Reviewerly/exHarmony huggingface dataset:
Color Image Encryption Employing Cellular Automata and Three-Dimensional Chaotic Transforms

Concerns about information security are relevant to almost every facet of human activity and daily life in today's contemporary civilization. The problem of information being leaked to unauthorized parties is becoming a far more serious issue. The creation of procedures that efficiently preserve information security has emerged as a prominent focus of study in recent years. Classical algorithms for encrypting data, such as RSA and DES, have already been extensively used in encrypting textual information. However, due to developments in the electronics industry and the field of computer science, the abilities of human computers have advanced at an accelerated rate. As a direct consequence, conventional techniques for encrypting data risk being broken. On the other hand, despite the vast improvements in computer computational power, people still need faster encryption rates. The amount of image data is much greater than text data, and there is a strong association between neighbouring pixels. As a result, there is a need to develop image encryption algorithms that are both more effective and safer.. Special algorithms have been proposed to crypt digital images to retain the effectiveness, security, and throughput of encryption since color image data differs in many ways from conventional data types like text and binary data. The current work offers a method for encrypting digital images utilizing chaotic mapping and reversible cell automation. The principles of Shannon's confusion and diffusion technique are used as the foundation for the encryption process in the suggested approach, which has two primary steps. The input is a simple image, which is then shuffled using a 3D chaotic transform and an appropriate key value in the first phase. The second stage involves extracting the cypher image from the first step onto 24 one-bit plates and XORing it using the appropriate 2D reversible cell automata. The suggested technique exhibits excellent performance outcomes when compared to other cryptographic algorithms.
Surveilling Systems Used to Detect Lingering Threats on Dark Web

Cybersecurity investigators, counter-terrorism agents and law enforcement pursues their continuous scrutiny on perpetrators, extremists, and others via studying their actions on dark web platform enabled by The Onion Route (TOR). Content on dark web forums and TOR hidden services offers a rich source of buried knowledge for many professionals. Often these hidden services are exploited to execute illicit operations including but not limited to disinformation-as-a-service, child pornography, circulating propagandas, drug and weapon smuggling and hiring assassins. Determining whether dark web platform is criminogenic in nature and designing approaches to surveil trends and fluctuations of TOR are crucial to regulate varied activities thriving within dark web. Qualitative research approach was used on determining the reality of dark web and TOR hidden services by identifying popular trends on dark web forums, types of user cohorts and their motivations to perform such activities. Moreover, the literature review provides several technologies employed to monitor popular trends on dark web and TOR traffic. Results shows that, illicit and immoral activities are abundant within dark web. Additionally, by employing real-time automated monitoring systems such as BlackWidow will help to either mitigate or eradicate known and unknown illicit activities on dark web to bring about security to the cyberworld.
Design and Development Applications HD’R Comic Cafe Using Augmented Reality

Augmented Reality is a technology that is able to bring virtual objects into the real environment. Augmented reality environment allows users to interact with virtual objects so that users will get a hands-on experience. For this reason, we designed an AR application that will be used as a presentation medium in redesigning interactive elements in one of the interior cafes in the city of Malang, namely HD’R Comic Cafe, which uses comics and board game media commonly known as cafe libraries. The design of the use of AR uses the Unity 3D engine that can display objects in real time using the marker-based tracking method. The results of this design are interactive products for the first floor and for the second floor which aims to test the use of applications in the form of AR in the scope of interior design, especially users of interactive products at the HD’R Comic Cafe.
Review for Person Recognition Using Siamese Neural Network

Person recognition can be used in automatic attendance for a person attendance at a certain location, e.g., at the school, to replace a card or fingerprint sensor. In this paper, the Siamese Neural Network is reviewed and used as a person recognition system using the reference coding from https://github.com/nicknochnack/FaceRecognition . The result shows that it can detect the intended person with a training loss of around 1%.
Thermal Condition Evaluation of Farmhouse Using Ecotect Analysis as an Effort to Optimize Cultural Activity of Enclave Villagers (Case Study: Ngadas, Bromo Tengger Semeru National Park)

