Reviewerly/exHarmony huggingface dataset:
DNA Strands Level Scrambling Based Color Image Encryption Scheme

In the past, many image encryption schemes have been developed through the swapping operations at the different levels of granularity.These levels span bits, Deoxyribonucleic acid (DNA) molecules, pixels, blocks of pixels.In this study, a new scheme for the encryption of color images based on the DNA strands level scrambling (DNASLS) and chaotic system has been proposed.After a color image is input, it is decomposed into the red, green and blue components.After it, these components are merged to form a big single image.Intertwining logistic map (ILM) has been used for the random data which generates three streams of random numbers.These streams have been further manipulated in such a way that nine streams are spawned out of them.One stream out of these nine streams has been used for the generation of a key image.Two streams have been used to DNA-encode the big single image and the key image.Afterwards, the strands of DNA-encoded single image are swapped with each other.Four streams determine the addresses of two DNA encoded pixels for the selection.A yet another stream is being used to select a particular strand from the DNA strands.To create the diffusion effects, an XOR operation has been done between the DNA encoded image after the swapping of strands and the DNA encoded key image.Finally, the last and ninth stream has been used to decode the DNA-encoded pixels into the decimal form.Purely random numbers with no inter-dependence have been employed in the entire encryption process.The effects of plaintext sensitivity have been achieved through the incorporation of Secure Hash Algorithm-256 or SHA-256 hash codes.In the end, the experiment and the security analysis have been performed.The results of the validation metrics like information entropy(7.9973),average key sensitivity(99.61%)and mean absolute error(84.7158)demonstrate the security, defiance to the number of attacks and a potential for real world application of the proposed image cipher.
Virtual reality services in academic libraries: deployment experience in Hong Kong

Purpose Virtual Reality (VR) has become a popular topic recently, and the quality of immersive experience nowadays is beyond our imagination. While VR applications for entertainment are common, it is a new and popular trend in academic libraries. Although many academic libraries in the West have started to provide VR services to catch up with the trend, the deployment is not quite popular in the East. This research aims to identify the reasons behind such phenomenon. Design/methodology/approach This research explores this phenomenon by studying two selected cases through interviews, site visits, and website/document analysis: the CAVE of the City University of Hong Kong Library and the VR Experience Zone of the Hong Kong Polytechnic University Library. Findings The findings indicated that VR library services are well-received and meeting user needs. However, the major limitations of using VR in academic libraries are technical capability, space and budget, causing libraries to hesitate in introducing and developing VR services. Originality/value Scant studies focus on the development, management and user feedback of VR services in academic libraries, especially in the East. Based on the findings, possible solutions for academic libraries interested in taking part in this trend are suggested.
General Concept of Real Estate Tokenization on Blockchain

Abstract This paper presents a concept of real estate tokenization, which includes legal, technological, and organizational aspects. The research introduces a theory of a Title Token – a digital record of ownership on the blockchain. It is discussed the principle of technological neutrality, where the traditional land registry is not necessarily abandoned in favor of blockchains, but instead, people gain the right to choose. Nowadays, public administrations use central-server databases, giving no alternatives for citizens. Recognition of the right of citizens to choose which technology to apply for managing their property rights creates a basis for free competition and the development of new technologies for better public services. Decentralized distributed ledgers are the key to decentralization. They enable more secure automation of legal procedures. On the contrary, centralization is a source of many issues in governance: abuse of power, corruption, inefficient governance, and high costs, slowness and complexity of bureaucratic procedures. With automation and reduction of intermediaries, the role of the government does not decrease but significantly changes, i.e. land cadaster bodies should not be monopolistic providers on the market. The paper introduces a theoretical basis for developing a new type of property registries.
On Testing Quantum Programs

A quantum computer (QC) can solve many computational problems more efficiently than a classic one.The field of QCs is growing: companies (such as DWave, IBM, Google, and Microsoft) are building QC offerings.We position that software engineers should look into defining a set of software engineering practices that apply to QC's software.To start this process, we give examples of challenges associated with testing such software and sketch potential solutions to some of these challenges.
Near-Lossless Binarization of Word Embeddings

Word embeddings are commonly used as a starting point in many NLP models to achieve state-of-the-art performances. However, with a large vocabulary and many dimensions, these floating-point representations are expensive both in terms of memory and calculations which makes them unsuitable for use on low-resource devices. The method proposed in this paper transforms real-valued embeddings into binary embeddings while preserving semantic information, requiring only 128 or 256 bits for each vector. This leads to a small memory footprint and fast vector operations. The model is based on an autoencoder architecture, which also allows to reconstruct original vectors from the binary ones. Experimental results on semantic similarity, text classification and sentiment analysis tasks show that the binarization of word embeddings only leads to a loss of ~2% in accuracy while vector size is reduced by 97%. Furthermore, a top-k benchmark demonstrates that using these binary vectors is 30 times faster than using real-valued vectors.
Template Guided Text Generation for Task-Oriented Dialogue

Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language.In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain-independent model across a large number of APIs.First, we propose a schemaguided approach which conditions the generation on a schema describing the API in natural language.Our second method investigates the use of a small number of templates, growing linearly in number of slots, to convey the semantics of the API.To generate utterances for an arbitrary slot combination, a few simple templates are first concatenated to give a semantically correct, but possibly incoherent and ungrammatical utterance.A pre-trained language model is subsequently employed to rewrite it into coherent, natural sounding text.Through automatic metrics and human evaluation, we show that our method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency. 1
Predicting quantum advantage by quantum walk with convolutional neural networks

Abstract Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enabling in these cases quantum algorithmic applications and quantum-enhanced energy transfer. However, little is known about the possible advantages on arbitrary graphs not having explicit symmetries. For these graphs one would need to perform simulations of classical and quantum walk dynamics to check if the speedup occurs, which could take a long computational time. Here we present a new approach for the solution of the quantum speedup problem, which is based on a machine learning algorithm that predicts the quantum advantage by just ‘looking’ at a graph. The convolutional neural network, which we designed specifically to learn from graphs, observes simulated examples and learns complex features of graphs that lead to a quantum advantage, allowing to identify graphs that exhibit quantum advantage without performing any quantum walk or random walk simulations. The performance of our approach is evaluated for line and random graphs, where classification was always better than random guess even for the most challenging cases. Our findings pave the way to an automated elaboration of novel large-scale quantum circuits utilizing quantum walk based algorithms, and to simulating high-efficiency energy transfer in biophotonics and material science.
Quantifying the Value of Real‐Time Geodetic Constraints for Earthquake Early Warning Using a Global Seismic and Geodetic Data Set

Abstract Geodetic earthquake early warning (EEW) algorithms complement point‐source seismic systems by estimating fault‐finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because such earthquakes are rare, it has been difficult to demonstrate that geodetic warnings improve ground motion estimation significantly. Here, we quantify and compare timeliness and accuracy of magnitude and ground motion estimates in simulated real time from seismic and geodetic observations for a suite of globally distributed, large earthquakes. Magnitude solutions saturate for the seismic EEW algorithm (we use ElarmS) while the ElarmS‐triggered Geodetic Alarm System (G‐larmS) reduces the error even for its first solutions. Shaking intensity (Modified Mercalli Intensity, MMI) time series calculated for each station and each event are assessed based on MMI threshold crossings, allowing us to accurately characterize warning times per station. We classify alerts and find that MMI 4 thresholds result in true positive alerts for 11.2% of sites exceeding MMI 4 with a median warning time of 20.1 s for ElarmS, while G‐larmS issues true positive alerts for 20.9% of all sites exceeding MMI 4 with a significantly longer median warning time of 34.2 s. The geodetic EEW system reduces the number of missed alerts for a threshold of MMI 4 from 20.5% to 10.8% for all sites, but also increases the number of false positive alerts from 1.6% to 19.9% of all sites. By quantifying increased accuracy in magnitude, ground motion estimation, and alert timeliness, we demonstrate that finite‐fault geodetic algorithms add significant value, including better cost savings performance, to point‐source seismic EEW systems for large earthquakes.
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" - brittle, ad hoc ML systems. To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems. First, we introduce ML primitives, a unified API and specification for data processing and ML components from different software libraries. Next, we compose primitives into usable ML pipelines, abstracting away glue code, data flow, and data storage. We further pair these pipelines with a hierarchy of AutoML strategies - Bayesian optimization and bandit learning. We use these components to create a general-purpose, multi-task, end-to-end AutoML system that provides solutions to a variety of data modalities (image, text, graph, tabular, relational, etc.) and problem types (classification, regression, anomaly detection, graph matching, etc.). We demonstrate 5 real-world use cases and 2 case studies of our approach. Finally, we present an evaluation suite of 456 real-world ML tasks and describe the characteristics of 2.5 million pipelines searched over this task suite.
Arabic text summarization using deep learning approach

Abstract Natural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a sequence-to-sequence framework of encoder–decoder model, which is composed of neural networks trained jointly on both input and output. Deep neural networks take advantage of big datasets to improve their results. These networks are supported by the attention mechanism, which can deal with long texts more efficiently by identifying focus points in the text. They are also supported by the copy mechanism that allows the model to copy words from the source to the summary directly. In this research, we are re-implementing the basic summarization model that applies the sequence-to-sequence framework on the Arabic language, which has not witnessed the employment of this model in the text summarization before. Initially, we build an Arabic data set of summarized article headlines. This data set consists of approximately 300 thousand entries, each consisting of an article introduction and the headline corresponding to this introduction. We then apply baseline summarization models to the previous data set and compare the results using the ROUGE scale.
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

