Reviewerly/exHarmony huggingface dataset:
Deep Multi-Resolution Diffusion Image Denoising Via Low-Frequency-To-High-Frequency Channel Translation

A deep denoising network called DeepDTI has recently been proposed to restore the diffusion image quality. For diffusion images under large variability of image appearances due to an arbitrary choice of gradient directions, the relatively shallow network proposed in the DeepDTI can be ineffective. Noting that the diffusion data is memory consuming, it can also be infeasible to train a large-scale deep network for this data when limited GPU memory is available. We find the multiresolution analysis based on fast wavelet transform particularly fit for large volumetric image restoration with deep learning since it allows the restoration algorithms to be applied independently to the decomposed images, a.k.a. the wavelet coefficients, of significantly reduced spatial sizes. Therefore, we propose a multi-resolution extension of the original DeepDTI denoising framework, in which the wavelet coefficients in each frequency sub-band can be denoised using an independently trained large network. Furthermore, since high-frequency coefficients are usually more severely contaminated by noise, we propose to reconstruct them using the more reliable low-frequency channels instead of pursuing direct restoration. This leads up to a channel-to-channel translation problem, or channel transfer. Additionally, to preserve the fine details in the high-frequency channels we propose to incorporate the spatial adaptive normalization (SPADE) layer in our network. Experimental results on the HCP dataset show that the proposed approach achieves superior performance over the standard DeepDTI network, and the proposed channel transfer model compares favorably to plain CNN based channel synthesis network.
Scale-Aware Cnn for Crowd Density Estimation and Crowd Behaviour Analysis

Visual surveillance is a highly demanded system in various real-time applications for civil and military sectors. For huge gatherings such as rallies and stadiums, crowd monitoring becomes a tedious task. Moreover, crowd density estimation also plays an important role to improve the performance of crowd monitoring system. Several techniques have been presented for crowd counting or density estimation and crowd behaviour analysis. The crowd scenes contain noise, occlusion, cluttered environment which creates complexity to analyze the crowd behaviour. To overcome the challenges of crowd monitoring, we present a combined solution to perform the crowd density estimation and crowd behaviour analysis. The crowd density estimation is carried out by developing a new CNN based architecture which considers scale information and overcome the issues of scale variations.In order to deal with this issue of scale variation, we introduce a scale-aware attention module where multiple branches of self-attention module are concatenated to improve the scale variation realization. Further, we present the crowd behaviour analysis model which uses motion map and energy level distribution based features to detect the abnormal crowd behaviour. The comparative study shows the proposed approach achieves better performance
Cognitive Workload Associated with Different Conceptual Modeling Approaches in Information Systems

Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient information. This trade-off between model complexity and clarity is crucial to prevent failure of information system development. Past studies have found that more expressive models lead to higher performance on tasks measuring a user’s deep understanding of the model and attributed this to lower experience of cognitive workload associated with these models. This study examined this hypothesis by measuring users’ EEG brain activity while they completed a task with different conceptual models. 30 participants were divided into two groups: One group used a low ontologically expressive model (LOEM), and the other group used a high ontologically expressive model (HOEM). Cognitive workload during the task was quantified using EEG Engagement Index, which is a ratio of brain activity power in beta as opposed to the sum of alpha and theta frequency bands. No significant difference in cognitive workload was found between the LOEM and HOEM groups indicating equal amounts of cognitive processing required for understanding of both models. The main contribution of this study is the introduction of neurophysiological measures as an objective quantification of cognitive workload in the field of conceptual modeling and information systems.
Tabular Value-Based Reinforcement Learning

This chapter will introduce the classic, tabular, field of reinforcement learning, to build a foundation for the next chapters. First, we will introduce the concepts of agent and environment. Next come Markov decision processes, the formalism that is used to reason mathematically about reinforcement learning. We discuss at some length the elements of reinforcement learning: states, actions, values, and policies.
&lt;P Class="Els-Title"&gt;&lt;A Name="_Hlk104546208"&gt;&lt;/A&gt;A Reference Framework for Variability Management of Software Product Lines&lt;O:P&gt;&lt;/O:P&gt;&lt;/P&gt;

