Reviewerly/exHarmony huggingface dataset:
Studying WebAssembly and comparison of its performance with JavaScript

Over the long history of the Internet, JavaScript has been the primary programming language in web development. Because of its simple syntax and support in all popular browsers, the language has gained popularity among the developers. However, as computer power and user demands evolved, simple sites turned into the web applications that are full-fledged analogues of the desktop applications. As capabilities grew, so did the performance requirements of such programs. Browser engines have developed ways to optimize the code they run, and intense competition between browsers has contributed to a qualitative increase in performance. Despite all the ways to increase the speed of execution of JavaScript code, the main stopping factor was the dynamic typing of the language. Because of dynamic typing, the browser engine needs to check each time the program is executed whether the variable is an integer, a float, or any other valid type. Thus, each JavaScript instruction has to go through several type checks and conversions, which slows down the execution. This led to the idea of using languages with strict typing, which could compensate for this drawback. But the browser engines cannot execute the code of other languages, that is why there appeared the technology WebAssembly. It allows writing code in languages with static typing, and then parses it into &#x0D; a more native and machine-readable format, which speeds up execution of programs compared to JavaScript. The technology is cross-platform and supports the main programming languages: C++, C, Java, C#. WebAssembly is also implemented using JavaScript, which allows to use the concise JavaScript syntax and computing power of WebAssembly together.
Intent-Aware Propensity Estimation via Click Pattern Stratification

Counterfactual learning to rank via inverse propensity weighting is the most popular approach to train ranking models using biased implicit user feedback from logged search data. Standard click propensity estimation techniques rely on simple models of user browsing behavior that primarily account for the attributes of the presentation context that affect whether the relevance of an item to the search context is observed. Most notably, the inherent effect of the listwise presentation of the items on users’ propensity for engagement is captured in the position of the presented items on the search result page. In this work, we enrich this position bias based click propensity model by proposing an observation model that further incorporates the underlying search intent, as reflected in the user’s click pattern in the search context. Our approach does not require an intent prediction model based on the content of the search context. Instead, we rely on a simple, yet effective, non-causal estimate of the user’s browsing intent from the number of click events in the search context. We empirically characterize the distinct rank decay patterns of the estimated click propensities in the characterized intent classes. In particular, we demonstrate a sharper decay of click propensities in top ranks for the intent class identified by sparse user clicks and the higher likelihood of observing clicks in lower ranks for the intent class identified by higher number of user clicks. We show that the proposed intent-aware propensity estimation technique helps with training ranking models with more effective personalization and generalization power through empirical results for a ranking task in a major e-commerce platform.
Aspect-based Summarization of Legal Case Files using Sentence Classification

Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.
HAPENS: Hardness-Personalized Negative Sampling for Implicit Collaborative Filtering

For training implicit collaborative filtering (ICF) models, hard negative sampling (HNS) has become a state-of-the-art solution for obtaining negative signals from massive uninteracted items. However, selecting appropriate hardness levels for personalized recommendations remains a fundamental, yet underexplored, problem. Previous HNS works have primarily adjusted the hardness level by tuning a single hyperparameter. However, applying the same hardness level to each user is unsuitable due to varying user behavioral characteristics, the quantity and quality of user records, and different consistencies of models’ inductive biases. Moreover, increasing the number of hyperparameters is not practical due to the massive number of users. To address this important and challenging problem, we propose a model-agnostic and practical approach called hardness-personalized negative sampling (HAPENS). HAPENS uses a two-stage approach: in stage one, it trains the ICF model with a customized objective function that optimizes its worst performance on each user’s interacted item set. In stage two, it utilizes these worst performances as personalized hardness levels with a well-designed sampling distribution, and trains the final model with the same architecture. We evaluated HAPENS on the collected Bing advertising dataset and one public dataset, and the comprehensive experimental results demonstrate its robustness and superiority. Moreover, HAPENS has delivered significant benefits to the Bing advertising system. To the best of our knowledge, we are the first to study this important and challenging problem.
The PLASMA Framework: Laying the Path to Domain-Specific Semantics in Dataspaces

