Reviewerly/exHarmony huggingface dataset:
Compliance of “Principles of transparency and best practice in scholarly publishing” in academic society published journals

the Directory of Open Access Journals, the Committee on Publication Ethics, and the World Association of Medical Editors declared the third version of "Principles of transparency and best practice in scholarly publishing" to ensure transparency and quality in journal publications.This study is aimed at assessing the guidelines from the journals' websites manually.Methods: In this study, three researchers investigate the homepages of 781 academic society-published journals that are registered in the Science Citation Index Expanded and whether these journals are effectively adopting these new guidelines.In this paper, 33 items from the guidelines are examined.The 33 items are rearranged into four different categories: basic journal information; publication ethics; copyright and archiving information; and profit model.The researchers count yes or no after checking the adopting status on the journal homepage and dividing into four scales: 0% to 25% for is rarely practiced, 26% to 50% for is poorly practiced, 51% to 75% for is adequately practiced, and 76% to 100% for is well practiced.Results: Of the 33 items, 10 are found to be poorly or rarely practiced, including readership, data sharing, archiving policies, and profit model information. Conclusion:It could be the most up-to-date indicator of the current status of applying best practice guidelines.Society journal editors especially from Asia should evaluate their journals regarding "Principles of transparency and best practice in scholarly publishing".
A Comparison of Radial and Linear Charts for Visualizing Daily Patterns

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.
Performing Deep Recurrent Double Q-Learning for Atari Games

Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.
Quantum Coherence and Intrinsic Randomness

The peculiar uncertainty or randomness of quantum measurements stems from coherence, whose information-theoretic characterization is currently under investigation. Under the resource theory of coherence, it is interesting to investigate interpretations of coherence measures and the interplay with other quantum properties, such as quantum correlations and intrinsic randomness. Coherence can be viewed as the resource for the intrinsic randomness in the measurement outcomes of a state in the computational basis. We observed in our previous work that the coherence of formation, which measures the asymptotic coherence dilution rate, indeed quantifies the uncertainty of a (classical) correlated party about the system measurement outcome. In this work, we re-derive the result from a quantum point of view and then connect the intrinsic randomness to the relative entropy of coherence, another important coherence measure that quantifies the asymptotic distillable coherence. Even though there does not exist bound coherent states, these two intrinsic randomness quantified by coherence of formation and the relative entropy of coherence are different. Interestingly, we show that this gap is equal to the quantum discord, a general form of quantum correlations, in the state of the system of interest and the correlated party, after a local measurement on the former system.
Information and document space in education, science and culture in the contemporary context of society digitalization. (The annual paper presented at the Fifth World Professional Forum “Crimea-2019”)

Traditionally, the annual analytical paper opens the World Professional “Crimea” Forum. The author introduces the concept of digitalization and defines its key differences from informatization. He emphasizes that digital transformation facilitates digital economics being characterized by the advance of the Internet, resources digitization, mobile communications, digital technologies, primarily in finances and economics, and merging online and offline services. He quotes statistical data on the digital economics in various countries. In the Russian Federation, the digital economy develops intensively and embraces industries, banking, healthcare, government, and, in particular, libraries and archives. The principal international documents to facilitate global digitalization are analyzed. In Russia, the fundamental documents are adopted with the reference to the global practice of digital transformations. The author formulates the trends in the digitalization that make direct impact on the librarianship and information industry; he also specifies the goals for the libraries in the digital epoch. The latest changes in the world book market are discussed along with its statistics. The focus is made on the open access and copyright issues. The author concludes that the libraries of the future make the inherent part of the global information infrastructure.
Auditing Deep Learning processes through Kernel-based Explanatory Models

