Reviewerly/exHarmony huggingface dataset:
RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression

This study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, this study combines causal attention modules with residual connections, proposing a residual causal attention module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also expands the receptive field. It facilitates the extraction of crucial image details while capturing the global structural information of the image, significantly enhancing the modeling capabilities for pulsar data. In the experiments, the model is trained and validated using the HTRU1 dataset. This study compares the average negative log-likelihood score with baseline models like the GMM, STM, and PixelCNN. The results demonstrate the superior performance of our model over other models. Finally, this study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.
Accelerating quantum optimal control of multi-qubit systems with symmetry-based Hamiltonian transformations

We present a novel, computationally efficient approach to accelerate quantum optimal control calculations of large multi-qubit systems used in a variety of quantum computing applications. By leveraging the intrinsic symmetry of finite groups, the Hilbert space can be decomposed and the Hamiltonians block diagonalized to enable extremely fast quantum optimal control calculations. Our approach reduces the Hamiltonian size of an n-qubit system from 2n×2n to O(n×n) or O((2n/n)×(2n/n)) under Sn or Dn symmetry, respectively. Most importantly, this approach reduces the computational runtime of qubit optimal control calculations by orders of magnitude while maintaining the same accuracy as the conventional method. As prospective applications, we show that (1) symmetry-protected subspaces can be potential platforms for quantum error suppression and simulation of other quantum Hamiltonians and (2) Lie–Trotter–Suzuki decomposition approaches can generalize our method to a general variety of multi-qubit systems.
Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

In recent times, global cities have been transforming from traditional cities to sustainable smart cities. In text sentiment analysis (SA), many people face critical issues namely urban traffic management, urban living quality, urban information security, urban energy usage, urban safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges in text SA. In such scenarios, the classification of COVID-19-related tweets for text SA includes using natural language processing (NLP) and machine learning methodologies to classify tweet datasets based on their content. This assists in disseminating relevant information, understanding public sentiment, and promoting sustainable practices in urban areas during this pandemic. This article introduces a modified aquila optimizer with a stacked deep learning-based COVID-19 tweet Classification (MAOSDL-TC) technique for text SA. The presented MAOSDL-TC technique incorporates FastText, an effective and powerful text representation approach used for the generation of word embeddings. Furthermore, the MAOSDL-TC technique utilizes an attention-based stacked bidirectional long short-term memory (ASBiLSTM) model for the classification of sentiments that exist in tweets. To improve the detection results of the ASBiLSTM model, the MAO algorithm is applied for the hyperparameter tuning process. The presented MAOSDL-TC technique is validated on the benchmark tweets dataset. The experimental outcomes implied the promising results of the MAOSDL-TC technique compared to recent models in terms of different measures. This MAOSDL-TC technique improves accuracy and interpretability of sentiment prediction.
Novel hybrid chaotic map-based secure data transmission between smart meter and HAN devices

ABSTRACTHome area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks.KEYWORDS: Smart gridsmart meterhome area networkchaos theoryAMIadvanced metering infrastructure Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementOn behalf of all authors, I, Lokesh Goel, declare that this article does not involve any generated or analyzed datasets. Data sharing does not apply to this study.Compliance with ethical standardsOn behalf of all authors, I, Lokesh Goel, hereby declare that: No external funding was obtained for this study.The authors and the submitted manuscript have no conflicts of interest to declare.This article does not include any studies involving human participants or animals that were performed by any of the authors.
A Hybrid Application of Quantum Computing Methodologies to AI Techniques for Paddy Crop Leaf Disease Identification

