Reviewerly/exHarmony huggingface dataset:
Laplacian pyramid based non-linear coherence diffusion for real-time ultrasound image speckle reduction

The inherent speckle noise in medical ultrasound image degrades valuable clinical information and affects the diagnosis quality. Therefore, commercial ultrasound systems always face a big challenge to improve the visual quality of ultrasound images by reducing the speckle noise. A new hybrid algorithm is proposed in the present paper for speckle reduction by combining nonlinear coherence diffusion (NCD) with pyramid-transform-based multi-scale filters (named as Laplacian Pyramid Based Non-linear Coherence Diffusion; viz. LPNCD). The new approach exhibits good capability in preserving tissue features while enhancing the coherence. The theory and implementation of the approach are presented and verified based on phantom and clinical data. By comparing with previous techniques on quantified signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), the results suggest that the LPNCD method may demonstrate superior performance than other commonly used AD-class filters. Meanwhile, the performance of GPU implementations of the current technique is also evaluated, and it is verified that the LPNCD approach may have good performance efficiency and wonderful visual effect as running on mainstream commercial ultrasound systems.
User Experience in Augmented Reality: A Holistic Evaluation of a Prototype for Assembly Instructions

Industries are under development with new upcoming tools that will further streamline the work of operators, not least in assembly. Assembly instructions are usually visualized by traditional paper or data bases. A new way of showing instruction is provided by augmented reality (AR). The focus of this paper is the user experience (UX) of AR based instructions for assembly. In order to study the UX in AR an evaluation matrix and an AR prototype has been developed and evaluated in a UX test, where data regarding both hedonic and pragmatic qualities was collected. The UX test yielded a result of three out of nine sub-goals completed while six did not. There was a general low degree of cognitive load while assembling but not low enough. However, there are promising results for AR based instructions, though the technology still needs improvement and more testing is also necessary. The assembly scenario for this study was somewhat simple and could be one reason why this study generated ambiguous results.
It’s Not My Problem: How Healthcare Models Relate to SME Cybersecurity Awareness

Small and medium enterprises (SMEs) make up a significant part of European economies. Despite their economic importance, they are often described as poorly placed to deal with cyber risks because of resource constraints or commercial interests. Providing appropriate tooling would facilitate a greater appreciation of the risks and provide mitigation strategies. In a series of workshops demonstrating visualization tools for cybersecurity, constructs from healthcare models such as awareness, self-efficacy, and a willingness to engage were investigated to throw light on the likelihood that the technologies would be adopted. Although most constructs were validated, it turns out that self-efficacy could more appropriately be interpreted as a desire to understand a broader company narrative rather than empowering any individual to identify and manage cyber risk. As part of an ongoing examination of technology acceptance, this work provides further evidence that technology must be contextualised to make sense for the individual as part of the SME rather than as individual employee.
Efficient multi‐key fully homomorphic encryption over prime cyclotomic rings with fewer relinearisations

Multi-key fully homomorphic encryption (MKFHE) allows computations on ciphertexts encrypted by different users, which can be applied to implement secure multi-party computing (MPC). The current NTRU-based MKFHE has the following two drawbacks: One is that the relinearisation process during homomorphic evaluation is so complicated that the corresponding computation time is costly. The other is that a class of subfield attacks are proposed and affects the security of NTRU schemes over power-of-2 cyclotomic rings for large moduli q, especially for the NTRU-based fully homomorphic encryption (FHE) schemes. In this work, an efficient MKFHE scheme is proposed over prime cyclotomic rings with fewer relinearisations, which seems a good choice because of its potential to resist a subfield attack. More specifically, the time of the relinearisation process is reduced by half in homomorphic evaluations by separating the homomorphic multiplication and the relinearisation process (implementing two homomorphic multiplication operations together before relinearisation), while in current NTRU-type MKFHE schemes, these two processes are usually performed together. The error bound of the basic function components is re-analysed over prime cyclotomic rings in the average case, which can be used in the error analysis of our scheme. We construct an efficient NTRU-based single-key FHE scheme and an efficient MKFHE scheme over prime cyclotomic rings through relinearisation and modulus-switching techniques. The MKFHE scheme proposed has the on-the-fly property and has a tight ciphertext size compared with the GSW-type and BGV-type MKFHE schemes. An experiment shows that the homomorphic evaluation of the optimised single-key FHE scheme proposed is 1.9 times faster than an efficient NTRU-type MKFHE DHS16 proposed at DCC 2016.
Cloud Rendering Scheme for Standalone Virtual Reality Headset

