Reviewerly/exHarmony huggingface dataset:
DSSF: Dynamic Semantic Sampling and Fusion for One-Stage Human-Object Interaction Detection

Human-object interaction (HOI) detection is a fundamental task for machines to understand high-level scenarios. Nowadays, most conventional HOI detectors are usually computationally inefficient due to the decoupled two-stage manner. Although the limitation of cascaded object detection is broken through by the latest one-stage methods of direct parallel detection for HOI triplets, the problem of insufficient extracted features and lack of HOI specific interactive semantic context still exists. Therefore, an improved one-stage framework is proposed in this paper, in which dynamic semantic sampling and fusion (DSSF) play key roles. Given the observation that numerous HOI classes have very limited positive samples, we tackle the long-tailed challenge by sampling dynamically using interaction semantic regularities. Meanwhile, the semantic expression ability of interaction points is explicitly enhanced by the dynamic semantic fusion with aggregated contextual information. Moreover, we design a feature fusion module for parallel branches to reduce the conflict of multi-task optimization, along with a point matching strategy to filter out low-probability HOI pairs. Finally, without introducing extra features, our DSSF outperforms previous state-of-the-art methods by a large scale on two challenging HOI detection benchmarks: V-COCO and HICO-DET.
Application Prospect of High-Precision Map Mode and Pure Vision Mode in Automatic Driving

With the introduction of NiO's Pilot Navigation (NOP), autonomous driving is becoming increasingly important, because Tesla's Autopilot navigation (NoA) is a purely visual mode, while NoP needs high-precision map support. In max et al., high-precision maps are not necessarily required in an environment of autonomous driving technology close to L3. L3 autonomous driving is achieved in pure vision mode. This paper analyzes and forecasts autonomous driving technology based on high-precision maps and pure vision. The conclusion is that the future of self-driving cars must be inextricably linked to a variety of multiple fusion technologies such as vision, radar, and high-precision maps.
Exploring the Implementation of Web, Mobile, and VR Technology for Cultural Heritage Presentation

As an important base for collecting cultural heritage resources, cultural and museum institutions in smart cities play an increasingly important role in public education. VR technology has received extensive attention because of its immersion, interaction, and imagination advantages. This paper explores the current Implementation and problems of digital cultural heritage in the world and proposes the optimization scheme of VR Museum design and development. VR museum can provide multi-modal information provision in five aspects: exhibition hall, collection display, interpretation, collection interaction, and guide, which involves human sight, hearing, and tactile sense. From the perspective of user experience, this paper analyzes the problems of the current digital cultural and museum websites, app and VR applications in terms of software features, hardware features, and digital content, designed and constructed a VR Museum application to show a potential future development path of VR cultural heritage applications,
A Framework for False Negative Detection in NER/NEL

Finding the false negatives of a NER/NEL system is fundamental to improve it, and is usually done by manual annotation of texts. However, in an environment with a huge volume of unannotated texts (e.g. a hospital) and a low frequency of positives (e.g. a mention of a particular disease in the clinical notes) the task becomes very inefficient. This paper presents a framework to tackle this problem: given an existing NER/NEL system, we propose a technique consisting of using text similarity search to rank texts by probability of containing false negatives of a given concept, using as a query those texts where the existing NER/NEL system has found positives of this concept. We formulate text similarity as a function of shared medical entities between texts, and we re-purpose an existing public dataset (CodiEsp) to propose an evaluation strategy.
LiDAR-Based Autonomous Self Driving Mini Vehicle

We propose to develop an Autonomous Self-Driving Vehicle using autonomous technology, considering the recent boom in the automotive industry and the future scope of Autonomous vehicles in this modern world. We envisage this system as a product capable of navigating itself in the Indian conditions thus having an immense value in the Indian automotive industry. The vehicle will be an application of celebrated self-driving technology. Our approach will be to use a LiDAR device and a Camera for object detection and environment analysis. Through this, we will be generating a 2D map of the vehicleâ€™s surroundings. Our motive is to design a system using SLAM and Sensor fusion supported by Deep Learning over images that will ensure a flawless operation. The vehicle will be capable of maneuvering itself in real-life scenarios. This functionality will be possible by the use of image data, and by the usage and tracking of real-time physical activity with the help of sensor data.
IT Infrastructure for Smart City: Issues and Challenges in Migration from Relational to NoSQL Databases

