Reviewerly/exHarmony huggingface dataset:
A Hybrid Denoising Algorithm of BM3D and KSVD for Gaussian Noise in DoFP Polarization Images

In this paper, we present a hybrid denoising algorithm dedicated to division-of-focal plane (DoFP) polarization images. The proposed algorithm, centered around the Block-Matching and 3D Filtering (BM3D) and K-times Singular Value Decomposition (KSVD) denoising algorithms, is capable of significantly enhancing the grouping step in the second round of collaborative filtering by purifying the “Semi-Filtered” image yielded by the first round of collaborative filtering. To achieve this, the BM3D denoising method's chain of operation is broken, and the “Semi-Filtered” image is passed through a round of KSVD denoising method before the second round of collaborative filtering is conducted. According to our extensive experimental results, the proposed algorithm visually outperforms the state-of-the-art BM3D denoising algorithm and a wide range of other denoising algorithms for DoFP polarization images. Quantitative results presented using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) Index metrics further highlight the superior performance of the proposed algorithm.
Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods

Malicious attackers on the Internet use automated attack programs to disrupt the use of services via mass spamming, unnecessary bulletin boarding, and account creation. Completely automated public turing test to tell computers and humans apart (CAPTCHA) is used as a security solution to prevent such automated attacks. CAPTCHA is a system that determines whether the user is a machine or a person by providing distorted letters, voices, and images that only humans can understand. However, new attack techniques such as optical character recognition (OCR) and deep neural networks (DNN) have been used to bypass CAPTCHA. In this paper, we propose a method to generate CAPTCHA images by using the fast-gradient sign method (FGSM), iterative FGSM (I-FGSM), and the DeepFool method. We used the CAPTCHA image provided by python as the dataset and Tensorflow as the machine learning library. The experimental results show that the CAPTCHA image generated via FGSM, I-FGSM, and DeepFool methods exhibits a 0% recognition rate with ε=0.15 for FGSM, a 0% recognition rate with α=0.1 with 50 iterations for I-FGSM, and a 45% recognition rate with 150 iterations for the DeepFool method.
Maximum Correntropy Criterion Kalman Filter with Adaptive Kernel Size

Kernel size plays a significant role in the performance of the maximum correntropy Kalman (MCC-KF). Kernel size is usually chosen by trail and error. If the kernel size is large, the MCC-KF reduces to the Kalman (KF). However, if the kernel size is small, the MCC-KF may diverge, or converge slowly. We propose a novel method for adaptive kernel size selection. We calculate kernel size as a weighted sum of the innovation term and the covariance of the filter-indicated estimation error at each time step. We call this the MCC with adaptive kernel size filter (MCC-AKF). We analytically prove that the true mean square error (TMSE) of the MCC-AKF is less than or equal to that of the MCC-KF under certain conditions. A simulation example is provided to illustrate the analytical results.
Application of Affordance Factors for User-Centered Smart Homes: A Case Study Approach

Smart homes improve quality of life by providing various services based on information and communication technologies. However, smart home systems are complicated and user interfaces for the interaction between smart home and user are often not user-friendly, causing potential difficulties and inconvenience for the user. Therefore, in order for smart homes to become user-centered, usability needs to be improved. This study aims to present guidelines for improving the usability of smart homes based on the concept of affordance, which is highly meaningful in user-centered design. To do this, firstly, the affordance factors that could be applied to improve the usability of active devices and user interfaces in smart homes were extracted, secondly, a case study was conducted to analyze the application of affordance factors, focusing on active devices (e.g., control devices and smart appliances) and user interfaces that directly interact with users in smart homes. Lastly, guidelines on the application of affordance factors were presented by combining case analysis results with relevant guidelines. Active devices and user interfaces should provide users with appropriate cognitive, physical, functional, and sensory affordances so that users can use the smart home services easily and conveniently.
Digital Cultural Heritage and Linked Data: Semantically-informed conceptualisations and practices with a focus on intangible cultural heritage