This paper proposes a cloud rendering framework for three-degree-of-freedom-oriented standalone virtual reality headset which worked on wireless network to solve the shortcomings of VR devices using cables connections and insufficient local rendering capabilities. This framework not only solves the problem of the VR experience being affected by cables, but also improves the image quality of the standalone headset. In this solution, we use the standalone headset as the client, the Unreal Engine 4(UE4) as the rendering engine and the server combining Asynchronous TimeWarping (ATW) and Fixed Foveated Rendering (FFR) technology to achieve cloud rendering. The results show that the solution can support the 2560×1440 resolution. Experiments show that the framework can significantly improve the image quality of the standalone headset, and it can also maintain a frame rate of 70Hz.
Visualizing Decision Diagrams for Quantum Computing (Special Session Summary)

With the emergence of more and more applications for quantum computing, also the development of corresponding methods for design automation is receiving increasing interest. In this respect, decision diagrams provide a promising basis for many design tasks such as simulation, synthesis, verification, and more. However, users of the corresponding tools often do not have a proper background or an intuition about how these methods based on decision diagrams work and what their strengths and limits are. In an effort to make decision diagrams for quantum computing more accessible, we present a visualization tool which visualizes quantum decision diagrams and allows to explore their behavior when used in the design tasks mentioned above. The installation-free web-tool allows users to interactively learn how decision diagrams can be used in quantum computing, e.g., to (1) compactly represent quantum states and the functionality of quantum circuits, (2) to efficiently simulate quantum circuits, and (3) to verify the equivalence of two circuits. The tool is available at https://iic.jku.at/eda/research/quantum_dd/tool
A Traffic Sign Recognition Method Based on Improved YOLOv3

With the continuous development and wide application of deep learning, object detection algorithms based on deep learning have become a new trend. In order to further improve the detection accuracy of convolutional neural network YOLO v3 (You Only Look Once v3), we use DenseNet to replace the ResNet in the original algorithm. The experimental results on the Tsinghua-Tencent 100K (TT100k) dataset show that the improved YOLO v3 has fewer parameters and faster detection speed in traffic sign detection.
Robust Optimization Framework for Training Shallow Neural Networks Using Reachability Method

In this paper, a robust optimization framework is developed to train shallow neural networks based on reachability analysis of neural networks. To characterize noises of input data, the input training data is disturbed in the description of interval sets. Interval-based reachability analysis is then performed for the hidden layer. With the reachability analysis results, a robust optimization training method is developed in the framework of robust least-square problems. Then, the developed robust least-square problem is relaxed to a semi-definite programming problem. It has been shown that the developed robust learning method can provide better robustness against perturbations at the price of loss of training accuracy to some extent. At last, the proposed method is evaluated on a robot arm model learning example.
The Human-side of Emerging Technologies and Cyber Risk: A case analysis of blockchain across different verticals

Blockchain, or distributed ledger, continues to be deployed in the healthcare, energy, manufacturing, financial services sectors, and retail. Blockchain advocates argue that this technology constitutes a new architectural foundation for building trust based on its unique technological characteristics (Glaser 2017). However, cybersecurity is not a function of technological architecture but, in fact, is critically bound by human elements, processes, and actors. Therefore, all emerging technologies have cyber risk. Given the disruptive nature of this technology, there is a strong business need to understand cyber risk associated with blockchain. With this in mind, this investigation posed the following research questions: What are the risks associated with blockchain? Can a risk-based approach be validated or extended by exploring cases of blockchain deployment and highlighting risk? Multiple qualitative methods were used to analyze the data and to identify trends. This analysis identified cyber risk, in the context of blockchain, using the simple and generally accepted definition of cyber security as confidentiality, integrity, and availability (CIA). Based on the results, a cyber-physical risk-based approach was developed and blockchain deployment was examined in specific cases to identify and analyze the breaches according to this framework. The results demonstrate that beyond technological and data architecture there a strong need to further identify and analyze the cybercriminal actors and behavior in order to address cyber risk.
AC Prediction Error Propagation-based Encryption for Texture Protection of JPEG Compressed Images

A recent attention of JPEG format-compliant encryptions has been concentrated to randomize the specific syntaxes of JPEG file interchange format (JFIF) or to flip the signs of quantized discrete cosine transform (QDCT) coefficients. Although the random sign flip (RSF) achieves low-complexity encryption while preserving the JPEG coding efficiency, the texture information represented by nonzero AC coefficients are still easy to reveal for an attacker using replacement attacks. To reinforce the perceptual degradation and the texture protection at the same complexity as the RSF, we propose an AC prediction error propagation-based encryption (AC-WPE) in the JPEG QDCT domain. Since the AC-WPE encrypts the coefficient signs in the residual QDCT domain obtained by a provisional AC predictor such as differential pulse-code modulation (DPCM) and propagates the encrypted prediction errors among the several blocks by the AC reconstructor, the attack resilience is obviously reinforced. Moreover, to suppress the decrease in coding efficiency, we introduce the randomized prediction intervals into the AC predictor/reconstructor. Experiments with JPEG encryption show that the AC-WPE reinforces the perceptual degradation and the resilience to the replacement attacks, while reasonably expensing the bitrate overheads of crypto-compressed images.
A Literature Review on a Neuro-Psychological Approach to Immersive Technology Research

