Reviewerly/exHarmony huggingface dataset:
Evolution of Cybersecurity Concerns: A Systematic Literature Review

This paper provides a systematic literature review of cybersecurity concerns in public administration scholarship. The main intent is to outline the major shifts in the cybersecurity issues faced by public and nonprofit sectors. We undertake this exercise also to identify the future research agenda of emerging cybersecurity research gaps. Our principal finding from the literature is that public administration scholars and practitioners have not given attention to cyber-security until recently. Cyber-security did not emerge as a significant theme until the mid-2000s. There is a dichotomy in treating cyber-security as a technical problem or a management problem. Public administration scholars deemed cyber-security presumably as a technical problem, even though security policies have been in effect since the mid-1980s. The technical concerns have evolved from that of individual computer problems (e.g., viruses) to that of systemic network problems (e.g., ransomware attacks). Recent publications are showing the consideration of cyber-security as a management problem. Even so, the literature shows that cyber-security is not often on the radar screen of public administrators. We argue that our future research should focus on the administrative dimensions of cybersecurity. In this context, emerging research needs to focus on leadership awareness, budgetary allocation, and cyber insurance policies.
Healthcare Information Exchange Using Blockchain Technology

Today, both medical facilities and individuals produce a significant amount of healthcare data every day. The medical industry has significantly benefited from healthcare information exchange (HIE), as has been demonstrated. It is crucial but difficult to store and share a vast amount of healthcare data. Healthcare Information Exchange (HIE) offers outstanding benefits for patient care, such as improving the standard of health care and aiding in the organization of healthcare data. With the aid of blockchain technology, users can confirm, safeguard, and synchronize the information contained in a data sheet (also known as a transaction ledger) that is cloned by numerous users. For enabling better services, blockchain technology has offered significant benefits and incentives to industries. This analysis aims to examine the advantages, difficulties, and utilities that influence the applications of blockchain in the medical sector.
THE TEACHING AND RESEARCH LABORATORY - AN OPPORTUNITY FOR INTELLECTUAL AND CREATIVE EXPRESSION IN THE EDUCATIONAL PROCESS

THE TEACHING AND RESEARCH LABORATORY - AN OPPORTUNITY FOR INTELLECTUAL AND CREATIVE EXPRESSION IN THE EDUCATIONAL PROCESS
Intellectual Property Implications of Artificial Intelligence and Ownership of AI-Generated Works

This dissertation explores the complex and evolving relationship between artificial intelligence (AI) and intellectual property (IP) law, specifically focusing on the ownership of the products created through AI. With the rapid advancements in AI technology, machines are increasingly capable of autonomously generating creative works, raising novel legal challenges. This study examines the existing legal frameworks, evaluates the adequacy of current IP laws, and proposes potential solutions to address the intellectual property implications of AI-generated works.
Extract-Transform-Load for Video Streams

Social media, self-driving cars, and traffic cameras produce video streams at large scales and cheap cost. However, storing and querying video at such scales is prohibitively expensive. We propose to treat large-scale video analytics as a data warehousing problem: Video is a format that is easy to produce but needs to be transformed into an application-specific format that is easy to query. Analogously, we define the problem of Video Extract-Transform-Load ( V-ETL ). V-ETL systems need to reduce the cost of running a user-defined V-ETL job while also giving throughput guarantees to keep up with the rate at which data is produced. We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper , a system tailored to V-ETL. Skyscraper can execute arbitrary video ingestion pipelines and adaptively tunes them to reduce cost at minimal or no quality degradation, e.g., by adjusting sampling rates and resolutions to the ingested content. Skyscraper can hereby be provisioned with cheap on-premises compute and uses a combination of buffering and cloud bursting to deal with peaks in workload caused by expensive processing configurations. In our experiments, we find that Skyscraper significantly reduces the cost of V-ETL ingestion compared to adaptions of current SOTA systems, while at the same time giving robustness guarantees that these systems are lacking.
Advanced low blaze angle x-ray gratings via nanoimprint replication and plasma etch

