Reviewerly/exHarmony huggingface dataset:
An Efficient Public Key Cryptosystem Based on Dihedral Group and Quantum Spin States

The enciphering schemes based on medium transformations by following the strict guidelines are almost used everywhere. We have developed the structure to simulate the digital data with quantum spin states rather than following or creating the strict guidelines. We simulate the pixels of an image with the dihedral group and spin states for a defined phase to create confusion in it. The scope of this article is concerned in the development and deployment of public key cryptosystem, its performance and security analyses.
Preference-Aware Mask for Session-Based Recommendation with Bidirectional Transformer

User profiles are not always visible in E-commerce scenarios, in which case the recommender systems can only summarize users' preferences through sessions of historical records. However, the items in a session might be irrelevant to users' preferences or become the disturbances for modelling the users' portraits, and thus degrade the performance of the recommender systems. In this paper, we propose the preference-aware mask to capture user preferences over the items within the sessions, which adapts to the preference-irrelevant items within the sessions and provides explainable evidence for the recommendation. Evaluation over three real-world datasets verifies that MBTREC performs well on the new-item recommendation task, and outperforms several state-of-the-art recommender systems on the general metrics.
Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers

The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads.
BOOST: Medical Image Steganography Using Nuclear Spin Generator

In this study, we present a medical image stego hiding scheme using a nuclear spin generator system. Detailed theoretical and experimental analysis is provided on the proposed algorithm using histogram analysis, peak signal-to-noise ratio, key space calculation, and statistical package analysis. The provided results show good performance of the brand new medical image steganographic scheme.
Self‐guided filter for image denoising

The guided filter has been acknowledged as an exceptional edge-preserving filter whose output is a locally linear transform of the guidance image. However, the traditional guided filter heavily relies on the guidance image and fails to achieve the desired result when performing image denoising without a clear guidance image. In this study, to address this limitation, the authors propose a simple yet effective guided filter variant for the single image noise removing. They further show that the proposed denoising strategy can be easily realised by using the iterative framework. Moreover, the weak textured patches based image noise estimation is utilised to generate a clear intermediate image which makes the proposed method highly adaptable to the local noise level. Experimental results demonstrate that their proposed algorithm can compete with the state-of-the-art local denoising methods in edge-preserving.
Anticipatory experience in everyday autonomous driving

Abstract In this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.
A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.
Distributed Ledger Technology as a Tool for Environmental Sustainability in the Shipping Industry

In recent years, many industries have adopted technology and digital systems to automate, expedite and secure specific processes. Stakeholders in maritime transport continue to exchange physical documents in order to conduct business. The monitoring of supply chain goods, communication among employees, environmental sustainability and longevity control, along with time framing, all create challenges to many industries. Everyday onboard work, such as cargo operations, navigation and various types of inspections in shipping, still requires paper documents and logs that need to be signed (and stamped). The conversion of traditional paper contracts into smart contracts, which can be digitalized and read through automation, provides a new wave of collaboration between eco systems across the shipping industry. Various data collected and stored on board ships could be used for scientific purposes. Distributed ledger technology (DLT) could be used to collect all those data and improve shipping operations by process expediting. It could eliminate the need to fill in various documents and logs and make operations safer and more environmentally friendly. Information about various important procedures onboard ships could be shared among all interested stakeholders. This paper considers the possible application of distributed ledger technology as an aid for the control of overboard discharge of wastewater from commercial ships. The intended outcome is that it could help protect the environment by sending data to relevant stakeholders in real time, thus providing information regarding the best discharge areas. The use of a structured communal data transference would ensure a consistent and accurate way to transmit data to all interested parties, and would eliminate the need to fill in various paper forms and logs. Wastewater overboard discharges would be properly monitored, recorded and measured, as distributed ledger technology would prevent any possibility of illegal actions and falsification of documents, thus ensuring environmental sustainability.
Robust head pose estimation based on key frames for human-machine interaction

