Reviewerly/exHarmony huggingface dataset:
Workflow Scheduling Scheme for Optimized Reliability and End-to-End Delay Control in Cloud Computing Using AI-Based Modeling

In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.
Approximation of Brownian Motion on Simple Graphs

This article is based on chapters 9 and 19 of the new neural network approximation monograph written by the first author. We use the approximation properties coming from the parametrized and deformed neural networks based on the parametrized error and q-deformed and β-parametrized half-hyperbolic tangent activation functions. So, we implement a univariate theory on a compact interval that is ordinary and fractional. The result is the quantitative approximation of Brownian motion on simple graphs: in particular over a system S of semiaxes emanating from a common origin radially arranged and a particle moving randomly on S. We produce a large variety of Jackson-type inequalities, calculating the degree of approximation of the engaged neural network operators to a general expectation function of this kind of Brownian motion. We finish with a detailed list of approximation applications related to the expectation of important functions of this Brownian motion. The differentiability of our functions is taken into account, producing higher speeds of approximation.
Certificateless Encryption Supporting Multi-Ciphertext Equality Test with Proxy-Assisted Authorization

Public key encryption with equality test (PKEET) is a cryptographic primitive that enables a tester to determine, without decryption, whether two ciphertexts encrypted with different public keys generate from the same message. In previous research, public key encryption with equality test (PKEET) was extended to include identity-based encryption with equality test (IBEET), thereby broadening the application of PKEET. Subsequently, certificateless encryption with equality test (CLEET) was introduced to address the key escrow problem in IBEET. However, existing CLEET schemes suffer from inefficiency and potential information leakage when dealing with multiple ciphertexts due to the need for pairwise equality tests. To address this issue, we propose a concept of certificateless encryption supporting multi-ciphertext equality test with proxy-assisted authorization (CLE-MET-PA). CLE-MET-PA incorporates the functionality of the multi-ciphertext equality test into CLEET, enabling a tester to perform a single equality test on multiple ciphertexts to determine whether the underlying plaintexts are equal, without revealing any additional information. This enhances the security of our scheme while significantly reducing the computational overhead compared to multiple pairwise equality tests, making our scheme more efficient. Additionally, our approach integrates proxy-assisted authorization, allowing users to delegate a proxy to grant authorizations for equality tests on their behalf when offline. Importantly, the proxy token used in our scheme does not include any portion of the user’s private key, providing enhanced protection compared to traditional PKEET schemes in which the user token is often part of the user’s private key. We construct a concrete CLE-MET-PA scheme and prove that it achieves CPA security and attains CCA security through an FO transformation.
Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division

Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations.
SIT6: Indirect touch-based object manipulation for DeskVR

Virtual reality (VR) has the potential to significantly boost productivity in professional settings, especially those that can benefit from immersive environments that allow a better and more thorough way of visualizing information. However, the physical demands of mid-air movements make it difficult to use VR for extended periods. DeskVR offers a solution that allows users to engage in VR while seated at a desk, minimizing physical exhaustion. However, developing appropriate motion techniques for this context is challenging due to limited mobility and space constraints. This work focuses on object manipulation techniques, exploring touch-based and mid-air-based approaches to design a suitable solution for DeskVR, hypothesizing that touch-based object manipulation techniques could be as effective as mid-air object manipulation in a DeskVR scenario while less physically demanding. Thus, we propose Scaled Indirect Touch 6-DOF (SIT6), an indirect touch-based object manipulation technique incorporating scaled input mapping to address precision and out-of-reach manipulation issues. The implementation of our solution consists of a state machine with error-handling mechanisms and visual indicators to enhance interaction. User experiments were conducted to compare the SIT6 technique with a baseline mid-air approach, revealing comparable effectiveness while demanding less physical exertion. These results validated our hypothesis and established SIT6 as a viable option for object manipulation in DeskVR scenarios.
Old house new home: Facilitating interior design with RedesignUS in virtual reality

