Reviewerly/exHarmony huggingface dataset:
Design of Dynamic Diffusion Simulation System for Atmospheric Pollutants in Coastal Cities under Persistent Inverse Temperature

Gong, Y. and Luo, X., 2020. Design of dynamic diffusion simulation system for atmospheric pollutants in coastal cities under persistent inverse temperature. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 526–529. Coconut Creek (Florida), ISSN 0749-0208.The prediction of the dynamic diffusion of atmospheric pollutants in coastal cities under continuous inverse temperature is the key to establish the dynamic treatment of atmospheric pollutants. In view of the low accuracy of the traditional nonlinear feature estimation simulation method, a continuous inverse temperature simulation model for the dynamic diffusion of atmospheric pollutants in coastal cities under continuous inverse temperature is proposed. A self-similar regression model is constructed for the statistical information of atmospheric pollutant dynamic diffusion in coastal cities under continuous inverse temperature, and a multi-scale decomposition and factor analysis are carried out for the time series of atmospheric pollutant dynamic diffusion in coastal cities under continuous inverse temperature. The simulation results show that the model has high accuracy and good convergence in the simulation and prediction of atmospheric pollutant dynamic diffusion in coastal cities.
Experimental Study of Secure PRNG for Q-trits Quantum Cryptography Protocols

Quantum cryptography doesn’t depend on computational capabilities of intruders; it uses inviolability of quantum physics postulates (postulate of measurement, no-cloning theorem, uncertainty principle). Some quantum key distribution protocols have absolute (theoretical and informational) stability, but quantum secure direct communication (deterministic) protocols have only asymptotic stability. For a whole class of methods to ensure Q-trit deterministic quantum cryptography protocols stability, reliable trit generation method is required. In this paper, authors have developed a high-speed and secure pseudorandom number (PRN) generation method. This method includes the following steps: initialization of the internal state vector and direct PRN generation. Based on this method TriGen v.2.0 pseudo-random number generator (PRNG) was developed and studied in practice. Therefore, analysing the results of study it can be concluded following: 1) Proposed Q-trit PRNG is better then standard C ++ PRNG and can be used on practice for critical applications; 2) NIST STS technique cannot be used to evaluate the quality (statistical stability) of the Q-trit PRNG and formed trit sequences; 3) TritSTS 2020 technique is suitable for evaluating Q-trit PRNG and trit sequences quality. A future research study can be related to developing a fully-functional version of TritSTS technique and software tool.
Precision-Recall Curves Using Information Divergence Frontiers

Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace. Recent developments have investigated metrics that quantify which parts of the true distribution is modeled well, and, on the contrary, what the model fails to capture, akin to precision and recall in information retrieval. In this paper, we present a general evaluation framework for generative models that measures the trade-off between precision and recall using Renyi divergences. Our framework provides a novel perspective on existing techniques and extends them to more general domains. As a key advantage, this formulation encompasses both continuous and discrete models and allows for the design of efficient algorithms that do not have to quantize the data. We further analyze the biases of the approximations used in practice.
Diversity-enhanced particle swarm optimization algorithm based on the group behaviour of social spiders

Zhao Liua, Han Lib & Ping Zhub*a School of Design, Shanghai Jiao Tong University, Shanghai, People’s Republic of Chinab State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
An Embedded ANN Raspberry PI for Inertial Sensor Based Human Activity Recognition

Human Activity Recognition (HAR) is one of the critical subjects of research in health and human machine interaction fields in recent years. Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Decision Tree (DT) and many other algorithms were previously implemented to serve this common goal but most of the traditional Machine learning proposed solutions were not satisfying in term of accuracy and real time testing process. For that, a human activities analysis and recognition system with an embedded trained ANN model on Raspberry PI for an online testing process is proposed in this work. This paper includes a comparative study between the Artificial Neural Network (ANN) and the Recurrent Neural Network (RNN), using signals produced by the accelerometer and gyroscope, embedded within the BlueNRG-Tile sensor. After evaluate algorithms performance in terms of accuracy and precision which reached an accuracy of 82% for ANN and 99% for RNN, obtained ANN model was implemented in a Raspberry PI for real-time predictions. Results show that the system provides a real-time human activity recognition with an accuracy of 86%.
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this paper, we introduce Auto-PyTorch, which brings the best of these two worlds together by jointly and robustly optimizing the architecture of networks and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several tabular benchmarks by combining multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data. To thoroughly study our assumptions on how to design such an AutoDL system, we additionally introduce a new benchmark on learning curves for DNNs, dubbed LCBench, and run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art competitors on average.
Identifying Fake Profile in Online Social Network: An Overview and Survey