Abstract Humans can interact with several kinds of machine (motor vehicle, robots, among others) in different ways. One way is through his/her head pose. In this work, we propose a head pose estimation framework that combines 2D and 3D cues using the concept of key frames (KFs). KFs are a set of frames learned automatically offline that consist the following: 2D features, encoded through Speeded Up Robust Feature (SURF) descriptors; 3D information, captured by Fast Point Feature Histogram (FPFH) descriptors; and target’s head orientation (pose) in real-world coordinates, which is represented through a 3D facial model. Then, the KF information is re-enforced through a global optimization process that minimizes error in a way similar to bundle adjustment. The KF allows to formulate, in an online process, a hypothesis of the head pose in new images that is then refined through an optimization process, performed by the iterative closest point (ICP) algorithm. This KF-based framework can handle partial occlusions and extreme rotations even with noisy depth data, improving the accuracy of pose estimation and detection rate. We evaluate the proposal using two public benchmarks in the state of the art: (1) BIWI Kinect Head Pose Database and (2) ICT 3D HeadPose Database. In addition, we evaluate this framework with a small but challenging dataset of our own authorship where the targets perform more complex behaviors than those in the aforementioned public datasets. We show how our approach outperforms relevant state-of-the-art proposals on all these datasets.
A novel data representation framework based on nonnegative manifold regularisation

Representation learning techniques have been frequently applied in multimedia content analysis and retrieval. In this study, an efficient multimedia data clustering method is presented, which consists of two independent algorithms. First, we propose a new representation framework by incorporating sparse coding and manifold regularisation in an optimisation objective function, the cluster indicator matrix is estimated by introducing ℓ1 sparsity norm coarsely. Second, we refine the estimated cluster indicator matrix by performing spectral rotation such that an optimal assignment for clustering can be learned. Compared with existing methods, we have the following merits: our method takes into account the global matrix reconstruction information and locality manifold information simultaneously. Therefore, global and locality information both are respected. Additionally, theoretical justification about the novel representation method is presented in this study. Comprehensive experiments demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art clustering methods on six real-world image datasets.
End-to-End Text-to-Image Synthesis with Spatial Constrains

Although the performance of automatically generating high-resolution realistic images from text descriptions has been significantly boosted, many challenging issues in image synthesis have not been fully investigated, due to shapes variations, viewpoint changes, pose changes, and the relations of multiple objects. In this article, we propose a novel end-to-end approach for text-to-image synthesis with spatial constraints by mining object spatial location and shape information. Instead of learning a hierarchical mapping from text to image, our algorithm directly generates multi-object fine-grained images through the guidance of the generated semantic layouts. By fusing text semantic and spatial information into a synthesis module and jointly fine-tuning them with multi-scale semantic layouts generated, the proposed networks show impressive performance in text-to-image synthesis for complex scenes. We evaluate our method both on single-object CUB dataset and multi-object MS-COCO dataset. Comprehensive experimental results demonstrate that our method significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.
Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions

Abstract Background Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they do not consider the discriminative contributions of different phrases and words. Moreover, local information and context information of EMRs should be deeply integrated. Results A new method based on the f usion of a convolutional neural network ( CN N) and b idirectional l ong short-term memory ( B i L STM) with a ttention mechanisms is proposed for predicting a disease related to a given EMR, and it is referred to as FCNBLA . FCNBLA deeply integrates local information, context information of the word sequence and more informative phrases and words. A novel framework based on deep learning is developed to learn the local representation, the context representation and the combination representation. The left side of the framework is constructed based on CNN to learn the local representation of adjacent words. The right side of the framework based on BiLSTM focuses on learning the context representation of the word sequence. Not all phrases and words contribute equally to the representation of an EMR meaning. Therefore, we establish the attention mechanisms at the phrase level and word level, and the middle module of the framework learns the combination representation of the enhanced phrases and words. The macro average f-score and accuracy of FCNBLA achieved 91.29 and 92.78%, respectively. Conclusion The experimental results indicate that FCNBLA yields superior performance compared with several state-of-the-art methods. The attention mechanisms and combination representations are also confirmed to be helpful for improving FCNBLA’s prediction performance. Our method is helpful for assisting doctors in diagnosing diseases in patients.
Deep Wavelet Network with Domain Adaptation for Single Image Demoireing