One of the Indonesian agrarian society characteristics can be seen in the Ngadas society. A tough farmer who spends most of his time in the fields so builds a simple construction called a farmhouse as a potato nursery, a shed/stable, and a rest room. In Ngadas Village, there are five types of farmhouses that need to be studied through a thermal analysis. It aims to simulate the thermal conditions that exist in each typology of farmhouses and to find the ideal typology for optimal potato/Solanum growth. This study uses a qualitative comparative descriptive method and simulation using Ecotect software. The most optimal type of farmhouse for potato nurseries is a farmhouse with a rectangular pattern. Every unit is separate, main room (storage, potato nursery and rest room) on the front lane and shed is on the side. With this type, animal husbandry, potato farming, and gegenen cultural activities become more optimal and do not interfere with each other. This building can also be categorized as a smart building because of its energy efficiency and space use.
Graphical posterior predictive classification: Bayesian model averaging with particle Gibbs

In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emphasis is then placed on the<italic>Bayesian model averaging</italic>which takes full account of the class-specific model uncertainty by averaging over the posterior graph model probabilities. An explicit evaluation of the model probabilities is well known to be infeasible. To address this issue, we consider the particle Gibbs strategy of J. Olsson, T. Pavlenko, and F. L. Rios [Electron. J. Statist. 13 (2019), no. 2, 2865–2897] for posterior sampling from decomposable graphical models which utilizes the so-called<italic>Christmas tree algorithm</italic>of J. Olsson, T. Pavlenko, and F. L. Rios [Stat. Comput. 32 (2022), no. 5, Paper No. 80, 18] as proposal kernel. We also derive a strong hyper Markov law which we call the<italic>hyper normal Wishart law</italic>that allows to perform the resultant Bayesian calculations locally. The proposed predictive graphical classifier reveals superior performance compared to the ordinary Bayesian predictive rule that does not account for the model uncertainty, as well as to a number of out-of-the-box classifiers.
Achieving Microsecond-Scale Tail Latency Efficiently with Approximate Optimal Scheduling

Datacenter applications expect microsecond-scale service times and tightly bound tail latency, with future workloads expected to be even more demanding. To address this challenge, state-of-the-art runtimes employ theoretically optimal scheduling policies, namely a single request queue and strict preemption.
GEMINI: Fast Failure Recovery in Distributed Training with In-Memory Checkpoints

Large deep learning models have recently garnered substantial attention from both academia and industry. Nonetheless, frequent failures are observed during large model training due to large-scale resources involved and extended training time. Existing solutions have significant failure recovery costs due to the severe restriction imposed by the bandwidth of remote storage in which they store checkpoints.
PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor).
Auxiliary Features‐Guided Super Resolution for Monte Carlo Rendering

Abstract This paper investigates super‐resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super‐resolution technologies, it is essentially an ill‐posed problem and cannot recover high‐frequency details in renderings. To address this problem, we exploit high‐resolution auxiliary features to guide super‐resolution of low‐resolution renderings. These high‐resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high‐frequency details to assist super‐resolution. To this end, we develop a cross‐modality transformer network that consists of an auxiliary feature branch and a low‐resolution rendering branch. These two branches are designed to fuse high‐resolution auxiliary features with the corresponding low‐resolution rendering. Furthermore, we design Residual Densely Connected Swin Transformer groups to learn to extract representative features to enable high‐quality super‐resolution. Our experiments show that our auxiliary features‐guided super‐resolution method outperforms both super‐resolution methods and Monte Carlo denoising methods in producing high‐quality renderings.
Environment for UAV Education

Simulation tools are often used in robotics education to transfer theoretical knowledge into practical experience. The correlation between practical and real-world experience depends on the quality of the simulation environment and its ability to serve as a replacement for the real world. In this study an environment for UAV education is proposed, where the transfer of theoretical knowledge into practical experience is achieved through the implementation of both simulation and real UAV deployment. Initially, this environment allows students to develop their applications in the simulation. Subsequently, in the later stages, they are able to easily deploy them into the educational UAV and test the solution in the drone laboratory. Compatibility between simulation and physical UAV is achieved by combining the appropriate components in both the simulation and physical UAV. The software technologies used include ROS, ArduPilot, and MavLink, while the hardware platform for the educational UAV was chosen as DroneCore.Suite by Airvolute s.r.o. These technologies are not only suitable for the proposed environment but are also commonly used across the robotics field, which enhances relevant students competencies. Later in the article, an example assignment is presented and described how to use the environment for UAV education in a classroom setting.
Metaverse CAN: Embracing Continuous, Active, and Non-intrusive Biometric Authentication

