Reviewerly/exHarmony huggingface dataset:
Assessment of Model-based Methodologies to Architect Cyber-Physical Systems

The steady progression of the digital transformation within all business sectors and society thrive the importance of Cyber-Physical Systems (CPSs), whereas their areas of application grow and expand to an unmanageable amount. Since the model-based engineering concept has been used for an integral part of CPSs, i.e., embedded devices, these methodologies should be carefully assessed, whether they still can be applied to design the emerging CPSs and to which extent. Accordingly, we first identify the specific challenges of CPSs. We then benchmark the established model-based engineering concepts concerning the identified challenges. In order to address the identified critical deficiencies and weaknesses, we propose to adopt carefully selected methods and strategies from the cyber-space domain as well as approaches followed by science and industry. Consequently, we propose a tailored engineering framework for CPS design, which represents a blueprint for future CPS projects. Our blueprint combines the strengths of the analyzed methods and concepts from all three domains -- model-based, cyber-space engineering and currently available solutions or strategies.
Mapping quantum circuits on 3D nearest-neighbor architectures

In some quantum technologies, an interaction is only allowed between physically adjacent qubits, hence the nearest-neighbor requirement is needed. In such technologies, quantum gates are limited to operate on adjacent qubits. To make a quantum circuit compliant with the nearest-neighbor requirement, SWAP gates are inserted into the circuit to move the interacting qubits of a gate to be adjacent to each other. The mapping of qubits on the physical environment has an important role on reducing the number of SWAP gates and thus the circuit latency. Focusing on this issue, in this paper, a method is proposed that maps a quantum circuit onto a 3D physical hardware such as a 3D optical lattice. A new methodology for this mapping problem is proposed based on a complex network spectral clustering algorithm and graph theory, which are suitable for very large scale networks. It includes three steps: finding the order of qubit mapping, placing physical qubits, and routing. Simulation results show that the proposed mapping approach not only decreases the average number of SWAP gates by about 37% but also improves the average runtime by about 87% for the 2D architecture compared to PAQCS. Moreover, it reduces the average number of SWAP gates for the 3D architecture by 17.1% compared to the best studies in the literatures.
Sensation of Deep Learning in Image Processing Applications

This chapter will address challenges with IoT and machine learning including how a portion of the difficulties of deep learning executions while planning the arrangement and choice of right calculation. Existing research in deep learning and IoT was focused to find how garbage in will deliver waste out, which is exceptionally appropriate for the scope of the informational index for machine learning. The quality, sum, readiness, and choice of information are essential to the achievement of a machine learning arrangement. Consequently, this chapter aims to provide an overview of how the system can use technologies along with deep learning and challenges to realize the security challenges IoT can support. Even though calculations can work in any nonexclusive conditions, there are particular rules to determine which calculation would work best under which circumstances. How reinforcement learning deep learning is useful for IoT will also be covered in the chapter.
Asymmetrical hyperentanglement concentration for entanglement of polarization and orbital angular momentum

We propose two hyperentanglement concentration protocols (hyper-ECPs) for two-photon entangled states in the polarization and orbital angular momentum degrees of freedom. The two cases distilling a maximally hyperentangled state from partially entangled pure state with unknown parameters and known parameters are dissected respectively. Both of the protocols require only linear optical elements which make our protocols more feasible for current technologies. In our protocols, the remote parties perform different local operations, which will reduce everyone's operation and improve the total efficiency. Each of them has the theoretical maximum success probability in the corresponding situation. The hyper-ECPs can be exploited simply to hyperentangled Greenberger-Horne-Zeilinger states.
Applying Scrum in New Product Development Process

For all organisations New Product Development (NPD) [1, 2], is a sophisticated process, necessary for introducing successfully the product on the market. Successfully means: customer satisfaction, benefits for organisation, new organisational and personal skills and experiences. The NPD process fulfils all conditions of project’s definition; that’s why many authors present the possibilities of applying to NPD process management instruments, specific for project management. Such attempts are limited to classical project managements methodologies and tools. Recently we observe growing interest in Scrum methodology, [3, 4]. Introduced for IT project management, nowadays is transferring completely or partially to manage the projects in other than IT fields of activities - healthcare, finance, consulting, education, and others, giving good results. This paper is a conceptual one, presenting an attempt made to show the possibility, utility and manner of transferring principal elements of Scrum toward NPD process. The objective of the paper is turn spotlight of New Product Development specialists on such possibility and encourage them to apply Scrum in this process management. The objective is achieved mainly in Sect. 5, where for all steps and activities in NPD process, their classification has been done, dividing them on unique or cyclical. The last ones (they are in the majority) can be managed according to Scrum methodology. It has been shown, for respective Scrum elements: roles, events and artefacts, how they can work in NPD process management. This study needs further research and empirical verification.
Engaging Lived and Virtual Realities

We examined the integration of VR into informal and less-structured learning environments in Atlanta (USA) and Mumbai (India) through a process of co-design, co-creation, and co-learning with students and teachers where students learned to use VR to engage with their economic, social, and cultural realities. Using qualitative methods, we engaged students and teachers at both sites in VR content creation activities; through these activities, we attempt to uncover a deeper understanding of the challenges and opportunities of introducing low-cost mobile VR for content generation, consumption, and sharing in underserved learning contexts. We also motivate future work that looks at integrating VR in new contexts, using flexible methods, across borders. The larger vision of our research is to advance us towards greater accessibility and inclusivity of VR across diverse learning environments.
Task design in APOS Theory

This paper discusses the role of task design in APOS Theory. The role played by the genetic decomposition in the theory and in task design is discussed. An example of a genetic decomposition for the concepts of inverse matrix transformation and inverse matrix is given. Tasks designed using this tool as a guide are exemplified as well as a description of their relationship to the genetic decomposition. In this way we provide insights about each task and the specific detailed construction it has as its aim. Therole of the tasks in the classroom is discussed since the combination of collaborative work of students in sequences of tasks and in group discussions are the foundation of APOS Theory’s potential to promote essential constructions needed for a deep learning of mathematical concepts.
Conversation Design

With the rise of digital assistants, chatbots, and other conversational interfaces, there's a huge demand for detail and instruction for Conversation Design. This course provides a focused walkthrough of the principles, strategies, and process of Conversation Design. Topics include understanding users, defining persona, analyzing conversation components, dialog writing strategies, and the detailed process of creating natural dialog. Interactive components at each stage engage participants with individual worksheets, small group exercises and reviews, and a team project. Participants will gain an understanding of the complexity and challenges of Conversation Design, and learn about resources and tools for doing it well.
Graph-Based Salient Object Detection using Background and Foreground Connectivity Cues

Salient object detection is an active research topic due to several potential applications in image compression, scene understanding, image retrieval, and so forth. In this paper, a salient object detection method is proposed by leveraging the recent advances in graph signal processing. Since, the image boundary regions generally belong to the image background, a distribution-based boundary contrast map is generated. Also, the graph representation of the image is used to compute the connectivity of the image regions to the image boundary as well as those to their local neighbors and the image foreground. The connectivity maps obtained are fused with the boundary contrast map in order to obtain the image saliency map. Several experiments are conducted to evaluate the performance of the proposed salient object detection method and to compare it with the state-of-the-arts. Results on datasets of images demonstrate that the proposed method achieves superior performance to the state-of-the-art methods in terms of precision, recall, and mean absolute error values.
Optimization of video steganography with additional compression and encryption

Currently, data such as text, images and video are very important. Therefore, data must be secured from unauthorized parties. In this paper, we propose a number of security levels, first using compression techniques on the data that will be hidden to reduce the size of the data, second using encryption techniques on data that has been compressed so that data is more secure, third using video steganography techniques on compressed and encrypted data so that unauthorized parties are increasingly difficult to extract data. Measurement of differences in quality of cover-video and stego-video using MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), evolution histogram of video tags, and video playback. The results show that the proposed algorithm gives better results than the previous algorithm which has a smaller MSE, larger PSNR, smaller histogram evolution of video tags, and play video without distortion.
Watching Sports in Augmented Reality

Recent years have seen enormous advances in the area of augmented and virtual reality (AR/VR). Established companies including Google, Facebook, and Microsoft, together with a considerable number of start-ups, are pushing the limits of AR/VR technology in terms of both hardware and software. Despite such efforts, there still isn't a killer app that can make these technologies appealing to large consumer audiences, and the currently available 3D content focuses mostly on games or simple prototypes of holographic demos.
Financial Data Visualization in 3D on Immersive Virtual Reality Displays

Within the scope of this publication, we present a design prototype for the 3D visualization of financial data of Australia's Energy sector with a large scale immersive virtual reality environment. We review existing 3D visualization approaches in finance industry; discuss the technical setup of the prototype; explore design considerations; and approaches to make financial information understandable for everyone. We conclude with a discussion about the potential that immersive virtual environments provide to support the understanding of market events, as well as the exploration of large multidimensional data-sets.
Digital Companion for Industry

The industrial domain offers a high degree of standardization, a variety of very specialized use cases, and an abundance of resources. These characteristics provide perfect conditions for Digital Companion systems. A Digital Companion is a cognitive agent that assists human users by taking on three roles: as guardians, assistants or mentors, and partners. This paper describes the characteristics, conceptual architecture, use cases and open challenges regarding Digital Companions for industry.
Fast Modular Squaring with AVX512IFMA

Modular exponentiation represents a significant workload for public key cryptosystems. Examples include not only the classical RSA, DSA, and DH algorithms, but also the partially homomorphic Paillier encryption. As a result, efficient software implementations of modular exponentiation are an important target for optimization. This paper studies methods for using Intel's forthcoming AVX512IFMA instructions in order to speed up modular (Montgomery) squaring, which dominates the cost of the exponentiation. We further show how a minor tweak in the architectural definition of AVX512IFMA has the potential to further speed up modular squaring.
Word Embedding-Based Automatic

Hiroshi Echizen’ya, Kenji Araki, Eduard Hovy. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Graph Convolutional Gaussian Processes

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. The structure of these models allows for high dimensional inputs while retaining expressibility, as is the case with convolutional neural networks. We present applications of graph convolutional Gaussian processes to images and triangular meshes, demonstrating their versatility and effectiveness, comparing favorably to existing methods, despite being relatively simple models.
Efficiency Improvement of Classification Model Based on Altered K-Means Using PCA and Outlier

In the generation and analysis of Big Data following the development of various information devices, the old data processing and management techniques reveal their hardware and software limitations. Their hardware limitations can be overcome by the CPU and GPU advancements, but their software limitations depend on the advancement of hardware. This study thus sets out to address the increasing analysis costs of dense Big Data from a software perspective instead of depending on hardware. An altered [Formula: see text]-means algorithm was proposed with ideal points to address the analysis costs issue of dense Big Data. The proposed algorithm would find an optimal cluster by applying Principal Component Analysis (PCA) in the multi-dimensional structure of dense Big Data and categorize data with the predicted ideal points as the central points of initial clusters. Its clustering validity index and [Formula: see text]-measure results were compared with those of existing algorithms to check its excellence, and it had similar results to them. It was also compared and assessed with some data classification techniques investigated in previous studies and we found that it made a performance improvement of about 3–6% in the analysis costs.
Slice interpolation of medical images using enhanced fuzzy radial basis function neural networks

Volume data composed of complete slice images play an indispensable role in medical diagnoses. However, system or human factors often lead to the loss of slice images. In recent years, various interpolation algorithms have been proposed to solve these problems. Although these algorithms are effective, the interpolated images have some shortcomings, such as less accurate recovery and missing details. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network to improve the performance of the interpolation method. The neural network includes an input layer (six input neurons), three hidden layers of neurons, and the output layer (one output neuron), and we propose a patch matching method to select the input variables of the neural network. Accordingly, we use two normal pending images to be interpolated as the input. Final output data is obtained by applying the trained neural network. In examining four groups of medical images, the proposed method outperforms five other methods, achieving the highest similarity image metric (ESSIM) values of 0.96, 0.95, 0.94, and 0.92 and the lowest mean squared difference (MSD) values of 35.5, 41.2, 50.9, and 47.1. In addition, for a whole MRI brain volume data experiment, the average MSD and ESSIM values of the proposed method and other methods are (41.62, 0.95) and (57.13, 0.90), respectively. The results indicate that the proposed method is superior to the other methods.
DENSE: Environment Perception in Bad Weather—First Results

This paper presents the first results of the publicly funded ECSEL project DENSE (Adverse weather environmental sensing system). DENSE seeks to eliminate one of the most pressing problems of automated driving: the inability of current systems to sense their surroundings under all weather conditions. The task in DENSE is to develop a sensor suite for automatic driving, by means of which the vehicle environment can be reliably detected 24/7 under these bad weather conditions. In this paper, the state of the art of environmental sensor technology is first examined and evaluated in the CEREMA weather chamber. Then, the architecture of the DENSE Sensor Suite is presented and the development results of the most important system components are described. The results show that the realization of a 24/7 all-weather sensor suite is absolutely feasible with these components .
STYX: A Hierarchical Key Management System for Elastic Content Delivery Networks on Public Clouds

