Reviewerly/exHarmony huggingface dataset:
Modelling a Framework to Obtain Violence Detection with Spatial-Temporal Action Localization

A system that detects and identifies human activities are named Human Action Recognition. In the video approach, human activity can be classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action. The categories are gestures, actions, interactions, and activities. This type of classification is quite challenging since systems must capture valuable and discriminating characteristics caused by variations in the human body. Thus, deep learning techniques have provided the development of practical applications in various fields of signal processing, generally surpassing traditional large-scale signal processing. Recently, several applications, namely surveillance, human-computer interaction and content-based video retrieval, have studied the detection and recognition of violence. This paper presents an experiment to identify and detect violence with spatial action location, adapting a public dataset for this purpose. The idea was to use an annotated dataset of recognition of general actions and adapted to detect violence. The results show that it is possible to adapt a general public dataset of action recognition to detect and recognize violence.
COMatchNet: Co-Attention Matching Network for Video Object Segmentation

Semi-supervised video object segmentation (semi-VOS) predicts pixel-accurate masks of the target objects in all frames according to the ground truth mask provided in the first frame. A critical challenge to this task is how to model the dependency between the query frame and other frames. Most methods neglect or do not make full use of the inherent relevance. In this paper, we propose a novel network called CO-Attention Matching Network (COMatchNet) for semi-VOS. The COMatchNet mainly consists of a co-attention module and a matching module. The co-attention module extracts frame correlation among the query frame and the previous frame and the first frame. The matching module calculates pixel-level matching scores and finds the most similar regions to preceding frames in the query frame. The COMatchNet integrates two level information and generates fine-grained segmentation masks. We conduct extensive experiments on three popular video object segmentation benchmarks, i.e. DAVIS 2016; DAVIS 2017; YouTube-VOS. Our COMatchNet achieves competitive performance (J&F) of 86.8%, 75.9%, and 81.4% on the above benchmarks, respectively.
Deep Domain Adaptation

While recent advances in deep learning have led to a significant performance boost in most computer vision tasks, the success of deep learning methods typically depends on the availability of large amounts of annotated training data. However, acquiring data annotations is often prohibitively costly as it requires a significant manual effort. Furthermore, the success of deep learning relies on the assumption that the training and test data are drawn from the same distribution, which is not always the case. In practice, the available labeled training data may follow very different distributions from the test data on which the model will be evaluated. As discussed in the previous chapters, domain adaptation provides a solution to both of these challenges, by effectively exploiting both the unlabeled target data and the knowledge acquired on the annotated source data. In this chapter, we discuss the main trends that have emerged for domain adaptation in the deep learning realm, and, when appropriate, relate them to the traditional methods discussed in Chapter 3. Note that, while different deep DA strategies can be grouped together according to different criteria (see also Wang and Deng [2018]), many methods, especially the most recent ones, combine several ideas, and thus truly belong to multiple categories. Note also that, several methods described in this chapter rely on target pseudo-labels, self-training, and curriculum learning that will be discussed more in detail in Chapter 5.
The NiuTrans’s Submission to the IWSLT22 English-to-Chinese Offline Speech Translation Task

This paper describes NiuTrans's submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task.The end-to-end and bilingual system is built by constrained English and Chinese data and translates the English speech to Chinese text without intermediate transcription.Our speech translation models are composed of different pre-trained acoustic models and machine translation models by two kinds of adapters.We compared the effect of the standard speech feature (e.g.log Mel-filterbank) and the pre-training speech feature and try to make them interact.The final submission is an ensemble of three potential speech translation models.Our single best and ensemble model achieves 18.66 BLEU and 19.35 BLEU separately on MuST-C En-Zh tst-COMMON set.
Improvement of On-Road Object Detection Using Inter-region and Intra-region Attention for Faster R-CNN

In this paper, we incorporate the attention module and the lambda layer into the existing object detection method, Faster R-CNN, to improve its detection accuracy. We propose three methods that incorporate mechanisms based on the attention module to capture the relationship between object candidate regions within an input frame, or a mechanism based on the lambda layer to improve the feature representation within each candidate region. We evaluated the performance of the proposed methods on BDD100K, which includes diverse scene types, weather conditions and times of the day. The results show that the detection accuracy of the proposed methods are improved compared to Faster R-CNN.
From Rewriting to Remembering: Common Ground for Conversational QA Models

In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it emerges and select the relevant information at every turn. We show that CG offers a more efficient and human-like way to exploit conversational information compared to existing approaches, leading to improvements on Open Domain Conversational QA.
Compiling Single Round QCCP-X Quantum Circuits by Genetic Algorithm