Variability management (VM) in software product line engineering (SPLE) is introduced as an abstraction that enables the reuse and customization of assets. VM is a complex task involving identification, representation, and instantiation of variability for specific products, as well as the evolution of variability itself. This work presents a comparison and contrast between existing VM approaches using “qualitative meta-synthesis” to determine the underlying perspectives, metaphors, and concepts of existing methods. A common frame of reference for the VM was proposed as the result of this analysis. Putting metaphors in the context of the dimensions in which variability occurs and identifying its key concepts provides a better understanding of its management and enables several analyses and evaluation opportunities. Finally, the proposed framework was evaluated using a qualitative study approach. The results of the evaluation phase suggest that the organizations in practice only focus on one dimension. The presented frame of reference will help the organization to cover this gap in practice.
Multiscale Progressive Complementary Fusion Network for Fine-Grained Visual Classification

In fine-grained visual classification(FGVC), the small inter-class variations and the large intra-class variations are always inherent attributes, so it is much more challenging than traditional classification tasks.Recent works mainly tackle this problem by employing attention mechanisms to locate the most discriminative parts.However, these methods tend to neglect other inconspicuous but distinguishable parts, and can not effectively fuse the features information of different scales and different degrees of discrimination.In this work, we propose a multi-scale progressive complementary fusion network (MPCF-Net) to tackle these problems.In particular, we propose: (i) A three-step multi-scale progressive training method, which employs the image slicer to generate puzzle images at different scales followed by multi-step progressive training.This enables the network to capture multi-granularity local feature information and gradually expand its attention to the global structural information as the training progresses for multi-granularity information fusion.(ii) A plug-and-play feature complementary enhancement module (FCEM) that explicitly enhances the features extracted by the current layer of the network, while also enabling the next layer of the network to extract potential complementary feature information to diversify the features.Our experiments were conducted on four FGVC benchmark datasets and yielded state-of-the-art or competitive results.
A Graph Representation Learning Algorithm for Approximate Local Symmetry Feature Extraction to Enhance Malicious Device Detection Preprocessing

Existing malicious device detection preprocessing ignores the topological similarity of node neighborhood structures in the network. According to the structural equivalence hypothesis, devices with approximately local symmetry in the device-account graph have similar topological embeddings after preprocessing. In order to improve the performance of malicious device detection, we propose the Graph Structural-topic Similar Subgraph Merging, abbreviated GraphSTSGM, to extract topological similarity between nodes. GraphSTSGM extracts approximate local symmetry features by adding local neighborhood structural patterns merge to Graph Structural-topic Neural Network (GraphSTONE). In this algorithm, we build a device-account relationship graph G with devices and accounts as nodes, build an edge between associated devices and accounts, and then calculate the approximate local symmetry of each device via the merge-similar-substructures-based anonymous walk in G. Then, the approximate local symmetry features and device features of the nodes are aggregated through Graph Convolutional Network (GCN). We use the above algorithm as a preprocessing method to enhance the ability of malicious device detection by accurately characterizing the approximate local symmetry features of nodes. Finally, the obtained aggregated features are used as the input of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model for malicious device detection. Experiments based on Alibaba Cloud Security data show that the proposal outperforms the state-of-the-art algorithms by 3.6% with respect to the AUC of malicious device detection. In addition, experiments based on the graph dataset Cora show that the proposal outperforms the state-of-the-art algorithms by 2.6% with respect to the AUC of node classification.
Finding Noisy Data in Social Websites Based on Semantic-Enhanced Method

Cyberbullying has emerged as a major problem for internet mates, teenagers, and young adults as a result of increasingly mainstream online media. AI techniques allow for the programmable detection of vexing messages in online media, which could aid in the development of a stable and secure online media environment. One fundamental problem in this important. The research sector is efficient and discriminating. Mathematical representation learning of instant messages. Our process, dubbed Semantically or Meaningfully Enhanced or established Marginalized Denoising Auto-Encoder, works by enhancing the mainstream profound learning model stacked denoising autoencoder with semantic. The lexical augmentation consists of Meaningful dropout clamor and very little Demand, with the semantic dropout clamor planned using cloud data and the Letters and words implant process. My suggested technique can be used to exploit the shrouded highlight the tormenting framework knowledge familiarize oneself with a vigorous often discriminatory text reflexive. Full experiments on two public cyberbullying sites (Twitter) have been performed, with the findings showing that our suggested methods outperform other pattern text representation learning techniques.
AI and the limits of human creativity in urban planning and design