Modern data management is evolving from centralized integration-based solutions to a non-integration-based process of finding, accessing and processing data, as observed within dataspaces. Common reference dataspace architectures assume that sources publish their own domain-specific schema. These schemas, also known as semantic models, can only be partially created automatically and require oversight and refinement by human modellers. Non-expert users, such as mechanical engineers or municipal workers, often have difficulty building models because they are faced with multiple ontologies, classes, and relations, and existing tools are not designed for non-expert users. The PLASMA framework consists of a platform and auxiliary services that focus on providing non-expert users with an accessible way to create and edit semantic models, combining automation approaches and support systems such as a recommendation engine. It also provides data conversion from raw data to RDF. In this paper we highlight the main features, like the modeling interface and the data conversion engine. We discuss how PLASMA as a tool is suitable for building semantic models by non-expert users in the context of dataspaces and show some applications where PLASMA has already been used in data management projects.
An Analogical Reasoning Method Based on Multi-task Learning with Relational Clustering

Analogical QA task is a challenging natural language processing problem. When two word pairs are similar in their relationships, we refer to their relations as analogous. Although the analogy method based on word embedding is well developed, the analogy reasoning is far beyond this scope. At present, the methods based on pre-trained language models have explored only the tip of the iceberg. In this paper, we proposed a multi-task learning method for analogical QA task. First, we obtain word-pair representations by leveraging the output embeddings of the [MASK] token in the pre-trained language model. The representations are prepared for two tasks. The first task aims to train an analogical classifier by supervised learning. The second task is an auxiliary task based on relation clustering to generate relation pseudo-labels for word pairs and train relation classifier. Our method guides the model to analyze the relation similarity in analogical reasoning without relation labels. The experiments show that our method achieve excellent performance on four analogical reasoning datasets without the help of external corpus and knowledge. In the most difficult data set E-KAR, it has increased by at least 4%.
Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems

Recommender systems are used to suggest items to users based on the users’ preferences. Such systems often deal with massive item sets and incredibly sparse user-item interactions, which makes it very challenging to generate high-quality personalized recommendations. Reinforcement learning (RL) is a framework for sequential decision making and naturally formulates recommender-system tasks: recommending items as actions in different user and context states to maximize long-term user experience. We investigate two RL policy parameterizations that generalize sparse user-items interactions by leveraging the relationships between actions: parameterizing the policy over action features as a softmax or Gaussian distribution. Our experiments on synthetic problems suggest that the Gaussian parameterization—which is not commonly used on recommendation tasks—is more robust to the set of action features than the softmax parameterization. Based on these promising results, we propose a more thorough investigation of the theoretical properties and empirical benefits of the Gaussian parameterization for recommender systems.
Deep learning-based foreign object detection method for aviation runways

Abstract Airport runway foreign object detection systems can quickly and accurately detect and identify foreign runway objects, which is significant for ensuring airport flight safety. Because of the drawbacks shown by the algorithm, the paper proposes to combine a new system scheme based on deep learning to obtain multiple feature information for identifying foreign objects on airport runways and improve the recognition accuracy of foreign object detection. This paper designs and constructs a dataset for accomplishing airport runway foreign object detection based on the data distribution and attribute semantics of actual airport runway foreign object scenarios and the technical features of deep learning, designs FOD detection and multi-attribute recognition networks, further design algorithms, and perform validation. The results show that the deep learning technology can accomplish all tasks of the airport runway foreign object detection system, which has not only good robustness to different environments but also has practical value for multi-tasking, and the localization task can accurately obtain the location information of foreign objects and improve the recognition accuracy of foreign object detection. Therefore, the deep learning-based airport runway foreign object recognition system designed in this paper is effective and can improve the accuracy of foreign object recognition.
Trusting Decentralised Knowledge Graphs and Web Data at the Web Conference