While NLP systems become more pervasive, their accountability gains value as a focal point of effort.Epistemological opaqueness of nonlinear learning methods, such as deep learning models, can be a major drawback for their adoptions.In this paper, we discuss the application of Layerwise Relevance Propagation over a linguistically motivated neural architecture, the Kernel-based Deep Architecture, in order to trace back connections between linguistic properties of input instances and system decisions.Such connections then guide the construction of argumentations on the network's inferences, i.e., explanations based on real examples that are semantically related to the input.We also propose here a methodology to evaluate the transparency and coherence of analogy-based explanations modeling an audit stage for the system.Quantitative analysis on two semantic tasks, i.e., question classification and semantic role labeling, shows that the explanatory capabilities (native in KDAs) are effective and they pave the way to more complex argumentation methods.
SAFe Adoptions in Finland: A Survey Research

Scaled Agile Framework (SAFe) was released in the year 2011. Since then it has become the most popular agile scaling framework in use. In this paper we examine the benefits and obstacles of SAFe adoptions in Finland. The data is based on a survey we conducted in Finland in 2018, when many respondents had already been following SAFe for some years. The biggest benefits reported are transparency, co-operation, and cadence. The biggest obstacles are the old organizational culture, that the SAFe model has not been fitted to the organization and implementation problems. The results indicate that although many respondents of the survey (80%) use SAFe, they are still struggling with their agile transformation, while using a mix of old and new methods and only a subset of the SAFe practices.
Coupling Global and Local Context for Unsupervised Aspect Extraction

Ming Liao, Jing Li, Haisong Zhang, Lingzhi Wang, Xixin Wu, Kam-Fai Wong. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Fast Approximate Geodesics for Deep Generative Models

The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches are limited to low-dimensional latent spaces, due to the computational complexity of solving a non-convex optimisation problem. We propose finding shortest paths in a finite graph of samples from the aggregate approximate posterior, that can be solved exactly, at greatly reduced runtime, and without a notable loss in quality. Our approach, therefore, is hence applicable to high-dimensional problems, e.g., in the visual domain. We validate our approach empirically on a series of experiments using variational autoencoders applied to image data, including the Chair, FashionMNIST, and human movement data sets.
Transformation of education in the conditions of digitalization

Purpose of the study is carrying out the analysis of current trends in education in the conditions of digitalization. In the put modern economic and technological conditions new modern approaches to development of key competences of digital economy, ensuring mass digital literacy of experts and personalisation of educational process are required. For realization of the specified purpose the passport of the Federal project “Shots for Digital Economy” is approved on May 28, 2019. According to target indicators of the Project by 2024 have to be trained on online to programs of development of digital literacy; 120 thousand people have to be accepted on training in programs of the higher education in the sphere of information technologies; about 8 programs of the higher education for the benefit of digital economy are created. The research problem – to establish as far as realizuyema goals. Materials and methods. For the purpose of the description of the current situation methods of descriptive statistics are used. The analysis of information collected during the sociological poll organized and held by PPS of department of statistics together with ANO Council for Questions of Management and Development at Department of work and social protection of the population of the city of Moscow is provided. Methods of visualization of data are applied to evident data presentation. Data processing was carried out with application of MS of Excel and a package of the IBM SPSS Statistics application programs. Results. The carried-out analysis allowed to define top trends in education. The revealed trends proved need of improvement of quality of education with application of information and communication technologies. Results of a research show about demand in labor market of experts in the sphere of information and communication technologies, including among persons with limited opportunities. The world rankings testify to low positions of Russia on the level of involvement of the experts having the specified skills. Conclusion. At the same time, the programs implemented by the state in education will allow to build a qualitative education system at all levels, including both support of talented youth in mathematics and informatics, and training of the highly qualified personnel conforming to new requirements of digital economy and also programs of retraining and professional development for the most demanded professions.
You Talk Too Much: Limiting Privacy Exposure Via Voice Input