One of the challenges of immersive technology research is that its increasing system complexity makes evaluating the user experience difficult. The use of an electroencephalogram (EEG) has been suggested as a promising approach to understanding the user's cognitive, emotional, and behavioral responses to immersive technology. However, the translation of this method into clear applications for user research remains challenging. To address this challenge, this paper outlines a systematic literature review to identify the applications of EEG measures currently adopted in immersive technology research. The full range of journal articles and major conference proceedings that reference the adoption of EEG measures to address immersive technology usage issues were searched. Based on rigorous inclusion and exclusion criteria, 84 relevant papers were identified and reviewed in the study. This literature review involves analysis of bibliometric data, research contexts, EEG analysis methods, and EEG stimuli. Presented in this paper are research gaps identified and opportunities for future research recommended based on the analysis results. This study contributes to advancing our knowledge about how to collect and analyze EEG data in immersive technology research.
Application of Machine Learning in Cryptanalysis Concerning Algorithms from Symmetric Cryptography

As machine learning is used to analyse data and produce some action based on that data, the application in the domain of cryptanalysis opens new points of view, since the key space of any complex cipher system is large. When concerning machine learning with the algorithms of cryptography, the possibility to learn from the experience that is provided by information retrieval, is being considered. With the understanding that cryptanalysis can be used for improvement and producing more complex solutions in the field of cryptography, common features are sought between these three fields. This paper aims to present the application of machine learning in the field of cryptanalysis, as a prime step for auditing and checking the strength of cryptosystems. Some of these cryptosystems ensure confidentiality and security of a variety of information exchanged from source to destination using symmetric key cryptography. Accordingly, understanding how the algorithms are maintaining the stability is the fundamental aspect for proceeding with the analysis. The experimental part of this paper provides a solution for application of neural networks in cryptanalysis. We show that AES with ECB mode is vulnerable to ciphertext-plaintext attacks. The analysis is focused on trying different sizes of keys for obtaining the ciphertexts from plaintexts. At the end, we discuss how that is impacting the difficulty of data reveal by decrypting with DNN (Artificial Neural Network).
Reading Line Classification Using Eye-Trackers

Eye-tracking while reading is an emerging application where the goal is to track the progression of reading. The challenges for accurate tracking of the reading progression are due to the measurement noise of the eye-tracker and the rapid and uncertain movement of the eye gaze. Solutions to this problem developed in the recent past suffer from many limitations, such as the need to know the text context and the need to have a batch of one page of data for classification. In this article, we relax these assumptions and develop a novel, real-time line classification approach. The proposed solution consists of an improved slip-Kalman smoother (slip-KS) that is designed to detect new line returns and to reduce the variance in the eye-gaze measurements. After preprocessing of the data by the slip-KS, a classification approach is employed to track the lines being read in real-time. Two such classifiers are demonstrated in this article; one is based on Gaussian discriminants, and the other is based on support vector machines. The proposed approaches were tested using realistic eye-gaze data from seven participants. Analysis based on the collected data using the proposed algorithms shows significantly improved performance over existing methods.
Accurate Grid Keypoint Learning for Efficient Video Prediction

Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction. However, keypoint locations are often modeled only as continuous coordinates, so noise from semantically insignificant deviations in videos easily disrupt learning stability, leading to inaccurate keypoint modeling. In this paper, we design a new grid keypoint learning framework, aiming at a robust and explainable intermediate keypoint representation for long-term efficient video prediction. We have two major technical contributions. First, we detect keypoints by jumping among candidate locations in our raised grid space and formulate a condensation loss to encourage meaningful keypoints with strong representative capability. Second, we introduce a 2D binary map to represent the detected grid keypoints and then suggest propagating keypoint locations with stochasticity by selecting entries in the discrete grid space, thus preserving the spatial structure of keypoints in the long-term horizon for better future frame generation. Extensive experiments verify that our method outperforms the state-of-the-art stochastic video prediction methods while saves more than 98% of computing resources. We also demonstrate our method on a robotic-assisted surgery dataset with promising results. Our code is available at https://github.com/xjgaocs/Grid-Keypoint-Learning.
A unified approach to the study of language contact: Cross-language priming and change in adjective/noun order

