Reviewerly/exHarmony huggingface dataset:
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation

Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution. To fit a 3D CNN model on CT or MRI images on commodity GPUs, one usually has to either downsample input images or use cropped local regions as inputs, which limits the utility of 3D models for multi-organ segmentation. In this work, we propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions, but guided by spatial contextual information extracted from a low-resolution 3D model. We implement a self-attention mechanism to control which 3D features should be used to guide 2D segmentation. Our model is light on memory usage but fully equipped to take 3D contextual information into account. Experiments on multiple organ segmentation datasets demonstrate that by taking advantage of both 2D and 3D models, our method is consistently outperforms existing 2D and 3D models in organ segmentation accuracy, while being able to directly take raw whole-volume image data as inputs.
A Security Analysis of Blockchain-Based Did Services

Decentralized identifiers (DID) has shown great potential for sharing user identities across different domains and services without compromising user privacy. DID is designed to enable the minimum disclosure of the proof from a user's credentials on a need-to-know basis with a contextualized delegation. At first glance, DID appears to be well-suited for this purpose. However, the overall security of DID has not been thoroughly examined. In this paper, we systemically explore key components of DID systems and analyze their possible vulnerabilities when deployed. First, we analyze the data flow between DID system components and analyze possible security threats. Next, we carefully identify potential security threats over seven different DID functional domains, ranging from user wallet to universal resolver. Lastly, we discuss the possible countermeasures against the security threats we identified.
Generalized Zero-Shot Learning With Multiple Graph Adaptive Generative Networks

Generative adversarial networks (GANs) for (generalized) zero-shot learning (ZSL) aim to generate unseen image features when conditioned on unseen class embeddings, each of which corresponds to one unique category. Most existing works on GANs for ZSL generate features by merely feeding the seen image feature/class embedding (combined with random Gaussian noise) pairs into the generator/discriminator for a two-player minimax game. However, the structure consistency of the distributions among the real/fake image features, which may shift the generated features away from their real distribution to some extent, is seldom considered. In this paper, to align the weights of the generator for better structure consistency between real/fake features, we propose a novel multigraph adaptive GAN (MGA-GAN). Specifically, a Wasserstein GAN equipped with a classification loss is trained to generate discriminative features with structure consistency. MGA-GAN leverages the multigraph similarity structures between sliced seen real/fake feature samples to assist in updating the generator weights in the local feature manifold. Moreover, correlation graphs for the whole real/fake features are adopted to guarantee structure correlation in the global feature manifold. Extensive evaluations on four benchmarks demonstrate well the superiority of MGA-GAN over its state-of-the-art counterparts.
A Blockchain-based approach for matching desired and real privacy settings of social network users

Social networks store a considerable amount of personal data, which are also a source of information for business. To comply with users’ privacy rights, all social networks allow users to select the level of privacy they desire. However, what occurs if the privacy choices of a user are modified unilaterally by the social network? The privacy settings chosen by the user are stored by the social network, which acts as a privileged party, which could tamper with the user’s choices at any time. This paper addresses this problem and proposes a decentralized approach to manage the privacy settings of a user. Any change in the privacy settings of a social network user is validated by a smart contract to ensure that it is compliant with users’ expectations. The proposed solution has been implemented as an Ethereum-based decentralized application to validate the effectiveness of the proposed approach.
Resource-Constrained Neural Architecture Search on Edge Devices

The performance requirement of deep learning inevitably brings up with the expense of high computational complexity and memory requirements, to make it problematic for the deployment on resource-constrained devices. Edge computing, which distributedly organizes the computing node close to the data source and end-device, provides a feasible way to tackle the high-efficiency demand and substantial computational load. Whereas given edge device is resource-constrained and energy-sensitive, designing effective neural network architecture for specific edge device is urgent in the sense that deploys the deep learning application by the edge computing solution. Undoubtedly manually design the high-performing neural architectures is burdensome, let alone taking account of the resource-constraint for the specific platform. Fortunately, the success of Neural Architecture Search techniques come up with hope recently. This paper dedicates to directly employ multi-objective NAS on the resource-constrained edge devices. We first propose the framework of multi-objective NAS on edge device, which comprehensively considers the performance and real-world efficiency. Our improved MobileNet-V2 search space also strikes the scalability and practicality, so that a series of Pareto-optimal architectures are received. Benefits from the directness and specialization during search procedure, our experiment on JETSON NANO shows the comparable result with the state-of-the-art models on ImageNet.
Property Inference from Poisoning

Property inference attacks consider an adversary who has access to a trained ML model and tries to extract some global statistics of the training data. In this work, we study property inference in scenarios where the adversary can maliciously control a part of the training data (poisoning data) with the goal of increasing the leakage. Previous works on poisoning attacks focused on trying to decrease the accuracy of models. Here, for the first time, we study poisoning attacks where the goal of the adversary is to increase the information leakage of the model. We show that poisoning attacks can boost the information leakage significantly and should be considered as a stronger threat model in sensitive applications where some of the data sources may be malicious.We theoretically prove that our attack can always succeed as long as the learning algorithm used has good generalization properties. Then we experimentally evaluate our on different datasets (Census dataset, Enron email dataset, MNIST and CelebA), properties (that are present in the training data as features, that are not present as features, and properties that are uncorrelated with the rest of the training data or classification task) and model architectures (including Resnet-18 and Resnet-50). We were able to achieve high attack accuracy with relatively low poisoning rate, namely, 2–3% poisoning in most of our experiments. We also evaluated our attacks on models trained with DP and we show that even with very small values for $\epsilon$, the attack is still quite successful <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/smahloujifar/PropertyInferenceFromPoisoning.git
An Energy-Aware Combinatorial Virtual Machine Allocation and Placement Model for Green Cloud Computing

Resource allocation is an important problem for cloud environments.This paper introduces an energy-aware combinatorial auction-based model for the resource allocation problem in clouds.The proposed model allows users of a cloud to submit their virtual resource requests as bids using the provided bidding language which allows complementarities and substitutabilities among those resources to be declared.The model finds the most profitable mutually satisfiable set of winning bids, and the corresponding allocation of virtual resources to the users while considering the placement of virtual resources to the available physical resources in the cloud by executing an optimization problem.During the optimization, the model also takes account of the non-linear energy requirements of the physical resources based on their utilization levels to find a placement with the lowest energy cost, thus, providing an energy-aware solution to the resource allocation problem.The associated optimization problem is formally defined and formulated using integer programming.Since the optimization problem is intractable, four heuristic methods are also proposed.To evaluate the performance of the model and the proposed heuristic methods, several experiments are conducted on a comprehensive test suite.The results demonstrate the benefits of the proposed model, and the high-quality solutions provided by the proposed methods.
Identifying the fingerprint of permanganate oxidizable carbon as a measure of labile soil organic carbon using Fourier transform mid‐infrared photoacoustic spectroscopy

Abstract Permanganate oxidizable carbon (POXC) is a sub‐pool of labile soil organic carbon (SOC) and is defined as the carbon (C) that can be oxidized by potassium permanganate (KMnO 4 ). Determination of the content of POXC has been used as a way to measure labile C and may be related to soil microbial biomass and particulate organic C. Fourier transform mid‐infrared photoacoustic spectroscopy (FTIR‐PAS) has been used for characterizing the chemical composition of soil organic matter. The objective of the current study was to identify the chemical information in the FTIR‐PAS spectra that is unique to POXC and therefore associated with labile C. FTIR‐PAS was used for characterizing SOC using a set of soil samples collected in four different countries (Laos, Malaysia, Peru and Thailand, n = 575). Partial least squares (PLS) regression was used to correlate the collected FTIR‐PAS spectra with both POXC and total SOC. The FTIR‐PAS spectra combined with the PLS method was useful for predicting both SOC and POXC. However, SOC and POXC were also highly correlated ( R 2 = 0.84), and because POXC can be seen as a fraction of total SOC, a method for extracting the FTIR‐PAS fingerprint of the POXC model that is independent of SOC was applied for the first time. The results showed that the POXC prediction was mainly based on its strong correlation with SOC and there was little information unique to POXC. However, the results do indicate four dominant peaks at 1,574, 1,495, 1,138 and 1,041 cm −1 to be specific for POXC. These peaks can be ascribed to amide II, aromatics, polysaccharides and carbohydrates, respectively. Highlights Permanganate oxidizable carbon (POXC) was predicted using FTIR‐photoacoustic spectroscopy (FTIR‐PAS). The POXC prediction using FTIR‐PAS is mainly based on its high correlation with SOC. A method extracting the unique spectral features of POXC independently of SOC was applied. Around 11% of the information used to predict POXC was identified as unique. Four dominant peaks were found to represent the POXC fingerprint.
CoFF: Cooperative Spatial Feature Fusion for 3-D Object Detection on Autonomous Vehicles

To reduce the amount of transmitted data, feature map-based fusion is recently proposed as a practical solution to cooperative 3-D object detection by autonomous vehicles (AVs). The precision of object detection, however, may require significant improvement, especially for objects that are far away or occluded. To address this critical issue for the safety of AVs and human beings, we propose a cooperative spatial feature fusion (CoFF) method for AVs to effectively fuse feature maps for achieving a higher 3-D object detection performance. Especially, CoFF differentiates weights among feature maps for a more guided fusion, based on how much new semantic information is provided by the received feature maps. It also enhances the inconspicuous features corresponding to far/occluded objects to improve their detection precision. The experimental results show that CoFF achieves a significant improvement in terms of both detection precision and effective detection range for AVs, compared to previous feature fusion solutions.
Pancreas segmentation using a dual-input v-mesh network

Accurate segmentation of the pancreas from abdomen scans is crucial for the diagnosis and treatment of pancreatic diseases. However, the pancreas is a small, soft and elastic abdominal organ with high anatomical variability and has a low tissue contrast in computed tomography (CT) scans, which makes segmentation tasks challenging. To address this challenge, we propose a dual-input v-mesh fully convolutional network (FCN) to segment the pancreas in abdominal CT images. Specifically, dual inputs, i.e., original CT scans and images processed by a contrast-specific graph-based visual saliency (GBVS) algorithm, are simultaneously sent to the network to improve the contrast of the pancreas and other soft tissues. To further enhance the ability to learn context information and extract distinct features, a v-mesh FCN with an attention mechanism is initially utilized. In addition, we propose a spatial transformation and fusion (SF) module to better capture the geometric information of the pancreas and facilitate feature map fusion. We compare the performance of our method with several baseline and state-of-the-art methods on the publicly available NIH dataset. The comparison results show that our proposed dual-input v-mesh FCN model outperforms previous methods in terms of the Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD) and Hausdorff distance (HD). Moreover, ablation studies show that our proposed modules/structures are critical for effective pancreas segmentation.
Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies

Summary The expectation–maximization (EM) algorithm is a familiar tool for computing the maximum likelihood estimate of the parameters in hidden Markov and semi‐Markov models. This paper carries out a detailed study on the influence that the initial values of the parameters impose on the results produced by the algorithm. We compare random starts and partitional and model‐based strategies for choosing the initial values for the EM algorithm in the case of multivariate Gaussian emission distributions (EDs) and assess the performance of each strategy with different assessment criteria. Several data generation settings are considered with varying number of latent states, of variables as well as of the level of fuzziness in the data, and discussion on how each factor influences the obtained results is provided. Simulation results show that different initialization strategies may lead to different log‐likelihood values and, accordingly, to different estimated partitions. A clear indication of which strategies should be preferred is given. We further include two real‐data examples, widely analysed in the hidden semi‐Markov model literature.
Methods for accelerating geospatial data processing using quantum computers

Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning processes. Machine learning processes enable automated image classification in geospatial data. Quantum algorithms provide novel approaches for solving these problems and a potential future advantage over current classical techniques. Universal Quantum Computers, developed by Rigetti Computing and other providers, enable fully general quantum algorithms to be executed, with theoretically proven speed-up over classical algorithms in certain cases. This paper describes an approach to satellite image classification using a universal quantum enhancement to convolutional neural networks: the quanvolutional neural network. Using a refined method, we found a performance improvement over previous quantum efforts in this domain and identified potential refinements that could lead to an eventual quantum advantage. We benchmark these networks using the SAT-4 satellite imagery dataset in order to demonstrate the utility of machine learning techniques in the space industry and the potential benefits that quantum machine learning can offer.
Face Mask Detection Using AI

Covid-19 has built a completely new frequency, and the people have realized themselves moving into a new world. While currently our society speedily transforming, we need to be quick to be able to answer fresh specifications, which have encircled all of us. Making risk-free surroundings will be priority of every person's mind so that the life can be conductive just as before. Options need to be taken to secure all those going back to our workplace and to keep ourselves and our loved ones devoid of problems. Brand-new plans have methodized every day in order to meet policies and regulation. Although, face masks are getting to be a whole new implemented standard for daily life, yet, to build safe environment that contributes to public safety, it becomes necessary to be observant throughout day and to take action against those who have not wearing mask in public places or work places. Many parts of society seem to be accepting some Covid tracking tools for safety. One of the most important tools is face mask detector. This system enables to identify who is without a required face mask. These systems work with existing surveillance systems along with innovative neural network algorithms to check whether a person has worn a face mask or not. In this chapter, we will briefly discuss the artificial intelligence and its subsets namely machine learning and deep learning, deep learning frameworks followed by a simple implementation for face mask detection system.
On Inductive–Transductive Learning With Graph Neural Networks

Many real-world domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The graph neural network (GNN) is a machine learning model capable of directly managing graph-structured data. In the original framework, GNNs are inductively trained, adapting their parameters based on a supervised learning environment. However, GNNs can also take advantage of transductive learning, thanks to the natural way they make information flow and spread across the graph, using relationships among patterns. In this paper, we propose a mixed inductive-transductive GNN model, study its properties and introduce an experimental strategy that allows us to understand and distinguish the role of inductive and transductive learning. The preliminary experimental results show interesting properties for the mixed model, highlighting how the peculiarities of the problems and the data can impact on the two learning strategies.
Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the “Natural Language Processing” course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.
Carfentanil on the darknet: Potential scam or alarming public health threat?

In an age of global insecurity, highly potent synthetic drugs have become a major public health issue. Their online advertisement and sale are facilitated by surface web, darknet markets and social media fuelling substance abuse and addiction, as well as various types of new criminal activities and their growth in sophistication. This study presents a systematic analysis of the darknet sale of one of the most potent synthetic opioids: Carfentanil. With an equianalgesic potency of 10.000 times a unit of morphine, its toxicity is comparable to traditional nerve agents, and it has been previously used as a chemical weapon, causing human fatalities. Digital trace data was collected retrospectively from all the darknet marketplaces, which have been active in the past five years. Data on vendors offering Carfentanil on Agartha, Empire and Yakuza marketplaces were analysed with regard to items sold and sellers’ features as these were the only active markets at the time of search. Searches were carried out in the English language only. 63 Cartfentanil vendors operating on 19 darknet marketplaces were identified. Contacts and payments were facilitated with end-to-end encryption messaging mobile applications and content-expiring messages. Although it is known that Agartha is a scam market, and no operative sellers were found on Yakuza, several sellers promoting Carfentanil sales were active in the Empire marketplace with a number of transaction ranging from 4 to 1223. The availability of highly potent drugs such as Carfentanil on the darknet requires the urgent development of novel scientific methods and tools able to monitor and to predict such new threats, while informing policymaking and protecting the health and the security of citizens.
Provotum: A Blockchain-based and End-to-end Verifiable Remote Electronic Voting System

While the existence of Public Bulletin Boards (PBB) is often formulated as an assumption in related work on Remote Electronic Voting (REV) systems, this work here on Provotum focuses on the practical design and architecture of such a PBB, including its distributed execution. Further, Provotum leverages a public permissioned Blockchain (BC) as a PBB, where only authorized entities can sign blocks, while the general public can verify all BC data. Therefore, Provotum defines a new and fully decentralized BC-based REV system, which deploys a permissioned BC as a PBB and allows for the explicit distribution of trust across different permissioned BC nodes. Provotum is operated in a fully distributed fashion by using Smart Contracts (SC), Distributed Key Generation (DKG), Homomorphic Encryption (HE), and Cooperative Decryption (CD), as well as employing client-side encryption, which enables ballot secrecy, while the BC forms an audit trail, enabling public and End-to-end Verifiability (E2E-V).