Online Social Networks (OSNs) presently engage the majority of people, from a child to an adult and even old age people as they spend a good amount of time on these platforms exchanging their information and creating interaction with other people of the world. On one hand, these social networks provide the advantage of direct connectivity between people, information sharing, ways to create a large audience, etc. on the other hand people also misuse them in many ways. Social networking sites are suffering from people who own bulk of fake accounts to take advantage of vulnerabilities for their immoral benefits such as intriguing targeted accounts to click on malicious links or to attempt any other cybercrimes. These actions motivate researchers to develop a system that can detect fake accounts on these OSNs. Several attempts have been made by the researchers to detect the accounts on social networking sites as fake or real, relying on account's features (user-based, graph-based, content-based, time-based) and various classification algorithms. In this paper, we provide an overview of various studies done in this direction and a survey of all the techniques already used and can be used in the future.
Approximate Cross-Validation in High Dimensions with Guarantees

Leave-one-out cross-validation (LOOCV) can be particularly accurate among cross-validation (CV) variants for machine learning assessment tasks -- e.g., assessing methods' error or variability. But it is expensive to re-fit a model $N$ times for a dataset of size $N$. Previous work has shown that approximations to LOOCV can be both fast and accurate -- when the unknown parameter is of small, fixed dimension. But these approximations incur a running time roughly cubic in dimension -- and we show that, besides computational issues, their accuracy dramatically deteriorates in high dimensions. Authors have suggested many potential and seemingly intuitive solutions, but these methods have not yet been systematically evaluated or compared. We find that all but one perform so poorly as to be unusable for approximating LOOCV. Crucially, though, we are able to show, both empirically and theoretically, that one approximation can perform well in high dimensions -- in cases where the high-dimensional parameter exhibits sparsity. Under interpretable assumptions, our theory demonstrates that the problem can be reduced to working within an empirically recovered (small) support. This procedure is straightforward to implement, and we prove that its running time and error depend on the (small) support size even when the full parameter dimension is large.
Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration

A profound understanding of the pathology processes is an essential precondition for successful introduction of changes and innovations, such as for example AI and Machine Learning, into pathology. Process modeling helps to build up such a profound understanding of the pathology processes among all relevant stakeholders. This paper describes the state of the art in modeling pathology processes and shows on an example how to create a reusable multipurpose process model for the diagnostic pathology process.
Drawing a Map of Elections in the Space of Statistical Cultures

We consider the problem of forming a testbed of elections to be used for numerical experiments (such as testing algorithms or estimating the frequency of a given phenomenon). We seek elections that come from well-known statistical distributions and are as diverse as possible. To this end, we define a (pseudo)metric over elections, generate a set of election instances, and measure distances between them, to assess how diverse they are. Finally, we show how to use these elections to test election-related algorithms.
Alternative Presents for Dynamic Fabric

In this paper we investigate how a combination of "speculative" design methods can be used to generate theoretical understandings for dynamic, colour-changing fabrics for garments. Specifically, we combine a first-person, autobiographical, research through design (RtD) approach that draws strategies from speculative design. We call this approach alternative presents, inspired by the work of James Auger, and explore it as a way to generate theoretical propositions for dynamic fabric that emphasize the lived experience over technological innovation. The contributions of this framing are twofold. Firstly, we offer a theoretical contribution to the literature on dynamic fabric. Secondly, we make a methodological contribution for how autobiographical design and RtD can be oriented speculatively to generate intermediate knowledge, with particular emphasis on social-technical aspects.
Anomaly Detection Using Federated Learning

Federated learning is the new tide that is being associated with machine learning territory. It is an attempt to enable smart edge devices to confederate a mutual prediction model while the training data is residing at the respective edge device. This facilitates our data to be more secure, use less bandwidth, lower latency, and power consumption. We exercise the concept of federated learning in our neural network autoencoder model to detect the anomaly. Anomaly detection is finding the unusual pattern in a given data stream which may be a false or mal-entry in the pool of transactions. It helps us to prevent many online theft and scams which are detected using state-of-the-art machine learning and deep learning algorithms. All this has to be implemented in smart edge devices that have enough computing power to train the models provided to them.
Towards AGI Agent Safety by Iteratively Improving the Utility Function