Convolutional neural networks have made a prominent progress in low-level image restoration tasks. Moire is a kind of high-frequency and irregular interference stripe that appears on the photosensitive element of digital cameras or scanners. It can bring in unpleasant colorful artifacts on images. In this paper, we propose a deep wavelet network with domain adaptation mechanism for single image demoireing, dubbed AWUDN. The feature mapping is mainly performed in the wavelet domain, which can not only cut down computation complexity, but also reduce information loss. Moreover, considering that the images provided by the challenge organizers have strong self-similarity, the global context block is adopted for the learning of feature dependency in different positions. Finally, we introduce the domain adaptation mechanism to fine-tune the pretrained model for reducing the domain gap between training moire dataset and testing moire dataset. Benefiting from these improvements, the proposed method can achieve superior accuracy on the public testing dataset in the NTIRE 2020 Single Image Demoireing Challenge.
Alleviation of Gradient Exploding in GANs: Fake Can Be Real

In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples are considered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens. We show the process of an unbalanced generation and a vicious circle issue resulted from gradient exploding in practical training, which explains the instability of GANs. We also theoretically prove that gradient exploding can be alleviated by penalizing the difference between discriminator outputs and fake-as-real consideration for very close real and fake samples. Accordingly, Fake-As-Real GAN (FARGAN) is proposed with a more stable training process and a more faithful generated distribution. Experiments on different datasets verify our theoretical analysis.
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue

Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation. In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility. In this architecture, word generation is decomposed into a series of attention-based information selection steps, performed by the novel recurrent Deliberation, Abandon and Memory (DAM) module. Each DAM module performs an adaptive combination of the response-level semantics captured from the encoder and the word-level semantics specifically selected for generating each word. Therefore, the responses contain more detailed and non-repetitive descriptions while maintaining the semantic accuracy. Furthermore, DAM is flexible to cooperate with existing visual dialogue encoders and adaptive to the encoder structures by constraining the information selection mode in DAM. We apply DAM to three typical encoders and verify the performance on the VisDial v1.0 dataset. Experimental results show that the proposed models achieve new state-of-the-art performance with high-quality responses. The code is available at https://github.com/JXZe/DAM.
Adversarial Oracular Seq2seq Learning for Sequential Recommendation

Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for sequential recommendation by recommending the next items with the highest scores based on the user histories while few efforts have been made on identifying the transition dependency and behavior continuity in the recommended sequences. In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality. We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios.
A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a data-driven machine learning approach for tungsten mineralisation. The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets. The data-driven Random Forest™ algorithm is employed to model tungsten mineralisation in SW England using a range of geological, geochemical and geophysical evidence layers which include a depth to granite evidence layer. Two models are presented, one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step. The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model. The typically subjective approach is guided using the Receiver Operating Characteristics (ROC) curve tool where transformed data are compared to known training samples. The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model. The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets. Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known. The Confidence Metric, derived from model variance, is employed to further evaluate the models. The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation. The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables. Finally, legacy mining data, from drilling reports and mine descriptions, is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits. Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 ​m. In summary, the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling, while the newly derived Confidence Metric generates reliable mineral exploration targets.
Strategic Directions of Development of Informatization of Domestic Education of Digital Information Technologies Period

The article presents significant changes in the field of education, taking place in connection with the active and systematic application of digital information technologies. The main directions of development of informatization of domestic education in conditions of realization of opportunities of digital information technologies are justified and described. The high-level characteristics of the main components of traditional pedagogical science (including didactics) and pedagogical science in conditions of informatization of education of the period of digital technologies have been revealed. The definition of didactics of digital information technology period is presented.