analogy presents with the aid of an automated environment an eminently practical technique. As a consequence, this approach impacts estimation accuracy and thus success of industrial software development projects.
Case-Based Learning and Reasoning Using Layered Boundary Multigraphs

Instance-based and case-based learning algorithms learn by remembering instances. When scaling such approaches to datasets of sizes that are typically faced in today’s data-rich and data-driven decade, basic approaches to case retrieval and case learning quickly come to their limits. In this paper, we introduce a novel scalable algorithm for both, the retrieval and the retain phase of the CBR cycle. Our approach builds an efficient graph-based data structure when learning new cases which it exploits in a stochastic any-time manner during retrieval. We investigate its characteristics both, theoretically and empirically using established benchmark datasets as well as a specific larger-scale dataset.
Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods

Model-agnostic methods in eXplainable Artificial Intelligence (XAI) propose isolating the explanation system from the AI model architecture, typically Machine Learning or black-box models. Existing XAI libraries offer a good number of explanation methods, that are reusable for different domains and models, with different choices of parameters. However, it is not clear what would be a good explainer for a given situation, domain, AI model, and user preferences. The choice of a proper explanation method is a complex decision-making process itself. In this paper, we propose applying Case-Based Reasoning (CBR) to support this task by capturing the user preferences about explanation results into a case base. Then, we define the corresponding CBR process to help retrieve a suitable explainer from a catalogue made of existing XAI libraries. Our hypothesis is that CBR helps the task of learning from the explanation experiences and will help to retrieve explainers for other similar scenarios.
Never Judge a Case by Its (Unreliable) Neighbors: Estimating Case Reliability for CBR

It is known, in the Case-based Reasoning community, that the effectiveness of a Case-Based Reasoner is heavily dictated by the quality of cases in its Case Base, and therefore the presence of poor quality cases can adversely influence its predictions. While it is common practice for a domain expert to periodically check the cases in the case base of a reasoner, it often becomes a time-intensive exercise in practice. Existing literature provides potential measures of reliability of cases in the case base, however, they fail to provide robust estimates of the reliability of a case when its neighborhood comprises of mixed quality cases. In this work, we propose RelCBR which builds upon a circular definition of case reliability - a case is reliable if it is well-aligned with its reliable neighbors. This formulation allows us to arrive at more robust estimates of reliability and results in streamlining the case base maintenance process by drawing the attention of the expert to cases that are more likely of being incorrect. In addition, these reliability values can also be used to discount the contribution of unreliable cases in the reasoning process that would consequently boost the performance of the reasoner.
GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning

In Process-Oriented Case-Based Reasoning (POCBR), determining the similarity between cases represented as semantic graphs often requires some kind of inexact graph matching, which generally is an NP-hard problem. Heuristic search algorithms such as A* search have been successfully applied for this task, but the computational performance is still a limiting factor for large case bases. As related work shows a great potential for accelerating A* search by using GPUs, we propose a novel approach called AMonG for efficiently computing graph similarities with an A* graph matching process involving GPU computing. The three-phased matching process distributes the search process over multiple search instances running in parallel on the GPU. We develop and examine different strategies within these phases that allow to customize the matching process adjusted to the problem situation to be solved. The experimental evaluation compares the proposed GPU-based approach with a pure CPU-based one. The results clearly demonstrate that the GPU-based approach significantly outperforms the CPU-based approach in a retrieval scenario, leading to an average speedup factor of 16.
Classification Analysis of Unilak Informatics Engineering Students Using Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN)

This research is entitled “Classification Analysis of the Study Period of Informatics Engineering Study Program Students at Unilak with the Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN)" method. an attempt to understand whether there are factors that influence the length of a student's study period. Basically, the length of the study period is not a measure of a student's non-academic academic ability, but most people judge that students with a study period of more than 8 semesters or long are not good. Therefore, the researcher chose to classify the factors that affect the length of the student's study period at the Faculty of Computer Science, Lancang Kuning University. This study uses 4 (four) calculation methods. With the several methods used, the authors can compare the results of the four calculation methods so that they can determine which method is better calculated. The result of this research is a comparison between 4 (four) calculation methods in determining which method has good classification ability
Multistage adaptive control strategy based on image contour data for autonomous endoscope navigation

The physician burnout, poor ergonomics are hardly conducive to the sustainability and high quality of colonoscopy. In order to reduce doctors' workload and improve patients' experiences during colonoscopy, this paper proposes a multistage adaptive control approach based on image contour data to guide the autonomous navigation of endoscopes. First, a fast image preprocessing and contour extraction algorithms are designed. Second, different processing algorithms are developed according to the different contour information that can be clearly extracted to compute the endoscope control parameters. Third, when a clear contour cannot be extracted, a triple control method inspired by the turning of a novice car driver is devised to help the endoscope capture clear contours. The proposed multistage adaptive control approach is tested in an intestinal model over a variety of curved configurations and verified on the actual colonoscopy image. The results reveal the success of the strategy in both straight sections of this intestinal model and in tightly curved sections as small as 6 cm in radius of curvature. In the experiment, processing time for a single image is 20-25 ms and the accuracy of judging steering based on intestinal model pictures is 96.7%. Additionally, the average velocity reaches 3.04 cm/s in straight sections and 2.49 cm/s in curved sections respectively.
Toward a Systematic Understanding of “Heaviness” in Metal Music Production

