Reviewerly/exHarmony huggingface dataset:
VTO Model Tester with MindAR Library

Augmented Reality is not a new technology, with more than 30 years of age, AR, which used to require large resources, can now even be run using a website application. The advantage of using a website is that the website application can be run on almost any device that supports a web browser. Web AR can also be used as a trial tool that can be used by customers to try on clothes or other products which is also known as VTO or Virtual Try On. The problem that arises from VTO Application is from the 3D designer side, which must make 3D Object and always readjust if the 3D is not suitable for VTO that requires accuracy of shape and size, that must be adjusted between 3D objects and human faces. The focus of this research is creating a website application that can connect to the internet so that 3D designers can easily access the VTO tester from any device to test 3D objects that have been created for Virtual Try On. The result of this research is that we can use local storage as a temporary storage medium and proof that local storage cannot also be used to store documents but also to store 3D objects. By immediately deleting temporary storage in local storage, the storage quota will not increase so that our local storage drives are not affected, the memory usage of only 10MB is also an advantage of this VTO Tester application, because it does not require a large memory to run the VTO Tester. In terms of memory usage, there is no memory leak that results when using VTO Tester application, this proves that the application developed can be used repeatedly without worrying about burdened computer performance.
Creating Educational Digital Artefacts Collaboratively in a Virtual Reality Environment

This paper discusses the use of virtual reality (VR) technologies in education and the collaborative creation of educational digital artifacts in a virtual reality environment. The paper presents a case study of students who collaborated creating digital artifacts in a VR environment. The artifacts were designed with the purpose of presenting the history of a local community. The study analyzed the interactions between learners, the effectiveness of the VR environment in facilitating collaboration, and the impact of the VR environment on learning outcomes. The study concludes that creating educational digital artifacts collaboratively in a virtual reality environment can lead to improved student engagement, motivation, and learning outcomes.
Machine Learning As A Communications Theory Problem

Machine learning, by far, has been the most impactful research discipline in the last decade. Motivated by the success of machine learning, almost all branches of academia have attempted to apply machine learning algorithms for solving some of their open problems. For example, employing machine learning for improving the performance of communications schemes is one of the main research directions in wireless communications. In this article, we will argue that the communications research community should also do the opposite, i.e., to use the communications\information theory framework for improving machine learning. To this end, we will show that the main machine learning problems can be represented equivalently as communications theory problems. This communications theory representation of the machine learning problems will enable researchers from the fields of communications\information theory to see the machine learning problems as familiar problems and thereby enable the vast body of knowledge and intuition from communications\information theory to be used for tackling the open problems in machine learning.
Interleaving of Polymorphic Gabidulin Codes : Dedicated to the memory of Ernst M. Gabidulin (1937–2021)

We define and analyze a new broad class of L-interleaved generalized Gabidulin codes over a cyclic Galois extension of a finite or an infinite field. Component codes can have different lengths, dimensions, supports, and field automorphisms, therefore, they are called polymorphic. Given a list Θ = (θ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , …, θ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> ) of the field automorphisms, we define the Θ-distance, that coincides with the rank distance if θ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> = const. We show which codes in this class reach the Singleton bound. For the proposed codes we give an efficient unique syndrome decoding algorithm based on the skew transform, which corrects errors of Θ-weight beyond half the code Θ-distance.
Drone-Assisted Path Planning Optimization for Mobile Robots in Dynamic Scenarios

In this study, the seamless integration of drones into military distribution operations is facilitated by coordinating the movements of mobile robots in both time and space. The approach consists of two key stages: first, the development of an iterative exact solution methodology to establish the optimal route for the mobile robot, and second, the optimization of the drone's route through a mixed integer linear programming model. The performance of this approach is assessed by initiating the process with the shortest route for the mobile robot and iteratively refining assignment and routing decisions. The primary objective of this study is to enhance the simultaneous deployment of drones and mobile robots within military zones. By doing so, the aim is to harness the full potential of unmanned aerial vehicles (UAVs) more efficiently and lay a solid foundation for future technological advancements.
Blockchain, IoT and AI: The Bets are Open!