Aims and objectives/purpose/research questions: The connection between language contact and the bilingual speaker goes back to foundational authors in the field of contact linguistics. Yet there is very little work that combines these two levels in a single study. In this paper, we propose a unified approach to language contact by testing the role of cross-language priming (CLP) on contact-induced change at the level of complex noun phrases (NPs). Design/methodology/approach: We conducted three studies with different types of data. In Study 1, we analyse the Romani Morpho-Syntax database to identify word order preferences in Romani dialects from different countries. In Study 2, we examine a corpus of interviews in Romani from Romania. In Study 3, we conduct an experiment to test short-term priming in adjective (ADJ)/noun (N) order from Romanian to Romani and within Romani. Data and analysis: In Study 1, we examine the word order in approximately 3000 NPs from 119 Romani speakers. In Study 2, we analyse a speech corpus of 9400 words from four elderly Romani–Romanian bilinguals. In Study 3, 90 Romani–Romanian bilinguals participated in a priming experiment. We used multinomial mixed-effects logistic regression, Bayesian models and Random Forests to analyse the experimental results. Findings/conclusions: Study 1 shows that Romani speakers from Romania stand out for their frequent use of postnominal ADJs. Study 2 confirms these uses in free speech. Study 3 reveals significant CLP effects, whereby speakers favour the use of determiner (DET)–N–ADJ order in Romani immediately following a noun with a suffixed determiner (N DET) –ADJ sentence read in Romanian. Originality: Our study is the first to demonstrate CLP effects in ADJ/N order. Significance/implications: We illustrate a unified approach to language contact by introducing theoretical and methodological advances from the field of bilingualism into the study of contact-induced change.
Implementation of New Message Encryption using Elliptic Curve Cryptography Over Finite Fields

Elliptic curve cryptography (ECC) is public-key cryptography that is increasingly used today because it provides better security. In this study, we propose a new message encryption algorithm using an elliptic curve over finite fields. This new method converts each character of the message to its hexadecimal Unicode value and then separates the value divided into blocks that contain one character. Unicode contains many more characters than ASCII. In the Unicode table, the hexadecimal values of the characters range from one to six digits. The proposed new encryption algorithm can encrypt not just by using the values in the ASCII table but all values in the Unicode table, thus can use different alphabets and characters. The algorithm is implemented in C++.
Predictive analytics of the state of computer devices in the inventory system

This article discusses processes for collecting, recording, and analyzing data about equipment and its condition. A general description of the inventory system is given and the possibilities of using a mobile application to improve the data collection process are discussed. The article describes the possibilities of using qr codes to mark equipment and simplify the process of obtaining information during the inventory process. Specific system design changes are described to be able to predict the condition of equipment and prevent breakdowns and data loss. At the end of the article possible ways of development and further application of predictive analytics technology are described.
Figure search by text in large scale digital document collections

Summary Digital document collections have been created with the transfer of a large number of documents to digital media. These digital archives have provided many benefits to users. As the diversity and size of digital image collections have grown exponentially, it has become increasingly important and difficult to obtain the desired image from them. The images on the document might contain critical information about the subject of it. In this study, an architecture is developed that can work on large‐scale data by creating regular expressions together with full‐text search approaches. The performance of the system has been tested on different academic documents and Elasticsearch and Apache Solr insert times are compared. Compared to Elasticsearch, Apache Solr achieved faster and more successful results.
Machine learning applications and process intelligence for cement industries

Estimating energy consumption in cement mills is critical for the cement industry. Following data science practices and adopting machine learning (ML) technologies, we developed energy consumption prediction models for a cement mill of TITAN SA plant in Kamari Viotia. The models exploit historical sensor measurements and operational data and give predictions for energy consumption with accuracy better than almost one order of magnitude compared to existing baseline methods.
Number and Quality of Diagrams in Scholarly Publications is Associated with Number of Citations

Diagrams are often used in scholarly communication. We analyse a corpus of diagrams found in scholarly computational linguistics conference proceedings (ACL 2017), and find inclusion of a system diagram to be correlated with higher numbers of citations after three years. Inclusion of more than three diagrams in this 8-page limit conference was found to correlate with a lower citation count. Focusing on neural network system diagrams, we find a correlation between highly cited papers and “good diagramming practice” quantified by level of compliance with a set of diagramming guidelines. This study suggests that diagrams may be a useful source of quality data for predicting citations, and that “graphicacy” is a key skill for scholars with insufficient support at present.