The circuit model is one of the leading quantum computing architectures. In this model, a quantum algorithm is given by a set of quantum gates that must be distributed on the quantum computer over time, subject to a number of constraints. This process gives rise to the Quantum Circuit Compilation Problem (QCCP), which is in fact a hard scheduling problem. In this paper, we consider a compilation problem derived from the general Quantum Approximation Optimization Algorithm (QAOA) applied to the MaxCut problem and consider Noisy Intermediate Scale Quantum (NISQ) hardware architectures, which was already tackled in some previous studies. Specifically, we consider the problem denoted QCCP-X (QCCP with crosstalk constraints) and explore the use of genetic algorithms to solve it. We performed an experimental study across a conventional set of instances showing that the proposed genetic algorithm, termed $$GA_{X}$$ , outperforms a previous approach.
Re-CRUD Code Automation Framework Evaluation using DESMET Feature Analysis

A unified view of web application design and development is crucial for dealing with complexity.However, the literature proposes many denominations, depending on the development methodology, frameworks or tools.This multitude of Create, Read, Update and Delete (CRUD) approaches does not allow a holistic view of the web application.Besides, in a web application, the search for good practice in design, features and essential functions is still a relevant issue.A subset of essential CRUD operations is to provide code automation for web application rapid prototyping.Re-CRUD articulates the records management features into CRUD operation.This study aims to provide insight into the effectiveness and efficiency of Re-CRUD in web application development and to compare it with other web application frameworks' CRUD output.The qualitative feature analysis is used based on the evaluation guideline proposed in DESMET and reviewed by experts for validation.A document management system is developed and used as a case study for Re-CRUD evaluation.The feature analysis comprises Re-CRUD and four other web application frameworks CRUD, namely, CakePHP, Laravel, Symfony and FuelPHP.According to the review, Re-CRUD satisfies its expectations by providing more useful features and delivering higher code automation in the web application development process.Compared to the other existing CRUD generator, Re-CRUD has integrated records management features that are useful in providing support in managing borndigital data and also contributes to effectiveness and efficiency in web application development.
Application of Rail Segmentation in the Monitoring of Autonomous Train’s Frontal Environment

One of the key factors in achieving an autonomous vehicle is understanding and modeling the driving environment. This step requires a considerable amount of data acquired from a wide range of sensors. To bridge the gap between the Roadway and Railway fields in terms of datasets and experimentation, we provide a new dataset called RailSet as the second large dataset after Railsem19, specialized in Rail segmentation. In this paper we present a multiple semantic segmentation using two deep networks UNET and FRNN trained on different data configuration involving RailSet and Railsem19 datasets. We show comparable results and promising performance to be applicable in monitoring autonomous train’s ego perspective view.
Two-Stream Transformer Encoders for Skeleton-Based Action Recognition

Extracting motion features from skeletal sequences efficiently is still an open issue in skeleton-based action recognition. This paper proposes a two-stream Transformer encoder (TSTE) network utilizing motion spatiotemporal feature embedding and shape transformation. Each stream contains two Transformer encoder layers with two self-attention heads. Shape transformation rearranges the features by frames. A full connection layer is introduced before Transformer architecture to distribute the features denser. To extend the feature dimension, the two streams input the coordinate data reflecting the joint spatial characteristics and the coordinate changes reflecting motion temporal characteristics respectively. Compared with the existing methods, it can regard all the spatial and temporal characteristics as a whole to calculate correlation without graph topologies or traversal rules. The parameters of the TSTE network is 30% of general graph convolution methods. The experiment is performed on NTU-60 and NTU-120 datasets, and better recognition accuracy is achieved using single GPU.
Persuasive Design for Healthy Eating: A Scoping Review

In this scoping review, we aimed to summarize and analyze the latest research developments of persuasive design for healthy eating behavior and explore future design opportunities. This paper initially collected 1231 papers from 2011 to 2021 in three different databases: the Association for Computing Machinery (ACM) digital library, IEEE Xplore and SpringerLink databases. Based on a selection process, 28 papers that mainly focused on addressing dietary health by persuasive designs were eventually included in final analysis. These 28 papers were sorted by three characteristics: research specifications, methodologies, and design rationales. Our data analyses revealed that the reviewed papers primarily utilized persuasive technologies for eating behaviors monitoring, recording, and healthy eating suggestion. Moreover, six types of design applications were commonly implemented, including mobile applications, persuasive messages, digital products and service systems, wearable devices, chatbots/assistants, and public devices. Our review showed that persuasive design, as a generic approach for promoting healthy eating, lacked research investigations on personalized solutions for particular user groups such as office workers and teenagers. Future works could explore persuasive design strategies by applying the research factors of user experience and examining the efficacy of persuasive technology tools to effectively promote healthy eating behaviors for various user groups in different contexts.
SciBERTSUM: Extractive Summarization for Scientific Documents