While it is still unclear if agents with Artificial General Intelligence (AGI) could ever be built, we can already use mathematical models to investigate potential safety systems for these agents. We present work on an AGI safety layer that creates a special dedicated input terminal to support the iterative improvement of an AGI agent’s utility function. The humans who switched on the agent can use this terminal to close any loopholes that are discovered in the utility function’s encoding of agent goals and constraints, to direct the agent towards new goals, or to force the agent to switch itself off. An AGI agent may develop the emergent incentive to manipulate the above utility function improvement process, for example by deceiving, restraining, or even attacking the humans involved. The safety layer will partially, and sometimes fully, suppress this dangerous incentive. This paper generalizes earlier work on AGI emergency stop buttons. We aim to make the mathematical methods used to construct the layer more accessible, by applying them to an MDP model. We discuss two provable properties of the safety layer, identify still-open issues, and present ongoing work to map the layer to a Causal Influence Diagram (CID).
The effectiveness of mobile learning role play game (rpg) maker mv in improving students’ critical thinking ability

Abstract The aim of this research were to improve students’ critical thinking and to know students’ responses in applying mobile learning RPG-based maker MV in Ecosystem learning. This research is experimental research by using quasi-experimental design with Randomized control-Group Pretest Posttest. The data was taken qualitatively by using observation results during students’ learning, students’ response questionnaire in experiencing mobile learning RPG based maker MV, and quantitative data of students’ critical thinking ability. The research findings showed (1) applying mobile learning RPG based maker MV had medium level, (2) Mobile learning RPG maker MV had improved students’ critical thinking, and (3) Students’ result after using this aplication of Ecosystem learning system in a good level or in the highest score.
Robust Spherical Panorama Image Watermarking Against Viewpoint Desynchronization

Although quantitative and qualitative growth of spherical panorama content has been achieved, watermarking methods for copyright protection are still insufficient. The spherical panorama image watermarking technique must be able to detect a watermark from the viewed images, but it is difficult to detect the watermark directly from the images due to various distortions. We propose a method for recovering the watermark-detection target viewed image to an equirectangular-formed source image and detecting the watermark after recovery. For recovery, we propose combining the scale-invariant feature transform point matching and the shift and rotation estimation method using a Euclidean transformation matrix. For watermark embedding and detection, a discrete Fourier transform magnitude coefficient is used. With the shift-invariant characteristic, our proposed method is robust against viewpoint desynchronization. Because finding an accurate viewpoint is difficult, our method offers a substantial advantage in the spherical panorama image watermarking scheme. Furthermore, it allows watermark detection on a continuous viewpoint spherical panorama system. In addition, our experiments reveal that our method is robust against signal attacks, such as JPEG compression, Gaussian filter blurring, noise addition, and histogram equalization.
The HiGHmed Didactical Framework for Online Learning Modules on Medical Informatics: First Experiences.

Within the HiGHmeducation consortium various online learning modules shall be developed by members of the consortium to address the increasing need for skilled professionals in a networked and digitalized healthcare system. Transferability of these modules to other locations is one main objective for the design of online learning modules. Thus, a didactical framework for online learning modules was developed. To ensure feasibility of the framework, the participating universities were analyzed concerning availability of e-learning support structures and infrastructures including learning management systems (LMS). The analysis especially focuses on the various LMS learning tools and their suitability for the framework. The framework is the basis for 12 HiGHmeducation online learning modules of which a part has firstly been conducted in winter 2019/20 and leads to a comparable structure of the modules.
Enhancing Credit Card Fraud Detection Using Deep Neural Network

With the development of technology, e-commerce became an essential part of an individual’s life, where individuals could easily purchase and sell products over the internet. However, fraud attempts; specifically credit card fraudulent attacks are rapidly increasing. Cards may potentially be stolen; fake records are being used and credit cards are subject to being hacked. Artificial Intelligence techniques tackle these credit card fraud attacks, by identifying patterns that predict false transactions. Both Machine Learning and Deep Learning models are used to detect and prevent fraud attacks. Machine Learning techniques provide positive results only when the dataset is small and do not have complex patterns. In contrast, Deep Learning deals with huge and complex datasets. However, most of the existing studies on Deep Learning have used private datasets, and therefore, did not provide a broad comparative study. This paper aims to improve the detection of credit card fraud attacks using Long Short-Term Memory Recurrent Neural Network (LSTM RNN) with a public dataset. Our proposed model proved to be effective. It achieved an accuracy rate of 99.4% which is higher compared to other existing Machine and Deep Learning techniques.
Fast Training of Deep Networks with One-Class CNNs