We developed a new method of making ultra-low blaze angle diffraction gratings for x-ray applications. The method is based on reduction of the blaze angle of a master grating by nanoimprint replication followed by a plasma etch. A master blazed grating with a relatively large blaze angle is fabricated by anisotropic wet etching of a Si single crystal substrate. The surface of the master grating is replicated by a polymer material on top of a quartz substrate by nanoimprinting. Then a second nanoimprinting is performed using the 1 st replica as a mold to replicate the saw-tooth surface into a resist layer on top of a Si grating substrate. A reactive ion etch is used to transfer the grating grooves into the Si substrate. The plasma etch provides reduction of the groove depth by a factor defined by the ratio of the etch rates for the resist and Si. We demonstrate reduction of the blaze angle of a master grating by a factor of 5 during fabrication of a 200 lines/mm blazed grating with a blaze angle of 0.2°. We investigated the quality and performance of the fabricated low blaze angle gratings and evaluate process accuracy and reproducibility. The new blaze angle reduction method preserves the planarity of the optical surface of the grating substrate and at the same time provides improvement in the grating groove quality during the reduction process.
Dynamical-corrected nonadiabatic geometric quantum computation

Recently, nonadiabatic geometric quantum computation has been received great attentions, due to its fast operation and intrinsic error resilience. However, compared with the corresponding dynamical gates, the robustness of implemented nonadiabatic geometric gates based on the conventional single-loop geometric scheme still has the same order of magnitude due to the requirement of strict multi-segment geometric controls, and the inherent geometric fault-tolerance characteristic is not fully explored. Here, we present an effective geometric scheme combined with a general dynamical-corrected technique, with which the super-robust nonadiabatic geometric quantum gates can be constructed over the conventional single-loop geometric and two-loop composite-pulse geometric strategies, in terms of resisting the systematic error, i.e., σx error. In addition, combined with the decoherence-free subspace (DFS) coding, the resulting geometric gates can also effectively suppress the σz error caused by the collective dephasing. Notably, our protocol is a general one with simple experimental setups, which can be potentially implemented in different quantum systems, such as Rydberg atoms, trapped ions and superconducting qubits. These results indicate that our scheme represents a promising way to explore large-scale fault-tolerant quantum computation.
Inference of Gene Regulatory Networks for Prostate Cancer using Bayesian Networks with Feedback and Feed Forward Loops

The solution to any problem depends on the depth of our understanding of it. Cancer is a disease that is being investigated at multiple levels and from multiple perspectives to understand the details of its origins and expansions in order to be able to figure a cure for it. We can now computationally analyze the biological data produced by genome analysis techniques like genomics, proteomics, and transcriptomics. DNA micro array technology has made available large gene expression datasets for entire genomes. It has been clinically observed that inside a human cell, activity of a gene often turns on or turns off one or more other genes. Such relationships in the co-regulation of genes is captured by gene regulatory network models which are computationally constructed from gene expression datasets. It has been observed that healthy and diseased states of a human cell show different regulatory interrelations between genes. In this paper, we have proposed to use a stochastic approach called Bayesian Networks with Feedback and Feedforward loops for inference of inter dependence in the regulation of genes in case of Prostate Cancer. It was observed that 4 of the networks revealed by the proposed approach matched the ones observed in clinical studies.
Surviving the storm: The key to cyber resilience and incident response in healthcare

This article underscores the significance of cyberdefences and response processes in healthcare, highlighting their contribution to cyber resilience through adherence to industry best practices. It emphasizes the value of hypothetical scenarios as a common practice in the field to validate the effectiveness of cyber resilient actions, systems, processes, and decision-making in the face of various cyberthreats. Focusing on the ransomware threat, the provided scenario examines its impact on healthcare systems and frontline support staff, while highlighting the time-sensitive challenges faced by response teams striving to restore essential services. Furthermore, it suggests replicating such analyses with key hospital personnel to precisely assess the impact of other types of cyberthreats, such as those originating from malicious insiders or technical data breaches facilitated through social engineering attacks. By doing so, healthcare organizations can develop comprehensive and cyber resilient responses to safeguard their operations.
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors

Maximum Inner Product Search or top- k retrieval on sparse vectors is well understood in information retrieval, with a number of mature algorithms that solve it exactly. However, all existing algorithms are tailored to text and frequency-based similarity measures. To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages. We consider, instead, a setup in which collections are streaming—necessitating dynamic indexing—and where indexing and retrieval must work with arbitrarily distributed real-valued vectors. As we show, existing algorithms are no longer competitive in this setup, even against naïve solutions. We investigate this gap and present a novel approximate solution, called Sinnamon , that can efficiently retrieve the top- k results for sparse real valued vectors drawn from arbitrary distributions. Notably, Sinnamon offers levers to trade off memory consumption, latency, and accuracy, making the algorithm suitable for constrained applications and systems. We give theoretical results on the error introduced by the approximate nature of the algorithm and present an empirical evaluation of its performance on two hardware platforms and synthetic and real-valued datasets. We conclude by laying out concrete directions for future research on this general top- k retrieval problem over sparse vectors.
A Novel Approach for Predicting Heavy Metal Contamination Based on Adaptive Neuro-Fuzzy Inference System and GIS in an Arid Ecosystem

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination.
Evaluation of Image Quality Assessment Metrics for Semantic Segmentation in a Machine-to-Machine Communication Scenario

Image and video compression aims at finding an optimal trade-off between rate and distortion. This is done through Rate-Distortion Optimization (RDO) in traditional en-coders with the use of Image Quality Assessment (IQA) metrics. While it is known that most IQA metrics are designed to be correlated with human perception, there is no evidence that this observation can be generalized in a Video Coding for Machines (VCM) context, where the receiver is not a human anymore but a machine. In this paper, we propose an evaluation protocol to measure the correlation level between conventional Full-Reference (FR) IQA metrics and machine perception through the semantic segmentation vision task. Experiments showed a relatively low correlation between them when measured on the block-level. This observation implies the need of RDO algorithms that are better suited for Machine-to-Machine (M2M) communications. In order to facilitate the emergence of IQA metrics that better reflect machine perception, the code and dataset used to perform this study is made freely available at https://github.com/albmarie/iqa_m2m_segmentation.
Anatomical structures, cell types, and biomarkers of the healthy human blood vasculature

More than 150 scientists from 17 consortia are collaborating on an international project to build a Human Reference Atlas, which maps all 37 trillion cells in the healthy adult human body. The initial release of this atlas provided hierarchical lists of the anatomical structures, cell types, and biomarkers in 11 organs. Here, we describe the methods we used as part of this initiative to build the first open, computer-readable, and comprehensive database of the adult human blood vasculature, called the Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF). It includes 993 vessels and their branching connections, 10 cell types, and 10 biomarkers. With this paper we are releasing additional details on vessel types and subtypes, branching sequence, anastomoses, portal systems, microvasculature, functional tissue units, mappings to regions vessels supply or drain, geometric properties of vessels, and links to 3D reference objects. Future versions will add variants and connections to the lymph vasculature; and, it will iteratively expand and improve the database as additional experimental data become available through the participating consortia.
Class-Specific Distribution Alignment for semi-supervised medical image classification

Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes. Furthermore, we propose a Variable Condition Queue (VCQ) module to maintain a proportionately balanced number of unlabeled samples for each class. Experiments on three public datasets HAM10000, CheXpert and Kvasir show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks.
A Systematic Investigation of DeFi Compositions in Ethereum

