Reviewerly/exHarmony huggingface dataset:
Virtual Reality in Clinical Practice and Research: Viewpoint on Novel Applications for Nursing

Virtual reality is a novel technology that provides users with an immersive experience in 3D virtual environments. The use of virtual reality is expanding in the medical and nursing settings to support treatment and promote wellness. Nursing has primarily used virtual reality for nursing education, but nurses might incorporate this technology into clinical practice to enhance treatment experience of patients and caregivers. Thus, it is important for nurses to understand what virtual reality and its features are, how this technology has been used in the health care field, and what future efforts are needed in practice and research for this technology to benefit nursing. In this article, we provide a brief orientation to virtual reality, describe the current application of this technology in multiple clinical scenarios, and present implications for future clinical practice and research in nursing.
Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition

Existing text recognition methods usually need large-scale training data. Most of them rely on synthetic training data due to the lack of annotated real images. However, there is a domain gap between the synthetic data and real data, which limits the performance of the text recognition models. Recent self-supervised text recognition methods attempted to utilize unlabeled real images by introducing contrastive learning, which mainly learns the discrimination of the text images. Inspired by the observation that humans learn to recognize the texts through both reading and writing, we propose to learn discrimination and generation by integrating contrastive learning and masked image modeling in our self-supervised method. The contrastive learning branch is adopted to learn the discrimination of text images, which imitates the reading behavior of humans. Meanwhile, masked image modeling is firstly introduced for text recognition to learn the context generation of the text images, which is similar to the writing behavior. The experimental results show that our method outperforms previous self-supervised text recognition methods by 10.2%-20.2% on irregular scene text recognition datasets. Moreover, our proposed text recognizer exceeds previous state-of-the-art text recognition methods by averagely 5.3% on 11benchmarks, with similar model size. We also demonstrate that our pre-trained model can be easily applied to other text-related tasks with obvious performance gain.
Reinforced Cross-modal Alignment for Radiology Report Generation

Medical images are widely used in clinical decision-making, where writing radiology reports is a potential application that can be enhanced by automatic solutions to alleviate physicians' workload.In general, radiology report generation is an image-text task, where cross-modal mappings between images and texts play an important role in generating high-quality reports.Although previous studies attempt to facilitate the alignment via the co-attention mechanism under supervised settings, they suffer from lacking valid and accurate correspondences due to no annotation of such alignment.In this paper, we propose an approach with reinforcement learning (RL) over a cross-modal memory (CMM) to better align visual and textual features for radiology report generation.In detail, a shared memory is used to record the mappings between visual and textual information, and the proposed reinforced algorithm is performed to learn the signal from the reports to guide the cross-modal alignment even though such reports are not directly related to how images and texts are mapped.Experimental results on two English radiology report datasets, i.e., IU X-Ray and MIMIC-CXR, show the effectiveness of our approach, where the state-of-the-art results are achieved.We further conduct human evaluation and case study which confirm the validity of the reinforced algorithm in our approach. 1
Holographic dynamics simulations with a trapped-ion quantum computer

Quantum computers have the potential to efficiently simulate the dynamics of many interacting quantum particles, a classically intractable task of central importance to fields ranging from chemistry to high-energy physics. However, precision and memory limitations of existing hardware severely limit the size and complexity of models that can be simulated with conventional methods. Here, we demonstrate and benchmark a new scalable quantum simulation paradigm--holographic quantum dynamics simulation--which uses efficient quantum data compression afforded by quantum tensor networks along with opportunistic mid-circuit measurement and qubit reuse to simulate physical systems that have far more quantum degrees of freedom than can be captured by the available number of qubits. Using a Honeywell trapped ion quantum processor, we simulate the non-integrable (chaotic) dynamics of the self-dual kicked Ising model starting from an entangled state of $32$ spins using at most $9$ trapped ion qubits, obtaining excellent quantitative agreement when benchmarking against dynamics computed directly in the thermodynamic limit via recently developed exact analytical techniques. These results suggest that quantum tensor network methods, together with state-of-the-art quantum processor capabilities, enable a viable path to practical quantum advantage in the near term.
Multi-Aspect enhanced Graph Neural Networks for recommendation