Voice synthesis uses a voice model to synthesize arbitrary phrases.Advances in voice synthesis have made it possible to create an accurate voice model of a targeted individual, which can then in turn be used to generate spoofed audio in his or her voice.Generating an accurate voice model of target's voice requires the availability of a corpus of the target's speech.This paper makes the observation that the increasing popularity of voice interfaces that use cloud-backed speech recognition (e.g., Siri, Google Assistant, Amazon Alexa) increases the public's vulnerability to voice synthesis attacks.That is, our growing dependence on voice interfaces fosters the collection of our voices.As our main contribution, we show that voice recognition and voice accumulation (that is, the accumulation of users' voices) are separable.This paper introduces techniques for locally sanitizing voice inputs before they are transmitted to the cloud for processing.In essence, such methods employ audio processing techniques to remove distinctive voice characteristics, leaving only the information that is necessary for the cloud-based services to perform speech recognition.Our preliminary experiments show that our defenses prevent state-of-the-art voice synthesis techniques from constructing convincing forgeries of a user's speech, while still permitting accurate voice recognition.
Direct Visual Servoing in the Frequency Domain

In this paper, we propose an original approach to extend direct visual servoing to the frequency domain.Whereas most of visual servoing approaches relied on the geometric features, recent works have highlighted the importance of taking into account the photometric information of the entire images.This leads to direct visual servoing (DVS) approaches.In this paper we propose no longer to consider the image itself in the spatial domain but its transformation in the frequency domain.The idea is to consider the Discrete Cosine Transform (DCT) which allows to represent the image in the frequency domain in terms of a sum of cosine functions that oscillate at various frequencies.This leads to a new set of coordinates in a new precomputed orthogonal basis, the coefficients of the DCT.We propose to use these coefficients as the visual features that are then considered in a visual servoing control law.We then exhibit the analytical formulation of the interaction matrix related to these coefficients.Experimental results validate our approach.
Camera calibration based on two-cylinder target

A large-size 2D target cannot be used for calibration in the application scenarios with confined spaces, neither can traditional 1D target because the need of constrained motion. To solve this problem, a camera calibration method based on two-cylinder (TC) target is proposed. The TC target is placed in the Field of View (FOV) arbitrarily and images of the target are acquired from multiple views, then the camera can be calibrated by establishing the perspective projection relationship between the TC target and its projective contours in each view. The experiments with both synthetic and real data show that the proposed method has better anti-noise ability and higher accuracy compared with small-size 2D target.
Domain Adaptation for Semantic Segmentation with Maximum Squares Loss

Deep neural networks for semantic segmentation always require a large number of samples with pixel-level labels, which becomes the major difficulty in their real-world applications. To reduce the labeling cost, unsupervised domain adaptation (UDA) approaches are proposed to transfer knowledge from labeled synthesized datasets to unlabeled real-world datasets. Recently, some semi-supervised learning methods have been applied to UDA and achieved state-of-the-art performance. One of the most popular approaches in semi-supervised learning is the entropy minimization method. However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer. To balance the gradient of well-classified target samples, we propose the maximum squares loss. Our maximum squares loss prevents the training process being dominated by easy-to-transfer samples in the target domain. Besides, we introduce the image-wise weighting ratio to alleviate the class imbalance in the unlabeled target domain. Both synthetic-to-real and cross-city adaptation experiments demonstrate the effectiveness of our proposed approach. The code is released at https://github. com/ZJULearning/MaxSquareLoss.
Software Defect Prediction via Deep Belief Network

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs. The problem of data redundancy exists in software defect data set, which has great influence on prediction effect. We propose a defect distribution prediction model (Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network (DBN) technology, which is an effective deep learning technique in image processing and natural language processing, whose features are similar to defects in source program. Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM), Locally linear embedding SVM (LLE-SVM), and Neighborhood preserving embedding SVM (NPE-SVM).
The Effects of an Impurity in an Ising‐XXZ Diamond Chain on Thermal Entanglement, on Quantum Coherence, and on Quantum Teleportation