The rapid growth of the Ethereum ecosystem since 2020 has been driven by the proliferation of several DeFi protocols [10], which are application-layer programs that provide Decentralized Finance (DeFi) services [14, 16] such as the exchange of cryptoassets on decentralized exchanges (DEXs) [2, 7, 15], their lending and borrowing [1, 4, 8], or the creation and trade of related derivative contracts [11].
Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics

Abstract Nanophotonics allows to employ light-matter interaction to induce nonlinear optical effects and realize non-conventional memory and computation capabilities, however to date, light-liquid interaction was not considered as a potential mechanism to achieve computation on a nanoscale. Here, we experimentally demonstrate self-induced phase change effect which relies on the coupling between geometric changes of thin liquid film to optical properties of photonic waveguide modes, and then employ it for neuromorphic computing. In our optofluidic silicon photonics system we utilize thermocapillary-based deformation of thin liquid film capable to induce nonlinear effect which is more than one order of magnitude higher compared to the more traditional heat-based thermo-optical effect, and allowing operation as a nonlinear actuator and memory element, both residing at the same compact spatial region. The resulting dynamics allows to implement Reservoir Computing at spatial region which is approximately five orders of magnitude smaller compared to state-of-the-art experimental liquid-based systems.
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill reliable semantic representatives for deep hashing to guide adversarial learning, and thereby it hinders the enhancement of adversarial robustness of deep hashing-based retrieval models. Moreover, current researches on adversarial training for deep hashing are hard to be formalized into a unified minimax structure. In this paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the adversarial robustness of deep hashing models. Specifically, we conceive a discriminative mainstay features learning (DMFL) scheme to construct semantic representatives for guiding adversarial learning in deep hashing. Particularly, our DMFL with the strict theoretical guarantee is adaptively optimized in a discriminative learning manner, where both discriminative and semantic properties are jointly considered. Moreover, adversarial examples are fabricated by maximizing the Hamming distance between the hash codes of adversarial samples and mainstay features, the efficacy of which is validated in the adversarial attack trials. Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes. Extensive experiments on benchmark datasets show superb attack performance against the state-of-the-art algorithms, meanwhile, the proposed adversarial training can effectively eliminate adversarial perturbations for trustworthy deep hashing-based retrieval. Our code is available at https://github.com/xandery-geek/SAAT.
Intelligent Video Surveillance Mechanisms for Abnormal Activity Recognition in Real-Time: A Systematic Literature Review

Video surveillance plays a crucial role in securing indoor and outdoor locations in today's unreliable world, particularly in real-time applications for behaviour detection, comprehension, and labelling activities as normal or suspicious. For example, in the development of automated video surveillance systems, smart video reconnaissance systems based on picture recognition and activity recognition that detect violent behaviours is basic to forestalling wrongdoings and giving public security. According to the literature, Artificial Intelligent, Machine Learning, and deep portrayal-based approaches have been effectively utilized in image recognition and human activity observation tasks. In this literature review, a 3D convolution neural network based on deep learning is used as the proposed methodology. Thus, this article completed a Systematic Literature Review (SLR) in light of intelligent video surveillance to real-time identify abnormal activities from 2016 to 2021. In this current study, 50 research papers were considered and based on the screen filtering, the most suitable 16 papers were filtered based on intelligent video surveillance and real-time abnormal activities. Furthermore, this study identifies potential areas for improvement in intelligent video surveillance systems that can enhance public safety and security, underscoring the importance of ongoing research in this field.
Analysis of Top Vulnerabilities in Security of Web-Based Applications

During the last few years, web applications have emerged as the most famous manner for providing services on the internet. The world today runs on the internet. Ranging from online banking to ecommerce, everything is made available at your disposal. As their popularity is increasing, so is the complexity in their layout and implementation. This increasing complexity makes them number one goal for hackers on the web. New vulnerabilities are being discovered every day in the applications, network, and software. The goal of this paper is to summarize some of these well-known vulnerabilities, how various tools and techniques can be used to mitigate them, and what impact it has on the world.