“Heaviness” has been one of metal music’s defining features in three decades of metal scholarship. Research acknowledged its essential quality, but no comprehensive definition of musical heaviness was developed. This article, therefore, elaborates a preliminary framework of musical heaviness by identifying its constituent production, performance, and compositional components, including their relationships. The findings suggest that while structural and performative components provide the necessary foundation for heaviness, metal has been increasingly driven by its production. Since musicians reach limits, technology has become one of the primary means to produce ever heavier metal music in the genre-defining quest for greater heaviness.
Cine-AI: Generating Video Game Cutscenes in the Style of Human Directors

Cutscenes form an integral part of many video games, but their creation is costly, time-consuming, and requires skills that many game developers lack. While AI has been leveraged to semi-automate cutscene production, the results typically lack the internal consistency and uniformity in style that is characteristic of professional human directors. We overcome this shortcoming with Cine-AI, an open-source procedural cinematography toolset capable of generating in-game cutscenes in the style of eminent human directors. Implemented in the popular game engine Unity, Cine-AI features a novel timeline and storyboard interface for design-time manipulation, combined with runtime cinematography automation. Via two user studies, each employing quantitative and qualitative measures, we demonstrate that Cine-AI generates cutscenes that people correctly associate with a target director, while providing above-average usability. Our director imitation dataset is publicly available, and can be extended by users and film enthusiasts.
Integration and Recommendation of Multimedia Network-Assisted English Instructional Resources Based on Association Rules Mining

The integration of multimedia teaching refers to a novel teaching mode that combines multimedia, information resources, and information methods with content and teaching process in the teaching process with the guidance of contemporary educational ideas and organically unifies them on a spatial and temporal platform, so as to better accomplish the teaching tasks. However, with the deepening of network teaching and school connotation construction, various resources such as media materials, courseware, network courses, and teaching cases generated by education and teaching, as well as academic works, papers, patents, project results, and lectures of experts and professors gathered by scientific research, are increasing. In this situation, how to integrate teaching resources and courses to provide students with an independent learning platform and an information-based learning environment has become an urgent issue. For this problem, data mining has shown a strong vitality, and data mining can find out the potential connection between datasets. Association rule mining is the most researched and widely used data mining method. In this paper, we propose a multimedia network-assisted English integration of educational resources method based on association rule algorithm to give full play to the role of “multimedia technology” and realize the integration of traditional English teaching and information network culture, so as to achieve the purpose of optimizing English teaching. The experimental results show that with the increase of data volume, the average testing time of Apriori is reduced by 56.49 s compared with MapReduce, so the advantage of Apriori algorithm is more obvious. Therefore, this research can effectively provide students with rich and colorful personalized resources, realize high-performance interactive learning, and help students cultivate their independent learning ability as well as their lifelong learning habits.
On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork

Abstract It is essential for agents to work together with others to accomplish common objectives, without pre-programmed coordination rules or previous knowledge of the current teammates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm of others in an on-line manner in order to decide its own actions for effective teamwork. A common approach is to assume a set of possible types and parameters for teammates, reducing the problem into estimating parameters and calculating distributions over types. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, de-mining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques can be developed. Hence, we present On-line Estimators for Ad-hoc Task Execution (OEATE), a novel algorithm for teammates’ type and parameter estimation in decentralised task execution. We show theoretically that our algorithm can converge to perfect estimations, under some assumptions, as the number of tasks increases. Additionally, we run experiments for a diverse configuration set in the level-based foraging domain over full and partial observability, and in a “capture the prey” game. We obtain a lower error in parameter and type estimation than previous approaches and better performance in the number of completed tasks for some cases. In fact, we evaluate a variety of scenarios via the increasing number of agents, scenario sizes, number of items, and number of types, showing that we can overcome previous works in most cases considering the estimation process, besides robustness to an increasing number of types and even to an erroneous set of potential types.
Evaluation Method of College Students’ Education Management Quality Based on Collaborative Filtering Algorithm and IoT Apps

Considering the ongoing unfortunate evaluation impact of College Students’ education management quality, this paper advances an undergrad’ education management quality evaluation technique in light of collaborative filtering algorithm, gathers the impact indicators of College Students’ education management quality, and builds an understudies’ education management quality evaluation record framework joined with collaborative filtering algorithm. At long last, it is affirmed by tests that the quality evaluation technique for College Students’ Education Management Based on collaborative filtering algorithm has high precision and practicability during the time spent down to earth application, which gives reference and help for college education management.
Research on the Current Situation and Improvement Countermeasures of Farmers' Information Security Literacy Based on New Media

Research on the current situation and Improvement Countermeasures of farmers' information security literacy under the background of new media information security has become the cornerstone of national overall security in the information age. Improving farmers' information security literacy is an inevitable requirement to protect farmers' personal privacy and property security. Using the questionnaire survey to analyze the four aspects of farmers' information security awareness, information security knowledge, information security ability and information ethics in five provinces, it is found that farmers' information security literacy is not high as a whole, and put forward suggestions and paths to improve information security literacy.