What can architects learn from AlphaGo? This chapter explores the lessons to be learnt from the famous match where AlphaGo, a machine-learning system developed by DeepMind, beat leading Korean Go player, Lee Sedol. It explores the ramifications of this victory on a series of different levels, from the global impact of the match on research into AI to the impact on Xkool Technologies and Spacemaker AI, two architectural start-ups developing AI systems for architecture and urban planning. It makes a particular comparison between the operations of AlphaGo and the strategic thinking of urban planning, arguing that AI now puts the future of urban planners—and possibly also architects—at risk. It then goes on to appraise the famous Move 37 made by AlphaGo in Game 2 of this match. It argues that, despite appearances, this move was not actually creative. Finally, it explores how we might view human creativity in the light of comments made about AlphaGo. The chapter concludes by speculating whether the ultimate lesson of AlphaGo is that creativity is simply a question of “perceived creativity.”
Blockchain and Supply Chain Finance: A Critical Literature Review at the Intersection of Operations, Finance and Law

In the current environment, where the COVID-19 pandemic has exposed the vulnerabilities of the incumbent paper-based trade and supply chain finance systems, digital transformation pledges to alleviate the friction on international trade. Here, we provide a timely review of state-of-the-art industry applications and theoretical perspectives on the use of blockchain as the medium toward digitalisation for supply chain finance systems. We argue that blockchain technology has an innovation promoting role in supply chain finance solutions through reducing inefficiencies and increasing visibility between different parties, which have hitherto constituted the main challenges in this sphere. Based on a review of the academic literature as well as an analysis of the industrial solutions that have emerged, we identify and discuss the financial, operational and legal challenges encountered in supply chain financing and the promise of blockchain to address these limitations. We discuss the bottlenecks as well as the benefits of blockchain and identify some necessary conditions required for the emergence of blockchain-enabled trade and supply chain financing, such as the establishment of co-opetition among supply chain actors, integration with IoT systems for data quality, and reform of regulatory and legal frameworks. We conclude by identifying promising research directions about the implementation process, inviting further research into the transformation of business models toward a more collaborative nature.
Mechanism Design Approaches to Blockchain Consensus

Blockchain consensus is a state whereby each node in a network agrees on the current state of the blockchain. Existing protocols achieve consensus via a contest or voting procedure to select one node as a dictator to propose new blocks. However, this procedure can still lead to potential attacks that make consensus harder to achieve or lead to coordination issues if multiple, competing chains (i.e., forks) are created with the potential that an untruthful fork might be selected. We explore the potential for mechanisms to be used to achieve consensus that are triggered when there is a dispute impeding consensus. Using the feature that nodes stake tokens in proof of stake (POS) protocols, we construct revelation mechanisms in which the unique (subgame perfect) equilibrium involves validating nodes propose truthful blocks using only the information that exists amongst all nodes. We construct operationally and computationally simple mechanisms under both Byzantine Fault Tolerance and a Longest Chain Rule, and discuss their robustness to attacks. Our perspective is that the use of simple mechanisms is an unexplored area of blockchain consensus and has the potential to mitigate known trade-offs and enhance scalability.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Semantics-Guided Disentangled Learning for Recommendation

Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users’ true interests from interaction data. Recent disentangled learning methods emphasize on untangling users’ true interests from historical interaction records, which however overlook auxiliary information to correct bias. In this paper, we design a novel method called SeDLR (Semantics Disentangled Learning Recommendation) to bridge this gap. Particularly, by leveraging rich heterogeneous information networks (HIN), SeDLR is capable of untangling high-order user-item relationships into multiple independent components according to their semantic user intents. In addition, SeDLR offers reliable explanations for the disentangled graph embeddings by the designed Monte Carlo edge-drop component. Finally, we conduct extensive experiments on two benchmark datasets and achieve state-of-the-art performance compared against recent strong baselines.
U Can’t (re)Touch This – A Deep Learning Approach for Detecting Image Retouching

Mobile applications make it easier than ever to edit images and share these images on the Internet. Even if this image manipulation is not a criminal offense in most cases, retouched images can have a number of negative effects. The goal of this work is to provide a solution for an automatic recognition of image retouching. For this, we adapt a well known convolutional neural network (CNN) from [5] to the domain of RGB images and create a data set to train it. Specifically, we process 1 000 images with nine different filters using Snapseed, an image editing app. Then, we use these 10 000 images in two different experiments to train the adapted CNN. The first experiment compares each single filter with the original images, while the second experiment tries to distinguish all ten classes at once. Overall, the first experiment achieves an accuracy of over 99% for most classes, and the second experiments a total accuracy of roughly 88%. Finally, we analyze, why some of the classes have lower accuracies.
Sentiment Analysis on Citizenship Amendment Act of India 2019 Using Twitter Data