The Metaverse is a virtual world, an immersive experience, a new human-computer interaction, built upon various advanced technologies. How to protect Metaverse personal information and virtual properties is also facing new challenges, such as new attacks and new expectations of user experiences. While traditional methods (e.g., those employed in smartphone authentication) generally pass the basic design considerations, they are repeatedly reported to be either unsafe or inconvenient in the Metaverse. In this paper, we address this discrepancy by introducing CAN: a new design consideration especially for the Metaverse. Specifically, we focus on the legacy and novel biometric authentication systems and evaluate them thoroughly with basic and CAN considerations. We also propose an ear-based method as one example of CAN systems. To conclude, a continuous, active and non-intrusive biometric system is suggested for Metaverse authentication for its capability in continuous sessions, against imposters, and immersive experience.
Visualizing Hand Force with Wearable Muscle Sensing for Enhanced Mixed Reality Remote Collaboration

In this paper, we present a prototype system for sharing a user's hand force in mixed reality (MR) remote collaboration on physical tasks, where hand force is estimated using wearable surface electromyography (sEMG) sensor. In a remote collaboration between a worker and an expert, hand activity plays a crucial role. However, the force exerted by the worker's hand has not been extensively investigated. Our sEMG-based system reliably captures the worker's hand force during physical tasks and conveys this information to the expert through hand force visualization, overlaid on the worker's view or on the worker's avatar. A user study was conducted to evaluate the impact of visualizing a worker's hand force on collaboration, employing three distinct visualization methods across two view modes. Our findings demonstrate that sensing and sharing hand force in MR remote collaboration improves the expert's awareness of the worker's task, significantly enhances the expert's perception of the collaborator's hand force and the weight of the interacting object, and promotes a heightened sense of social presence for the expert. Based on the findings, we provide design implications for future mixed reality remote collaboration systems that incorporate hand force sensing and visualization.
Image Segmentation Edge Detection Technology Based on Optimization Model

To meet the requirement of realtime and correct edge detection in complicated image segmentation, a speedy edge detection technological know-how based totally on optimization mannequin was once proposed. The optimization mannequin of deep mastering is adopted, the image object is taken as the segmentation unit, the image is divided through pixel, and then the segmentation image is merged based totally on certain rules. A giant wide variety of spots or noise after image segmentation are reduced, and the variety of pixel factors in the incorrect division is reduced It shortens the detection time and improves the detection efficiency, and has sure benefits in contrast with the classical Log, Canny and Pal. King algorithms.
Lapa: Multi-Label-Flipping Adversarial Attacks on Graph Neural Networks

In recent years, the robustness of graph neural networks (GNNs) adversarial attacks has been favored by researchers, and related research has intensified. However, existing graph adversarial attacks are mainly performed by modifying the topology of the graph and node feature vectors, and attacks from the labels of nodes are rarely considered, especially for multi-label graph adversarial attacks, where an attacker can manipulate an obscure portion of the training labels and flip the sample labels in the training data to other classes, resulting in incorrect predictions of the retrained GNNs model. In this work, we present graph adversarial attack against multi-label flipping. We propose an effective attack model-Lapa, based on an adaptive genetic algorithm that searches the solution space for suitable adversarial samples to obtain near-optimal solutions. The adaptive genetic algorithm performs crossover and mutation operations utilizing adaptive probabilities. We demonstrate the effectiveness of our proposed Lapa attack model on GNNs after extensive experiments on four real datasets.
An End-to-End Network for Multi-Scale Ship Detection in SAR Images

Automatic target recognition (ATR) technology of synthetic aperture radar (SAR) image is one of the key technologies of artificial image interpretation. Most of the traditional SAR ship target detection algorithms are limited by the scene and have poor generalization ability. In this paper, an end-to-end network for multi-scale ship detection in SAR images is proposed, which combines a multi-scale feature extraction module and an attention module to solve these problems. The multi-scale feature extraction module enhances the representation ability of the model to target size variations ad appearance changes by exploiting context information of different resolutions. The attention module enhances the target information and suppresses the background redundancy information in the spatial and channel dimensions of the feature. In the experiment, the effectiveness of the proposed method is verified on the public SAR ship detection dataset (SSDD), and the proposed method achieves 0.998 average precision (AP).
Status, Hotspots and Evolutionary Path of Foreign Research on Digital Immersion Technologies