Smart city applications collect massive amount of data from various types of IoT sensors, engines, and people. Most of the data generated are heterogeneous in nature. However, this data need to be integrated with legacy applications based on SQL. Some of these applications also require migration to NoSQL for improved performance and fault tolerance. This paper addresses challenges of working in hybrid environments and migration issues from SQL to NoSQL databases. Rapid rate of growth in heterogeneous data and characteristics of NoSQL database like easy scalability, high availability, high performance, and low cost are the motivating factors to migrate toward NoSQL from relational databases, especially for applications requiring dealing with unstructured data. NoSQL database gives dynamic schema, adjustable data model, scale out architecture, and allows storage of big data and access to it in an efficient manner. The relational databases store data in form of tables with fixed schema. Relational databases are structured and not capable of handling unstructured and big data. In relational databases, because of normalization, data is spread across multiple tables and expensive join operation is required to integrate data. Due to limitations of relational databases, most of the leading organizations are migrating toward NoSQL. NoSQL databases are analyzed into four categories- Key-value database, Column-oriented database, Document-based database, and Graph database. Key research issues and challenges in migrating from relational to NoSQL include model transformation (including mapping and schema conversion), application integration, strategies related to perform join in different scenarios, use of indexes, storage issues, etc. Massive growth is likely in the area of NoSQL over cloud because of initiatives like Smart City, however, these applications required integration with legacy applications built over SQL. Thus need for migration as well as bridges for connection leading to requirements of our research area. This chapter tends to explore the comparative study of Relational databases and NoSQL databases, classification of NoSQL databases, case study, popular approaches for migration, and some of the key issues and challenges in migrating from Relational to NoSQL databases. This chapter will be fruitful for students and researchers too.
A study of an intelligent algorithm combining semantic environments for the translation of complex English sentences

Abstract In order to improve the translation quality of complex English sentences, this paper investigated unknown words. First, two baseline models, the recurrent neural machine translation (RNMT) model and the transformer model, were briefly introduced. Then, the unknown words were identified and replaced based on WordNet and the semantic environment and input to the neural machine translation (NMT) model for translation. Finally, experiments were conducted on several National Institute of Standards and Technology (NIST) datasets. It was found that the transformer model significantly outperformed the RNMT model, its average bilingual evaluation understudy (BLEU) value was 42.14, which was 6.96 higher than the RNMT model, and its translation error rate (TER) was also smaller. After combining the intelligent algorithm, the BLEU values of both models improved, and the TER became smaller; the average BLEU value of the transformer model combined with the intelligent algorithm was 43.7, and the average TER was 57.68. The experiment verifies that the transformer model combined with the intelligent algorithm is reliable in translating complex sentences and can obtain higher-quality translation results.
Estimating Return Rate of Blockchain Financial Product by ANFIS-PSO Method

Today, blockchain technology is developing rapidly and the volume of blockchain financial product trading is increasing rapidly as well. The aim of this study is to predict the return rates of cryptocurrencies with the help of artificial learning applications, considering the complex and unstable structure of the financial system. The rate of return is one of the important criteria used for investment decisions. Therefore, an efficient method for return rate prediction will help investors in preparing their portfolios. Ethereum, one of the top three most traded cryptocurrencies in the world, was chosen for empirical analysis. The adaptive neuro-fuzzy inference system approach (ANFIS) has emerged as a method that has been frequently used in recent years. ANFIS uses optimization algorithms to obtain the best prediction performance based on neural network modeling. The ANFIS approach has a multilayered structure consisting of many nodes inside and connections between the layers. ANFIS retains the properties of a fuzzy system while applying the principles of a neural network. Computations in the layers are conducted to learn and reproduce the information of the system. In this study, the particle swarm optimization (PSO) algorithm is used to train the ANFIS network. PSO aims to find the best-performing model in predicting the prices of three major cryptocurrencies that are Bitcoin, Ethereum, and Tether. The prediction accuracy of the proposed models was checked on the test set with performance indicators of root mean squared error (RMSE) and mean absolute percentage error (MAPE). The ANFIS-PSO approach gives strong results in cryptocurrency rate of return estimation.
Gi22Pi Rule-based, index-preserving grapheme-to-phoneme transformations Rule-based, index-preserving grapheme-to-phoneme transformations