Disease is a common term used for ailments in all species, from humans to animals and plants. This research work helps to identify the diseases of plants. There are many methods to identify the plant’s disease using various techniques and algorithms; in this work, leaf disease is identified focusing on the ailment identification using quantum computing techniques to get a vivid result for better crop production. As there are many diseases in a paddy crop, they have been categorized into two types: nursery diseases and main field diseases. Both types cause a great loss in crop yield. In this research work, main field diseases are studied; though there are various main field diseases, research work targets leaf color changing, and colored spots. The existing methods use hybrid algorithms using AI techniques, but the result is not favorable, therefore, to get a precise result, the methodology is focused on techniques based on quantum computing. The technique to identify disease in a plant varies from leaf to leaf, but the base disease remains the same. Defects like yellow, black, and white spots are common problems. The fungi disease takes the energy from the plants in which they live, this leads to conditions like wilting, scabs, moldy coatings, rusts, blotches, and rotted tissues. All these problems have different existing solutions, through this research work, a minimal fault detection method is identified to reduce the flaws in the paddy crop. As the initial step, paddy crops are taken for research work; later, this can be extended to wheat and maize. The focus is on a hybrid approach of quantum computing and AI as quantum computing techniques help to spot patterns in large data sets. The concept of quantum image processing will help to elaborate the images of a leaf to identify its defect. QIP uses high varied quantum lattice methods to identify clear leaf images by reducing noise and paving the way to identify the discolored leaf at a minimal time. quantum computing possibly paves way for new opportunities in the techniques of Artificial Intelligence, for better predictions and decision-making involving a combination of large data collected from various resources, to produce very clear results. This research work aims to solve the problem of proper identification of paddy crops by studying the image carefully with the designed algorithms to achieve better results that lead to better productivity. The image detection helps to trigger the crop production by routine test work on the image captured thus optimizing the consistency and accuracy. This work also helps to detect the related issues during image computation using image classification techniques.
Exploring the role of assurance context in system security assurance evaluation: a conceptual model

Purpose Security assurance evaluation (SAE) is a well-established approach for assessing the effectiveness of security measures in systems. However, one aspect that is often overlooked in these evaluations is the assurance context in which they are conducted. This paper aims to explore the role of assurance context in system SAEs and proposes a conceptual model to integrate the assurance context into the evaluation process. Design/methodology/approach The conceptual model highlights the interrelationships between the various elements of the assurance context, including system boundaries, stakeholders, security concerns, regulatory compliance and assurance assumptions and regulatory compliance. Findings By introducing the proposed conceptual model, this research provides a framework for incorporating the assurance context into SAEs and offers insights into how it can influence the evaluation outcomes. Originality/value By delving into the concept of assurance context, this research seeks to shed light on how it influences the scope, methodologies and outcomes of assurance evaluations, ultimately enabling organizations to strengthen their system security postures and mitigate risks effectively.
Promoting Sustainable Travel Experiences: A Weighted Parallel Hybrid Approach for Personalized Tourism Recommendations and Enhanced User Satisfaction

With the growing significance of the tourism industry and the increasing desire among travelers to discover new destinations, there is a need for effective recommender systems that cater to individual interests. Existing tourism mobile applications incorporate recommendation systems to alleviate information overload. However, these systems often overlook the varying importance of different items, resulting in suboptimal recommendations. In this research paper, a novel approach is proposed: a weighted parallel hybrid recommendation system. By considering item weights and leveraging parallel processing techniques, this method significantly enhances the accuracy of the similarity between items, leading to improved recommendation quality and precision. With this approach, users can efficiently and effectively explore new destinations that align with their unique preferences and interests, thereby enhancing their overall tourism experience and satisfaction. To evaluate the effectiveness of the proposed weighted parallel hybrid recommendation system, we conducted experiments using a dataset consisting of 20 users. The results demonstrated that the proposed approach achieved an impressive classification accuracy of 80%. A comparative analysis revealed that the proposed approach outperformed that of existing systems and achieved the best results in terms of classification accuracy. This finding highlights the effectiveness and efficiency of the proposed method in generating and promoting sustainable travel experiences by developing a personalized recommendations system for the unique preferences and interests of individual users.
Continuous-variable quantum approximate optimization on a programmable photonic quantum processor

Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm, by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly distributed wave function to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.
Why Do We Need Neurosymbolic AI to Model Pragmatic Analogies?