Customized interiors not only reflect personality and offer protection against aesthetic exhaustion, but they also foster a sense of home. Virtual reality (VR) systems have previously leveraged the benefits of immersion, interactivity, and entertainment to empower interior design. These systems, however, largely ignore the need to renovate houses in favor of a more immersive VR experience. To demonstrate the significance of scene reconstruction in redesign, we conducted experience prototyping with 8 professionals, outlining our design goals. Then, we propose RedesignUS, a VR system that enables users to rebuild and customize their own homes. By providing users with the flexibility to synthesize existing room layouts and fine-tune various levels of their homes leveraging inspiration from others’ combinations, RedesignUS offers an unparalleled degree of customization. A 24-user empirical study confirms that our system boosts editing efficiency and allows users to explore more options of home decoration, broadening their design horizons. Furthermore, scene restoration leads to enhanced realism and familiarity, thereby increasing their trust in our system. Overall, our research investigates novel interactive paradigms and insights for DIY home improvement.
Alexa doesn't have that many feelings: Children's understanding of AI through interactions with smart speakers in their homes

As voice-based Conversational Assistants (CAs), including Alexa, Siri, Google Home, have become commonly embedded in households, many children now routinely interact with Artificial Intelligence (AI) systems. It is important to research children's experiences with consumer devices which use AI techniques because these shape their understanding of AI and its capabilities. We conducted a mixed-methods study (questionnaires and interviews) with primary-school children aged 5–12 in Scotland to establish children's understanding of how voice-based CAs work, how they perceive their cognitive abilities, agency and other human-like qualities, their awareness and trust of privacy aspects when using CAs and what they perceive as appropriate verbal interactions with CAs. Most children overestimated the CAs' intelligence and were uncertain about the systems' feelings or agency. They also lacked accurate understanding of data privacy and security aspects, and believed it was wrong to be rude to conversational assistants. Exploring children's current understanding of AI-supported technology has educational implications; such findings will enable educators to develop appropriate materials to address the pressing need for AI literacy.
The future of solar forecasting in China

The current grid code in China in regard to solar forecasting is, in my opinion, underdeveloped, especially in contrast to the rate at which photovoltaics are being installed. As such, explaining the limitations of the grid code and resetting pathways to improve it are thought utilitarian for those scientists and policymakers who are responsible for or aware of the grid code but have not themselves worked on the problem of forecasting. In this perspective article, I should first explain with respect to China's grid code the perceived deficiencies in the current forecasting research and practices, and then outline a five-stage workflow that could completely mitigate the situation. Among other things, the over-reliance on accuracy as the basis for gauging the goodness of forecasts is identified as a root cause for the status quo, and thus, I advocate a holistic forecast verification procedure that encompasses consistency, quality, and value. With that in mind, the proposed workflow for better solar forecasting for grid integration purposes relies on the effective information flow among the weather department, grid operators, and individual plant owners, which is inline with the current grid code. What goes beyond this is that the proposal further introduces a couple of concepts called “hierarchical reconciliation” and “firm forecasting,” which are new but are able to eliminate forecast errors wholly, thus making solar power dispatchable on the system level. With a slight premium incurred, it is now possible to manage solar plants, or variable renewables in general, in the same style as managing conventional fire-powered generators.
Using Decentralized Identifiers and InterPlanetary File System to Create a Recoverable Rare Disease Patient Identity Framework

This paper presents a novel framework for creating a recoverable rare disease patient identity system using blockchain and smart contracts, decentralized identifiers (DIDs), and the InterPlanetary File System (IPFS). Smart contracts are executable code that can be written into decentralized storage such as blockchains to enable tamper-proof data transactions. DIDs provide a secure, decentralized, and extensible way to create, store, and manage digital identities, while IPFS provides a distributed, immutable, and secure storage system for patient identities. Utilizing these technologies with smart contracts, we created a framework to store persistent medical records of patients. Smart contracts additionally allow account recovery without the use of any centralized authority. The framework provides a standardized identity layer that ensures healthcare providers have correct and secure access to a patient’s data. The paper explores the advantages of using a decentralized identity system and highlights the potential of this approach to improve the security and universality of medical records for patients with rare diseases.
PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