This paper describes the motivation and implementation details for a rule-based, indexpreserving grapheme-to-phoneme engine 'G i 2P i ' implemented in pure Python and released under the open source MIT license 8 .The engine and interface have been designed to prioritize the developer experience of potential contributors without requiring a high level of programming knowledge.G i 2P i already provides mappings for 30 (mostly Indigenous) languages, and the package is accompanied by a web-based interactive development environment, a RESTful API, and extensive documentation to encourage the addition of more mappings in the future.We also present three downstream applications of G i 2P i and show results of a preliminary evaluation.
Introduction and Preliminaries

This chapter first provides a brief overview of the contents of the book. Section 1.2 then provides a brief introduction to R, focusing on summarizing and visualizing data, and modelling data with (generalized) linear models. Section 1.3 introduces the datasets used throughout the book to illustrate the models, including their origin and main research questions.
Quantum Entanglement Inspired Correlation Learning for Classification

Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Entanglement inspired the Classification Algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, Quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.
Analysis of Current Recommendation Techniques and Evaluation Metrics to Design an Improved Book Recommendation System

Sometimes users face difficulty in searching required content on the digital platform due to an enοrmous amοunt of information. But this hassle may be solved with the assist of a recοmmender system (RS). RS plays a fundamental role in reducing information overloading. It also provides the item based on the interest of the user. RS plays a significant role on E-commerce sites, online auction, and on any online platform. The Bοok recοmmender system (BRS) is now mostly used by books e-commerce sites. This paper surveyed the machine learning techniques which have been implemented to design the BRS. Six types of approaches are identified to design the BRS like clustering, Collaborative Filtering, Content-Based, Association, Opinion Mining, and Hybrid technique. The hybrid technique is designed by the cοmbination of various techniques.
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games

Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.In these games, the agent learns to explore the environment via natural language interactions with the game simulator.A fundamental challenge in TGs is the efficient exploration of the large action space when the agent has not yet acquired enough knowledge about the environment.We propose COMMEXPL, an exploration technique that injects external commonsense knowledge, via a pretrained language model (LM), into the agent during training when the agent is the most uncertain about its next action.Our method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho.Additionally, the produced trajectory of actions exhibit lower perplexity, when tested with a pretrained LM, indicating better closeness to human language. 1!!+ "xEffect" * " = "PerxonX opens the trap door" !!+ "xEffect" * # = "PerxonX removes the trap door" !!+ "xWant" * $ = "PerxonX clean up the room" * $ + "xWant" * % = "PerxonX have a clean room" * $ + "xEffect" * & = "PerxonX gets tired from cleaning". ' ()) / ())
Development and Implementation of Tweet Stream Summarization Technique for Pernicious Tweet Detection

In contrast to traditional media, social media is populated by anonymous individuals who have the freedom to broadcast whatever they choose. This online social media culture is dynamic, and the move from traditional media to digital media is growing increasingly popular among people. While conventional media will continue to be used less frequently in the future, the increasing use of Online Social Networks will obfuscate the real information provided by traditional media. Genuine users provide material benefits to the broader public; on the other hand, spammers transmit irrelevant or misleading content that turns social media into a front for spreading false information. A precise statistical classification for news is not possible with the current systems because of data limitations and communication types. We will look at a variety of research publications that employ various strategies for master training in the prediction and detection of harmful material on social media websites and networks. This study attempted to identify spam tweets from an extensive collection of tweets by utilizing TVC Algorithm to identify them. When a content paper is incorporated in this manner, a summary produced by utilizing the most important keywords from the first document is known as summarization. It is necessary to have a dynamic approach to dealing with the condensed information supplied through Twitter feeds. This research presents a novel way to produce a significant substance-based summery in a shorter period than previously available. We also propose to detect harmful tweets both offline and online and to do so in real-time. Most notably, compared to the other current frameworks, the proposed framework performs multi-subject summarization on an online dataset, which reduces the amount of time required.
Comparison of Interpolation Methods for MRI Images Acquired with Different Matrix Sizes

