Reviewerly/exHarmony huggingface dataset:
Learning from adversarial medical images for X-ray breast mass segmentation

Simulation of diverse lesions in images is proposed and applied to overcome the scarcity of labeled data, which has hindered the application of deep learning in medical imaging. However, most of current studies focus on generating samples with class labels for classification and detection rather than segmentation, because generating images with precise masks remains a challenge. Therefore, we aim to generate realistic medical images with precise masks for improving lesion segmentation in mammagrams. In this paper, we propose a new framework for improving X-ray breast mass segmentation performance aided by generated adversarial lesion images with precise masks. Firstly, we introduce a conditional generative adversarial network (cGAN) to learn the distribution of real mass images as well as a mapping between images and corresponding segmentation masks. Subsequently, a number of lesion images are generated from various binary input masks using the generator in the trained cGAN. Then the generated adversarial samples are concatenated with original samples to produce a dataset with increased diversity. Furthermore, we introduce an improved U-net and train it on the previous augmented dataset for breast mass segmentation. To demonstrate the effectiveness of our proposed method, we conduct experiments on publicly available mammogram database of INbreast and a private database provided by Nanfang Hospital in China. Experimental results show that an improvement up to 7% in Jaccard index can be achieved over the same model trained on original real lesion images. Our proposed method can be viewed as one of the first steps toward generating realistic X-ray breast mass images with masks for precise segmentation.
TGM: A Generative Mechanism for Publishing Trajectories With Differential Privacy

We describe a new generative algorithm called trajectory generative mechanism (TGM) for publishing trajectory datasets with ε-differential privacy guarantee, which achieves substantially higher computational efficiency and utility (practical) than the state-of-the-art algorithms. Our algorithm first encodes (models) the data as a graphical generative model and accurately captures the statistics of moving object trajectories. Using this model, TGM then privately generates synthetic trajectories such that the noise is optimally added to capture the movement direction of an object. Our algorithm preserves both the spatial and temporal information of trajectories in the generated dataset, requires less memory and computation than the competing approaches, and preserves the properties of real trajectory data in terms of traveled distance and stay location. We demonstrate the performance of TGM on both real and simulated datasets with a wide range of settings. Our experimental results show that TGM achieves high utility and efficiency by using the properties of the data.
Instance Segmentation of Biomedical Images with an Object-Aware Embedding Learned with Local Constraints

Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available ( https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings/ ).
Liberalization of Digital Twins of IoT-Enabled Home Appliances via Blockchains and Absolute Ownership Rights

IoT-enabled home appliances are still in their infancy, especially from the perspective of customers, which hinders widespread market penetration. To relieve this skeptical attitude of customers toward IoT appliances, it is important to design human-centric business models rather than corporate-centric ones. IoT ecosystems that make use of cognitive outputs from the social network of things can assist in the establishment of such business models. The contributions of this article are twofold. First, we propose a reference model that grants the absolute ownership of the fog-located digital twin (DT) of a home appliance to its owner and promotes human-centric services and applications on this twin by utilizing blockchains (BCs) and BC-enabled clouds. Second, by enabling customers to have absolute ownership rights over the fog-located DTs, the model paves the way for the liberalization of DTs as well as the appliances coupled to them, which is an important step in revealing the true value that underlies human-centricity and things-to-things collaboration.
Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning

Automatic and reliable segmentation of the pancreas is an important but difficult task for various clinical applications, such as pancreatic cancer radiotherapy and computer-aided diagnosis (CAD). The main challenges for accurate CT pancreas segmentation lie in two aspects: (1) large shape variation across different patients, and (2) low contrast and blurring around the pancreas boundary. In this paper, we propose a two-stage, ensemble-based fully convolutional neural network (FCN) to solve the challenging pancreas segmentation problem in CT images. First, candidate region generation is performed by classifying patches generated by superpixels. Second, five FCNs based on the U-Net architecture are trained with different objective functions. For each network, 2.5D slices are used as the input to provide 3D image information complementarily without the need for computationally expensive 3D convolutions. Then, an ensemble model is utilized to combine the five output segmentation maps and achieve the final segmentation. The proposed method is extensively evaluated on a publicly available dataset of 82 manually segmented CT volumes via 4-fold cross-validation. Experimental results show its superior performance compared with several state-of-the-art methods with a Dice coefficient of 84.10±4.91% and Jaccard coefficient of 72.86±6.89%.
Blockchain and maritime supply-chain performance: dynamic capabilities perspective

Purpose Maritime supply chains rely on electronic and paper-based processes, leading to efficiency bottlenecks. The purpose of this paper is to propose a theory for how implementing digitalization in the form of blockchain technology (BCT) can improve the efficiency and effectiveness of maritime supply chains. Design/methodology/approach This conceptual paper is grounded in dynamic capabilities, supply-chain management and digital supply-chain theories. Relevant literature is scrutinized to explain how BCT can improve supply-chain performance. Findings BCT mainly benefits supply-chain performance through the development of intangible capabilities, by leveraging the capabilities (resources) that maritime supply chains provide. Research limitations/implications This framework sets an analytical basis for future empirical research on BCT and maritime supply chains. As such, it can give only indications and aid in the theory-building process. Discussion on the value and effects of BCT on maritime supply chains is ongoing. Finally, the framework focuses on the application of one information technology in managing logistics activities, rather than taking a bundling approach. Practical implications Investments in digitalization improve the overall information sharing, coordination and visibility capabilities and performances of supply chains. Originality/value Although literature on BCT is extensive, this framework paper is the first to link BCT with dynamics capabilities and maritime supply-chain theory and will serve as a road map for future research and practice.
Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark and Waymo Open dataset demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK

We consider the dynamic of gradient descent for learning a two-layer neural network. We assume the input $x\in\mathbb{R}^d$ is drawn from a Gaussian distribution and the label of $x$ satisfies $f^{\star}(x) = a^{\top}|W^{\star}x|$, where $a\in\mathbb{R}^d$ is a nonnegative vector and $W^{\star} \in\mathbb{R}^{d\times d}$ is an orthonormal matrix. We show that an \emph{over-parameterized} two-layer neural network with ReLU activation, trained by gradient descent from \emph{random initialization}, can provably learn the ground truth network with population loss at most $o(1/d)$ in polynomial time with polynomial samples. On the other hand, we prove that any kernel method, including Neural Tangent Kernel, with a polynomial number of samples in $d$, has population loss at least $\Omega(1 / d)$.
Object-and-Action Aware Model for Visual Language Navigation

Vision-and-Language Navigation (VLN) is unique in that it requires turning relatively general natural-language instructions into robot agent actions, on the basis of visible environments. This requires to extract value from two very different types of natural-language information. The first is object description (e.g., ‘table’, ‘door’), each presenting as a tip for the agent to determine the next action by finding the item visible in the environment, and the second is action specification (e.g., ‘go straight’, ‘turn left’) which allows the robot to directly predict the next movements without relying on visual perceptions. However, most existing methods pay few attention to distinguish these information from each other during instruction encoding and mix together the matching between textual object/action encoding and visual perception/orientation features of candidate viewpoints. In this paper, we propose an Object-and-Action Aware Model (OAAM) that processes these two different forms of natural language based instruction separately. This enables each process to match object-centered/action-centered instruction to their own counterpart visual perception/action orientation flexibly. However, one side-issue caused by above solution is that an object mentioned in instructions may be observed in the direction of two or more candidate viewpoints, thus the OAAM may not predict the viewpoint on the shortest path as the next action. To handle this problem, we design a simple but effective path loss to penalize trajectories deviating from the ground truth path. Experimental results demonstrate the effectiveness of the proposed model and path loss, and the superiority of their combination with a $$50\%$$ SPL score on the R2R dataset and a $$40\%$$ CLS score on the R4R dataset in unseen environments, outperforming the previous state-of-the-art.
An Investigation of Why Overparameterization Exacerbates Spurious Correlations

We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data. Through simulations and experiments on two image datasets, we identify two key properties of the training data that drive this behavior: the proportions of majority versus minority groups, and the signal-to-noise ratio of the spurious correlations. We then analyze a linear setting and theoretically show how the inductive bias of models towards memorizing fewer examples can cause overparameterization to hurt. Our analysis leads to a counterintuitive approach of subsampling the majority group, which empirically achieves low minority error in the overparameterized regime, even though the standard approach of upweighting the minority fails. Overall, our results suggest a tension between using overparameterized models versus using all the training data for achieving low worst-group error.
Z-set Based Approach to Control System Design

In real life problems If-Then rules antecedent and consequent parts are characterized with the bimodal information. The bimodal information covers probabilistic and fuzzy uncertainty (not separately) in existing literature. There are fuzzy reasoning approach and probabilistic approximate reasoning approach that deal separately with fuzzy uncertainty and probabilistic uncertainty. There is the need to develop new approach to approximate reasoning with If $$ \overline{\ldots} $$ Then rules which are based on bimodal information. In this paper, we suggest new approach for approximate reasoning with Z-set-based rules. Numerical example with 7 Z-set-based rules for approximate reasoning are considered.
Entertainment Chatbot for the Digital Inclusion of Elderly People Without Abstraction Capabilities

Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user's mood. Its novelty lies in the concept of “intelligent radio”, according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -background news- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.
Dynamics of superconducting qubit relaxation times

Abstract Superconducting qubits are a leading candidate for quantum computing but display temporal fluctuations in their energy relaxation times T 1 . This introduces instabilities in multi-qubit device performance. Furthermore, autocorrelation in these time fluctuations introduces challenges for obtaining representative measures of T 1 for process optimization and device screening. These T 1 fluctuations are often attributed to time varying coupling of the qubit to defects, putative two level systems (TLSs). In this work, we develop a technique to probe the spectral and temporal dynamics of T 1 in single junction transmons by repeated T 1 measurements in the frequency vicinity of the bare qubit transition, via the AC-Stark effect. Across 10 qubits, we observe strong correlations between the mean T 1 averaged over approximately nine months and a snapshot of an equally weighted T 1 average over the Stark shifted frequency range. These observations are suggestive of an ergodic-like spectral diffusion of TLSs dominating T 1 , and offer a promising path to more rapid T 1 characterization for device screening and process optimization.
Reconfigurable electronic circuits for magnetic fields controlled by structured light

Dynamic control over the conduction band electrons of a semiconductor is a central technological pursuit. Beyond electronic circuitry, flexible control over the spatial and temporal character of semiconductor currents enables precise spatiotemporal structuring of magnetic fields. Despite their importance in science and technology, the control of magnetic fields at the micrometre spatial scale and femtosecond temporal scale using conventional electromagnets remains challenging. Here, we apply structured light beams to interfering photoexcitation pathways in gallium arsenide to sculpt the spatial and momentum configuration of its conduction band population. Programmable control over several hundred micrometre-scale current elements is achieved by manipulating the wavefronts of an optical beam using a spatial light modulator, enabling vast flexibility in the excited current patterns. Using this platform, we demonstrate dynamic optoelectronic interconnects, circuits for spatially tailored magnetic fields and magnetic field lattices. Structured beams of light are used to engineer conduction band populations and pattern currents. Using the approach, dynamic optoelectronic interconnects and other applications are demonstrated.
Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.
Neuro-symbolic artificial intelligence

Neuro-Symbolic Artificial Intelligence – the combination of symbolic methods with methods that are based on artificial neural networks – has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.
A novel approach for dimension reduction using word embedding: An enhanced text classification approach

One of the challenging tasks in text classification is to reduce the dimensional feature space. This paper discusses an enhanced text classification method using Bag-of-Words representation model with term frequency-inverse document frequency (tf-idf) and word embedding technique ‘GloVe’ to find words with similar semantic meaning. We select the word with the highest sum of tf-idf as the most representative word with similar meanings. The performance of the proposed method is compared with other methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Latent Semantic Indexing (LSI), a hybrid approach PCA+LDA using the Naïve Bayes classifier. Experimental results on three datasets, namely BBC, Classic4, and 20-newsgroup datasets, show that the proposed algorithm gives better classification results than existing dimension reduction techniques. Lastly, we defined a new performance evaluation metric to check the classifier's performance on the reduced features.