Blockchain, IoT and AI are key technologies that revolutionized the world, each one on its side; let's see if the mix of the three can go hand in hand! Blockchain is the ingeniously simple, revolutionary protocol that increases trust, security, transparency, and the traceability of shared data. IoT is at the heart of essential areas like industrial automation, e-Health and smart cities. For its part, AI improves outcomes by detecting patterns and optimizing processes. The convergence between blockchains and AI has been discussed in many works, and the observation is the same for the convergence between blockchains and IoT. However, the question of the convergence of the three technologies altogether remains open. In this talk, I will try to answer questions like: Can these three innovations coexist and complement? Can they remedy each other's weaknesses? What place can they occupy in the context of the next wave of digital transformation?
Virtual Educational Environments in Mexican Legislation

The purpose of this essay is to approach the legal view in which virtual educational environments are developed, which were exposed to the health emergency by COVID-19 from 2019 to date, and how the parliamentary scenario was responding. The article is developed under a descriptive and analytical approach, where it is intended that Higher Education Institutions have knowledge of the regulatory framework and verify if their educational platforms are developed within it.
Development of a Multilingual Vocalization Methodology for Algerian SMS

We have developed an innovative methodology for the multilingual vocalization of Algerian SMS messages, drawing insights from an initial study that highlights the challenges posed by the complex and specific language used in these messages. In the course of our research, we worked with a dataset comprising 20,000 SMS messages, which, after segmentation, yielded 27,903 French words (class fr) and 42,300 Algerian words (class DZ). This dataset served as the foundation for our comprehensive workflow, outlining the various steps involved in implementing a vocalization system tailored to Algerian SMS messages. Our approach begins with data preprocessing, where we focus on text normalization and the extraction of textual features from these messages. Subsequently, the second phase of our methodology centers on developing a language identification system, recognizing that Algerian SMS messages often encompass multiple languages, including French and Arabic. To tackle this challenge, we employed a range of classifiers, including SVM, KNN, MNB, RFC, RNN, and CNN. Notably, SVM achieved the highest accuracy of 62.56% without SMS normalization. However, it's worth highlighting that the accuracy significantly improved to 73.18% after applying text normalization when using the CNN method. Following the language identification step, we proceed to perform speech synthesis based on the textual content, followed by unit concatenation to generate vocalized SMS messages. The results obtained through our methodology have provided valuable insights into both the strengths and limitations of our developed system, setting the stage for promising avenues of future research in the field of multilingual vocalization of SMS messages in dialects.
Image Encryption Techniques Using Dynamic Approach : An Article Review

In this study, a chaotic method is proposed that generates S-boxes similar to AES S-boxes with the help of a private key belonging to In this study, dynamic encryption techniques are explored as an image cipher method to generate S-boxes similar to AES S-boxes with the help of a private key belonging to the user and enable images to be encrypted or decrypted using S-boxes. This study consists of two stages: the dynamic generation of the S-box method and the encryption-decryption method. S-boxes should have a non-linear structure, and for this reason, K/DSA (Knutt Durstenfeld Shuffle Algorithm), which is one of the pseudo-random techniques, is used to generate S-boxes dynamically. The biggest advantage of this approach is the production of the inverted S-box with the S-box. Compared to the methods in the literature, the need to store the S-box is eliminated. Also, the fabrication of the S-box has a very large key space as it depends on the user's key. The encryption-decryption method allows changing pixel positions with the help of dynamically generated S-boxes, images, videos, etc. Thus, the study shows that a new method of S-boxes for dynamic cipher algorithms can be easily generated and applied to image encryption.
A Graph ATtention Networks Model for Session-Based Recommender Systems