The emerging field of Linked Data for Digital Cultural Heritage presents new challenges and possibilities for memory institutions and the way they model their digital collections for open access. The essay reviews current semantically-informed practices and conceptualisations, standards and applied frameworks on this topic, with a focus on Intangible Cultural Heritage (ICH). In particular, the essay identifies and reviews the scope of applied frameworks for a semantically informed digital documentation of intangible cultural heritage by memory institutions, a topic with limited contributions in academic literature so far. The essay starts with an overview of the current digital transformation in the GLAM sector. Following a data centered approach, it analyses conceptual models of linked data in connection to digital heritage. It further reviews the working models and metadata schemas of linked data in particular for Intangible Cultural Heritage including innovative examples and finally, discusses challenges and new concepts for future research on the topic.
Test and Evaluation for Artificial Intelligence

ABSTRACT Incorporating artificial intelligence (AI) leveraging statistical machine learning (ML) into complex systems poses numerous challenges to traditional test and evaluation (T&amp;E) methods. As AI handles varying decision levels, the underlying ML needs confidence to ensure testable, repeatable, and auditable decisions. Additionally, we need to understand failure modes and failure mitigation techniques. We need AI assurance–certifying ML and/or AI algorithms function as intended and are vulnerability free, either intentionally or unintentionally designed or inserted as data/algorithm parts. T&amp;E provides a process for AI assurance. This article highlights existing test and evaluation methods, the key challenges embedded‐AI exacerbates, and themes based for how T&amp;E will evolve to provide AI system assurance.
A cluster-based hybrid sampling approach for imbalanced data classification

When processing instrumental data by using classification approaches, the imbalanced dataset problem is usually challenging. As the minority class instances could be overwhelmed by the majority class instances, training a typical classifier with such a dataset directly might get poor results in classifying the minority class. We propose a cluster-based hybrid sampling approach CUSS (Cluster-based Under-sampling and SMOTE) for imbalanced dataset classification, which belongs to the type of data-level methods and is different from previously proposed hybrid methods. A new cluster-based under-sampling method is designed for CUSS, and a new strategy to set the expected instance number according to data distribution in the original training dataset is also proposed in this paper. The proposed method is compared with five other popular resampling methods on 15 datasets with different instance numbers and different imbalance ratios. The experimental results show that the CUSS method has good performance and outperforms other state-of-the-art methods.
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT presents a new methodology to produce the offspring that does not rely on the classic crossover. The synergy between such a methodology and linear scaling yields to final solutions with low approximation error and computational cost. We evaluate SGP-DT on eight well-known data sets and compare with $$\epsilon $$ -lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of $$\epsilon $$ -lexicase.
Relation Embedding for Personalised Translation-Based POI Recommendation

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.
Content and context features for scene image representation

Existing research works in scene image classification have focused on different aspects such as content features (e.g., visual information), context features (e.g., annotations, semantic information, etc.) and both. However, such works suffer from various issues such as higher feature size and lower classification performance. In this paper, we propose a new feature extraction approach for scene image representation using two kinds of rich information: content features and context features. Specifically, the new content features are generated by multi-scale foreground and background information. Similarly, the new context features are generated by the novel compact supervised codebook. Our compact supervised codebook minimizes irrelevant and redundant information, which, in result, achieves the lower-sized contextual feature vector. Finally, we combine both content and context features to represent the scene image. Our experiments on three widely used benchmark scene datasets using Support Vector Machine (SVM) classifier reveal that our proposed context and content features produce better results than existing context and content features, respectively. The fusion of the proposed two types of features significantly outperform numerous state-of-the-art features.
ScriptWriter: Narrative-Guided Script Generation

It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach.In dialogue systems, it would also be useful to drive dialogues by a dialogue plan.In this paper, we address a key problem involved in these applications -guiding a dialogue by a narrative.The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative.It keeps track of what in the narrative has been said and what is to be said.A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems.Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where endusers can upload their narratives freely when watching a movie.Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
Pengunaut trainer

