Reviewerly/exHarmony huggingface dataset:
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems

The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data. GNNs have achieved state-of-the-art results across an impressive array of graph-based machine learning problems. Nevertheless, despite their rapid pace of development, much of the work on GNNs has focused on graph classification and embedding techniques, largely ignoring regression tasks over graph data. In this paper, we develop a Graph Mixture Density Network (GraphMDN), which combines graph neural networks with mixture density network (MDN) outputs. By combining these techniques, GraphMDNs have the advantage of naturally being able to incorporate graph structured information into a neural architecture, as well as the ability to model multi-modal regression targets. As such, GraphMDNs are designed to excel on regression tasks wherein the data are graph structured, and target statistics are better represented by mixtures of densities rather than singular values (so-called “inverse problems”). To demonstrate this, we extend an existing GNN architecture known as Semantic GCN (SemGCN) to a GraphMDN structure, and show results from the Human3.6M pose estimation task. The extended model consistently outperforms both GCN and MDN architectures on their own, with a comparable number of parameters.
Towards a Theory of Decentralized Finance

Decentralized Finance (DeFi) has brought about decentralized applications which allow untrusted users to lend, borrow and exchange crypto-assets. Many of such applications fulfill the role of markets or market makers, featuring complex, highly parametric incentive mechanisms to equilibrate interest rates and prices. This complexity makes the behaviour of DeFi applications difficult to understand: indeed, ill-designed incentive mechanisms could potentially lead to emergent unwanted behaviours. We argue that theories, techniques and tools rooted in formal methods can provide useful instruments to better understand, specify and analyze DeFi systems. We summarize in this paper our first steps towards a theory of DeFi based on formal methods, and we overview the open challenges and opportunities for formal methods in DeFi.
An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization

Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.
A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World

Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the personalized medical decision support system. Our primary purpose is to study the impact of external case characteristics (ECC) on the effectiveness of the personalized medical decision support system for breast cancer assisted diagnosis (PMDSS-BCAD) in making accurate recommendations. Therefore, we designed a novel comprehensive framework for case-based reasoning (CBR) that takes the impact of external features of cases into account, made use of the naive Bayes and k-nearest neighbor (KNN) algorithms (CBR-ECC), and developed a PMDSS-BCAD system by using the CBR-ECC model and external features as system components. Under the new case-based reasoning framework, the accuracy of the combined model of naive Bayes and KNN with an optimal K value of 2 is 99.40%. Moreover, in a real hospital scenario, users rated the PMDSS-BCAD system, which takes into account the external characteristics of the case, better than the original personalized system. These results suggest that PMDSS-BCD can not only provide doctors with more personalized and accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the personalized system.
Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization

Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists researchers and practitioners in understanding data. Thereby, by utilizing feature selection, better performance and reduced computational consumption, memory complexity and even data amount can be expected. Although there exist approaches leveraging the power of deep neural networks to carry out feature selection, many of them often suffer from sensitive hyperparameters. This paper proposes a feature mask module (FM-module) for feature selection based on a novel batch-wise attenuation and feature mask normalization. The proposed method is almost free from hyperparameters and can be easily integrated into common neural networks as an embedded feature selection method. Experiments on popular image, text and speech datasets have shown that our approach is easy to use and has superior performance in comparison with other state-of-the-art deep-learning-based feature selection methods.
An adaptive representation algorithm for Multi-scale logo detection

The proliferation of logo has driven research into multiple applications, like logo duration monitoring in advertising videos and logo infringement detection. Recently, logo research has attracted a rapt attention and keen interest from researchers. In these studies, logo detection is challenging due to the characteristics of the logo (like multi-scale, large-scale categories, viewpoint and part deformation etc.) and the complexity of its background. In this paper, we design a new strong baseline method based on an adaptive representation algorithm, called obtaining sufficient features for logo (OSF-Logo). The method aims to address the challenges of the multi-scale objects, large-scale categories, viewpoint logos and part deformation via introducing two modules. Specifically, we introduce a regulated deformable convolution module with offsets and amplitudes to more convolution layers in the stage of feature extraction. In addition, we add an up-sampling operator to FPN for aggregating information into a large receptive field. The experimental results on several publicly available datasets demonstrate the effectiveness of OSF-Logo.
SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.
Performance of Low-Latency HTTP-based Streaming Players