Text summarization aims to compress a textual document to a short summary while keeping salient information.Extractive approaches are widely used in text summarization because of their fluency and efficiency.However, most of existing extractive models hardly capture intersentence relationships, particularly in long documents.They also often ignore the effect of topical information on capturing important contents.To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture intersentence relationships efficiently via graph-structured document representation.Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection.The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths.Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.
Deep triplet hashing network for case-based medical image retrieval

Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep hashing methods have a poor small-sample ranking performance for case-based medical image retrieval. The top-ranked images in the returned query results may be as a different class than the query image. This ranking problem is caused by classification, regions of interest (ROI), and small-sample information loss in the hashing space. To address the ranking problem, we propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes that preserve the classification, ROI, and small-sample information. We embed a spatial-attention module into the network structure of our ATH to focus on ROI information. The spatial-attention module aggregates the spatial information of feature maps by utilizing max-pooling, element-wise maximum, and element-wise mean operations jointly along the channel axis. To highlight the essential role of classification in direntiating case-based medical images, we propose a novel triplet cross-entropy loss to achieve maximal class-separability and maximal hash code-discriminability simultaneously during model training. The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes. Moreover, by adopting triplet labels during model training, we can utilize the small-sample information fully to alleviate the imbalanced-sample problem. Extensive experiments on two case-based medical datasets demonstrate that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods and boost the ranking performance for small samples. Compared to the other loss methods, the triplet cross-entropy loss can enhance the classification performance and hash code-discriminability.
Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps

Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based model to identify lung and colon cancer utilizing histopathological images with better augmentation techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16, NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet, and DenseNet169 are trained on LC25000 dataset. The model performances are assessed on precision, recall, f1score, accuracy, and auroc score. The results exhibit that all eight models accomplished noteworthy results ranging from 96% to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture the attention images of Pre-trained CNN models classifying malignant and benign images.
Efficient quantum digital signatures without symmetrization step

Quantum digital signatures (QDS) exploit quantum laws to guarantee non-repudiation, unforgeability and transferability of messages with information-theoretic security. Current QDS protocols face two major restrictions, including the requirement of the symmetrization step with additional secure classical channels and the quadratic scaling of the signature rate with the probability of detection events. Here, we present an efficient QDS protocol to overcome these issues by utilizing the classical post-processing operation called post-matching method. Our protocol does not need the symmetrization step, and the signature rate scales linearly with the probability of detection events. Simulation results show that the signature rate is three orders of magnitude higher than the original protocol in a 100-km-long fiber. This protocol is compatible with existing quantum communication infrastructure, therefore we anticipate that it will play a significant role in providing digital signatures with unconditional security.
Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.
A image encryption algorithm based on chaotic Lorenz system and novel primitive polynomial S-boxes

Abstract We have proposed a robust, secure and efficient image encryption algorithm based on chaotic maps and algebraic structure. Nowadays, the chaotic cryptosystems gained more attention due to their efficiency, the assurance of robustness and high sensitivity corresponding to initial conditions. In literature, there are many encryption algorithms that can simply guarantees security while the schemes based on chaotic systems only promises the uncertainty, both of them can not encounter the needs of current scenario. To tackle this issue, this article proposed an image encryption algorithm based on Lorenz chaotic system and primitive irreducible polynomial substitution box. First, we have proposed 16 different S-boxes based on projective general linear group and 16 primitive irreducible polynomials of Galois field of order 256, and then utilized these S-boxes with combination of chaotic map in image encryption scheme. Three chaotic sequences can be produced by the disturbed of Lorenz chaotic system corresponding to variables x , y and z . We have constructed a new pseudo random chaotic sequence k i based on x , y and z . The plain image is encrypted by the use of chaotic sequence k i and XOR operation to get a ciphered image. To show the strength of presented image encryption, some renowned analyses are performed.
How to Evaluate Object Selection and Manipulation in VR? Guidelines from 20 Years of Studies

The VR community has introduced many object selection and manipulation techniques during the past two decades. Typically, they are empirically studied to establish their benefits over the state-of-the-art. However, the literature contains few guidelines on how to conduct such studies; standards developed for evaluating 2D interaction often do not apply. This lack of guidelines makes it hard to compare techniques across studies, to report evaluations consistently, and therefore to accumulate or replicate findings. To build such guidelines, we review 20 years of studies on VR object selection and manipulation. Based on the review, we propose recommendations for designing studies and a checklist for reporting them. We also identify research directions for improving evaluation methods and offer ideas for how to make studies more ecologically valid and rigorous.