Magnetic resonance is one of the most comprehensive and safe radiological techniques. However, a serious limitation is signal strength because it is inversely proportional to image resolution. One of the most important parameters which determines signal-to-resolution ratio is matrix size, therefore a post-processing technique which allows the best-possible resolution to be obtained is desired. This paper concerns a study whose main goal was to evaluate seventeen popular interpolation methods and select the one that best estimates human tissues when enlarging MRI images. The experiment was conducted using data from twenty left shoulder MRI scans from different patients. In order to compare interpolation methods, lower-resolution images were upsampled to higher-resolution images, after which the quality of each method was checked using the structural similarity index measure and mean square error.
A Gentle Introduction to Deep Nets and Opportunities for the Future

The first half of this tutorial will make deep nets more accessible to a broader audience, following "Deep Nets for Poets" and "A Gentle Introduction to Fine-Tuning."We will also introduce, gft (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models).Based on the success of these methods on a number of benchmarks, one might come away with the impression that deep nets are all we need.However, we believe the glass is half-full: while there is much that can be done with deep nets, there is always more to do.The second half of this tutorial will discuss some of these opportunities.
Digital Education as a Condition for Maintaining a High Level of Socioeconomic and Managerial Effectiveness and Competitiveness of Higher Education Systems

The authors conduct a study of the main regularities determining the distinctive features of digital education. The conducted research allowed us to identify several trends in vocational education, providing an increase in socioeconomic and managerial efficiency and competitiveness of higher education systems in the current digital society. A system analysis, including the required methods and scientific techniques to evaluate the research results, constitutes the methodological basis of this research. The authors found that the digital transformation of education to maintain the socioeconomic and managerial efficiency and competitiveness of higher education systems covers all levels of the educational system and involves all interested parties. This work combines three areas: (1) development of digital educational infrastructure; (2) improvement of informational educational and methodological study manuals, tools, and servers, including digital assessment; (3) creation and replication of new ways to organize educational activities. It is proved that the digitalization of higher education is an objective process, expressing the general logic of the transformation of the digital society. It is proved that this process is associated with some problems and social risks of maintaining a high level of socioeconomic and managerial efficiency and competitiveness of higher education systems. Such risks and limitations must be studied and systematized so that the development of digitalization can be controlled and its negative consequences minimized as much as possible.
Preferences for Single-Turn vs. Multiturn Voice Dialogs in Automotive Use Cases—Results of an Interactive User Survey in Germany

Voice assistants have manifested their existence in the vehicle over the last decade. Further technological developments in the area of voice recognition and interactions with users open up the opportunity for new customer-centric user scenarios. In the following work, eight dialog use cases and two different interaction types were examined in detail. The focus of this work is to answer the research question to which extent do users prefer task-oriented multi-turn dialogs over the question-and-answer single-turn dialogs in certain driving situations. Employing a three-step online survey in 2020, participants were asked about their preferences for the assistant’s interaction type, use case as well as the perceived usefulness and pleasantness. The authors found that users preferred multi-turn conversations over single-turn conversations for all defined use case scenarios. As further challenges for the future development of voice assistants in the automotive context, the changed driving situation due to e.g. progress in autonomous driving and the focus on an integration of the voice modality as a direct function should be considered.
Deep Learning Basics

Deep learningDeep learning basics are essential for the visual question answering task since multimodal information is usually complex and multidimensional. Therefore, in this chapter, we present basic information regarding deep learningDeep learning, covering the following: (1) neural networks, (2) convolutional neural networks, (3) recurrent neural networks and their variants, (4) encoder/decoder structure, (5) attention mechanism, (6) memory networks, (7) transformer networks and BERT, and (8) graph neural networks.
An Intelligent Recommendation System Using Market Segmentation

Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers’ needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used.