A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning. In this article, we delve into the performance of large language models (LLMs) in dealing with progressively complex analogies expressed in unstructured text. We discuss analogies at four distinct levels of complexity: lexical, syntactic, semantic, and pragmatic. As the analogies become more complex, they require increasingly extensive, diverse knowledge beyond the textual content, unlikely to be found in the lexical co-occurrence statistics that power LLMs. We discuss neurosymbolic AI techniques that combine statistical and symbolic AI, informing the representation of unstructured text to highlight and augment relevant content, provide abstraction, and guide the mapping process. This maintains the efficiency of LLMs while preserving the ability to explain analogies for pedagogical applications.
Right HTML, Wrong JSON: Challenges in Replaying Archived Webpages Built with Client-Side Rendering

Many web sites are transitioning how they construct their pages. The conventional model is where the content is embedded server-side in the HTML and returned to the client in an HTTP response. Increasingly, sites are moving to a model where the initial HTTP response contains only an HTML skeleton plus JavaScript that makes API calls to a variety of servers for the content (typically in JSON format), and then builds out the DOM client-side, more easily allowing for periodically refreshing the content in a page and allowing dynamic modification of the content. This client-side rendering, now predominant in social media platforms such as Twitter and Instagram, is also being adopted by news outlets, such as CNN.com. When conventional web archiving techniques, such as crawling with Heritrix, are applied to pages that render their content client-side, the JSON responses can become out of sync with the HTML page in which it is to be embedded, resulting in temporal violations on replay. Because the violative JSON is not directly observable in the page (i.e., in the same manner a violative embedded image is), the temporal violations can be difficult to detect. We describe how the top level CNN.com page has used client-side rendering since April 2015 and the impact this has had on web archives. Between April 24, 2015 and July 21, 2016, we found almost 15,000 mementos with a temporal violation of more than 2 days between the base CNN.com HTML and the JSON responses used to deliver the content under the main story. One way to mitigate this problem is to use browser-based crawling instead of conventional crawlers like Heritrix, but browser-based crawling is currently much slower than non-browser-based tools such as Heritrix.
MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries

Metadata quality is crucial for digital objects to be discovered through digital library interfaces.However, due to various reasons, the metadata of digital objects often exhibits incomplete, inconsistent, and incorrect values.We investigate methods to automatically detect, correct, and canonicalize scholarly metadata, using seven key fields of electronic theses and dissertations (ETDs) as a case study.We propose MetaEnhance, a framework that utilizes state-of-the-art artificial intelligence methods to improve the quality of these fields.To evaluate MetaEnhance, we compiled a metadata quality evaluation benchmark containing 500 ETDs, by combining subsets sampled using multiple criteria.We tested MetaEnhance on this benchmark and found that the proposed methods achieved nearly perfect F1-scores in detecting errors, and correctness F1-scores ranging from 0.85 to 1.00 for five of seven fields.The codes and data are publicly available at
Zero-Shot Entailment of Leaderboards for Empirical AI Research

We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e., the automated mining of LEADERBOARDS for Empirical AI Research. The prior reported state-of-the-art models for LEADERBOARDS extraction formulated as an RTE task in a non-zero-shot setting are promising with above 90% reported performances. However, a central research question remains unexamined: did the models actually learn entailment? Thus, for the experiments in this paper, two prior reported state-of-the-art models are tested out-of-the-box for their ability to generalize or their capacity for entailment, given LEADERBOARD labels that were unseen during training. We hypothesize that if the models learned entailment, their zero-shot performances can be expected to be moderately high as well-perhaps, concretely, better than chance. As a result of this work, a zero-shot labeled dataset is created via distant labeling, formulating the LEADERBOARD extraction RTE task.
Artificial Intelligence for Sign Language Translation – A Design Science Research Study