Reducing end-to-end streaming latency is critical to HTTP-based live video streaming. There are currently two technologies in this domain: Low-Latency HTTP Live Streaming (LL-HLS) and Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). Many players support LL-HLS and/or LL-DASH protocols, including Apple's AVPlayer, Shaka player, HLS.js Dash.js, and others. This paper is dedicated to the analysis of the performance of low-latency players and streaming protocols. The evaluation is based on a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of real-world networks. Several performance metrics, such as average stream bitrate, the amounts of downloaded media data, streaming latency, as well as buffering and stream switching statistics are captured and reported in our experiments. These results are subsequently used to describe the observed differences in the performance of LL-HLS and LL-DASH-based players.
Adaptive Decision Forest: An incremental machine learning framework

In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, we introduce a new splitting strategy called iSAT, which allows ADF to classify new records even if they are associated with previously unseen classes. ADF is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches. We evaluate ADF on nine publicly available natural datasets and one synthetic dataset, and compare the performance of ADF against the performance of eight state-of-the-art techniques. We also examine the effectiveness of ADF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.
A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users. In this scheme, classification learning is used for identifying replicas and accessing granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.
Hierarchical Attention Link Prediction Neural Network

In this paper, a novel end-to-end neural link prediction model, named Hierarchical Attention Link Prediction Neural Network (HalpNet), is proposed. HalpNet comprehensively explores neighborhood information, which has proved important for link prediction, via the core component, i.e., hierarchical attention mechanism. The proposed hierarchical attention mechanism consists of two neural attention layers, modeling crucial structure information at node level and subgraph level, respectively. At node level, a structure-preserving attention is developed to preserve structure features of each node in the neighborhood subgraph. Based on the latent node features, at subgraph level, a structure-aggregating attention is designed to learn how important that each node in the subgraph is for the linkage of the target node pair and aggregate node features with learned attentions as a comprehensive subgraph representation. Given this expressive representation of neighborhood subgraph, HalpNet is able to predict link score of target node pair effectively. We evaluate HalpNet on 8 benchmark datasets against 14 popular and state-of-the-art approaches. The experimental results demonstrate its significant superiority and wide applicability on link prediction problem.
Synergistically Exploiting CNN Pruning and HLS Versioning for Adaptive Inference on Multi-FPGAs at the Edge

FPGAs, because of their energy efficiency, reconfigurability, and easily tunable HLS designs, have been used to accelerate an increasing number of machine learning, especially CNN-based, applications. As a representative example, IoT Edge applications, which require low latency processing of resource-hungry CNNs, offload the inferences from resource-limited IoT end nodes to Edge servers featuring FPGAs. However, the ever-increasing number of end nodes pressures these FPGA-based servers with new performance and adaptability challenges. While some works have exploited CNN optimizations to alleviate inferences’ computation and memory burdens, others have exploited HLS to tune accelerators for statically defined optimization goals. However, these works have not tackled both CNN and HLS optimizations altogether; neither have they provided any adaptability at runtime, where the workload’s characteristics are unpredictable. In this context, we propose a hybrid two-step approach that, first, creates new optimization opportunities at design-time through the automatic training of CNN model variants (obtained via pruning) and the automatic generation of versions of convolutional accelerators (obtained during HLS synthesis); and, second, synergistically exploits these created CNN and HLS optimization opportunities to deliver a fully dynamic Multi-FPGA system that adapts its resources in a fully automatic or user-configurable manner. We implement this two-step approach as the AdaServ Framework and show, through a smart video surveillance Edge application as a case study, that it adapts to the always-changing Edge conditions: AdaServ processes at least 3.37× more inferences (using the automatic approach) and is at least 6.68× more energy-efficient (user-configurable approach) than original convolutional accelerators and CNN Models (VGG-16 and AlexNet). We also show that AdaServ achieves better results than solutions dynamically changing only the CNN model or HLS version, highlighting the importance of exploring both; and that it is always better than the best statically chosen CNN model and HLS version, showing the need for dynamic adaptability.
Camouflaged people detection based on a semi-supervised search identification network

Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions. With the equipment gradually moving towards intelligence, unmanned aerial vehicles (UAVs) will be widely used for integrated reconnaissance/attack in the future. The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility. However, as the camouflage has been designed to blend in with surroundings, which greatly reduces the probability of the target being discovered. Moreover, the lack of training data on camouflaged people detection will inhibit the training of a deep model. To address these problems, a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images. In this paper, the camouflaged object detection dataset (COD10K) is first supplemented according to our mission requirements, then the edge attention is utilized to enhance the boundaries based on search identification network. Further, a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy. Experiments demonstrate that the proposed semi-supervised search identification network (Semi-SINet) performs well in camouflaged people detection compared with other object detection methods.
Efficient Non-Targeted Attack for Deep Hashing Based Image Retrieval

As the deep hashing technique has been widely used in large-scale image retrieval, the corresponding security issue is getting more and more attention. Recent studies have found that deep image classifiers are vulnerable to adversarial example attacks and produce misleading classifications. Therefore, in order to study the robustness of deep hashing based retrieval system to adversarial example, in this letter, we propose a novel adversarial example generation algorithm, non-targeted deep hashing attack (NDHA), which uses the anchor-moving strategy to continuously modify anchor image to cross the search correlation boundary and maximize Hamming distance between the hash codes of adversarial example and query image. The generated adversarial example can make the retrieved result semantically irrelevant to query image. Extensive experiments show that the proposed NDHA can efficiently produce imperceptible perturbation, which is effective for attacking deep hashing based retrieval systems.
Multi-aspect self-supervised learning for heterogeneous information network

Graph neural networks (GNNs) have made remarkable advancements in processing graph-structured data with all nodes and edges belonging to the same type. However, various types of node and relations exist in heterogeneous information networks (HINs), and due to this, HINs contain rich structural and semantic information. To tackle this heterogeneity, existing methods usually apply several well-designed metapaths to HINs to obtain the corresponding homogeneous subgraphs. However, these methods either fail to capture the interconnections between the same nodes in different subgraphs or require qualified labels. To address these issues, we propose a new multi-aspect self-supervised learning (SSL) framework for HIN representation in an unsupervised manner: (1) we design a new contrastive learning model to capture the similarities between the same nodes in different homogeneous subgraphs, and (2) we maximize the mutual information between the local patches and the global representation in one subgraph. Extensive experiments on various downstream tasks demonstrate the superiority of our model in comparison to the existing state-of-the-art methods.
Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a 2D end-to-end model of attention R2U-Net with multi-task deep supervision (MTDS). MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning. Furthermore, we propose the attention pre-activation residual module (APR), which is an attention mechanism based on multi-scale fusion methods. APR is suitable for a deep learning model to help the network locate the tumor area accurately. Finally, we evaluate our proposed model on the public BraTS 2020 validation dataset which consists of 125 cases, and got a competitive brain tumor segmentation result. Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.
Analytics of high average-utility patterns in the industrial internet of things

Abstract Recently, revealing more valuable information except for quantity value for a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure for pattern analytics and evaluations. HAUIM provides a more fair assessment than generic high utility itemset mining and ignores the influence of the length of itemsets. There are several high-performance HAUIM algorithms proposed to gain knowledge from a disorganized database. However, most existing works do not concern the uncertainty factor, which is one of the characteristics of data gathered from IoT equipment. In this work, an efficient algorithm for HAUIM to handle the uncertainty databases in IoTs is presented. Two upper-bound values are estimated to early diminish the search space for discovering meaningful patterns that greatly solve the limitations of pattern mining in IoTs. Experimental results showed several evaluations of the proposed approach compared to the existing algorithms, and the results are acceptable to state that the designed approach efficiently reveals high average utility itemsets from an uncertain situation.
Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization

Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk.