Reviewerly/exHarmony huggingface dataset:
Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation

Rendering programs have changed the design process completely as they permit to see how the products will look before they are fabricated. However, the rendering process is complicated and takes a significant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in order to get the best quality results. Nevertheless, the optimal output may not be obtained in the first render. This all makes the rendering process a tedious process. Since Goodfellow et al. introduced Generative Adversarial Networks (GANs) in 2014 [1], they have been used to generate computer-assigned synthetic data, from non-existing human faces to medical data analysis or image style transfer. GANs have been used to transfer image textures from one domain to another. However, paired data from both domains was needed. When Zhu et al. introduced the CycleGAN model, the elimination of this expensive constraint permitted transforming one image from one domain into another, without the need for paired data. This work validates the applicability of CycleGANs on style transfer from an initial sketch to a final render in 2D that represents a 3D design, a step that is paramount in every product design process. We inquiry the possibilities of including CycleGANs as part of the design pipeline, more precisely, applied to the rendering of ring designs. Our contribution entails a crucial part of the process as it allows the customer to see the final product before buying. This work sets a basis for future research, showing the possibilities of GANs in design and establishing a starting point for novel applications to approach crafts design.
Big data architectures for data lakes: A systematic literature review

The rise in big technologies has been demanding different concepts and practices for data exploitation; among them data lake is a recently emerged concept that is meant to deal with the heterogeneous data. Data lakes have been residing in the big data era since 2010, but there has not been any systematic review yet over data lake implementation. In this research survey, we conduct a review and provide a road map to researcher that elaborates what has happened to data lakes till now. We aim to give understanding for basic concept of data lakes and propose a novel data lake definition that could best describe the concept based on the literature review. One of the main problem while implementing data lake is deciding the technologies to use, this study covers technologies that can potentially be used for data lake implementation. Furthermore, data lake architectures and their variants are discussed in detail. Moreover, we analyze current state, challenges, pros and cons of the data lake. This study is all in one place for researchers who try to understand data lake concept, architectures, technologies, approaches, current state and challenges.
Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories

With the popularity of GPS-enabled devices, a huge amount of trajectory data has been continuously collected and a variety of location-based services have been developed that greatly benefit our daily life. However, the released trajectories also bring severe concern on personal privacy, and several recent studies have demonstrated the existence of personally-identifying information in spatial trajectories. Trajectory anonymization is nontrivial due to the trade-off between privacy protection and utility preservation. Furthermore, recovery attack has not been well studied in the current literature. To tackle these issues, we propose a frequency-based randomization model with a rigorous differential privacy guarantee for trajectory data publishing. In particular, we introduce two randomized mechanisms to perturb the local/global frequency distributions of significantly important locations in trajectories by injecting Laplace noise. We design a hierarchical indexing along with a novel search algorithm to support efficient trajectory modification, ensuring the modified trajectories satisfy the perturbed distributions without compromising privacy guarantee or data utility. Extensive experiments on a real-world trajectory dataset verify the effectiveness of our approaches in resisting individual re-identification and recovery attacks, and meanwhile preserving desirable data utility as well as the feasibility in practice.
Online music learning based on digital multimedia for virtual reality

In the era of multimedia digital education, virtual reality (VR) technologies have become extremely popular teaching tools. The research compares the learning outcomes of the VR students group using the VR4EDU virtual reality application with the learning outcomes of the control students group taking the traditional online music lessons. The sample consisted of 96 students from a Shandong Management University in China. The research is based on a quasi-experimental design. The scholars identified the statistically significant improvements in learning music supported by VR in the following areas: for Exercise 1, improvements were from M:30% to M:45%; for Exercise 2, improvements were from M:32% to M:54%; and for Exercise 3 improvements were from M:57% to M:83%. The research highlights technical features of the VR environment in music education that may help developers and designers to develop effective curricula for students. The spatial design reveals the potential of spatial learning, but further research is still required to analyze the role and advantages of spatial learning in detail. The present research underlines that more complex structures can be developed to teach students different aspects of musical genres.
A variational Bayesian approach for partly resolvable group tracking

The partly resolvable group tracking is significant for the anti multi-agent system, in which some individual targets of the group may generate only one measurement, and the association relationships between the individual targets and measurements are unknown. To estimate the group state, the individual target states, and the association variables accurately, this paper proposed a novel tracking algorithm considering the resolvability of group. Firstly, a unified Bayesian framework for partly resolvable group tracking is formulated, including data association and state estimation. Secondly, a variational Bayesian approach is derived using the Kalman filter and random matrix model, which recursively estimates the states of the group and its individual targets. Thirdly, to reduce the computational burden, the complex data association problem is modeled by a factor graph that is solved by message passing. Finally, simulation results show that the proposed algorithm has a significant improvement over the previous variational group tracking algorithm in terms of state estimation.
LOD-RoadTran18: Supporting the cross-border use of Road Traffic Data with Linked Open Data based on DATEX II

In this article we demonstrate a particular use of ontologies based on the European specifications of the DATEX II UML model with Safety Related Traffic Information (SRTI) version 3 scope. These specifications are designed and developed as an exchange of traffic data and mechanisms for sending information by a working group to establish and standardize the interface between traffic control and information centers. It is the standard reference for sending and receiving traffic information between applications developed by different countries in Europe. This specification of DATEX II XML/UML describes concepts and data structures related to traffic, but the description is only syntactic, not semantic. Therefore, the objective to be achieved in this part of the research has been to develop a semantic description of the contents based on the UML data model and to link the data of the new general ontology with other vocabularies or related ontologies already developed and available on the internet.
A Secure Framework for Remote Healthcare Monitoring using the Internet of Medical Things

In this paper, we propose a secure framework for healthcare monitoring using the Internet of Medical Things (IoMT). In spite of their deployment, these devices still vulnerable to several cyber-attacks, ranging from unauthorized access to private medical data to data modification and injection. These attacks can compromise the privacy of the monitored patient, reduce the reliability of the monitoring system and may harm the life of monitored patient. In this paper, we propose a new framework to detect attacks and secure the communications in IoMT. To prevent eavesdropping and modification attacks, we propose the Ephemeral Elliptic Curves Diffie-Hellman (EECDH) to derive a session key used to provide confidentiality and authenticity. To detect injected measurements, flooding triggered by compromised devices and medical changes in physiological data, we applied the sequential change point detection algorithm Pruned Exact Linear Time (PELT) followed by the boxplot. Our experimental results show that our approach is able to increase the reliability and the accuracy of remote monitoring system, while reducing the false alarms triggered by injected measurements.
A high-order convex variational model for denoising MRI data corrupted by Rician noise

Rician noise removal is an essential problem in magnetic resonance imaging (MRI). Numerous variational models have been proposed in the literature dealing with Rician noise for MRI data. They, however, are first-order variational models, which suffer the staircase effect and smooth out fine structural details. In this paper, we propose a high-order convex variational model for Rician noise reduction. Unlike other works, our model employs the bounded Hessian regularizer to remedy the staircase effect and preserve small structures. The proofs for mathematical properties, including the convexity of the model, the existence and uniqueness of the solution, are provided. A split Bregman algorithm is developed to solve the proposed minimization problem. All subproblems are solved efficiently by either closed-form solutions or Newton’s method. Experimental results on simulated and real MRI data demonstrate the effectiveness of our proposed model compared with some state-of-the-art variational models for Rician noise removal.
AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

Abstract The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi‐run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi‐level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.
Data Provenance for healthcare: a blockchain-based approach

With the advent of Industry 4.0, information has become a key aspect for virtually any system. Critical infrastructures haven't been excluded by this technological revolution, which has led to several advantages in terms of communication, interoperability, and scalability. On the other hand, these systems are now targeted by new attacks, previously exclusive to cybersystems. The potential of data violation ranges from the interruption of the service provided to, in the worst cases, disastrous consequences in environmental, economic and safety terms. Consequently, ensuring data reliability is an essential task to prevent these kinds of attacks. Data provenance, a kind of metadata that identifies the derivation history of a data, can provide a possible solution. This paper aims to discuss solutions for a tamper proof data provenance extended, but not limited, to the healthcare scenario. The proposed approach is based on blockchain technology to ensure protection of sensitive data, such as medical records and healthcare data.
Block-based multi-scale haze image enhancement method for surveillance application

Images captured in surveillance systems suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The basic idea is to apply the frequency domain image enhancement approach for different image block scales. The parameter of transform coefficient enhancement for every block is driven through optimization of measure of enhancement. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. To test the performance of the proposed algorithm, the public database O-HAZE is used.
Eye-tracking-based robotic arm control system

People with severe speech and motor impairments are unable to actively use their muscles, with the result being that they have difficulty communicating with the external world. In this study, we developed a non-invasive robot-arm control system based on eye tracking. We conducted a user-centered design process with eight commands and an intermediate real-time video transmission user interface after fully considering the spatial characteristics of the robotic arm. Additionally, we evaluated three eye-gaze point processing algorithms. Among them, density-based spatial clustering applied with a noise algorithm achieved an average accuracy of 99.3%. On this basis, we designed and conducted offline experiments, in which all five participants were able to send commands with accuracy higher than 99% for a total of 80 random commands.
Temporal Question Answering in News Article Collections

The fields of automatic question answering and reading comprehension have been rapidly advancing recently [6]. Open-domain question answering, in particular, assumes answering arbitrary user questions from a large document collection. The existing QA approaches are however generally working on synchronic document collections such as Wikipedia, Web data or short-term news corpora. This talk is about our latest efforts in automatic question answering over temporal news collections which can contain millions of news articles published during several decades’ long time frames. Temporal aspects of both news articles and user questions form an additional challenge for this kind of a temporal question answering task. To correctly answer questions over such collections one usually needs first to find candidate documents that are likely to contain these answers. We will first discuss a re-ranking approach [3] for news articles which works by utilizing temporal information embedded in questions and in the underlying document collection, thus combining methods from Temporal Information Retrieval [1, 2] and Natural Language Processing. Next, we will discuss a dedicated solution for answering ”When” type questions which require finding occurrence dates of events described in input questions based on the underlying news archive [5]. Finally, we will introduce ArchivalQA [4] - a large-scale question answering dataset which has been automatically created from a two decades’ long news article collection, and which contains over 500k question-answer pairs. The dataset has been processed to remove temporally ambiguous questions for which more than one correct answer exist.
Disruptive Quantum Safe Technologies

