Reviewerly/exHarmony huggingface dataset:
An Edge-Aware Transformer Framework for Image Inpainting Detection

Image inpainting methods based on Generative Adversarial Networks are very powerful in producing visually realistic images. It is widely used in image processing and computer vision, such as recovering damaged photos. However, image inpainting may also be maliciously used to change or delete contents, e.g. removing key objects to report fake news. Such inpainting forged images can bring serious adverse effects to society. Most existing inpainting forgery detection approaches using convolutional neural networks (CNN) have limited receptive fields and do not fully exploit the edge information of the forged regions, making them fail to effectively model the global information of forged regions and well preserve their edges. To fight against inpainting forgeries (not only deep learning (DL) based but also traditional ones), in this work, we propose an edge-aware transformer framework for image inpainting detection. To better perform feature extraction and learn discriminative features, we propose a two-stream Transformer to learn the global body features and fake edge features respectively. Further, a multi-modality cross attention module is employed to propagate their information interactively, thus greatly improving the detection results. Extensive experiments demonstrate the superiority of our scheme over existing ones, and our method exhibits desirable detection generalizability for both DL-based inpainting and traditional inpainting.
Virtual World Under AI: Augmented Reality and Deep Synthesis

The relationship between people and digital information has been changing all the time. On the one hand, people can obtain an enormous amount of information from texts, images and videos in various ways. On the other hand, from keyboard, mouse, touch screen to voice, the interactive modes of information are getting closer to the five natural senses of the human being. The popularity of the virtual world will promote the evolution of the relationship once again. The presentation of information will move toward the integration of virtual and real information, and the interaction of information will step toward a totally new form driven by technologies such as image recognition and gesture recognition.
Incorporating Syntactic and Semantic Information into Compound Sentence Discrimination and Relation Recognition

Compound sentences contain at least two independent clauses which account for a large proportion in natural language, especially in Chinese. Therefore, the discrimination and relation recognition of compound sentences are essential yet crucial to text understanding. Based on the presence of connective words, compound sentences can be categorized as explicit sentences and implicit sentences. The current researches have achieved significant performance on explicit sentences, while that of implicit sentences remains unsatisfactory due to the missing of connective words. To address this issue, this paper proposes a joint model based on the enhanced sentence representation combining the syntactic information, semantic roles and local feature within the sentence to distinguish the compound sentence and recognize its semantic relation at once. First, utilize the syntactic tree of the sentence and encode it with Tree-LSTM to obtain the contextual and syntactical information of the sentences, then semantic role information encoded by Bi-LSTM is used to capture deep semantic information for sentence representation. In the discrimination module, CNN is used to obtain local semantic features, while in the relation recognition module, sentence matching is introduced to fuse the information between the two clauses. Experimental results on CAMR and CDTB corpus show that the F1 value of compound sentence discrimination reaches 95.90%, which increases by about 10.24% compared with state of the art of this task, and that of semantic relation recognition reaches 83.88%, which increases about 4.47% than state of the art.
A Participative and Collaborative Approach for Security Risk Management Modelling in the Context of Essential Services

This paper introduces a participative and collaborative approach for security risk management modelling in the context of Essential Services for NIS Directive compliance. This approach allows building sectoral models using the necessary knowledge of domain experts and obtaining a better acceptability and handling of these models. The application of the methodology is related based on our experience of national modelling of several essential sectors/sub-sectors in Luxembourg. This modelling implied more than forty participatory workshops, involving more than a hundred experts to gather their knowledge. The methodology as well as the different workshops are presented in detail, and then analysed in terms of relevance, adequacy and results produced.
Sentiment Analysis using Ensemble of Machine Learning and Lexicon Based Models

The study aims to look into the relationship between digitization and financial performance of banks and its implication on competitiveness between the banks. In order to test the association between digitization and performance, regression using panel data is used in this paper. The Granger causality test is applied to bank-level data for Indian banks to assess the relationship between digitization and competitiveness from 2009 to 2019. The Lerner Index is a control over the market metric that is used to estimate competition. Panel data analysis technique is applied for measuring association between digitization and financial performance along with granger causality test to check relationship between digitization and competitiveness. For the purpose of examining competitiveness and digitization in the banking sector, this study examined more than 90% of banking assets. Policymakers/advisors can attempt to improve banking digital infrastructure in order to increase/decrease financial performance as well as competitive advantage in the banking sector, which will aid in the development of the investment-savings cycle.
Forecasting Future Instance Segmentation with Learned Optical Flow and Warping