Knowledge Graphs have become a foundation for sharing data on the web and building intelligent services across many sectors and also within some of the most successful corporations in the world. The over centralisation of data on the web, however, has been raised as a concern by a number of prominent researchers in the field. For example, at the beginning of 2022 a €2.7B civil lawsuit was launched against Meta on the basis that it has abused its market dominance to impose unfair terms and conditions on UK users in order to exploit their personal data. Data centralisation can lead to a number of problems including: lock-in/siloing effects, lack of user control over their personal data, limited incentives and opportunities for interoperability and openness, and the resulting detrimental effects on privacy and innovation. A number of diverse approaches and technologies exist for decentralising data, such as federated querying and distributed ledgers. The main question is, though, what does decentralisation really mean for web data and Knowledge Graphs? What are the main issues and tradeoffs involved? These questions and others are addressed in this workshop.
Challenges to Sustainable Development in China’s Banking Industry: A Structural Equipment Modeling Approach for Fighting Phishing in China

This research aims to explore how China’s banking industry is influenced by phishing. We used a statistical analysis method and the Structural Equation Model (SEM). The results show that the degree of public security force involvement, the function of telecom operators, the implementation of relative legislation initiatives, and the issues of cooperative governance are vital to limit the occurrence rate of phishing and related cybercrimes, which challenge the development of China’s banking industry. This research will hopefully guide Chinese policy makers to create and implement more progressive initiatives for preventing phishing and facilitate the development of China’s banking industry.
Review Paper on Android-based Mobile Application for Specific Counsel or of Specific Domain

The paper discusses the design and development of an Android application that provides career guidance on a specific domain to users and students. The application can be used by students and researchers as it is a Knowledge sharing platform for specific domains in the field of Computer and Information Technology. It provides complete details of counselors, counselors’ profiles, the expertise of the counselor, interaction with counselors using chats, the features like sharing documents for sharing knowledge with users, privately chatting with counselors, the platform on which publicly users can post the question and counselors are present to help them out
The Weaponisation of Public Comment Rules in Policy Deliberations

The Covid-19 pandemic heightened already intense and increased scrutiny of public education in recent years. The administrative impulse to stage community engagement efforts to deliberate upon these questions, however well-intentioned, rarely realises full community engagement and reflection. Based on an examination of public engagement events held at Florida schools related to the Covid-19 health crisis, the proposed essay identifies a more concerning transformation of “public comment” into a weaponisable prop for lawmakers seeking the public legitimacy necessary for their agenda, marrying the worlds of critical studies with those of public administration and its orientations. More than merely failing to genuinely engage the public, we argue that such events forestall a more productive arrangement of the democratic form that does not rely on publicness and the leader that secures that space. Ultimately, we suggest a path that affords the possibility of public engagement, but that does not seal off the possibility of that more radical democratic future to come.
A Novel Article Recommendation System Empowered by the Hybrid Combinations of Content-Based State-of-the-Art Methods

The initial literature reviewing step is of great importance during any scientific reporting. Nevertheless, finding relevant papers grows tough as the number of online scientific publications rapidly increases. Correspondingly, the need for article recommendation systems has emerged, which aim to recommend new papers suitable for the researchers’ interests. Using these systems provides researchers access to related publications quickly and effectively. In this study, a novel article recommendation system, which is empowered by the hybrid combinations of content-based state-of-the-art methods, is proposed. Various methods are utilized comparatively for an in-depth analysis, and user profiles are evaluated. 41,000 articles collected from the ARXIV dataset are used in the performance evaluation. In the experiments in which Word2vec and LDA are combined, Precision@50, Recall@50, and F1-score@50 achieve the highest performance with .206, .791, and .498 values, respectively. The in-depth analysis and the numerical findings justify that the proposed system is strong and promising compared to the literature.
Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity

We study the strategic similarity of game positions in two-player extensive games of perfect information by looking at the structure of their local game trees, with the aim of improving the performance of game-playing agents in detecting forcing continuations. We present a range of measures over the induced game trees and compare them against benchmark problems in chess, observing a promising level of accuracy in matching up trap states. Our results can be applied to chess-like interactions where forcing moves play a role, such as those arising in bargaining and negotiation.
A multi-view automatic table tennis umpiring framework

