Reviewerly/exHarmony huggingface dataset:
Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity.
State-level needs for social distancing and contact tracing to contain COVID-19 in the United States

Starting in mid-May 2020, many US states began relaxing social-distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of 22 July 2020, we found that only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing and distancing. Increased testing and contact-tracing capacity is paramount for mitigating the recent large-scale increases in US cases and deaths. Using a US state-level Bayesian susceptible, exposed, infectious, removed (SEIR) compartmental model, the authors demonstrate that, in almost all states, doubling rates of contact tracing and testing while also rolling back reopening by 25–50% via social distancing can mitigate the resurgence of COVID-19.
Do Adversarially Robust ImageNet Models Transfer Better

Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on standard datasets can be efficiently adapted to downstream tasks. Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect of transfer learning performance. In this work, we identify another such aspect: we find that adversarially robust models, while less accurate, often perform better than their standard-trained counterparts when used for transfer learning. Specifically, we focus on adversarially robust ImageNet classifiers, and show that they yield improved accuracy on a standard suite of downstream classification tasks. Further analysis uncovers more differences between robust and standard models in the context of transfer learning. Our results are consistent with (and in fact, add to) recent hypotheses stating that robustness leads to improved feature representations. Our code and models are available at https://github.com/microsoft/robust-models-transfer.
Image super-resolution via channel attention and spatial graph convolutional network

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and obtained remarkable performance. However, most of the existing CNN-based SR methods mainly focus on wider or deeper architecture design, neglecting to discover the latent relationship of features, hence limiting the representational ability of networks. To address this issue, we propose a channel attention and spatial graph convolutional network (CASGCN) for more powerful feature obtaining and feature correlations modeling. The CASGCN is formed by several channel attention and spatial graph (CASG) blocks that incorporate global spatial and channel inter-dependencies for rendering features of each pixel. Inside the CASG block, channel branch and spatial branch are first arranged in a paralleled way, and then are concatenated to effectively learn the representation of each image pixel. Specifically, we use attention mechanism to extract informative features in channel branch while the spatial-aware graph is used in spatial branch to model the global self-similar information. Furthermore, the adjacency matrix in spatial-aware graph is dynamically generated via the Gram matrix to model global correlations between pixels and is shared across the whole network without auxiliary parameters. Extensive experiments on SISR with different degradation models show the effectiveness of our CASGCN in terms of quantitative and visual results.
Application of Internet of Things and artificial intelligence for smart fitness: A survey

The revolution of Internet of Things (IoT) is pervading many facets of our everyday life. Among the multiple IoT application domains, well-being is becoming one of the popular scenarios in IoT which aims to offer new services including smart fitness. This paper focuses on smart fitness covering IoT-based solutions for this domain as well as the impacts of artificial intelligence and social-IoT. IoT-based smart fitness is divided into three categories: Fitness trackers (including wearable and non-wearable sensors), movement analysis and fitness applications. Data collected from IoT-based smart fitness and users could be used for enhancing training performance by Artificial Intelligence (AI)-based algorithms. Sensor to sensor relationship is another notable topic which can be implemented by social-IoT that can share data, information and experiences of users’ training from different places and times. In this his study a comprehensive review on different types of fitness trackers and fitness applications in provided and followed by a review of AI algorithms used in smart fitness scenarios. Lastly detail discussions on the benefits and the potential problems of smart fitness are presented and a shortlist of existing gaps and potential future work have been identified and proposed.
Exploring relationship development with social chatbots: A mixed-method study of replika

This mixed-method investigation proposes and empirically tests a human-Artificial Intelligence (AI) relationship development model in the context of social chatbots. Utilizing data from representative populations and employing method triangulation, the study uniquely combines existing human-computer interaction theoretical concepts (Computers are Social Actors, Perceived Social Presence, and Parasocial Interaction) with interpersonal relationship theories (Social Penetration and Attachment Theories) to advance an explanatory model of human – AI relationship development mechanism. We identify AI Anthropomorphism and AI Authenticity as antecedents, AI Social Interaction as a mediator, and Attachment to AI as an outcome of this process, moderated by the AI usage motivations. Meaningful theoretical, managerial, and societal implications, as well as suggestions for chatbot designers and future research are provided.
<scp>TNT</scp>version 1.6, with a graphical interface for<scp>MacOS</scp>and Linux, including new routines in parallel

A new graphical user interface (GUI) for the parsimony program TNT is presented that works under the Linux and Mac operating systems, as well as the Cygwin environment (which runs under Windows). The new interface is based on the GIMP Tool Kit, GTK (version 3). Formerly, only Windows versions of TNT had a GUI. The new interface improves upon the existing Windows GUI in several respects. These changes, together with several additions to the program since the publication of version 1.5, warrant a change in minor version, thus moving from version 1.5 to 1.6. Among the most notable improvements are the possibility to access graphical user dialogs by means of simple commands, to easily save trees in SVG format ("Scalable Vector Graphics") directly from any tree-diagram being displayed, and to manage analyses in parallel (using multiple processors, by means of the PVM system or "Parallel Virtual Machine"), as well as a generally more stable and consistent behaviour. As the binaries for the new version are compiled as native 64-bit applications, this removes the limitations for accessing large amounts of memory in the previous GUI Windows interface (which is a 32-bit application).
Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.
Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCALContext and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling

Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity. We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. Also, the generated interpretative time-aware attention weights shed some lights on the progression behaviors of septic shock.
Sustainability of Educational Technologies: An Approach to Augmented Reality Research

Technological advancements have posed a challenge to educational institutions in the sustainability of management, research and teaching activities. Virtual technologies have allowed digital transformation to be incorporated into university education—among them, augmented reality (AR). The study examined the evolution of global research on this topic in the period 2005–2019. A bibliometric analysis of 1977 articles was performed, obtaining results of scientific productivity. The evidence showed a growing interest in studying the sustainability of AR in higher education. According to the results, the main category was computer science; the most productive journal was Computer and Education; the authors with the largest number of publications were Nee and Ong, while Wang was the most frequently cited author; the most productive institution was the National Taiwan University of Science and Technology; the United States was the country with the largest number of publications and citations; and, finally, the United States and Spain were the countries with the largest number of international collaborations in their articles. Five lines of research were identified, among which those oriented to the topics of technological resources, computer, simulation, education and learning stand out. It was verified that this research topic has a growing and dynamic interest in scientific activity, and its sustainable approach is confirmed by being linked to the Sustainable Development Goals (SDGs).
Invertible Zero-Shot Recognition Flows

Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with ZSL-oriented regularizers and the limited generation quality) hinder the existing generative ZSL models from fully bypassing the seen-unseen bias. To tackle the above limitations, for the first time, this work incorporates a new family of generative models (i.e., flow-based models) into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings (i.e., the semantic factors and the non-semantic ones) with the forward pass of an invertible flow network, while the reverse pass generates data samples. This procedure theoretically extends conventional generative flows to a factorized conditional scheme. To explicitly solve the bias problem, our model enlarges the seen-unseen distributional discrepancy based on a negative sample-based distance measurement. Notably, IZF works flexibly with either a naive Bayesian classifier or a held-out trainable one for zero-shot recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods, in both classic and generalized settings.
PDPChain: A Consortium Blockchain-Based Privacy Protection Scheme for Personal Data

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.
Application of improved version of multi verse optimizer algorithm for modeling solar radiation

For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust soft computing method is applied to predict solar​ radiation of two stations located in the southeast region of China. For modeling solar radiation of selected stations, the improved version of multi verse optimizer algorithm (IMVO) is utilized with integration of least square support vector machine (LSSVM) for better tuning the hyperparameters of LSSVM model. For validation, the newly developed method is compared with other algorithms integrated with LSSVM models, such as LSSVM with genetic algorithm (LSSVM-GE), LSSVM with gray wolf optimization (LSSVM-GWO), LSSVM with sine–cosine algorithm (LSSVM-CSA) and LSSVM with multi verse algorithm original version (LSSVM-MVO). It is found that newly developed method, LSSVM-IMVO, provided more accurate results in comparison to other models. For better visualization of data and model application, three different training testing data splitting strategies are used. It is found that the increase in training sample size considerably improved the models’ accuracies.
Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot

As the key point for auto-navigation of mobile robot, path planning is a research hotspot in the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the commonly used approaches aiming to solve the problem of path planning of mobile robot. Nevertheless, the traditional ACO has the shortcomings such as slow convergence speed, inefficiency and easily fall into local optimal values. Thus, a novel variant of ACO is proposed in this study. In detail, a new heuristic mechanism with orientation information is firstly introduced to add direction guidance during the iteration process, further to advance the convergence speed of algorithm. Secondly, an improved heuristic function is presented to enhance the purposiveness and reduce the number of turn times of planned path. Then, an improved state transition probability rule is introduced to improve the search efficiency significantly and increase the swarm diversity. Moreover, a new method for unevenly distributing initial pheromone concentration is proposed to avoid blind searching. After integrating the four improvements, the new variation of ACO called modified adaptive ant colony optimization algorithm (MAACO) is formed. Subsequently, parameter optimization of MAACO is carried out. For verifying the effectiveness of the proposed MAACO, a series of experiments are conducted based on five static space environment modes and one dynamic environment mode. Comparing with 13 existing approaches for solving the problem of path planning of mobile robot, including several variants of ACO and two commonly used algorithms (A* algorithm and Dijkstra algorithm), the experimental results demonstrate the merits of MAACO in terms of decreasing the path length, reducing the number of turn times, and promoting the convergence speed. In detail, in all the static simulation experiments, the proposed MAACO generates the shortest path length with a standard deviation of zero, and achieves the least number of turn times within the smallest convergence generation. In terms of the five experiments, the average number of reducing turn times is two with a generally reduction ratio of 22.2% compared with the best existing results. The obtained results of MAACO prove its practicality and high-efficiency for path planning.
Deep Generalized Unfolding Networks for Image Restoration

Deep neural networks (DNN) have achieved great suc-cess in image restoration. However, most DNN methods are designed as a black box, lacking transparency and inter-pretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined degradation processes or hand-crafted assumptions, making it difficult to deal with complex and real-world applications. In this paper, we propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradi-ent descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation. In addition, we design inter-stage in-formation pathways across proximal mapping in different PGD iterations to rectify the intrinsic information loss in most deep unfolding networks (DUN) through a multi-scale and spatial-adaptive way. By integrating the flexible gradi-ent descent and informative proximal mapping, we unfold the iterative PGD algorithm into a trainable DNN. Exten-sive experiments on various image restoration tasks demon-strate the superiority of our method in terms of state-of-the-art performance, interpretability, and generalizability. The source code is available at github.com/MC-E/DGUNet.