The summarization literature focuses on the summarization of news articles. The news articles in the CNN-DailyMail are relatively short documents with about 30 sentences per document on average. We introduce SciBERTSUM, our summarization framework designed for the summarization of long documents like scientific papers with more than 500 sentences. SciBERTSUM extends BERTSUM to long documents by 1) adding a section embedding layer to include section information in the sentence vector and 2) applying a sparse attention mechanism where each sentences will attend locally to nearby sentences and only a small number of sentences attend globally to all other sentences. We used slides generated by the authors of scientific papers as reference summaries since they contain the technical details from the paper. The results show the superiority of our model in terms of ROUGE scores. (The code is available at https://github.com/atharsefid/SciBERTSUM ).
An Ontological Approach for Recommending a Feature Selection Algorithm

Feature selection plays an important role in machine learning or data mining problems. Removing irrelevant features increases model accuracy and reduces the computational cost. However, selecting important features is not a simple task as one feature selection algorithm does not perform well on all the datasets that are of interest. This paper tries to address the recommendation of a feature selection algorithm based on dataset characteristics and quality. The research uses three types of dataset characteristics along with data quality metrics. The main contribution of the work is the utilization of Semantic Web techniques to develop a novel system that can aid in robust feature selection algorithm recommendations. The system’s strength lies in assisting users of machine learning algorithms by providing more relevant feature selection algorithms for the dataset using an ontology called Feature Selection algorithm recommendation based on Data Characteristics and Quality (FSDCQ). Results are generated using six different feature selection algorithms and four types of classifiers on ten datasets from UCI repository. Recommendations take the form of “Feature selection algorithm X is recommended for dataset i, as it performed better on dataset j, similar to dataset i in terms of class overlap 0.3, label noise 0.2, completeness 0.9, conciseness 0.8 units". While the domain-specific ontology FSDCQ was created to aid in the task of algorithm recommendation for feature selection, it is easily applicable to other meta-learning scenarios.
Research on User Requirement Analysis Based on Online Comment Mining and Kano Model

This paper explores the application of online comment mining and Kano model in air purifier design optimization from the perspective of user needs with the aim of improving user experience and enhancing market competitiveness. Firstly, the Octopus software was used to mine the comments of each air purifier on the JD online shopping platform, then ROST was used to visualise the collected comments in terms of feature word co-occurrence, to identify user concerns against the feature functions. Then build a Kano questionnaire based on this, to conduct a questionnaire survey, combined with the user satisfaction coefficient analysis method, to determine the types of requirements corresponding to different features and the degree of importance. Through the analysis of user concerns, the qualitative analysis of the Kano model and the quantitative analysis of the user satisfaction coefficient analysis, the 12 features that have a significant impact on user satisfaction were identified, and categorised and ranked in importance. This method can help designers to more clearly explore user requirements and determine design priorities, so as to better enhance the market competitiveness of their products.
SAP S/4HANA on AWS Elastic Compute Cloud – Concepts and Architecture

This chapter introduces Amazon AWS Hyperscaler Cloud. Starting with a brief history of AWS, it goes on to introduce the most important services required for hosting, managing, and running the SAP S/4HANA application on AWS. Further, it explains the possible deployment scenarios for SAP S/4HANA on AWS Hyperscaler Cloud and covers the integration aspects.
Research on User Conversational Sentiment Analysis Based on Deep Learning

As people's demand for electricity continues to increase, the demand for customer service quality of power supply companies is also increasing. In order to meet the increasing demand for customer service and ensure the quality of customer service, this article takes user sentiment analysis as the starting point and proposes a sentiment analysis model based on CNN-XGBoost to analyze the sentiment of users in the process of customer service and provide guidance for further customer service. Experiments show that the sentiment analysis model proposed in this paper is effective which can provide sentiment analysis performance far superior to traditional models.
Hydraulic-driven piezoelectric ozonation process for nitrobenzene degradation: synergy, energy consumption, impact factors, mechanism, and application potential

The IBP is efficiently degraded by ˙OH in the PE-O 3 process with less energy consumption and excellent stability.
Hyperparameter Optimization