Although our digitalized society is able to foster social inclusion and integration, there are still numerous communities suffering from inequality. This is also the case with deaf people. About 750,000 deaf people in the European Union and over 4 million deaf people in the United States face daily challenges in terms of communication and participation. This occurs not only in leisure activities but also, and more importantly, in emergency situations. To provide equal environments and allow people with hearing handicaps to communicate in their native language, this paper presents an AI-based sign language translator. We adopted a transformer neural network capable of analyzing over 500 data points from a person’s gestures and face to translate sign language into text. We have designed a machine learning pipeline that enables the translator to evolve, build new datasets, and train sign language recognition models. As proof of concept, we instantiated a sign language interpreter for an emergency call with over 200 phrases. The overall goal is to support people with hearing inabilities by enabling them to participate in economic, social, political, and cultural life.
Virtual Reality Telepresence: 360-Degree Video Streaming with Edge-Compute Assisted Static Foveated Compression

Real-time communication with immersive 360° video can enable users to be telepresent within a remotely streamed environment. Increasingly, users are shifting to mobile devices and connecting to the Internet via mobile-cellular networks. As the ideal media for 360° videos, some VR headsets now also come with cellular capacity, giving them potential for mobile applications. However, streaming high-quality 360° live video poses challenges for network bandwidth, particularly on cellular connections. To reduce bandwidth requirements, videos can be compressed using viewport-adaptive streaming or foveated rendering techniques. Such approaches require very low latency in order to be effective, which has previously limited their applications on traditional cellular networks. In this work, we demonstrate an end-to-end virtual reality telepresence system that streams ∼6K 360° video over 5G millimeter-wave (mmW) radio. Our use of 5G technologies, in conjunction with mobile edge compute nodes, substantially reduces latency when compared with existing 4G networks, enabling high-efficiency foveated compression over modern cellular networks on par with WiFi. We performed a technical evaluation of our system's visual quality post-compression with peak signal-to-noise ratio (PSNR) and FOVVideoVDP. We also conducted a user study to evaluate users' sensitivity to compressed video. Our findings demonstrate that our system achieves visually indistinguishable video streams while using up to 80% less data when compared with un-foveated video. We demonstrate our video compression system in the context of an immersive, telepresent video calling application.
Exemplar-Based Inpainting for 6DOF Virtual Reality Photos

Multi-layer images are currently the most prominent scene representation for viewing natural scenes under full-motion parallax in virtual reality. Layers ordered in diopter space contain color and transparency so that a complete image is formed when the layers are composited in a view-dependent manner. Once baked, the same limitations apply to multi-layer images as to conventional single-layer photography, making it challenging to remove obstructive objects or otherwise edit the content. Object removal before baking can benefit from filling disoccluded layers with pixels from background layers. However, if no such background pixels have been observed, an inpainting algorithm must fill the empty spots with fitting synthetic content. We present and study a multi-layer inpainting approach that addresses this problem in two stages: First, a volumetric area of interest specified by the user is classified with respect to whether the background pixels have been observed or not. Second, the unobserved pixels are filled with multi-layer inpainting. We report on experiments using multiple variants of multi-layer inpainting and compare our solution to conventional inpainting methods that consider each layer individually.
ZKBdf: A ZKBoo-Based Quantum-Secure Verifiable Delay Function with Prover-Secret

Since the formalization of Verifiable Delay Functions (VDF) by Boneh et al. in 2018, VDFs have been adopted for use in blockchain consensus protocols and random beacon implementations. However, the impending threat to VDF-based applications comes in the form of Shor's algorithm running on quantum computers in the future which can break the discrete logarithm and integer factorization problems that existing VDFs are based on. Clearly, there is a need for quantum-secure VDFs. In this paper, we propose ZKBdf, which makes use of ZKBoo, a zero-knowledge proof system for verifiable computation, as the basis for realizing a quantum-secure VDF. We describe the algorithm, provide the security proofs, implement the scheme and measure the execution and size requirements. In addition, as ZKBdf extends the standard VDF with an extra "Prover-secret" feature, new VDF use-cases are also explored.