Reviewerly/exHarmony huggingface dataset:
Information fusion in visual question answering: A Survey

Visual question answering automatically answers natural language questions according to the content of an image or video. The task is challenging because it requires the understanding of semantic information in the textual and visual channels, as well as their interplay. A typical solver is composed of three components: feature extraction from singular modality, feature fusion between visual and textual channels, and answer prediction based on the learnt joint representation. Among them, information fusion plays a key role in enhancing the overall accuracy and various types of approaches have been proposed, such as simple vector operators, deep neural networks, bilinear pooling, attention mechanisms, and memory networks. The primary objective of this survey is to provide a clear organization and comprehensive review on the ever-proposed fusion techniques in the domain of visual question answering. We propose an abstract fusion framework that can fit the majority of existing VQA models, making it convenient for readers to quickly understand their key contributions. Finally, we summarize the effective fusion strategies that have been widely adopted so as to benefit readers in their model design.
ShieldStore

The shielded computation of hardware-based trusted execution environments such as Intel Software Guard Extensions (SGX) can provide secure cloud computing on remote systems under untrusted privileged system software. However, hardware overheads for securing protected memory restrict its capacity to a modest size of several tens of megabytes, and more demands for protected memory beyond the limit cause costly demand paging. Although one of the widely used applications benefiting from the enhanced security of SGX, is the in-memory key-value store, its memory requirements are far larger than the protected memory limit. Furthermore, the main data structures commonly use fine-grained data items such as pointers and keys, which do not match well with the coarse-grained paging of the SGX memory extension technique. To overcome the memory restriction, this paper proposes a new in-memory key-value store designed for SGX with application-specific data security management. The proposed key-value store, called ShieldStore, maintains the main data structures in unprotected memory with each key-value pair individually encrypted and integrity-protected by its secure component running inside an enclave. Based on the enclave protection by SGX, ShieldStore provides secure data operations much more efficiently than the baseline SGX key-value store, achieving 8--11 times higher throughput with 1 thread, and 24--30 times higher throughput with 4 threads.
Análisis de la Competencia Digital Docente: Factor Clave en el Desempeño de Pedagogías Activas con Realidad Aumentada

In a time conditioned by continuous technological advances, it is essential to train teachers in digital competence in order to respond to the needs and concerns of students. The educational processes of the teacher acquire great relevance to have the required skills against the use of new emerging resources. This study focuses on knowing the digital teaching competence in the development and application of augmented reality resources. A non-experimental design of descriptive and correlational type has been followed through a quantitative method. Two questionnaires were used, applied to a sample of 2631 teachers. The results obtained reveal that the teaching staff carries out continuous training, carrying out 2-3 courses per year related to ict and other areas, in person. They demonstrate skills in certain areas of digital teaching competence, such as security and communication and collaboration, as well as a deficit in the creation of digital content. Deepening in the areas more analogous to augmented reality, according to the type of center and educational stage, it is the public and primary education centers that have revealed the highest level of competence.
Credit-Based Payments for Fast Computing Resource Trading in Edge-Assisted Internet of Things

The introduction of edge computing into blockchain-enabled Internet of Things (IoT) for offloading computational tasks is attracting increasing attention. Computing resource trading unavoidably happens in edge-assisted IoT. However, efficient computing resource trading cannot be achieved because of the “cold start” and “long return” problems. To address these challenges, we propose to use a credit-based payment for fast computing resource trading in edge-assisted blockchain-enabled IoT; therefore, the IoT nodes can finish fast payment and frequent trading by borrowing resource coins from other IoT nodes based on their credit values. In our resource-coin loan problem, we propose an iterative double-auction-based algorithm, where a broker is introduced to solve the loan allocation problem and to determine the size of the loan each lender would provide to each borrower. Furthermore, the broker enforces specific loan pricing rules to induce the borrowers and lenders to bid truthfully. Then, the hidden privacy information could be extracted to achieve the optimal resource-coin allocation and loan pricing. The proposed algorithm can maximize the economic benefits while protecting privacy. Simulations showed that the proposed algorithm can maximize social welfare. In addition, we compared the proposed algorithm with the credit-bank-based method in terms of the satisfaction function and payments. The experimental results demonstrated that the proposed algorithm was individually rational, truthful, and budget-balanced.
Designing a general type-2 fuzzy expert system for diagnosis of depression