Hosting content delivery networks (CDNs) on clouds has the potential to improve the performance as resources and caches can be placed closer to subscribers. However, avoiding data leakage over an untrusted public cloud is critical, especially for sensitive data such as the SSL private key. The popular Keyless SSL solution allows content owners to retain on-premise custody of SSL private keys on their own key servers, but this solution likely causes performance bottlenecks and impedes the elasticity of CDNs. This paper describes a novel key management system, named STYX, for transmitting trusted data over untrusted channels and storing them on untrusted platforms. STYX accomplishes secure key provisioning for CDN scale-out and the key is securely protected with full revocation rights for CDN scale-in. STYX is implemented as a three-phase hierarchical key management scheme by leveraging Intel Software Guard Extensions (SGX) and QuickAssist Technology (QAT). Furthermore, STYX supports CDN services by integrating Nginx as the SSL termination proxy and the popular Redis/Memcached/Apache as backend caching engines. The performance evaluation shows that STYX significantly outperforms the native HTTPS servers on the CDN node due to QAT acceleration, providing up to a 5× enhancement in throughput and a 50 percent reduction in latency.
Human activity recognition using support vector machine for automatic security system

Home is a place for human beings to live and also to socialize with the society. In this rapid technology development, a home security system is a must to prevent crimes. One of the innovation is home automation which enables controlling the doors automatically. RGB-D camera can detect human body movements which later can be used to recognize the activity through the Support Vector Machine (SVM) algorithm. The result of activity prediction can be used as home automation input for the home security system. In this paper, a security system analysis has been carried out which is beneficial for the users in maintaining the security system through combining the RGB-D Camera and Skeleton Tracking which then classified by using SVM algorithm. The result shows that the optimal data resulted through 1 meter distance with 100% accuracy, while testing variable C of SVM resulting in C = 2 as the best score for C variable with 92% accuracy.
Story Ending Prediction by Transferable BERT

Recent advances, such as GPT and BERT, have shown success in incorporating a pre-trained transformer language model and fine-tuning operation to improve downstream NLP systems. However, this framework still has some fundamental problems in effectively incorporating supervised knowledge from other related tasks. In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task. Particularly, we propose utilizing three kinds of transfer tasks, including natural language inference, sentiment classification, and next action prediction, to further train BERT based on a pre-trained model. This enables the model to get a better initialization for the target task. We take story ending prediction as the target task to conduct experiments. The final result, an accuracy of 91.8%, dramatically outperforms previous state-of-the-art baseline methods. Several comparative experiments give some helpful suggestions on how to select transfer tasks. Error analysis shows what are the strength and weakness of BERT-based models for story ending prediction.
Unsupervised Neural Network for Modulation Format Discrimination and Identification

We propose a new method to discriminate and identify the modulation format of signals based on an unsupervised neural network named convolutional Gaussian-Bernoulli restricted Boltzmann machine (CGBRBM). Tests are performed to demonstrate how the proposed method works and to evaluate the discrimination/identification accuracy for different input combinations. Signals of five modulation formats are used to test the CGBRBM-based algorithm including QPSK, 8QAM, 16QAM, 32QAM, and 64QAM. The results indicate the performance of the proposed method when dealing with various application scenarios and reveal the relation between discrimination accuracy and identification accuracy.
The Dynamically Modified BoW Algorithm Used in Assessing Clicks in Online Ads

In this paper we present an algorithm that identifies fraud in online advertising systems such as CPC (Cost Per Click also called PPC Pay-Per-Click). This model used in online advertising is particularly sensitive because it can be exploited by making invalid clicks on an advertisement. This results in additional costs for the advertiser, reduced possibility of reaching the most interested viewers and fraudulent results of an advertising campaign. The dynamically modified BoW (Bag-Of-Words) algorithm presented in the article allows us to identify repetitive clicks made by dishonest publishers or by automatic software, i.e. bots. The algorithm uses data obtained directly on an advertiser’s website. The paper also presents the results of an experimental research confirming effectiveness of the proposed methods.