Reviewerly/exHarmony huggingface dataset:
It’s (DOM) Clobbering Time: Attack Techniques, Prevalence, and Defenses

DOM Clobbering is a type of code-less injection attack where attackers insert a piece of non-script, seemingly benign HTML markup into a webpage and transform it to executable code by exploiting the unforeseen interactions between JavaScript code and the runtime environment. The attack techniques, browser behaviours, and vulnerable code patterns that enable DOM Clobbering has not been studied yet, and in this paper, we undertake one of the first evaluations of the state of DOM Clobbering on the Web platform. Starting with a comprehensive survey of existing literature and dynamic analysis of 19 different mobile and desktop browsers, we systematize DOM Clobbering attacks, uncovering 31.4K distinct markups that use five different techniques to unexpectedly overwrite JavaScript variables in at least one browser. Then, we use our systematization to identify and characterize program instructions that can be overwritten by DOM Clobbering, and use it to present TheThing, an automated system that detects clobberable data flows to security-sensitive instructions. We instantiate TheThing on the top of the Tranco top 5K sites, quantifying the prevalence and impact of DOM Clobbering in the wild. Our evaluation uncovers that DOM Clobbering vulnerabilities are ubiquitous, with a total of 9,467 vulnerable data flows across 491 affected sites, making it possible to mount arbitrary code execution, open redirections, or client-side request forgery attacks also against popular websites such as Fandom, Trello, Vimeo, TripAdvisor, WikiBooks and GitHub, that were not exploitable through the traditional attack vectors. Finally, in this paper, we also evaluate the robustness of the existing countermeasures, such as HTML sanitizers and Content Security Policy, against DOM Clobbering.
Effect of Bank Lending on Agricultural Output in Nigeria From 1981 – 2021 (Auto-regresive Distribution Approach)

Abstract Lending plays a vital role in expediting agricultural modernization and encouraging farmer involvement in the development process. By alleviating financial constraints, lending offers the motivation to embrace new technologies that might otherwise be adopted at a slower pace. This study investigates the effect of bank lending on agricultural activities in Nigeria from 1981 - 2021 using a secondary form of analysis. Different diagnostic tests were carried out, which include descriptive statistics, a pear wise correlation matrix, unit root test, co-integration test, and an auto regressive distributed model (ARDL). The result of the study shows that commercial bank lending to agriculture and interest rates have a positive and statistically significant effect on agricultural output in Nigeria. The study recommends that the government should strengthen financial institutions, which are the credit agencies, in order to ensure efficient disbursement of funds to agricultural and manufacturing sectors and ensure that funds are not misappropriated for other purposes, in order to boost economic growth.
Assessing Resolvability, Parsability, and Consistency of RDF Resources: A Use Case in Rare Diseases

Abstract Introduction: Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable' to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods: Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results: For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion: We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages

Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, intents and slots with translated unstructured utterance. To address this, we propose an Inter-bilingual Seq2seq Semantic parsing dataset IE-SemParse Suite for 11 distinct Indian languages. We highlight the proposed task's practicality, and evaluate existing multilingual seq2seq models across several train-test strategies. Our experiment reveals a high correlation across performance of original multilingual semantic parsing datasets (such as mTOP, multilingual TOP and multiATIS++) and our proposed IE-SemParse suite.
Applications of Neural Network-Based AI in Cryptography

Artificial intelligence (AI) is a modern technology that allows plenty of advantages in daily life, such as predicting weather, finding directions, classifying images and videos, even automatically generating code, text, and videos. Other essential technologies such as blockchain and cybersecurity also benefit from AI. As a core component used in blockchain and cybersecurity, cryptography can benefit from AI in order to enhance the confidentiality and integrity of cyberspace. In this paper, we review the algorithms underlying four prominent cryptographic cryptosystems, namely the Advanced Encryption Standard, the Rivest–Shamir–Adleman, Learning with Errors, and the Ascon family of cryptographic algorithms for authenticated encryption. Where possible, we pinpoint areas where AI can be used to help improve their security.
A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering

Visual Question Answering (VQA) is a well-known problem for which deep-learning is key. This poses a challenge for explaining answers to questions, the more if advanced notions like contrastive explanations (CEs) should be provided. The latter explain why an answer has been reached in contrast to a different one and are attractive as they focus on reasons necessary to flip a query answer. We present a CE framework for VQA that uses a neurosymbolic VQA architecture which disentangles perception from reasoning. Once the reasoning part is provided as logical theory, we use answer-set programming, in which CE generation can be framed as an abduction problem. We validate our approach on the CLEVR dataset, which we extend by more sophisticated questions to further demonstrate the robustness of the modular architecture. While we achieve top performance compared to related approaches, we can also produce CEs for explanation, model debugging, and validation tasks, showing the versatility of the declarative approach to reasoning.
Systematic literature review and future research directions for service robots in hospitality and tourism industries

Service robots create a touchless experience for travellers; therefore, this research aims to conduct a systematic literature review (SLR) of service robots in the tourism and hospitality sector. This study used the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) approach for reviewing the articles. One hundred eighteen articles are selected for the final review. A thematic analysis divided the articles into three themes and nine sub-themes. For establishing the future research directions Theory, Methodology, and Context (TMC) framework was used and forty-one future research questions are proposed. The study provides various research implications concerning theory and practical perspective. Different management theories are linked with future research questions. Few review papers discussed the service robot in the context of the tourism sector, but none of these studies used the SPAR-4-SLR and TMC framework. This study comprehensively reviews the articles and provides potential future research directions.
Strip Attention for Image Restoration

As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing long-range dependencies, Transformer based methods have achieved promising performance on multifarious image restoration tasks. However, the canonical self-attention leads to quadratic complexity with respect to input size, hindering its further applications in image restoration. In this paper, we propose a Strip Attention Network (SANet) for image restoration to integrate information in a more efficient and effective manner. Specifically, a strip attention unit is proposed to harvest the contextual information for each pixel from its adjacent pixels in the same row or column. By employing this operation in different directions, each location can perceive information from an expanded region. Furthermore, we apply various receptive fields in different feature groups to enhance representation learning. Incorporating these designs into a U-shaped backbone, our SANet performs favorably against state-of-the-art algorithms on several image restoration tasks. The code is available at https://github.com/c-yn/SANet.
Multi-Robot Coordination and Layout Design for Automated Warehousing

With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. We extend existing automatic scenario generation methods to optimize warehouse layouts. Results show that our optimized warehouse layouts (1) reduce traffic congestion and thus improve throughput, (2) improve the scalability of the automated warehouses by doubling the number of robots in some cases, and (3) are capable of generating layouts with user-specified diversity measures.
Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways (Extended Abstract)

Recommender systems typically suggest to users content similar to what they consumed in the past. A user, if happening to be exposed to strongly polarized content, might be steered towards more and more radicalized content by subsequent recommendations, eventually being trapped in what we call a "radicalization pathway". In this paper, we investigate how to mitigate radicalization pathways using a graph-based approach. We model the set of recommendations in a what-to-watch-next (W2W) recommender as a directed graph, where nodes correspond to content items, links to recommendations, and paths to possible user sessions. We measure the segregation score of a node representing radicalized content as the expected length of a random walk from that node to any node representing non-radicalized content. A high segregation score thus implies a larger chance of getting users trapped in radicalization pathways. We aim to reduce the prevalence of radicalization pathways by selecting a small number of edges to rewire, so as to minimize the maximum of segregation scores among all radicalized nodes while maintaining the relevance of recommendations. We propose an efficient yet effective greedy heuristic based on the absorbing random walk theory for the rewiring problem. Our experiments on real-world datasets confirm the effectiveness of our proposal.
Augmenting Automated Spectrum Based Fault Localization for Multiple Faults

Spectrum-based Fault Localization (SBFL) uses the coverage of test cases and their outcome (pass/fail) to predict the "suspiciousness'' of program components, e.g., lines of code. SBFL is, perhaps, the most successful fault localization technique due to its simplicity and scalability. However, SBFL heuristics do not perform well in scenarios where a program may have multiple faulty components. In this work, we propose a new algorithm that "augments'' previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher. We implement our ideas in a tool, ARTEMIS, that attempts to "bubble up'' faulty components which are ranked lower by base SBFL metrics. We compare our technique to the most popular SBFL metrics and demonstrate statistically significant improvement in the developer effort for fault localization with respect to the basic strategies.
ViT-CX: Causal Explanation of Vision Transformers

Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been designed specially for ViTs thus far. They mostly use attention weights of the [CLS] token on patch embeddings and often produce unsatisfactory saliency maps. This paper proposes a novel method for explaining ViTs called ViT-CX. It is based on patch embeddings, rather than attentions paid to them, and their causal impacts on the model output. Other characteristics of ViTs such as causal overdetermination are considered in the design of ViT-CX. The empirical results show that ViT-CX produces more meaningful saliency maps and does a better job revealing all important evidence for the predictions than previous methods. The explanation generated by ViT-CX also shows significantly better faithfulness to the model. The codes and appendix are available at https://github.com/vaynexie/CausalX-ViT.
Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.
Application of IoT technology based on neural networks in basketball training motion capture and injury prevention

Basketball players need to frequently engage in various physical movements during the game, which puts a certain burden on their bodies and can easily lead to various sports injuries. Therefore, it is crucial to prevent sports injuries in basketball teaching. This paper also studies basketball motion track capture. Basketball motion capture preserves the motion posture information of the target person in three-dimensional space. Because the motion capture system based on machine vision often encounters problems such as occlusion or self occlusion in the application scene, human motion capture is still a challenging problem in the current research field. This article designs a multi perspective human motion trajectory capture algorithm framework, which uses a two-dimensional human motion pose estimation algorithm based on deep learning to estimate the position distribution of human joint points on the two-dimensional image from each perspective. By combining the knowledge of camera poses from multiple perspectives, the three-dimensional spatial distribution of joint points is transformed, and the final evaluation result of the target human 3D pose is obtained. This article applies the research results of neural networks and IoT devices to basketball motion capture methods, further developing basketball motion capture systems.
AOGC: Anchor Free Oriented Object Detection Based on the Gaussian Centerness

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, which has attracted extensive attention in recent years with the development of deep learning. Currently, mainstream oriented object detectors are based on preset anchor boxes. This method increases the computational load of the network and cause a large amount of anchor box redundancy. To solve this problem, we propose anchor-free oriented object detection based on the Gaussian centerness(AOGC), a single-stage anchor-free detection method. Our method uses contextual attention FPN(CAFPN) to obtain the contextual information of the target. Then we design a label assignment method for oriented objects. Finally, we develop a Gaussian kernel-based centerness branch, which can effectively determine the significance of different anchors. AOGC achieves mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. AOGC exhibits superior performance to other methods in oriented anchor-free object detection methods.
Educational Technology Innovation: AI-Integrated Learning System Design in AILS-Based Education

Information and Communication Technology (ICT) has seamlessly integrated itself into modern society, revolutionizing education through its innovations. One prominent innovation is the Artificial Intelligence (AI)-based learning system, promising an elevated quality of education. The AI-based learning system harnesses machine learning to tailor approaches for each student, monitoring their progress and providing pertinent feedback. A noteworthy example is the Intelligent Adaptive Learning System (AILS), which amalgamates Natural Language Processing (NLP) technology and 3D animation with the Google Search Engine. AILS aids comprehension via tablet devices, with teachers concurrently assuming a supportive role. AILS empowers students to autonomously acquire knowledge, accessing information through NLP and the Google Search Engine. The visualization of teachers through 3D animations enhances interactivity. Through AILS, students can learn from home or in-class, guided and mentored by teachers. AILS efficiently processes data, monitors progress, offers feedback, and identifies student needs. Teachers continue to facilitate profound learning and critical thinking skills. AILS fosters self-directed learning and lifelong skills. Furthermore, AILS augments the quality of the educational framework, integrating AI technology to realize student self-directedness in the Society 5.0 era.&#x0D;
Learning Specialized Activation Functions for Physics-informed Neural Networks

Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and activation functions. Specifically, we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties. Existing works usually choose activation functions by inefficient trial-and-error. To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs, we introduce adaptive activation functions to search for the optimal function when solving different problems. We compare different adaptive activation functions and discuss their limitations in the context of PINNs. Furthermore, we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization, which has a higher requirement for the smoothness and diversity on learned functions. This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand. We further enhance the search space with adaptive slopes. The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way. Its effectiveness is demonstrated on a series of benchmarks. Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.
Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours without Edges (ACWE)

This paper presents an intramethod ensemble for coronal hole (CH) detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all on disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region's proximity to, and homogeneity with, the core of identified CH regions. CHs are regions of open magnetic field lines, resulting in high speed solar wind. Accurate detection of CHs is vital for space weather prediction. By relying on region homogeneity, and not intensity (which can vary due to various factors including line of sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions and different intensity resolutions. Furthermore, using the same metrics, the method is shown to be robust across short timescales, indicating self-consistent segmentations. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field.