Depression is a common and important mental disorder that affects the quality of human life. Since people with depression are not aware of their disorder and sometimes suffer from physical symptoms such as chronic pain, refer to a physician instead of a psychologist. Hence, physician’s diagnosis is not always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis, considering two main objectives, was developed. These objectives include accuracy of the system and diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system has five steps. In the first step, we generate general type-2 membership function by using zSlices method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The proposed general type-2 expert system has been tested and evaluated to show its performance. This Intelligent system is able to diagnose depression accurately at a suitable time.
Voice User Interfaces in Schools

Voice user interfaces (VUIs) are increasingly popular, particularly in homes. However, little research has investigated their potential in other settings, such as schools. We investigated how VUIs could support inclusive education, particularly for pupils with visual impairments (VIs). We organised focused discussions with educators at a school, with support staff from local authorities and, through bodystorming, with a class of 27 pupils. We then ran a series of co-design workshops with participants with mixed-visual abilities to design an educational VUI application. This provided insights into challenges faced by pupils with VIs in mainstream schools, and opened a space for educators, sighted and visually impaired pupils to reflect on and design for their shared learning experiences through VUIs. We present scenarios, a design space and an example application that show novel ways of using VUIs for inclusive education. We also reflect on co-designing with mixed-visual-ability groups in this space.
Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network

Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole‐slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carcinoma (BCC) using smartphone‐captured images. To develop deep neural network frameworks for accurate BCC recognition and segmentation based on smartphone‐captured microscopic ocular images (MOIs). We collected a total of 8046 MOIs, 6610 of which had binary classification labels and the other 1436 had pixelwise annotations. Meanwhile, 128 WSIs were collected for comparison. Two deep learning frameworks were created. The ‘cascade’ framework had a classification model for identifying hard cases (images with low prediction confidence) and a segmentation model for further in‐depth analysis of the hard cases. The ‘segmentation’ framework directly segmented and classified all images. Sensitivity, specificity and area under the curve (AUC) were used to evaluate the overall performance of BCC recognition. The MOI‐ and WSI‐based models achieved comparable AUCs around 0·95. The ‘cascade’ framework achieved 0·93 sensitivity and 0·91 specificity. The ‘segmentation’ framework was more accurate but required more computational resources, achieving 0·97 sensitivity, 0·94 specificity and 0·987 AUC. The runtime of the ‘segmentation’ framework was 15·3 ± 3·9 s per image, whereas the ‘cascade’ framework took 4·1 ± 1·4 s. Additionally, the ‘segmentation’ framework achieved 0·863 mean intersection over union. Based on the accessible MOIs via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathology with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinical scenarios. What's already known about this topic? The diagnosis of basal cell carcinoma (BCC) is labour intensive due to the large number of images to be examined, especially when consecutive slide reading is needed in Mohs surgery. Deep learning approaches have demonstrated promising results on pathological image‐related diagnostic tasks. Previous studies have focused on whole‐slide images (WSIs) and leveraged classification on image patches for detecting and localizing breast cancer metastases. What does this study add? Instead of WSIs, microscopic ocular images (MOIs) photographed from microscope eyepieces using smartphone cameras were used to develop neural network models for recognizing BCC automatically. The MOI‐ and WSI‐based models achieved comparable areas under the curve around 0·95. Two deep learning frameworks for recognizing BCC pathology were developed with high sensitivity and specificity. Recognizing BCC through a smartphone could be considered a future clinical choice.
Research on Topic Detection and Tracking for Online News Texts

With the rapid development of the Internet, the amount of data has grown exponentially. On the one hand, the accumulation of big data provides the basic support for artificial intelligence. On the other hand, in the face of such huge data information, how to extract the knowledge of interest from it has become a matter of general concern. Topic tracking can help people to explore the process of topic development from the huge and complex network texts information. By effectively organizing large-scale news documents, a method for the evolution of news topics over time is proposed in this paper to realize the tracking and evolution of topics in the news text set. First, the LDA (latent Dirichlet allocation) model is used to extract topics from news texts and the Gibbs Sampling method is used to speculate parameters. The topic mining using the K-means method is compared to highlight the advantages of using LDA for topic discovery. Second, the improved single-pass algorithm is used to track news topics. The JS (Jensen-Shannon) divergence is used to measure the topic similarity, and the time decay function is introduced to improve the similarity between topics with the similar time. Finally, the strength of the news topic and the content change of the topic in different time windows are analyzed. The experiments show that the proposed method can effectively detect and track the topic and clearly reflect the trend of topic evolution.
RGB-D sensing based human action and interaction analysis: A survey