Graph neural networks (GNNs) have achieved remarkable performance in personalized recommendation, for their powerful data representation capabilities. However, these methods still face several challenging problems: (1) the majority of user–item interaction graphs only utilize the interaction information, which cannot reflect the users’ specific preferences for different aspects, making it difficult to capture user preferences in a fine-grained manner. (2) there is no effective way to integrate multi-aspect preferences into a unified model to capture the comprehensive user interests. To address these challenges, we propose a Multi-Aspect enhanced Graph Neural Networks (MA-GNNs) model for item recommendation. Specifically, we learn the aspect-based sentiments from reviews and use them to construct multiple aspect-aware user–item graphs, thus giving the edge practical meaning. And aspect semantic features are introduced into the information aggregation process to adjust users’ preferences for different items. Furthermore, we design a routing-based fusion mechanism, which adaptively allocates weights to different aspects to realize the dynamic fusion of aspect preferences. We conduct experiments on four publicly available datasets, and the experimental results show that the proposed MA-GNNs model outperforms state-of-the-art methods. Further analysis proves that fine-grained interest modeling can improve the interpretability of recommendations.
Temporal Knowledge Graph Completion: A Survey

Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
The IITM Model: A Simple and Expressive Model for Universal Composability

Abstract The universal composability paradigm allows for the modular design and analysis of cryptographic protocols. It has been widely and successfully used in cryptography. However, devising a coherent yet simple and expressive model for universal composability is, as the history of such models shows, highly non-trivial. For example, several partly severe problems have been pointed out in the literature for the UC model. In this work, we propose a coherent model for universal composability, called the IITM model (“Inexhaustible Interactive Turing Machine”). A main feature of the model is that it is stated without a priori fixing irrelevant details, such as a specific way of addressing of machines by session and party identifiers, a specific modeling of corruption, or a specific protocol hierarchy. In addition, we employ a very general notion of runtime. All reasonable protocols and ideal functionalities should be expressible based on this notion in a direct and natural way, and without tweaks, such as (artificial) padding of messages or (artificially) adding extra messages. Not least because of these features, the model is simple and expressive. Also the general results that we prove, such as composition theorems, hold independently of how such details are fixed for concrete applications. Being inspired by other models for universal composability, in particular the UC model and because of the flexibility and expressivity of the IITM model, conceptually, results formulated in these models directly carry over to the IITM model.
The Semantic Web identity crisis: In search of the trivialities that never were

For a domain with a strong focus on unambiguous identifiers and meaning, the Semantic Web research field itself has a surprisingly ill-defined sense of identity. Started at the end of the 1990s at the intersection of databases, logic, and Web, and influenced along the way by all major tech hypes su ch as Big Data and machine learning, our research community needs to look in the mirror to understand who we really are. The key question amid all possible directions is pinpointing the important challenges we are uniquely positioned to tackle. In this article, we highlight the community's unconscious bias toward addressing the Paretonian 80% of problems through research – handwavingly assuming that trivial engineering can solve the remaining 20%. In reality, that overlooked 20% could actually require 80% of the total effort and involve significantly more research than we are inclined to think, because our theoretical experimentation environments are vastly different from the open Web. As it turns out, these formerly neglected "trivialities" might very well harbor those research opportunities that only our community can seize, thereby giving us a clear hint of how we can orient ourselves to maximize our impact on the future. If we are hesitant to step up, more pragmatic minds will gladly reinvent technology for the real world, only covering a fraction of the opportunities we dream of.
Unsupervised adaptive hashing based on feature clustering