For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others. This can be done using different sensors and modalities. In this paper we investigate the usage of optical flow for predicting future semantic segmentations. To do so we propose a model that forecasts flow fields autoregressively. Such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames. Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow methods.
Toward Adaptive Trust Management for Human-automation Teaming Using an Instance-based Learning Cognitive model

Trust in automation is seen as a core factor affecting human-automation teaming. Inappropriate calibration of trust in automation can damage the performance and safety goals of the collaborative team. It is essential to develop automation that can correctly calibrate human trust in it. Herein, based on the view that trust comes from interaction, we use an instance-based learning cognitive model to obtain the cognitive process involved in the interaction between dispatchers and automated Decision Support Systems(DSSs) in the Fully Automatic Operation (FAO) circumstances, and obtain from the model an internal estimate of the calibration state of human trust. We consider integrating the model into automation so that it can judge the hidden calibration status of the human teammate's trust, and respond to the trust dynamics in an online and adaptive way. We discuss our results and the potential of the instance-based computational cognitive process model to improve human-automation teaming. Our model has great potential to avoid the sluggish effect caused by dispatchers failing to obtain effective decision support in time in the FAO circumstances, especially when dealing with emergencies under high time pressure.
A Data Reconciliation Model Based on QLDPC for Satellite-Ground Quantum Key Distribution Network

In the satellite-ground quantum key distribution network for electric IoT, the transmitted data may have errors caused by the external environment and equipment. In order to ensure the correctness and consistency of the transmitted data, we propose a data reconciliation model based on quantum low density parity check (QLDPC) code for satellite-ground quantum key distribution network. We perform the check operator of QLDPC on the obtained raw keys, and use belief propagation algorithm and ordered statistics decoding technology to implement error correction. Compared with MET-LDPC and Turbo code, our model based on QLDPC has higher error correction efficiency with the increase of bit error rate.
Multimodal Augmented Reality and Subtle Guidance for Industrial Assembly – A Survey and Ideation Method

Industrial manual assembly is a relatively established use case for emerging head-mounted Augmented Reality (AR) platforms: operators get visual support in placing pieces depending on where they are in the assembly process. However, is vision the only suitable sensory modality for such guidance? We present a systematic review of previous work done on multimodal guidance and subtle guidance approaches, confirming that explicit visual cues dominate. We then outline a three-step method for generating multisensory guidance ideas intended for real-world task support based on task observation that led to identification of 18 steps in truss assembly, brainstorming AR guidance approaches related to assembly and maintenance, and mapping of brainstorming results to the observed task. We illustrated the use of the method by deploying it on our current mission in producing AR guidance approaches for an industrial partner involved in designing and assembling wooden trusses. In this work, we went beyond the standard visual AR guidance in two ways, 1) by opening for guidance through auditory, tactile, and olfactory sensory channels, 2) by considering subtle guidance as alternative or complement to explicit information presentation. We presented a resulting set of multisensory guidance ideas, each tied to one of the 18 steps in the observed truss assembly task. To mention a few which we intend to investigate further: smell for gradual warning about non-imminent potential hazardous situations; 3D sound to guide operators to location of different tools; thermos-haptics for subtle notifications about contextual events (e.g., happening at other assembly stations). The method presented helped us to explore all modalities and to identify new possibilities. More work is needed to understand how different modalities can be combined and the impact of different modality distractions on task performance.
Single Shot Detector for Multi-vehicle Detection and Tracking in Different Lighting and Weather Conditions

Intelligent communities use innovative ideas to make human life better. Smart transportation is one of such idea to be implemented in smart cities. As number of vehicles are increasing too fast, need of smart transportation is increasing day by day. Vehicle detection and tracking has become essential part of such smart transportation systems. Self-driving car, accident detection, traffic flow statistics, license plate recognition are some related research works which can be undertaken based on vehicle detection and tracking. This paper compares different approaches of vehicle detection techniques. Multiple vehicle detection and tracking in various atmospheric and lighting circumstances by making use of deep learning-based single shot detection algorithm is described in detail by authors.
Mammogram Mass Classification: A CNN-Based Technique Applied to Different Age Groups