The application of machine learning models, particularly in cybersecurity, has surged significantly in the past few years. However, the effectiveness of these models is predominantly tethered to the quality and breadth of the training data they ingest. The scarcity of realistic datasets within the cybersecurity field constitutes a considerable challenge to the development of industry-grade tools intended for real-world application scenarios. Specifically, current datasets are either significantly outdated or fall short on both qualitative and quantitative fronts, primarily because many organizations exhibit reluctance in data sharing, stemming from privacy concerns or the potential threat to trade secrets. To address this challenge, the paper introduces PAC-GPT, a novel framework to generate reliable synthetic data for machine learning methods based on Open AI’s Generative Pre-trained Transformer 3 (GPT-3). The core components of this framework are two modules, namely a Flow Generator, which is responsible for capturing and regenerating patterns in a series of network packets, and Packet Generator, which can generate individual network packets given the network flow. We also propose a packet generator based on LLM chaining and then proceed to assess, compare, and evaluate its performance using metrics such as loss, accuracy and success rate, concluding that transformers are a suitable approach for synthetic packet generation with minimal fine-tuning performed. Lastly, a streamlined command line interface (CLI) tool has been devised to facilitate the seamless access of this innovative data generation strategy by professionals from various disciplines.
E-RRT*: Path Planning for Hyper-Redundant Manipulators

A hyper-redundant manipulator(HRM) can flexibly accomplish tasks in narrow spaces. However, its excessive degrees of freedom pose challenges for path planning. In this letter, an ellipsoid-shape rapidly-exporing random tree (E-RRT*) method is proposed for path planning of HRMs in workspace, particularly those with angle limits. This method replaces line segments with ellipsoids to connect adjacent nodes. Firstly, an analysis of angle constraints of the HRM is conducted, providing restrictions on node selection during path planning. Secondly, a slow-speed informed guiding approach is introduced to optimize the sampling process. Finally, the obtained path is enhanced by adding control points and applying cubic polynomial interpolation to achieve path smoothing. Simulations demonstrate that the proposed E-RRT* method effectively solves the path planning problem for HRMs. Especially in narrow environments, appropriate informed guiding speeds enable E-RRT* to outperform other methods.
Automatic Specialization of Third-Party Java Dependencies

Large-scale code reuse significantly reduces both development costs and time. However, the massive share of third-party code in software projects poses new challenges, especially in terms of maintenance and security. In this paper, we propose a novel technique to specialize dependencies of Java projects, based on their actual usage. Given a project and its dependencies, we systematically identify the subset of each dependency that is necessary to build the project, and we remove the rest. As a result of this process, we package each specialized dependency in a <monospace>JAR</monospace> file. Then, we generate specialized dependency trees where the original dependencies are replaced by the specialized versions. This allows building the project with significantly less third-party code than the original. As a result, the specialized dependencies become a first-class concept in the software supply chain, rather than a transient artifact in an optimizing compiler toolchain. We implement our technique in a tool called <small>DepTrim</small> , which we evaluate with 30 notable open-source Java projects. <small>DepTrim</small> specializes a total of 343 (86.6%) dependencies across these projects, and successfully rebuilds each project with a specialized dependency tree. Moreover, through this specialization, <small>DepTrim</small> removes a total of 57,444 (42.2%) classes from the dependencies, reducing the ratio of dependency classes to project classes from 8.7 <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> in the original projects to 5.0 <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> after specialization. These novel results indicate that dependency specialization significantly reduces the share of third-party code in Java projects.
A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU to construct a graph neural network recommendation model; Considering the long and short term preferences of users, a graph neural network algorithm integrating long and short term preferences is constructed. The research results indicated that when the embedding dimension was 64, the batch size of selected samples was 64, the learning rate was 0.0005, the vertical stacking layer of GRU was 2, the iteration period was 5, and the dropout probability was 50% with the best performance. The graph neural network algorithm based on long and short term preferences had higher recommendation accuracy compared to other algorithms. Modeling users’ short-term and long-term preferences can achieve the goal of comprehensively improving recommendation effectiveness.
Coverage Path Planning for UAV Based on Improved Back-and-Forth Mode