For the perspective of the latest happening news or some events occurring worldwide, social media is widely used and the reaction given by the people’s opinion is in the form of raw natural data in many languages and environments. All those written views have some unbalanced statements, i.e., some sensitive information or some slang words and uneven words. This makes opinion mining and making strategic decision useful in the future market. The structured and unbalanced data, Natural Language Processing (NLP) and Data Mining techniques are used for sentiment analysis. In the developed method, the study focuses on Twitter data on Citizenship Amendment Act of India, 2019 to detect the sentiment of the views from people all over the world using machine learning techniques. Many people had given their opinions and views about this new rule for CAA throughout that time. By purifying and analyzing the data using NLP techniques, VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment polarity is calculated. The dataset is normalized to be used by machine learning algorithms and prepared using natural language techniques such as Word Tokenization, Stemming and lemmatization, and Part of Speech (POS) Tagging. All that input variables are converted into vectors by Term Frequency-Inverse Document Frequency (TF-IDF). This method is implemented in a python programming language. The evaluation parameters such as accuracy, precision, recall, and F1-score were obtained for Naïve Bayes, SVM (support vector machine), K-Nearest Neighbor, Neural Network, Logistic Regression, Random Forest and LSTM (Long-short Term Memory) based RNN (Recurrent Neural Network). Finally, the results are compared. A One-way Analysis of Variance (ANOVA) test was performed on the mean values of performance metrics on all the methods.
Geometric Searchable Encryption Without False Positive And Its Applications

Abstract As a prominent cryptographic tool, geometric searchable encryption (GSE) can be applied in many scenarios, such as location-based services (LBS), social networks and vehicle networks. Unfortunately, most of existing searchable encryption schemes supporting the functionality of geometric range searches suffer from false positives, which will lead people to make a wrong decision and further raise some serious consequences such as financial loss. In addition, some of them are designed under a symmetric system, which is not enough flexible deployed in LBS since in a symmetric system only a private key holder creates ciphertext, whereas in a public-key system anyone who holds a public key can produce ciphertext. In this paper, we intend to design a novel GSE scheme without any false positive under a public-key system supporting arbitrary geometric area searches, which is able to guarantee an accurate query result. Toward this goal, we develop a novel technique in handling the relation between a point and any convex polygon in combination with an inner product encryption, which is able to support arbitrary convex polygon range searches without any false positive. A comprehensive experiment demonstrates that, compared with the known schemes, our scheme possesses a 100% accuracy as well as an acceptable efficiency in the sense that it can guarantee that all files retrieved by users are exactly matched ones. Finally, we provide two practical examples of our GSE scheme: privacy-preserving friend-nearby notification with a common point of interest and privacy-preserving parking monitor and guiding system.
Histo-Fusion: A Novel Domain Specific Learning to Identify Invasive Ductal Carcinoma (IDC) from Histopathological Images

Today breast cancer is the leading type of cancer among women undergoing cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible convolutions. Histopathological images from the biopsy examination present a large amount of structural information that can significantly improve the prognosis for breast cancer. The pathological analysis, which involves the microscopic examination of the histopathological slides, is a challenging task. An automated computer-aided detection (CAD) procedure is inevitable, as it may decrease the pathologist examination time and help detect the disease at an early stage. Lately, deep learning methods using artificial neural networks are consistently in use to improve the performance of CAD methods. A common practice among recent studies is to use the transfer learning approach of training deep neural network architectures. Transfer Learning is an established learning approach that facilitates a deep neural network to train quickly on a specific dataset and resolve an interdisciplinary problem. Deep Learning methods employing the transfer learning approach have provided highly competitive results on the datasets consisting of the whole slide images, which are captured generally at high resolutions. However, the performance is not remarkably appreciable on the small and low-resolution image datasets, in particular the datasets that include patch samples. In this direction, the present study proposes a novel domain-specific learning strategy, Breast Histo-Fusion, which aims to detect breast cancer even from images of low resolution and small size. Further, four state-of-the-art deep CNNs (AlexNet, VGG19, ResNet, and DenseNet) are trained using both learning approaches: Transfer Learning and Histo-Fusion on the IDC dataset. The proposed Histo-Fusion learning approach has improved the discriminating abilities and performance of each deep CNN by providing better results of (AlexNet – 95.75%, VGG19 – 95.96%, ResNet34 – 96.17%, ResNet50 – 96.67%, and DenseNet121 – 97.49%) compared to (AlexNet – 90.41, VGG19 – 90.51%, ResNet34 – 90.83%, ResNet50 – 92.27%, and DenseNet121 – 93.05%) using the transfer learning strategy. As a result, the procedure can help expert pathologists to perform accurate diagnoses and reduce false-positive rates.