The World Health Organization (WHO) reports that breast cancer is one of the most frequent cancers among women, affecting almost 2.1 million women/year. As a consequence, fatality rate is high: 627,000 women died from breast cancer in 2018, which is approximately 15% [1] of all cancer deaths among women. Early detection (using AI-driven tools) can prevent from being worse. In this paper, we propose a Convolutional Neural Network (CNN) based approach for mammogram mass classification. The proposed method outperforms our existing feature-learning based model [2] on the same dataset. We experimentally achieved the best average recognition accuracy of 95.25% in separating malignant from benign masses. In our study on different age groups for mammogram mass classification, better results were observed from the age group: 61–75 years.
Secure Predictive Analysis on Heart Diseases Using Partially Homomorphic Machine Learning Model

Cardiovascular disease is the most important reason for death worldwide and significant public health distress. Timely prevention and treatment are possible by early prediction of the disease. However, there is necessary to include or vary new risk factors to improve the prediction models' performance. It is one of the best ways to make development towards human-level AI. The machine learning (ML) algorithms PCA (Principal Component Algorithm) and XGboost classification are used to process the user queries and send the prediction. A major constraint is to secure the user queries submitted to the prediction models in order that the patient can submit their questions in an encrypted format to ensure security. It motivates us to develop a predictive model PHML which combines Partial Homomorphic Encryption and Machine Learning algorithms PCA and XGboost classification. The model is implemented in Amazon SageMaker with the dataset stored in Amazon S3 using the above algorithms. The patient query was submitted to the cloud with the encrypted format by the proposed partial homomorphic encryption algorithm. The machine learning algorithms predict the user queries, which are in encrypted form. The dataset includes multiple attributes like age, height, weight, gender, smoking, alcohol intake, physical activity, systolic blood pressure, cholesterol, glucose, diastolic blood pressure, and medical notes. With all features, doctors try to predict whether our individual has a high risk of cardiovascular.
Knowledge-Based VQA

Tasks such as VQA often require common sense and factual information in addition to the information learned from a task-specific dataset. Therefore, a knowledge-based VQA task is established. In this chapter, we first introduce the main datasets proposed for knowledge-based VQA and knowledge bases such as DBpediaDBpedia and ConceptNetConceptNet. Subsequently, we classify methods from three aspects: knowledge embedding, question-to-query translation and querying knowledge base methods.
Multidimensional Information Alignment for Domain Adaptive Object Detector

Object detection in complex scenarios such as fog, rain, snow, cloudy day, dusk, occlusion and other scenarios has always been one of the difficulties and hotspots in computer vision. At present, domain adaption is mainly used to detect object in complex scenarios. However, current methods cannot make full use of the multi-dimensional information, such as context information, edge information and saliency information, which plays a very important role in the general object detection. Hence, we propose a multidimensional information fusion method for domain adaptive object detector. It mainly includes Non-local Alignment (NLA) and Center-aware with Saliency Map Alignment (CASMA). NLA focuses on not only the detail information of the image object, but also the context of the image object in the process of source domain and target domain alignment. CASMA focuses on saliency information such as saliency map in spatial domain and frequency domain, image gradient map, on the basis of the center-aware alignment method of source domain and target domain. Experiments on three challenging benchmarks show that our proposed method outperforms state of-the-art domain adaptive object detection methods.
Weighted Least-Squares Fitting of Circles with Variance Component Estimation

Although the least squares (LS) fitting of circle has been widely known in measurement, the weighted LS fitting of circle is not thoroughly investigated, in particular, when the prior weight information is only partly known. Based on the Gauss-Helmert model (GHM), we first investigate the invariance on translation and rotation. The results show that though the translational invariance holds, the rotation invariance is broken except for some specific weight structures. As the main finding of this paper, we develop the VCE theory directly adapting to the nonlinear GHM representation of the circle fitting problem where the weight information is not exactly known. In the simulated example and the real applications, we show that: (1) The conclusions about the invariance of translation and rotation are validated. (2) The estimated variance components can perfectly represent the uncertainty of different point groups or different coordinate components from the statistical perspective.
Weighted Median filtering based fusion of multiple exposure images for HDRI