We present the concept, design, and evaluation of a playful mobile virtual reality (VR) app for children to reduce anxiety and stress during MRI examinations. The Pengunaut Trainer aims to help children to familiarize themselves with the medical environment so that they can be examined without fear, rendering sedation unnecessary. The young patients learn about the procedure and train to lie still during a virtual MRI scan. We conducted a clinical trial focusing on an in-depth analysis of the game design. 29 children trained over 14 days on average before their MRI examination. The participants were impressed by the VR experience and motivated to train. They reported high levels of immersion and positive affect. Anxiety and negative feelings towards the upcoming MRI examination were significantly reduced after the training period. Moreover, our results indicate that the Pengunaut Trainer could be effective in reducing anxiety and stress during the MRI scan. Our results and the positive feedback from parents and medical professionals prove the validity of our approach.
Accuracy controlled iterative method for efficient sigmoid function approximation

The sigmoid activation function plays an essential role in implementing neural networks in hardware. However, due to a high degree of non-linearity, the hardware implementation of the activation function itself is a daunting job. In this Letter, the authors proposed an accuracy controlled iterative algorithm for the piecewise linear approximation of the sigmoid function. The proposed algorithm approximates sigmoid activation with an optimised number of linear functions for a given error. The accuracy and hardware complexity of the resulting approximation function are compared with the state-of-the-art literature.
LightGAN: A Deep Generative Model for Light Field Reconstruction

A light field image captured by a plenoptic camera can be considered a sampling of light distribution within a given space. However, with the limited pixel count of the sensor, the acquisition of a high-resolution sample often comes at the expense of losing parallax information. In this work, we present a learning-based generative framework to overcome such tradeoff by directly simulating the light field distribution. An important module of our model is the high-dimensional residual block, which fully exploits the spatio-angular information. By directly learning the distribution, our approach can generate both high-quality sub-aperture images and densely-sampled light fields. Experimental results on both real-world and synthetic datasets demonstrate that the proposed method outperforms other state-of-the-art approaches and achieves visually more realistic results.
Investigating the role of HRM practices on service innovation: empirical evidence from UAE government agencies

Purpose Government agencies are trying to develop strategies to improve their innovative activities. However, due to many challenges and obstacles, employees are reluctant to perform innovatively in such agencies. Human resource management (HRM) practises and an appropriate culture can help to improve service innovation. However, empirical evidence to prove this relationship is insufficient particularly in the government sector. Thus, this study aims to empirically analyses the effect of HRM practises and innovation culture on service innovation of the United Arab Emirates (UAE)’s Government agencies. Design/methodology/approach The data were collected from government entities in the UAE. Structural equation modelling through partial least squares modelling was used to test the proposed hypotheses. SPSS was also used to conduct preliminary analysis. Findings Statistical results provide strong evidence that HRM practises and innovation culture positively affected service innovation of UAE’s Government agencies. Research limitations/implications Further details and valuable implications are discussed throughout the study. Results have many practical and theoretical implications. Results can help government agencies develop their services innovation by tailoring HRM practises and establishing proper innovation culture in their agencies. Originality/value Although several contributions indicated that culture is a key determinant of innovation and a mediator in the link between HRM practises and service innovation, the literature lacks empirical studies investigating this link. Accordingly, this study seeks to bridge this gap and delivers evidence supporting them. In addition, this study is one of the unique studies that use these variables in government agencies in the UAE.
A survey on the construction methods and applications of sci-tech big data knowledge graph

Recently, the knowledge graph (KG) of sci-tech and big data technology has played a paramount role in the development of the science of science. We carry out a systematic and in-depth review of KG construction and big data technology application in the sci-tech field. Specifically, we explain the issues of sci-tech entity extraction, sci-tech entity disambiguation, sci-tech relationship extraction, and sci-tech relationship inference involved in the construction of sci-tech big data KG, and give a systematized summary of the analysis and mining methods of the sci-tech big data KG, such as sci-tech entity recommendation, sci-tech community detection, sci-tech entity evaluation, interdisciplinary research, and disciplinary evolution analysis. Lastly, we give the future research and application directions of the sci-tech KG.
Toward a Formal Scholarly Understanding of Blockchain-Mediated Decentralization: A Systematic Review and a Framework