Almost every deep learning model has a large number of hyperparameters. Choosing the proper hyperparameters is one of the most common problems in AutoML. A small change in one of the model's hyperparameters can significantly change its performance. Hyperparameter Optimization (HPO) is the first and most effective step in deep learning model tuning. Due to its ubiquity, Hyperparameter Optimization is sometimes regarded as synonymous with AutoML. This chapter will examine various neural network designs and how NNI can be applied to optimize their hyperparameters for particular problems.
Representative-Based Cluster Undersampling Technique for Imbalanced Credit Scoring Datasets

Credit scoring is an imbalanced binary classification problem, where the number of instances of bad customers is much less than that of good customers. Traditional classification algorithms may not give an effective performance while dealing with these imbalanced datasets, especially when classifying the minority class instances. To overcome the imbalanced problems, different undersampling and oversampling techniques have been proposed to reduce the majority class instances and oversample the minority class instances, respectively. In this paper, the clustering-based undersampling technique (CUTE) is proposed to tackle the imbalanced credit scoring problems. CUTE implements a new strategy to compute the representativeness of each member of the majority class subset. Here, the proposed model is compared with four traditional resampling techniques using two credit scoring datasets. Additionally, the performance of the proposed model consistently improves in different measures, such as accuracy, precision, recall, Fscore, and AUC.
College Admissions Counseling Using Intelligent Question Answering System

College admission is successful based on admission counseling for college entrance examinations. To understand the user’s intentions and provide students and parents with all necessary information about school admissions in a more rapid and efficient manner, the college admissions counseling system plays an important role. In this research, we build an intelligent question answering system to support enrollment counseling based on the question classification methodology using deep learning approach. In order to classify the question, we then use pre-train word embedding and bi-directional long short-term memory. Based on the outcomes of question classification, we will be able to get the answer for the question. As a practical experiment, we have built this application and applied for the college admissions counseling of Thu Dau Mot University.
Assisted Human-Robot Interaction for Industry Application Based Augmented Reality

Augmented Reality provides more possibilities for industrial manufacturing to solve the current problems. However, previous researches have been focused on virtual scene visualization and robot trajectory prediction. The investigation of robotic real-time teleoperation using AR is limited, which is still facing great challenges. In this research, a novel method is presented for human-robot interaction for industrial applications based on AR, like assembly. Users can intuitively teleoperate a 6-DOFs industry robot to recognize and locate entities by multi-channel operation via HoloLens2. Augmented reality, as a medium, bridges human zones and robot zones. The system can transform user instructions from virtual multi-channel user interface to robots by communication module in AR environment. The above approaches and related devices are elaborated in this paper. An industrial case is presented to implement and validate the feasibility of the AR-based human-robot interactive system. The results provide evidence to support that AR is one of the efficient methods to realize multi-channel human-robot interaction.
Introduction

Though the phrase “knowledge graph” has been used in the literature since at least 1972 [Schneider, 1973], the modern incarnation of the phrase stems from the 2012 announcement of the Google Knowledge Graph [Singhal, 2012], followed by further announcements of the development of knowledge graphs by Airbnb [Chang, 2018], Amazon [Krishnan, 2018], eBay [Pittman et al., 2017], Facebook [Noy et al., 2019], IBM [Devarajan, 2017], LinkedIn [He et al., 2016], Microsoft [Shrivastava, 2017], Uber [Hamad et al., 2018], and more besides. The growing industrial uptake of the concept proved difficult for academia to ignore: more and more scientific literature is being published on knowledge graphs, which includes books (e.g., Fensel et al. [2020], Kejriwaletal. [2021], Pan etal. [2017], Qi etal. [2020]), as well as papers outlining definitions (e.g., Ehrlinger and Wofi [2016]), novel techniques (e.g., Lin et al. [2015], Pujara et al. [2013], Wang et al. [2014]), and surveys of specific aspects of knowledge graphs (e.g., Paulheim [2017], Wang et al. [2017]).
Capturing the Lighting Inconsistency for Deepfake Detection

The rapid development and widely spread of deepfake techniques have raised severe societal concerns. Thus detecting such forgery contents has become a hot research topic. Many deepfake detection methods have been proposed in an artifacts-driven manner. They are well-designed to capture subtle artifacts of the face region in different domains. But since the lighting information is usually ignored during the forgery process, which may cause inconsistent lighting between the original face and forged one, we believe that this kind of semantic information can be useful to promote detection accuracy. In this paper, we propose a lighting inconsistency based deepfake detection method. We apply the color constancy technique to each sample and obtain a pre-processed image. Then the unique lighting information of each sample can be obtained by calculating the difference between the processed image and the original one. The lighting information will be used as an assistant channel for better detection accuracy. Extensive experiments show that our method can achieve obvious enhancements compared to the baseline method.