The advances in quantum computation are leading to higher computation capabilities, which is expected to reshape the cybersecurity environment. Quantum computers are expected to break most of the public-key security schemes, like RSA, Diffie-Hellman or Elliptic-Curve Cryptography, due to the implications of quantum algorithms like the Shor’s or Grover’s. As a consequence of the quantum computing threat, many studies are being carried out in the recent years in order to develop security schemes and cryptosystems. Within these researches, two main fields stand out due to their security implications: Quantum cryptography, with special emphasis on the Quantum Key Distribution (QKD) and Post-Quantum Crypthography (PQC). Here we make an analysis of the current landscape of key establishment and encapsulation protocols, algorithms and their applications of these solutions in military environments.
Improved Retinex for low illumination image enhancement of nighttime traffic

An image enhancement algorithm with improved Retinex is proposed for images captured at night that are easily affected by low light and suffer from narrow dynamic range, significant noise and loss of detail information. Firstly, the low illumination image adaptive algorithm is further proposed in combination with the traditional Retinex theory, and the global logarithmic mean is used in the calculation process, which has a very obvious adjustment effect for nighttime images. Then, the illumination part of the model is estimated using bilateral filters to obtain an accurate and reliable reflection part, so as to recover the true color and details of the image. The experimental results show that the improved algorithm is feasible and effective, and can further improve the visual effect of the image.
Theoretical Proposal of Optical Neural Network Architecture with Temporal Synthetic Dimension

We theoretically propose the construction of the optical neural network architecture for deep learning in a single ring resonator, with the concept of temporal synthetic dimension formed by arrival times of optical pulses.
Material Survey on Source Code Plagiarism Detection in Programming Courses

Plagiarism has always been a serious concern in programming courses. Most universities use online code evaluation platforms to assess students’ knowledge, making source code plagiarism a real concern. With the hope of reducing this problem, many source code plagiarism detection tools are proposed each year, either based on source code analysis or the changes in student's characteristics, such as behavior, history of submissions, source IP address, etc. The purpose of this research is to offer a detailed state-of-the-art overview on source code plagiarism detection in programming courses and suggest a direction for a robust approach.
AWFC: Preventing Label Flipping Attacks Towards Federated Learning for Intelligent IoT

Abstract Centralized machine learning methods require the aggregation of data collected from clients. Due to the awareness of data privacy, however, the aggregation of raw data collected by Internet of Things (IoT) devices is not feasible in many scenarios. Federated learning (FL), a kind of distributed learning framework, can be running on multiple IoT devices. It aims to resolve the issues of privacy leakage by training a model locally on the client-side, other than on the server-side that aggregates all the raw data. However, there are still threats of poisoning attacks in FL. Label flipping attacks, typical data poisoning attacks in FL, aim to poison the global model by sending model updates trained by the data with mismatched labels. The central parameter aggregation server is hard to detect the label flipping attacks due to its inaccessibility to the client in a typical FL system. In this work, we are motivated to prevent label flipping poisoning attacks by observing the changes in model parameters that were trained by different single labels. We propose a novel detection method called average weight of each class in its associated fully connected layer. In this method, we detect label flipping attacks by identifying the differences of classes in the data based on the weight assignments in a fully connected layer of the neural network model and use the statistical algorithm to recognize the malicious clients. We conduct extensive experiments on benchmark data like Fashion-MNIST and Intrusion Detection Evaluation Dataset (CIC-IDS2017). Comprehensive experimental results demonstrated that our method has the detection accuracy over 90% for the identification of the attackers flipping labels.
Improved spherical search with local distribution induced self-adaptation for hard non-convex optimization with and without constraints

Most metaheuristic optimizers rely heavily on precisely setting their control parameters and search operators to perform well. Considering the complexity of real-world problems, it is always preferable to adjust control parameter values automatically rather than clamping them to a fixed value. In recent years, Spherical Search (SS) has emerged as a population-based stochastic optimization method that exploits the concepts of random projection matrices in linear algebra. As a result of the success of SS in solving non-convex, real-parameter optimization problems of various complexity, we have significantly extended SS in this paper by introducing a set of new algorithms, collectively known as Self Adaptive Spherical Search (SASS). Our proposal aims to enhance the performance of SS by using different projection matrix schemes in conjunction with improved search-direction calculations and an adaptive modification of parameter values. In our proposed adaptation scheme, parameters are modified to relevant values by applying a self-adaptive process that does not rely upon prior knowledge of the correlation between the parameter values and characteristics of the problem space. Consequently, we may apply the algorithms to bound and nonlinearly constrained optimization problems. For the benchmark suites derived from the most recent IEEE Congress on Evolutionary Computation (CEC) competitions, simulation results indicate that the SASS family of algorithms performs better than or is comparable to state-of-the-art algorithms from the other paradigms concerning robustness and convergence.