In the field of aerial photography, complete coverage path planning (CCPP) is the key to improving the task efficiency of unmanned aerial vehicles (UAVs) with a limited battery capacity. Previous CCPP algorithms consisted of Cellular Decomposition (CD) class, Probabilistic Roadmap (PRM) class, and Bio-inspired Neural Network (BINN) class. However, when faced with the complex environment of multiple forbidden areas, CD and PRM have high computational complexity and a long path length, and BINN has a large number of turns. In this paper, an improved back-and-forth (IBF) algorithm is proposed to achieve good performance in these three aspects at the same time based on a newly straight-turn mode and the corresponding auxiliary approaches (i.e. Local Traversal, Global Traversal, and continuity constraint function). An important difference between straight-turn mode and back-and-forth mode is that the next straight direction is not restricted to being parallel to the last straight direction but is determined by Local Traversal, which is introduced to evaluate the benefits of different paths and find the best direction for the next straight-turn mode at the ending cell. Global Traversal is available to choose a “jump” cell when "Dead zone" are faced. The continuity constraint function is used to maintain the continuity of the unexplored area. Under the comprehensive consideration of path length, computation time, and number of turns, the simulation and experimental results indicate that the proposed IBF algorithm has better performance in multiple forbidden areas.
Subjective Quality Evaluation of Point Clouds using Remote Testing

Subjective quality assessment serves as a method to evaluate the perceptual quality of 3D point clouds. These evaluations can be conducted using lab-based or remote or crowdsourcing tests. The lab-based tests are time-consuming and less cost-effective. As an alternative, remote or crowd tests can be used, offering a time and cost-friendly approach. Remote testing enables larger and more diverse participant pools. However, this raises the question of its applicability due to variability in participants' display devices and environments for the evaluation of the point cloud. In this paper, the focus is on investigating the applicability of remote testing by using the Absolute Category Rating (ACR) test method for assessing the subjective quality of point clouds in different tests. We compare the results of lab and remote tests by replicating lab-based tests. In the first test, we assess the subjective quality of a static point cloud geometry for two different types of geometrical degradations, namely Gaussian noise, and octree-pruning. In the second test, we compare the performance of two different compression methods (G-PCC and V-PCC) to assess the subjective quality of coloured point cloud videos. Based on the results obtained using correlation and Standard deviation of Opinion Scores (SOS) analysis, the remote testing paradigm can be used for evaluating point clouds.
A Haptic-enabled, Distributed and Networked Immersive System for Multi-User Collaborative Virtual Reality

Virtual Reality (VR) is gaining attention in various domains such as entertainment, industry, mental healthcare and VR training. Al- though most of these use-cases are still limited to single-user tasks, a lot of applications are heavily depending on multi-user collaboration. Existing multi-user VR systems are most often created in a classic server-client architecture, however, which induces unpredictable network behaviour which can affect the end-user's Quality-of-Experience (QoE) and performance. In addition, the interaction methods in these systems are often constrained to either traditional VR controllers or very use-case specific interaction methods, such that general purpose haptic gloves form a somewhat under-explored part of literature. Therefore, we (i) present a networked, distributed multi-user VR system with synchronization of environments over a low-bandwidth networked connection. In addition, we (ii) enhance the experience by adding haptic gloves to the system, which we compare to the traditional VR controllers in a subjective experiment. As a proof-of-concept, a use case is implemented in which two users have to prepare and bake a virtual pizza. The results show that high framerates (> 90 Frames Per Second (FPS)) can be obtained while keeping network throughput to a minimum ( < 1 Mbps). The accompanying user study shows that haptic gloves are preferred when immersiveness is the main emphasis of the virtual environment, while controllers are more suited when performance is in the center of attention. In objective terms, the applicability of haptic feedback is highly dependent on the task at hand.