Reviewerly/exHarmony huggingface dataset:
Improved YOLOv3-tiny for Silhouette Detection Using Regularisation Techniques

Although recent advances in Deep Learning (DL) algorithms have been developed in many Computer Vision (CV) tasks with a high accuracy level, detecting humans in video streams is still a challenging problem. Several studies have, therefore, focused on the regularisation techniques to prevent the overfitting problem which is one of the most fundamental issues in the Machine Learning (ML) area. Likewise, this paper thoroughly examines these techniques, suggesting an improved You Only Look Once (YOLO)v3-tiny based on a modified neural network and an adjusted hyperparameters file configuration. The obtained experimental results, which are validated on two experimental tests, show that the proposed method is more effective than the YOLOv3-tiny predecessor model . The first test which includes only the data augmentation techniques indicates that the proposed approach reaches higher accuracy rates than the original YOLOv3-tiny model. Indeed, Visual Object Classes (VOC) test dataset accuracy rate increases by 32.54 % compared to the initial model. The second test which combines the three tasks reveals that the adopted combined method wins a gain over the existing model. For instance, the labelled crowd_human test dataset accuracy percentage rises by 22.7 % compared to the data augmentation model.
ESTABLISHMENT AND DEVELOPMENT OF COLLECTIVE MANAGEMENT ORGANIZATIONS OF COPYRIGHT PROPERTY RIGHTS IN UZBEKISTAN

The issues related to the history of the establishment of the institution of copyright protection, with the division of powers of collective management organizations, with the functions of organizations and their types, with the difference between collective management of copyright and such a concept as representation are touched upon. In addition, the essence of collective management is that participating organizations enter into agreements with authors and other rightholders and take responsibility for the management of these rights; conclude contracts with copyright holders, persons using works (objects of copyright and related rights) on their own behalf on the basis of the powers granted by collective management organizations; collect, distribute and pay royalties to copyright holders; The article discusses the role of copyright holders and other copyright holders in court, as well as the role of some organizations in providing social support to their members. Attention is paid to some urgent problems related to the improvement of this institution, international standards for copyright protection and comparative legal analysis of national legislation, as well as the opinions of scientists from Uzbekistan and other different countries. Suggestions and recommendations were put forward of scientific and practical importance for national copyright law.
Few-Shot Learning for Image Denoising

Deep Neural Networks (DNNs) have achieved impressive results on the task of image denoising, but there are two serious problems. First, the denoising ability of DNNs-based image denoising models using traditional training strategies heavily relies on extensive training on clean-noise image pairs. Second, image denoising models based on DNNs usually have large parameters and high computational complexity. To address these issues, this paper proposes a two-stage Few-Shot Learning for Image Denoising (FSLID). Our FSLID is a two-stage denoising strategy integrating Basic Feature Learner (BFL), Denoising Feature Inducer (DFI), and Shared Image Reconstructor (SIR). BFL and SIR are first jointly unsupervised to train on the base image dataset <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {D}_{base}$ </tex-math></inline-formula> consisting of easily collected high-quality clean images. Following this, the trained BFL extracts the guided features and constraint features for the noisy and corresponding clean images in the novel image dataset <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {D}_{novel}$ </tex-math></inline-formula> , respectively. Furthermore, DFI encodes the noisy features of the noisy images in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {D}_{novel}$ </tex-math></inline-formula> . Then, inducing both the guided features and noisy features, DFI can generate the denoising prior features for the SIR with frozen weights to adaptively denoise the noisy images. Furthermore, we propose refined, low-channel-count, recursive multi-branch Multi-Scale Feature Recursive (MSFR) to modularly formulate an efficient DFI to capture more diverse contextual features information under a limited number of feature channels. Thus, compared with the baseline models, the FSLID composed of the proposed MSFR can significantly reduce the number of model parameters and computational complexity. Extensive experimental results demonstrate our FSLID significantly outperforms well-established baselines on multiple datasets and settings. We hope that our work will encourage further research to explore the field of few-shot image denoising.
Precision information extraction for rare disease epidemiology at scale

Abstract Background The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations. Methods In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies. Results We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet’s collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm. Conclusions EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.
DSDNet: Toward single image deraining with self-paced curricular dual stimulations

A crucial challenge regarding the single image deraining task is to completely remove rain streaks while still preserving explicit image details. Due to the inherent overlapping between rain streaks and background scenes, the texture details could be inevitably lost when clearing rain away from the degraded image, making the two purposes contradictory. Existing deep learning based approaches endeavor to resolve the two issues successively in a cascaded framework or to treat them as independent tasks in a parallel structure. However, none of the models explores a proper interaction between rain distributions and hidden feature responses, which intuitively would provide more clues to facilitate the procedures of rain streak removal as well as detail restoration. In this paper, we investigate the impact of rain streak detection for single image deraining and propose a novel deep network with dual stimulations, namely, DSDNet. The proposed DSDNet utilizes a dual-stream pipeline to separately estimate rain streaks and a loss of details, and more importantly, an additional mask that indicates both location and intensity of rains is jointly predicted. In particular, the rain mask is involved in a tailored stimulation strategy that is deployed into each stream of the proposed model, serving as guidance for allowing the network to focus on rain removal and detail recovery in rain regions rather than non-rain areas. Moreover, we incorporate a self-paced semi-curriculum learning design to alleviate the learning ambiguity brought by the prediction of the rain mask and thus accelerate the training process. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art methods on several benchmarks, including in both synthetic and real-world scenarios. The effectiveness of the proposed method is also validated via joint single image deraining, detection, and segmentation tasks.
Pushing the Limits of Generic Side-Channel Attacks on LWE-based KEMs - Parallel PC Oracle Attacks on Kyber KEM and Beyond