This work analyses blockchain-mediated decentralization based on a systematic review of the scholarly understanding of the term ‘decentralization’ across multiple disciplines from computer to political sciences, examining how its various meanings are reflected in popular discourse on blockchains and distributed ledgers. The paper aims to capture the rigorous cross-domain understanding of decentralization and its most important features, and to map the commonalities and differences between it and some closely related concepts such as distribution, disintermediation and peer-to-peer (P2P). Across all domains, decentralization appears to be used as a solution to problems requiring non-trivial coordination across heterogeneous stakeholders. Blockchain-mediated decentralization appears to have unique characteristics reflecting an idiosyncratic set of authority-related values prevalent in so-called ‘crypto’ online communities. Within blockchain space, the article argues against the binary positioning of ‘decentralization’ and ‘centralization’, proposing a dialectical approach and arguing that a system’s authority allocation is a quality positioned on a spectrum between purely decentralized and completely centralized, noting how a blockchain set-up could simultaneously both have facets that are significantly centralized and others that are not. The authors document their systematic review findings and propose a framework for understanding blockchain-mediated decentralization, suggesting a definition, and outlining new directions for further human-centric research into distributed ledger technologies and for designing decentralized ecosystems.
Past, present and future of Virtual Reality: Analysis of its technological variables and definitions

Developments in Virtual Reality (VR) technology are currently arousing great scientific interest because in just a few years, VR has found its niche not only in the specialised public, but also in society in general and in different contexts, thanks to its many uses in different contexts and the decreasing price of VR viewing devices. To many, this technology may appear to be a novelty of the 21st century, but its origins go back several decades. Taking into account these aspects, this article aims to analyse the past and present of VR from two perspectives: one focused on its technological development and one on its conceptual evolution. This historical overview, in turn, will allow us to address the future applications of VR in different disciplines. The study provides the reader with an indepth analysis of VR that will contribute to the understanding of this technology and its uses.
DRAMDig: A Knowledge-assisted Tool to Uncover DRAM Address Mapping

As recently emerged rowhammer exploits require undocumented DRAM address mapping, we propose a generic knowledge-assisted tool, DRAMDig, which takes domain knowledge into consideration to efficiently and deterministically uncover the DRAM address mappings on any Intel-based machines. We test DRAMDig on a number of machines with different combinations of DRAM chips and microarchitectures ranging from Intel Sandy Bridge to Coffee Lake. Comparing to previous works, DRAMDig deterministically reverse-engineered DRAM address mappings on all the test machines with only 7.8 minutes on average. Based on the uncovered mappings, we perform double-sided rowhammer tests and the results show that DRAMDig induced significantly more bit flips than previous works, justifying the correctness of the uncovered DRAM address mappings.
Single Headed Attention Based Sequence-to-Sequence Model for State-of-the-Art Results on Switchboard

It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousand hours, is available for training. In this paper, we show that state-of-the-art recognition performance can be achieved on the Switchboard-300 database using a single headed attention, LSTM based model. Using a cross-utterance language model, our single-pass speaker independent system reaches 6.4% and 12.5% word error rate (WER) on the Switchboard and CallHome subsets of Hub5'00, without a pronunciation lexicon. While careful regularization and data augmentation are crucial in achieving this level of performance, experiments on Switchboard-2000 show that nothing is more useful than more data. Overall, the combination of various regularizations and a simple but fairly large model results in a new state of the art, 4.7% and 7.8% WER on the Switchboard and CallHome sets, using SWB-2000 without any external data resources.
Non-closure of Quantum Correlation Matrices and Factorizable Channels that Require Infinite Dimensional Ancilla (With an Appendix by Narutaka Ozawa)

We show that there exist factorizable quantum channels in each dimension $$\ge 11$$ which do not admit a factorization through any finite dimensional von Neumann algebra, and do require ancillas of type $$\hbox {II}_1$$, thus witnessing new infinite-dimensional phenomena in quantum information theory. We show that the set of $$n \times n$$ matrices of correlations arising as second-order moments of projections in finite dimensional von Neumann algebras with a distinguished trace is non-closed, for all $$n \ge 5$$, and we use this to give a simplified proof of the recent result of Dykema, Paulsen and Prakash that the set of synchronous quantum correlations $$C_q^s(5,2)$$ is non-closed. Using a trick originating in work of Regev, Slofstra and Vidick, we further show that the set of correlation matrices arising from second-order moments of unitaries in finite dimensional von Neumann algebras with a distinguished trace is non-closed in each dimension $$\ge 11$$, from which we derive the first result above.