Digital immersive art has received a lot of attention in recent years, and research results are abundant. In order to explore the progress of international research on digital immersive art and to grasp the development direction and evolution trend of the field of digital immersive art, the article borrows the knowledge mapping software CiteSpace and conducts a visual analysis of the countries or regions, highly productive authors and institutions, and keywords of 1457 documents on digital immersive art selected from the WOS core collection database. The study found that the current research in the field of digital immersive art continues to increase in interest, with close collaboration between authors and loose collaboration between institutions. Research hotspots are mainly focused on virtual reality, digital art, immersive interaction, and games. Neutral networks, artificial intelligence, and deep learning are also receiving continued attention.
The rising of platformed education. Theoretical notes and interpretations of lived experiences

Based on the theoretical elaboration of teachers' lived experience, our study lays the ground for a critical assessment of online higher education. On the one hand, our analysis investigates an alternative to the hegemonic understanding of learning objectives as fetishized by "measurability" and results-oriented outcomes, which are considered to be subsumed as products of the hyper-industrialized education. On the other, we claim that our research participants operate as neoliberal subjectivities who have to adapt to remote teaching, mobilizing tendencies towards self-empowerment and self-activation.
Improved Object Detectors Based on Semantic Feature Fusion

Object detection methods generally use fused features for box regression and category classification, but shallow fused features lack long-distance contextual information. To address the above issues, we propose an embeddable semantic feature fusion network called UN eck that extracts global information from deep features and iteratively fuses shallow features to improve the performance of the object detectors.
FAIR Data for Large Research Facilities

This workshop will bring together data managers, repository managers, administrators, and others who are responsible for, or interested in research data management at large research facilities. These facilities have unique issues due to a variety of factors, such as an extreme data volume, variety, and velocity. The workshop aims to provide cross-pollination between facilities that have similar desires to realize the FAIR principles. The organizers of this workshop are members of the NSF CI Compass FAIR Data Working Group, and the outcomes from these discussions will become a white paper and topics for future CI Compass webinars.
Research on Path Planning of Unmanned Combat Vehicle Based on Improved Potential Field Method

Unmanned combat vehicle is one of the important development trends of the future battlefield, and the unmanned combat vehicle that can realize positioning navigation and autonomous obstacle avoidance in the complex and changeable battlefield environment is an important research direction of unmanned combat vehicle. The path planning problem of unmanned combat vehicles is studied. Based on the classical potential field method, the inherent defects of the classical potential field method are emphatically analyzed. An improved model of repulsive potential field function based on the social force model is proposed. Aiming at the problem that unmanned combat vehicles are prone to fall into local minima, this solution of sub-target points is proposed. Through simulation verification, the inherent defects of the classical potential field method in the path process of unmanned combat vehicles are optimized, It shows that the model and simulation can provide an effective decision-making basis for the path planning of unmanned combat vehicles.
Research on Simulating Application of Artificial Intelligence in Aerospace Field

The project relies on the intelligent mechanical arm and omnidirectional intelligent mobile car developed by Yuejiang Technology Co., LTD to simulate the space missions such as spaceship departure, astronaut exit, material handling, base station construction, astronaut return and spaceship return, and integrates multiple technologies such as autonomous control of mechanical arm, path planning of unmanned vehicle, AI identification and sensor technology. The research of the project has a certain role in promoting the promotion of AI in higher vocational education, and also has a certain reference significance for the application of AI in aerospace, logistics and other fields.
New Centre/Surround Retinex-like Method for Low-Count Poisson Image Denoising

The problem of synthesizing weak image enhancement methods is under consideration. The proposed approach to the synthesis of enhancement methods for image processing is based on the principles and methods of statistical learning, considered as learning on the input data (adaptive filtering). The synthesis is focused on a special representation of images using controlled size sample of counts (sampling representation). Based on the specifics of this representation, a generative model of an ideal image is formulated, which is then concretized to a probabilistic parametric model in the form of a system of receptive fields (Retinex model). It is demonstrated how the problem of denoising with image detail preserving could be reduced to recurrent computational procedures of the maximum likelihood approach, popular in the field of machine learning.
Extreme Classification for Answer Type Prediction in Question Answering

Semantic answer type prediction (SMART) is known to be a useful step towards designing effective question answering (QA) systems. The SMART task involves predicting the top-k knowledge graph (KG) types for a given natural language question. This is challenging due to the large number of types in KGs. In this paper, we propose use of extreme multi-label classification using Transformer models (XBERT) by clustering KG types using structural and semantic features based on question text. We specifically improve the clustering stage of the XBERT pipeline using the features derived from KGs. We show that these features can improve end-to-end performance for the SMART task, and yield state-of-the-art results.