In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of recommender systems to help users in their different decision-making processes. The goal of SBRSs is to capture dynamic and short-term user preferences within sessions to provide more timely and accurate next-item recommendations that are sensitive to be adapted in different contexts. In the literature, the proposed approaches for SBRS development are limited to some models that lack more precision and efficacy, which are the primary purposes for this kind of recommender system. This paper aims to formally present a graph neural networks model for session-based recommender systems based on Graph ATtention networks (GAT). GAT can capture complex transitions between items through a session modelled as graph-structured data. The effectiveness of the pro-posed GAT-based model is extensively evaluated on three public real-world datasets. Experimental results show the superiority of our model against some used baselines in the SBRS field.
A Deep Cybersickness Predictor through Kinematic Data with Encoded Physiological Representation

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user’s kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.
MR.Sketch. Immediate 3D Sketching via Mixed Reality Drawing Canvases

Sketching is a fundamental technique for early design and form finding. Digital 3D sketching can improve the early design phase by improving spatial understanding and enriching the design with additional information. However, the tools used for sketching should not hinder the expression and thought process inherent in form finding. Methods already exist for using 2D pen-on-tablet input for 3D sketching via stroke projection onto 3D drawing canvases. However, positioning the canvas and sketching lines are separate work steps. This breaks the flow of the designer’s thought process. We propose a novel technique for mixed reality 3D sketching that involves the use of viewport-attached drawing canvases, spatial meshing and intersection canvas visualisation. By combining the inside-out tracking capabilities of current portable consumer devices with stylus-on-tablet freehand drawing input, we transform 2D to 3D projective sketching into a more seamless experience. Results of a pilot user study with 16 participants show significant user preference for our technique, as well as increased sketching speed and immediacy.
Leap to the Eye: Implicit Gaze-based Interaction to Reveal Invisible Objects for Virtual Environment Exploration

Cinematic virtual reality (CVR) brings viewers a novel and immersive movie-watching experience. However, they may miss story events and scene transitions that the director has designed as key points hidden in the VR scene. In this paper, we introduce implicit gaze-based interaction for enhancing the exploration experience in CVR. In contrast to most research on gaze-based selection of objects or explicit guidance of attention using visual cues, we focus on implicit interaction that utilizes the user’s natural gaze and attention to explore the scene. We design and implement different gaze trigger methods for implicit interaction, making the interaction more intuitive and natural when users reveal the invisible objects. We implemented the adaptive collider technique, offering users a better sense of exploration than raycasting and spotlight techniques. We have also conducted user studies to compare animation sequences for visual feedback, with each animation sequence offering different storytelling techniques. One of the sequences is better suited for describing spaces in the virtual world, while the other sequence offers users the feeling of constructing a world through their gaze.
Exploring the Causal Links of Public Spending on Bank-Based Financial Development in African Economies

This work examined the causal impact of types of government spending on bank-based financial development. It tests the influences of both government productive and non-productive spending on bank-based financial development. Selected 37 African economies between 1980-2018 were sampled. Both the short –run and long –run effects were assessed using either Feasible Generalized Least Squares (FGLS), Mean Group (MG), Pooled Mean Group (PMG) and Dynamic Common Correlated Effects Mean Group (DCCEMG) estimators. Evidences support the hypotheses that both types of spending contribute positively to bank-based financial development. Bank-based financial development is more responsive to non-productive spending than it is to productive spending. Also, confirm the supportive roles of trade openness and GDP per capita, and the detriment of inflation to bank-based financial development. This study comprehensively unearths the impact of government spending on bank-based financial development in Africa by isolating spending into productive and non-productive types. Governments need to promote dual policies that address spending and financial development. They should avoid detrimental spending and promote enhancing spending within each type above. Spending that attract private agents, investments, saving, and liquidity in the financial sector, trade openness, and economic output should be promoted since these enhances bank-based financial development.
Report on Logic in Computer Science (LICS'23)

Th 38th Annual ACM/ IEEE Symposium on Logic in Computer Science (LICS), took place at Boston University, Boston, USA, from June 26 to June 29, 2023, with co-located events taking place on June 24 and June 25. Five workshops were co-located with LICS 2023: combinatorial games in finite model theory, the decision problem in first order logic, international workshop on quantitative logical method, structure meets power, and the logic mentoring workshop.
Verification Column