Abstract The effects of an impurity plaquette on the thermal quantum correlations measurement by the concurrence, on quantum coherence quantified by the recently proposed l 1 ‐norm of coherence and on quantum teleportation in a Ising‐ diamond chain are discussed. Such an impurity is formed by the interaction between the interstitial Heisenberg dimers and the nearest‐neighbor Ising coupling between the nodal and interstitial spins. All the interaction parameters are different from those of the rest of the chain. By tailoring them, quantum entanglement and quantum coherence can be controlled and tuned. Therefore, the quantum resources—thermal entanglement and quantum coherence—of the model exhibit a clear performance improvement in comparison to the original model without impurities. It is demonstrated that quantum teleportation can be tuned by its inclusion. Thermal teleportation is modified in a significant way as well, and a strong increase in the average fidelity is observed. The exact solution is furnished by the use of the transfer‐matrix method.
Establishing Trusted I/O Paths for SGX Client Systems With Aurora

Today users' private data in edge computing devices (desktops, laptops, and tablets, etc.) is at high risk because they run applications on potentially compromised or malicious systems. To address this problem, hardware vendors propose Trusted Execution Environment (TEE). Particularly, Intel has released a new processor feature called Software Guard eXtension (SGX), and provisions shielded executions (i.e., enclaves) for security-sensitive computations. Regrettably, Intel SGX's design objectives omit trusted I/O paths. Without such guarantees, it is unlikely for an enclave to fulfill its security and privacy purposes because the source or sink of data may have been corrupted. To this end, we propose a novel architecture called Aurora to provide trusted I/O paths for enclave programs even in the presence of untrusted system software. Specifically, Aurora exploits two commercial-off-the-shelf features (System Management Mode, SMM and SGX) and establishes a secure channel between an enclave program and target device. Furthermore, we design and implement trusted paths for HID keyboard, serial port printer, hardware clocks, and USB mass storage, respectively. Leveraging these trusted paths, we protect real-world applications including OpenSSH client, OpenSSL server/client and SQLite database. Security and performance evaluations show that Aurora mitigates several kinds of I/O related attacks and introduces acceptable overheads. Our framework has been open-sourced and is available to the security community.
Natural Language Generation Challenges for Explainable AI

Good quality explanations of artificial intelligence (XAI) reasoning must be written (and evaluated) for an explanatory purpose, targeted towards their readers, have a good narrative and causal structure, and highlight where uncertainty and data quality affect the AI output.I discuss these challenges from a Natural Language Generation (NLG) perspective, and highlight four specific "NLG for XAI" research challenges.
Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks

Recurrent neural networks (RNN) are powerful tools to explain how attractors may emerge from noisy, high-dimensional dynamics. We study here how to learn the ∼N^{2} pairwise interactions in a RNN with N neurons to embed L manifolds of dimension D≪N. We show that the capacity, i.e., the maximal ratio L/N, decreases as |logε|^{-D}, where ε is the error on the position encoded by the neural activity along each manifold. Hence, RNN are flexible memory devices capable of storing a large number of manifolds at high spatial resolution. Our results rely on a combination of analytical tools from statistical mechanics and random matrix theory, extending Gardner's classical theory of learning to the case of patterns with strong spatial correlations.
Augmented Reality: Historical Development and Area of Usage

Technological developments bring along lots of innovations. One of the innovations that have become widespread in recent years is the concept of augmented reality. In current scientific literature there are a lot of studies in literature about augmented reality which can be defined as the combination of real and virtual worlds, experienced by humans in a unified view. Aim of this study is to present the historical development and area of usage of augmented reality. This study will hopefully provide a timely insight into the current state of research on the use of augmented reality.
Using 360-Degree Video as a Research Stimulus in Digital Health Studies: Lessons Learned

Due to the accessibility of omnidirectional cameras to record 360-degree videos and the technology to view the videos via mobile phones and other devices, 360-degree videos are being used more frequently to place people in different contexts and convey health-related information. Increasingly, 360-degree videos are being employed in health marketing because they have the potential to enhance health-related attitudes and behaviors. As a case study on how this technology may be used for health-related information and its effect on health care providers, we created a 360-degree video that portrays the experience of a migraine sufferer to be used as a stimulus in an online study. We describe the challenges and lessons learned in designing and implementing a 360-degree video as part of an online experiment focused on inducing empathy among clinicians for understanding patient experience. Given the rapid change in digital technology, future research can use this knowledge to design and implement 360-degree video studies more effectively.