In this work, we propose generic and novel adaptations to the binary Plaintext-Checking (PC) oracle based side-channel attacks for Kyber KEM. These attacks operate in a chosen-ciphertext setting, and are fairly generic and easy to mount on a given target, as the attacker requires very minimal information about the target device. However, these attacks have an inherent disadvantage of requiring a few thousand traces to perform full key recovery. This is due to the fact that these attacks typically work by recovering a single bit of information about the secret key per query/trace. In this respect, we propose novel parallel PC oracle based side-channel attacks, which are capable of recovering a generic P number of bits of information about the secret key in a single query/trace. We propose novel techniques to build chosen-ciphertexts so as to efficiently realize a parallel PC oracle for Kyber KEM. We also build a multi-class classifier, which is capable of realizing a practical side-channel based parallel PC oracle with very high success rate. We experimentally validated the proposed attacks (upto P = 10) on the fastest implementation of unprotected Kyber KEM in the pqm4 library. Our experiments yielded improvements in the range of 2.89× and 7.65× in the number of queries, compared to state-of-the-art binary PC oracle attacks, while arbitrarily higher improvements are possible for a motivated attacker, given the generic nature of the proposed attacks. We further conduct a thorough study on applicability to different scenarios, based on the presence/absence of a clone device, and also partial key recovery. Finally, we also show that the proposed attacks are able to achieve the lowest number of queries for key recovery, even for implementations protected with low-cost countermeasures such as shuffling. Our work therefore, concretely demonstrates the power of PC oracle attacks on Kyber KEM, thereby stressing the need for concrete countermeasures such as masking for Kyber and other lattice-based KEMs.
Heads in the Clouds? Measuring Universities’ Migration to Public Clouds: Implications for Privacy &amp; Academic Freedom

With the emergence of remote education and work in universities due to COVID-19, the 'zoomification' of higher education, i.e., the migration of universities to the clouds, reached the public discourse. Ongoing discussions reason about how this shift will take control over students' data away from universities, and may ultimately harm the privacy of researchers and students alike. However, there has been no comprehensive measurement of universities' use of public clouds and reliance on Software-as-a-Service offerings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities' core functions and services---starting long before the COVID-19 pandemic. We attribute this clustering to several socio-economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals' privacy towards questions of academic independence and integrity.
Privacy Property Graph: Towards Automated Privacy Threat Modeling via Static Graph-based Analysis

Privacy threat modeling should be done frequently throughout development and production to be able to quickly mitigate threats. Yet, it can also be a very time-consuming activity. In this paper, we use an enhanced code property graph to partly automate the privacy threat modeling process: It automatically generates a data flow diagram from source code which exhibits privacy properties of data flows, and which can be analyzed semi-automatically via queries. We provide a list of such reusable queries that can be used to detect various privacy threats. To enable this analysis, we integrate a taint-tracking mechanism into the graph using privacy-specific labels. Since no benchmark for such an approach exists, we also present a test suite for privacy threat implementations which comprises implementations for 22 privacy threats in multiple programming languages. We expect that our approach significantly reduces time consumption of threat modeling and show that it also has potential beyond the threat categories defined by LINDDUN, e.g. to detect privacy anti-patterns and verify compliance to privacy policies.
SoK: Content Moderation for End-to-End Encryption

Popular messaging applications now enable end-to-end-encryption (E2EE) by default, and E2EE data storage is becoming common. These important advances for security and privacy create new content moderation challenges for online services, because services can no longer directly access plaintext content. While ongoing public policy debates about E2EE and content moderation in the United States and European Union emphasize child sexual abuse material and misinformation in messaging and storage, we identify and synthesize a wealth of scholarship that goes far beyond those topics. We bridge literature that is diverse in both content moderation subject matter, such as malware, spam, hate speech, terrorist content, and enterprise policy compliance, as well as intended deployments, including not only privacy-preserving content moderation for messaging, email, and cloud storage, but also private introspection of encrypted web traffic by middleboxes. In this work, we systematize the study of content moderation in E2EE settings. We set out a process pipeline for content moderation, drawing on a broad interdisciplinary literature that is not specific to E2EE. We examine cryptography and policy design choices at all stages of this pipeline, and we suggest areas of future research to fill gaps in literature and better understand possible paths forward.
The Security Issues and challenges in Cloud Computing