Automated theorem proving can be seen as a logic-based approach for generating mathematical proofs mechanically. Important applications are hardware and software verification, general-purpose proof assistants and proof checking. Most state-of-the-art theorem provers rely on proof calculi for higher-order logic that incorporate superposition techniques or satisfiability modulo theories. The article by Bentkamp, Blanchette, Nummelin, Tourret and Waldmann provides an overview of the recently developed λ-superposition approach. It takes inspirations of classical superposition for first-order logic and extends it for higher-order constructs. This new approach paves the way for a new generation of automated theorem provers for powerful logics, still being impressively efficient.
Role of Artificial Intelligence on Leadership Decision Making: A Perspective of Business Sector Organization

Comparing the different digital technologies from a business perspective, artificial intelligence is the fastest-growing technology. Globally, all business organizations rapidly use various artificial intelligence applications for decision-making. This study explores how artificial intelligence will affect the leadership decision-making process in the business sector of Pakistan. A quantitative approach was used to analyze the problem in question. The data was gathered from different business leaders across Pakistan. The data was analyzed via SmartPLS. The results show that transformational leaders are the highest users of artificial intelligence in their decision-making, while transactional leaders are lower users, and servant leaders are the lowest users in their decision-making. It is recommended that business organizations across Pakistan utilize more and more artificial intelligence applications in their business processes because it also significantly impacts the organization's growth.
Dual Image Reversible Data Hiding Based on Coordinate Vector Directions via Communication Networking

In this paper, an innovative reversible dual image information hiding technique is proposed, which utilizes the coordinate vectors generated by the original pixel pairs to hide secret information at two combined locations in four directions. In the process of image transmission, it is not easy to be detected, and the receiver of the image data can completely restore the stego image to the original image, which is very suitable for the transmission of confidential information such as medicine or intelligent transportation. In recent years, many researchers have used this technique to improve the quality of embedded steganographic images and the ability to hide secret information. In the field of digital image information hiding, embedding capacity and Peak Signal-to-Noise Ratio (PSNR) are two important quantitative evaluation indicators. The higher the PSNR value, the closer the distorted digital image quality is to the original digital image. If the PSNR is higher than 30dB, it is generally considered to be ideal image quality. We extract two pixels from the original image and embed the secret information according to different hidden parameters and . The minimum hiding ability is 1 bit per pixel (bpp) or above. According to our experimental results, when the hidden capacity of the steganographic image is 1 bpp, the PSNR value of the image is 50.65 dB, and when the hidden capacity is 2 bpp, the PSNR value of the image is 37.88 dB. This means that our proposed method has good image quality and embedding capacity.
Analysis of Literacy Learning Methods in the Process of Forming a Conceptual Framework for Student Final Projects

Literacy learning at this time is very important because it can help students to think more critically and be able to think in a structured way. The literacy learning method is a method that invites students to think conceptually in their learning activities in class. The derivatives of this learning system include thinking, reading, and writing. The tendency problem encountered by students when compiling their final assignment is not being able to harmonize these three aspects, so a learning method with better content is needed for students to be able to complete their final assignment comfortably. The object of this study is the effectiveness of the literacy learning method for students. The method in this study uses a qualitative approach to data analysis with data triangulation. The results in this study are that the implementation of literacy learning methods must be carried out repetitively so that students are able to describe patterns of thinking, reading, and writing.
Behavior Oriented Design of Evaluation Function for Topic Crawler Search Algorithm

As the core of topic crawler search algorithm, the prevailing topic crawler search algorithm evaluation function cannot effectively deal with the local optimal problem in topic crawler search task. An evaluation function for crawler behavior and a corresponding memory-based calculation method are presented. Instead of evaluating the value of each object to be climbed, the method values the predictive results of the crawler's different behaviors. By querying the result of the similarity between the behavior and the behavior to be evaluated, the value of the behavior performed by the crawler under the current situation is obtained by weighting distance. Experimental results showed that the method have an improvement of more than 11.78% in harvest ratio and target recall, and more than 28.32% in velocity compared to baseline models.