One-class CNNs have shown promise in novelty detection. However, very less work has been done on extending them to multiclass classification. The proposed approach is a viable effort in this direction. It uses one-class CNNs i.e., it trains one CNN per class, for multiclass classification. An ensemble of such one-class CNNs is used for multiclass classification. The benefits of the approach are generally better recognition accuracy while taking almost even half or two-thirds of the training time of a conventional multi-class deep network. The proposed approach has been applied successfully to face recognition and object recognition tasks. For face recognition, a 1000 frame RGB video, featuring many faces together, has been used for benchmarking of the proposed approach. Its database is available on request via e-mail. For object recognition, the Caltech-101 Image Database and 17Flowers Dataset have also been used. The experimental results support the claims made.
Assessing the Impact of Script Gadgets on CSP at Scale

The Web, as one of the core technologies of modern society, has profoundly changed the way we interact with people and data. One of the worst attacks on the Web is Cross-Site Scripting (XSS), in which an attacker is able to inject their malicious JavaScript code into a Web application, giving this code full access to the victimized site. To mitigate the impact of markup injection flaws that cause XSS, support for the Content Security Policy (CSP) is nowadays shipped in all browsers. Deploying such a policy enables a Web developer to whitelist from where script code can be loaded, essentially constraining the capabilities of the attacker to only be able to execute injected code from the said whitelist. As recently shown by Lekies et al., injecting script markup is not a necessary prerequisite for a successful attack in the presence of so-called script gadgets. These small snippets of benign JavaScript code transform non-script markup contained in a page into executable JavaScript, opening the door for bypasses of a deployed CSP. Especially in combination with CSP's logic in handling redirected resources, script gadgets enable attackers to bypass an otherwise secure policy. In this paper, we, therefore, ask the question: is securely deploying CSP even possible without a priori knowledge of all files hosted on even a partially trusted origin? To answer this question, we investigate the severity of the findings of Lekies et al., showing real-world Web sites on which, even in the presence of CSP and without code containing such gadgets being added by the developer, an attacker can sideload libraries with known script gadgets, as long as the hosting site is whitelisted in the CSP. In combination with CSPs matching logic for redirects, this enables us to bypass 10% of otherwise secure policies in the wild. To further answer our main research question, we conduct a hypothetical what-if analysis. Doing so, we automatically generate sensible CSPs for all of the Top 10,000 sites and show that around one-third of all sites would still be susceptible to a bypass through script gadget sideloading due to heavy reliance on third parties that also host such libraries.
Two Faces of Privacy: Legal and Human-Centered Perspectives of Lifelogging Applications in Home Environments

In view of the consequences resulting from the demographic change, using assisting lifelogging technologies in domestic environments represents one potential approach to support elderly and people in need of care to stay longer within their own home. Yet, the handling of personal data poses a considerable challenge to the perceptions of privacy and data security, and therefore for an accepted use in this regard. The present study focuses on aspects of data management in the context of two different lifelogging applications, considering a legal and a human-centered perspective. In a two-step empirical process, consisting of qualitative interviews and an online survey, these aspects were explored and evaluated by a representative German sample of adult participants (N = 209). Findings show positive attitudes towards using lifelogging, but there are also high requirements on privacy and data security as well as anonymization of the data. In addition, the study allows deep insights into preferred duration and location of the data storage, and permissions to access the personal information from third parties. Knowledge of preferences and requirements in the area of data management from the legal and human-centered perspectives is crucial for lifelogging and must be considered in applications that support people in their daily living at home. Outcomes of the present study considerably contribute to the understanding of an optimal infrastructure of the accepted and willingly utilized lifelogging applications.
Virtual Reality (VR) in the Computer Supported Cooperative Work (CSCW) Domain: A Mapping and a Pre-study on Functionality and Immersion

This pre-study investigates how Virtual Reality (VR) can be used to support collaborative work in a business setting. Based on a literature review it’s explored how immersive technologies like Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) fit into the Computer Supported Cooperative Work (CSCW) domain. By using the time and space matrix the immersive technologies are classified. Based on the findings, immersive VR supporting synchronous and remote collaboration is chosen for further investigation, where the immersive VR application, MeetinVR, has been tested. Two small-scale experiments have been conducted (n = 3 and n = 10) to test the functionality of the MeetinVR application and the level of immersion when conducting interviews compared to an online communication tool, like Skype. The initial results indicate that an immersive VR application is relatively user-friendly and provide a high level of immersion.