Several study experts have been interested in fusing a changeable exposure stack in recent years. Scenes with a wide dynamic range are taken, and each photograph comprises some of the sections that are well described. Certain areas of the photos are either completely dark or completely brilliant. We merge these multiple exposure picture stacks to create a composite image that comprises of all the well-exposed sections. When working with an image in a 'hdr' format, which cannot be viewed on standard display devices or printed on standard media, the traditional tone-mapping method begins with the production of an image. We combine the variable exposure stack and do not create a mediator image but develop an approach to fuse the multi-exposure image stack. Filter based technique achieves preservation of edges using WMF. The under- and over-exposures are recovered by applying the coefficients generated using this method, which restores the image's weak edges and fine textures. As with Bilateral filtering and Savitsky-Golay filtering, the proposed solution utilises a Weighted Median Filetring (WMF) technique to maximise the likelihood of fusion. All exposure stacks are combined in the LDR domain and the resulting outputs are displayed on conventional LDR monitors. WMF based fusion methodology is compared to other popular methods, resulting in a conclusion to use this approach for merging images using the (WMF) which is novel in its nature as a first step towards merging variable exposure images using a non-linear filtering based technique.
Smart Pedagogy-Focused Design and Development of Innovative Curriculum for Data Cleaning

Data cleaning is a complex, multi-step process of modifying input data to ensure that it is free of irrelevant, incorrect, duplicated and/or incomplete data. It accounts for about 60% of the work by data scientists. Data cleaning in companies, business and organizations requires 1) an availability of knowledgeable and experienced expert(s) in a designated area of business operation (for example, teaching/learning, enrollment, sales, manufacturing, etc.), 2) a specialized set of data cleaning tools for automatic and/or manual modification of data, and 3) well-defined data cleaning techniques/procedures and secure protocols of working with corporate/organizational data. These days it is important not just to teach a new subject or topic, but also to teach it in a smart way - using smart pedagogy - with the goal to maximize student learning outcomes. This paper presents the up-to-date findings and outcomes of a multi-aspect project in the Department of Computer Science and Information Systems, the Department of Education, Counseling and Leadership, and the InterLabs Research Institute in Bradley University (IL, USA) that is aimed to design, develop and test the innovative data cleaning curriculum based on corresponding features of smart pedagogy.
Data De-Duplication Process and Authentication Using ERCE with Poisson Filter in Cloud Data Storage

The cloud storage is essential environment for users to access high confidential data. Every single data is most valued by users. If we count, day by day information as well as, memory storage are increasing gradually. Cost of memory will increase when data increases with demand for storage. At present data duplication or redundant data storing in the cloud became hard for storage provider. Also, it makes security issue if repeated data from various users stored in the server. It makes data duplication, which is very efficient for intruders. Also, when same data stored in cloud, there will be a waste of storage. Our research article is focused on security of original data by generating the key from owner and identifying the repeated data, while storing in cloud platform. This process is called as data de-duplication, which is also known as intelligent based computing. Storing the data in single instance is very challenging among cloud computing researchers. In this article we propose a content level de-duplication with re-encryption using enhanced Randomized convergent encryption (ERCE) based on Poisson filter (PF). First the data is encrypted and re-encrypted using the cipher methodology. Generated key only stored and processed by authenticated user. Owner of the data give permission to access key. Then the Poisson filter is used in de-duplication process. if key is authenticated, then the authenticated user can access data from cloud server. Data is stored only once and accessing key decides who can access the data. The result is evaluated with various existing algorithm. our proposed algorithm proves less time in downloading file and less computation cost when comparing with existing system.
VIRNet for Image Retrieval: One for All Top Based on Feature Fusion Technique

Image Classification is major aide in improving Human Computer Interaction (HCI) by providing semantic level user experience. Classification of images has ever since been on a rollercoaster ride with multiple deep learning models, each one of them with unique way of looking into the image for feature extraction. The classic problem with deep learning models is it consumes more time and requires huge training data. In the effort to address two major issues of deep learning, in search of one single model which is able to extract features given any small amount of data is proposed. Image classification from its finest models - VGG16, Inception-V3 and ResNet-50 is chosen for the job. Each one of them with its diverse technique for analyzing the image, a Feature Fusion Technique (FFT) which fuses together the features from different model to create a fused vector which is compatible for the One for All Top (OAT) Module. The OAT Module with its normalized fully connected layers outperforms the state-of- the art techniques. The proposed VIRNet is tested on standard dataset like Caltech-101, Caltech-256, Scene-15, MIT-CVPR, Eight sports, proved to achieve higher accuracy from small training data (15 images per category) without waiting for long hours.