An attractive method for image retrieval is binary hashing, which aims to reduce the dimensionality and generate similarity-preserving binary codes. To map the high-dimensional data into a low-dimensional subspace, majority of current unsupervised hashing approaches reduce the dimensionality by principal component analysis (PCA). However, PCA will yield unbalanced variances of projection directions and cause inconvenience in the quantization step. Besides, preserving the original similarity in existing unsupervised hashing methods remains as an NP-hard problem. For addressing these problems, we explore a novel hashing method based on feature clustering to simultaneously generate low-dimensional data with balanced variance and preserve the similarity in Euclidean space. Furthermore, we also propose an adaptive quantization approach to displace the fixed threshold quantization. Our novel method, dubbed as Feature Clustering Hashing (FCH), has shown its superiority to state-of-the-art methods on three benchmark datasets.
Size, efficiency, market power, and economies of scale in the African banking sector

There is a growing body of evidence that interest rate spreads in Africa are higher for big banks compared to small banks. One concern is that big banks might be using their market power to charge higher lending rates as they become larger, more efficient, and unchallenged. In contrast, several studies found that when bank size increases beyond certain thresholds, diseconomies of scale are introduced that lead to inefficiency. In that case, we also would expect to see widened interest margins. This study examines the connection between bank size and efficiency to understand whether that relationship is influenced by exploitation of market power or economies of scale. Using a panel of 162 African banks for 2001–2011, we analyzed the empirical data using instrumental variables and fixed effects regressions, with overlapping and non-overlapping thresholds for bank size. We found two key results. First, bank size increases bank interest rate margins with an inverted U-shaped nexus. Second, market power and economies of scale do not increase or decrease the interest rate margins significantly. The main policy implication is that interest rate margins cannot be elucidated by either market power or economies of scale. Other implications are discussed.
Interpretive Performance and Inter-Observer Agreement on Digital Mammography Test Sets

To evaluate the interpretive performance and inter-observer agreement on digital mammographs among radiologists and to investigate whether radiologist characteristics affect performance and agreement.The test sets consisted of full-field digital mammograms and contained 12 cancer cases among 1000 total cases. Twelve radiologists independently interpreted all mammograms. Performance indicators included the recall rate, cancer detection rate (CDR), positive predictive value (PPV), sensitivity, specificity, false positive rate (FPR), and area under the receiver operating characteristic curve (AUC). Inter-radiologist agreement was measured. The reporting radiologist characteristics included number of years of experience interpreting mammography, fellowship training in breast imaging, and annual volume of mammography interpretation.The mean and range of interpretive performance were as follows: recall rate, 7.5% (3.3-10.2%); CDR, 10.6 (8.0-12.0 per 1000 examinations); PPV, 15.9% (8.8-33.3%); sensitivity, 88.2% (66.7-100%); specificity, 93.5% (90.6-97.8%); FPR, 6.5% (2.2-9.4%); and AUC, 0.93 (0.82-0.99). Radiologists who annually interpreted more than 3000 screening mammograms tended to exhibit higher CDRs and sensitivities than those who interpreted fewer than 3000 mammograms (p = 0.064). The inter-radiologist agreement showed a percent agreement of 77.2-88.8% and a kappa value of 0.27-0.34. Radiologist characteristics did not affect agreement.The interpretative performance of the radiologists fulfilled the mammography screening goal of the American College of Radiology, although there was inter-observer variability. Radiologists who interpreted more than 3000 screening mammograms annually tended to perform better than radiologists who did not.
Text Classification Using SVM Enhanced by Multithreading and CUDA

With the sudden growth of the internet and digital documents available on the web, the task of organizing text data has become a major problem.In recent times, text classification has become one of the main techniques for organizing text data.The idea behind text classification is to classify a given piece of text to a predefined class or category.In the present research work, SVM has been used with linear kernel using the One-V-Rest strategy.The SVM is trained using various data sets collected from various sources.It may so happen that some particular words were not so common around 5-6 years ago, but are currently prevalent due to recent trends.Similarly, new discoveries may result in the coinage of new words.This process can also be applied to text blogs which can be crawled and then analyzed.This technique should in theory be able to classify blogs, tweets or any other document with a significant amount of accuracy.In any text classification process, preprocessing phase takes the most amount of timecleaning, stemming, lemmatization etc.Hence, the authors have used a multithreading approach to speed up the process.The authors further tried to improve the processing time of the algorithm using GPU parallelism using CUDA.
Mixed path planning for multi‐robots in structured hospital environment