A cloud computing model allows customers to use a pool of shared computer resources on-demand or pay-per-use basis. In terms of capital investment and operational cost reductions, cloud-based computing offers users and organizations many benefits. Despite these advantages, several challenges still limit the adoption of cloud computing. A crucial concern that is usually taken into account is security. Without this vital component, the computing model has a negative influence, which causes suffering on the human, ethical, and economic levels. This essay will look at the security issues that cloud entities must deal with. This group includes Cloud Service Provider, the Data Owner, and the Cloud User concentrating on the communication, computation, and service level agreements that make up the crypto-cloud. It will offer the required updates by evaluating the origins and consequences of different cyberattacks.
UAV Waypoint Strategy for COVID-19 Medicine Delivery based on Cheapest Link and Hamilton Circuit Algorithm

&lt;p&gt;The COVID-19 pandemic is an unavoidable disaster. Many COVID-19 sufferers must self-isolate at their respective residences. However, difficult access has hampered the distribution of medicines from nearby hospitals. This paper aims to find the shortest route for UAVs so that they can deliver drugs in the shortest possible time and the minimum distance. The method used is the application of the Hamilton Circuit with the Cheapest Link Algorithm approach so that the distance between houses can be reached without being neglected at all. Flying construction was carried out using Software in The Loop (SITL) and ArduPilot Mission Planner. The results obtained are that routes created using the Cheapest Link Algorithm have an average efficiency of 66.86% better than other Hamilton circuits formed on the same graph. &lt;/p&gt;
Cloud-hosted concept-hierarchy flex-based infringement checking system

Unlike competing online Infringement solutions, Infringement is copy-paste elimination software.Infringement is the illegal use of another person's words or ideas without permission.It's a dishonest thing to do.Copyscape is an anti-piracy programme that ensures all of our customers' essays and term papers are 100% unique.Users and organisations alike can now use cloud computing to access their data and run their programmes from any device with an internet connection, eliminating the need for local installations of software.Cloud computing is a method of storing and accessing information and software on remote servers and networks.Professors should be concerned about infringement because it happens frequently.Students in many engineering and computer science degree programmes take online courses where they are graded on the code they create on their own or in small groups.Multiple indicators make it hard to spot infringing code.Therefore, it is necessary to create an Infringement checking system that will accept code submissions from teachers and quickly identify instances of plagiarism.In other words, it analyses the structure of each uploaded assignment and finds any similarities.A straightforward style is used to summarise the similarity analysis results, which show which tasks are most similar to one another.This method has the advantage of relieving teachers of the tedious task of checking for Infringements.
Parallel Hybrid Networks: an interplay between quantum and classical neural networks

Quantum neural networks represent a new machine learning paradigm that has recently attracted much attention due to its potential promise. Under certain conditions, these models approximate the distribution of their dataset with a truncated Fourier series. The trigonometric nature of this fit could result in angle-embedded quantum neural networks struggling to fit the non-harmonic features in a given dataset. Moreover, the interpretability of neural networks remains a challenge. In this work, we introduce a new, interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to 1) a classical multi-layered perceptron and 2) a variational quantum circuit, and then the outputs of the two are linearly combined. We observe that the quantum neural network creates a smooth sinusoidal foundation base on the training set, and then the classical perceptrons fill the non-harmonic gaps in the landscape. We demonstrate this claim on two synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that the parallel hybrid network architecture could improve the solution optimality on periodic datasets with additional noise.
Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)

AI is promising in assisting UX evaluators with analyzing usability tests, but its judgments are typically presented as non-interactive visualizations. Evaluators may have questions about test recordings, but have no way of asking them. Interactive conversational assistants provide a Q&A dynamic that may improve analysis efficiency and evaluator autonomy. To understand the full range of analysis-related questions, we conducted a Wizard-of-Oz design probe study with 20 participants who interacted with simulated AI assistants via text or voice. We found that participants asked for five categories of information: user actions, user mental model, help from the AI assistant, product and task information, and user demographics. Those who used the text assistant asked more questions, but the question lengths were similar. The text assistant was perceived as significantly more efficient, but both were rated equally in satisfaction and trust. We also provide design considerations for future conversational AI assistants for UX evaluation.
A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements is difficult because many, technically diverse tools are available, but recent performance benchmarks are rare. Moreover, such benchmarks typically cover only a few content elements like header metadata or bibliographic references and use smaller datasets from specific academic disciplines. We provide a large and diverse evaluation framework that supports more extraction tasks than most related datasets. Our framework builds upon DocBank, a multi-domain dataset of 1.5 M annotated content elements extracted from 500 K pages of research papers on arXiv. Using the new framework, we benchmark ten freely available tools in extracting document metadata, bibliographic references, tables, and other content elements from academic PDF documents. GROBID achieves the best metadata and reference extraction results, followed by CERMINE and Science Parse. For table extraction, Adobe Extract outperforms other tools, even though the performance is much lower than for other content elements. All tools struggle to extract lists, footers, and equations. We conclude that more research on improving and combining tools is necessary to achieve satisfactory extraction quality for most content elements. Evaluation datasets and frameworks like the one we present support this line of research. We make our data and code publicly available to contribute toward this goal.