This paper presents a low-cost, portable, multi-view table tennis umpiring framework, as a viable alternative to the current expensive systems which are almost exclusively restricted to elite professional sports. Table tennis has been selected as the sport to evaluate this framework primarily because it comprises many different complex match elements, including the service, return and rally elements, which are governed by a strict set of regulations which need to be umpired. The aim is to develop novel methods to analyse and judge the legality of such key match facets, with ball detection and tracking in video frames being integral to reliably and accurately determining the ball’s position and flight path during rallies. While a low-cost option is attractive and offers several benefits, it is a technically challenging problem due to the small number and generally low-resolution cameras that are used. A novel multi-view camera setup and multi-agent system (MAS) framework is presented, which comprises computationally lightweight agents which detect and track the table tennis ball, create a 3D representation of the flight path of the ball, predict the ball’s trajectory, and identify and analyse key facets in a table tennis rally. The MAS correctly detects all state transitions in seven test table tennis sequences with minimal latency and while the processing rate of a standard computer may be unable to analyse long rallies in real time, the potential of running the MAS on a parallel architecture is a propitious alternative. The MAS is also scalable, enabling additional camera pairs to be deployed to achieve enhanced accuracy and coverage. The framework affords the potential to reform the way matches are umpired, especially for amateur players, providing an economic and objective manner of dispute resolution, while the multi-view facility is extendible to other relevant ball-based sports. The ball flight path analysis mechanism can be exploited as a valuable training tool for skill development.
Financial Transaction Management for NGO using Block Chain

Non-governmental organizations or NGOs, are primarily nonprofit groups, and a large portion of them are supported by members of NGO and public donations. The credibility of an NGO is substantially enhanced and donor confidence is greatly increased by maintaining transparency regarding how donations are used. Donor organizations provide funding to non-governmental organizations(NGOs) in developing various initiatives such as economic development, women’s empowerment, promoting education, responding to calamities and natural disasters. Due to some NGOs’ involvement in the misuse of funds, some donor agencies cease to have faith in NGOs method of operation. This system is decentralized, not owned or operated by a single person or organization but rather shared among users. We have developed a blockchain-based Donation System capable of decentralizing the resources used by a given organization. The user may create any new organization and others will be allowed to donate money in the organization. As a result, the public become the true owners of the resources, and the charity fund system as a whole becomes more transparent. Blockchain technology and distributed ledgers can reduce operational costs and bring us closer to real-time transactions between financial institutions. System uses cross chain protocol for reliably exchanging information without third party in a multiple Blockchain system
Multi-Granularity Context Network for Efficient Video Semantic Segmentation

Current video semantic segmentation tasks involve two main challenges: how to take full advantage of multi-frame context information, and how to improve computational efficiency. To tackle the two challenges simultaneously, we present a novel Multi-Granularity Context Network (MGCNet) by aggregating context information at multiple granularities in a more effective and efficient way. Our method first converts image features into semantic prototypes, and then conducts a non-local operation to aggregate the per-frame and short-term contexts jointly. An additional long-term context module is introduced to capture the video-level semantic information during training. By aggregating both local and global semantic information, a strong feature representation is obtained. The proposed pixel-to-prototype non-local operation requires less computational cost than traditional non-local ones, and is video-friendly since it reuses the semantic prototypes of previous frames. Moreover, we propose an uncertainty-aware and structural knowledge distillation strategy to boost the performance of our method. Experiments on Cityscapes and CamVid datasets with multiple backbones demonstrate that the proposed MGCNet outperforms other state-of-the-art methods with high speed and low latency.
Cognition Guided Human-Object Relationship Detection

Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos. Previous human-object relationship detection approaches are mainly developed with object features and relation features without exploring the specific information of humans. In this paper, we propose a novel Relation-Pose Transformer (RPT) for human-object relationship detection. Inspired by the coordination of eye-head-body movements in cognitive science, we employ the head pose to find those crucial objects that humans focus on and use the body pose with skeleton information to represent multiple actions. Then, we utilize the spatial encoder to capture spatial contextualized information of the relation pair, which integrates the relation features and pose features. Next, the temporal decoder aims to model the temporal dependency of the relationship. Finally, we adopt multiple classifiers to predict different types of relationships. Extensive experiments on the benchmark Action Genome validate the effectiveness of our proposed method and show the state-of-the-art performance compared with related methods.