Human activity recognition has been actively studied in the last three decades. Compared to human action performed by a single person, human interaction is more complex due to the involvement of more subjects and the interdependence between them. Recently, motivated by the remarkable success of deep learning techniques, many learning-based feature representations have been developed for activity recognition. This paper provides a comprehensive review of human action and interaction recognition methods, covering both hand-crafted features and learning-based features, with a special focus on data captured by RGB-D sensors. Furthermore, this review reveals practical challenges in human activity analysis along with their promising solutions and potential future directions.
Retinal image synthesis from multiple-landmarks input with generative adversarial networks

Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial networks (GANs) to enlarge training datasets for facilitating medical image analysis. For instance, conventional methods such as image-to-image translation techniques are used to synthesize fundus images with their respective vessel trees in the field of fundus image.In order to improve the image quality and details of the synthetic images, three key aspects of the pipeline are mainly elaborated: the input mask, architecture of GANs, and the resolution of paired images. We propose a new preprocessing pipeline named multiple-channels-multiple-landmarks (MCML), aiming to synthesize color fundus images from a combination of vessel tree, optic disc, and optic cup images. We compared both single vessel mask input and MCML mask input on two public fundus image datasets (DRIVE and DRISHTI-GS) with different kinds of Pix2pix and Cycle-GAN architectures. A new Pix2pix structure with ResU-net generator is also designed, which has been compared with the other models.As shown in the results, the proposed MCML method outperforms the single vessel-based methods for each architecture of GANs. Furthermore, we find that our Pix2pix model with ResU-net generator achieves superior PSNR and SSIM performance than the other GANs. High-resolution paired images are also beneficial for improving the performance of each GAN in this work. Finally, a Pix2pix network with ResU-net generator using MCML and high-resolution paired images are able to generate good and realistic fundus images in this work, indicating that our MCML method has great potential in the field of glaucoma computer-aided diagnosis based on fundus image.
Fractal

In this paper we propose Fractal, a high performance and high productivity system for supporting distributed graph pattern mining (GPM) applications. Fractal employs a dynamic (auto-tuned) load-balancing based on a hierarchical and locality-aware work stealing mechanism, allowing the system to adapt to different workload characteristics. Additionally, Fractal enumerates subgraphs by combining a depth-first strategy with a from scratch processing paradigm to avoid storing large amounts of intermediate state and, thus, improves memory efficiency. Regarding programmer productivity, Fractal presents an intuitive, expressive and modular API, allowing for rapid compositional expression of many GPM algorithms. Fractal-based implementations outperform both existing systemic solutions and specialized distributed solutions on many problems - from frequent graph mining to subgraph querying, over a range of datasets.
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: “polynomially coded sharding” scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system. We provide simulation results that numerically demonstrate the performance improvement over state of the arts, and the scalability of the PolyShard system. Finally, we discuss potential enhancements, and highlight practical considerations in building such a system.
GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain

Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored onchain and the raw data are outsourced to off-chain storage. To protect data integrity, a cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> *-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.
Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find 'contour-like' objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases.
An image encryption algorithm based on ZigZag transform and LL compound chaotic system

This paper proposes a new image encryption algorithm based on ZigZag transform and LL compound chaotic system. The improved ZigZag transform method and the Lü system are combined to scramble the original image block sub-channel, and complement it by a sorting scrambling algorithm with identification values. LL compound chaotic system is combined with Lü system and logistic chaotic map. It provides a secure and efficient key stream. The new chaotic encryption system destroys the pixel values and finally uses the adjacent-side XOR method to spread. The experimental results and security analysis show that the new algorithm has good encryption effect and can resist common attacks.
On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context

Recent advances in information technology have induced an explosive growth of data, creating a new era of big data. Unfortunately, traditional machine-learning algorithms cannot cope with the new characteristics of big data. In this paper, we address the problem of breast cancer prediction in the big data context. We considered two varieties of data, namely, gene expression (GE) and DNA methylation (DM). The objective of this paper is to scale up the machine-learning algorithms that are used for classification by applying each dataset separately and jointly. For this purpose, we chose Apache Spark as a platform. In this paper, we selected three different classification algorithms, namely, support vector machine (SVM), decision tree, and random forest, to create nine models that help in predicting breast cancer. We conducted a comprehensive comparative study using three scenarios with the GE, DM, and GE and DM combined, in order to show which of the three types of data would produce the best result in terms of accuracy and error rate. Moreover, we performed an experimental comparison between two platforms (Spark and Weka) in order to show their behavior when dealing with large sets of data. The experimental results showed that the scaled SVM classifier in the Spark environment outperforms the other classifiers, as it achieved the highest accuracy and the lowest error rate with the GE dataset.