Multi-robots are used in hospital for the delivery service. To increase the delivery efficiency, an appropriate multi-robots path planning algorithm is required. This study explains a mixed path planning algorithm for multi-robots in structured hospital environment which combines the ‘corridor path planning’ and ‘room path planning’, ‘corridor path planning’ uses graph search algorithm to plan the path in corridor for multiple hospital robots, and it uses the strongly connected digraph to describe corridor environment, so it could simplify the multi-robots path planning problem. While the ‘room path planning’ uses the artificial potential field method to plan the path in room for multiple hospital robots, it makes the hospital robot move flexibly in the room, so the robot could avoid the obstacle and other robots more flexibly. The algorithm is tested by simulation, and the result shows that mixed path planning algorithm makes the robot move fast in the corridor environment and move flexibly in the room environment.
Fully Deniable Interactive Encryption

Deniable encryption (Canetti et al., Crypto 1996) enhances secret communication over public channels, providing the additional guarantee that the secrecy of communication is protected even if the parties are later coerced (or willingly bribed) to expose their entire internal states: plaintexts, keys and randomness. To date, constructions of deniable encryption—and more generally, interactive deniable communication—only address restricted cases where only one party is compromised (Sahai and Waters, STOC 2014). The main question—whether deniable communication is at all possible if both parties are coerced at once—has remained open. We resolve this question in the affirmative, presenting a communication protocol that is fully deniable under coercion of both parties. Our scheme has three rounds, assumes subexponentially secure indistinguishability obfuscation and one-way functions, and uses a short global reference string that is generated once at system set-up and suffices for an unbounded number of encryptions and decryptions. Of independent interest, we introduce a new notion called off-the-record deniability, which protects parties even when their claimed internal states are inconsistent (a case not covered by prior definitions). Our scheme satisfies both standard deniability and off-the-record deniability.
New Inpainting Algorithm Based on Simplified Context Encoders and Multi-Scale Adversarial Network

Inpainting refers to reconstruct the incomplete image or video via analysing their context, feature of the tailing etc. Convolutional neural network with deep learning is proved to be an effective method to achieve inpainting. However, those algorithms existed now usually have vague and blurry results with huge amount of time to train the models. To address this issue, this article based on the construction of Context Encoders, continue to use the strategy of combining the encoders and generative adversarial networks (GANs), on which we add the global discriminator, consisting of the multi-scale adversarial network with the local discriminator altogether. The local discriminator ensures the local detail while the global discriminator guarantees the global consistency. Comparing with the Context Encoders, this network is simplified by reducing some of the redundant fabric, therefore this network is faster. Meantime, we re-calculate the loss function of the network and train it with the Paris dataset. The results proved that our network can achieve a better performance on street-view pictures than Context Encoders to some extent.
On Energy Efficiency and Performance Evaluation of Single Board Computer Based Clusters: A Hadoop Case Study

Energy efficiency in a data center is a challenge and has garnered researchers interest. In this study, we addressed the energy efficiency issue of a small scale data center by utilizing Single Board Computer (SBC)-based clusters. A compact layout was designed to build two clusters using 20 nodes each. Extensive testing was carried out to analyze the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput and energy consumption. Further, we investigated the cost of operating SBC-based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Results show that, although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is possible to tweak Hadoop configuration parameters for an ARM-based SBC cluster to efficiently utilize resources. We present a discussion on the effectiveness of the SBC-based clusters as a testbed for inexpensive and green cloud computing research.
A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.