Reviewerly/exHarmony huggingface dataset:
A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios

Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability (1–10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> ) and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm.
Narrative Query Graphs for Entity-Interaction-Aware Document Retrieval

Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one's information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user's intent. In contrast, distilling short narratives of the searchers' information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice.
A discriminative channel diversification network for image classification

Channel attention mechanisms in convolutional neural networks have been proven to be effective in various computer vision tasks. However, the performance improvement comes with additional model complexity and computation cost. In this paper, we propose a light-weight and effective attention module, called channel diversification block, to enhance the global context by establishing the channel relationship at the global level. Unlike other channel attention mechanisms, the proposed module focuses on the most discriminative features by giving more attention to the spatially distinguishable channels while taking account of the channel activation. Different from other attention models that plugin the module in between several intermediate layers, the proposed module is embedded at the end of the backbone networks, making it easy to implement. Extensive experiments on CIFAR-10, SVHN, and Tiny-ImageNet datasets demonstrate that the proposed module improves the performance of the baseline networks by a margin of 3% on average.
Wearable technologies, brand community and the growth of a transhumanist vision

By enabling users to digitally monitor their health and behaviour, wearable technologies foster the perspective of the quantified self, a cultural phenomenon emphasising personal improvement through self-tracking. This vision, in turn, provides a basis for new forms of social engagement. In this netnographic study, we explore a brand community for users of the Fitbit wearable device. Our analysis reveals two structural dynamics – material agency and quantitative anchoring – which create a foundation for what we label ‘accidental transhumanism’, a transitional movement towards a transhumanist vision based on self-quantification, self-extension, and integration with technology. We further highlight the social engagement mechanisms, including motivating empowerment, friendly rivalry, and trusting engagement, that are built upon this foundation. Leveraging these findings, we theorise a novel model of the interaction of consumers and system artefacts in socio-technical assemblages. We refer to this novel phenomenon of brand community centred on self-quantification as quantified self-in-community, and consider both the beneficial and potentially deleterious impacts that it presents.
Relational Reasoning Using Neural Networks: A Survey

Relational Networks (RN), as one of the most widely used relational reasoning techniques, have achieved great success in many applications such as action and image analysis, speech recognition and text understanding. The use of relational reasoning via RN in neural networks has often been used in recent years. In these instances, RN is composed of various deep learning-based algorithms in simple plug-and-play modules. This is quite advantageous since it circumvents the need for features engineering. This paper surveys the emerging research of deep learning models that make use of RN in tasks such as Natural Language Processing (NLP), Action Recognition, Temporal Relational Reasoning as well as Facial Emotion Recognition (FER). Since, RNs are easy to integrate they have been used in various tasks such as NLP, Recurrent Neural Networks (RNN), Action Recognition, Image Analysis, Object Detection, Temporal Relational Reasoning, as well as for FER. This is due to the fact that RNs use bidirectional LSTM and CNN to solve relational reasoning problems at character and word level. In this paper a comparative review of all relational reasoning-based RN models using deep learning techniques is presented.
3D Vehicle Detection Using Cheap LiDAR and Camera Sensors

Autonomous Vehicles (AVs) are expected to be intelligent enough to perceive the world accurately in terms of avoiding road obstacles. Remarkable progress has been made in 3D road scene perception of AVs through machine learning and computer vision methods, but existing solutions rely on expensive 64 beams LiDAR point clouds for the 3D positioning of objects. In this paper, we propose a simple yet effective approach that is based on the success of 2D object detection to estimate 3D positions of the vehicles in front of AVs. Our approach relies on camera RGB images for predicting size and orientation of 3D bounding boxes of AVs by using a novel deep neural network (DNN) and LiDAR 3D point clouds for distance estimation. For testing and training, KITTI and Waymo datasets are employed. We have converted 64 beams of LiDAR point clouds into 32 and 16 beams point clouds for model performance analysis. Based on the results, the proposed method proved to be robust with sparse point clouds without compromising accuracy.
Designing a data infrastructure for catalysis science aligned to FAIR data principles

The UK Catalysis Hub (UKCH) is designing and implementing an infrastructure to facilitate the management of research data produced by researchers, the Catalysis Data Infrastructure (CDI). The CDI is proposed to encompass the presentation of research outputs (publications and data) in a digital repository that brings together an array of heterogeneous data types. The CDI is designed to hold references to research outputs, maintains links between them and promotes publishing and sharing of data. The proposal is to create persistent relationships between the different types of data and publications complying with FAIR data principles (findability, accessibility, interoperability, and reuse). In this paper, we will discuss how the elicited requirements for data management are being incorporated in the design of the CDI. The prototype has been used in discussion with researchers and in presentations to the UKCH community, generating increased interest and providing ideas for further development. Additionally, the CDI prototype and its code are publicly available for further analysis.
Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments

Driven by the advancements of wearable sensors and signal processing algorithms, studies on continuous real-world monitoring are of major interest in the field of clinical gait and motion analysis. While real-world studies enable a more detailed and realistic insight into various mobility parameters such as walking speed, confounding and environmental factors might skew those digital mobility outcomes (DMOs), making the interpretation of results challenging. To consider confounding factors, context information needs to be included in the analysis. In this work, we present a context-aware mobile gait analysis system that can distinguish between gait recorded at home and not at home based on Bluetooth proximity information. The system was evaluated on 9 healthy subjects and 6 Parkinsons disease (PD) patients. The classification of the at home/not at home context reached an average F1-score of 98.2 ± 3.2 %. A context-aware analysis of gait parameters revealed different walking bout length distributions between the two environmental conditions. Furthermore, a reduction of gait speed within the at home context compared to walking not at home of 8.9 ± 9.4 % and 8.7 ±5.9 % on average for healthy and PD subjects was found, respectively. Our results indicate the influence of the recording environment on DMOs and, therefore, emphasize the importance of context in the analysis of continuous motion data. Hence, the presented work contributes to a better understanding of confounding factors for future real-world studies.
Mongolian emotional speech synthesis based on transfer learning and emotional embedding

In recent years, end-to-end speech synthesis based on attention has achieved better performance than traditional speech synthesis models, and the technology of end-to-end Mongolian speech synthesis has reached the application standard. However, due to the sparse training corpus, the research on Mongolian emotional speech synthesis is still far from perfect. In response to these problems, we established a Mongolian emotional corpus and constructed an emotionally controllable Mongolian speech synthesis system for the first time. Through combining transfer learning and emotional embedding, the Mongolian emotional speech synthesis system with 8 kinds of emotions (happy, angry, sadness, surprise, fear, disgust, boredom and neutral) has been achieved. We proposed the method that emotional labels are used as the input of the emotional embedding layer to generate emotional vectors, which are spliced with the output vectors of the bidirectional LSTM layer, so that the text representation vectors contain information about emotional category, thereby synthesize a variety of different emotional voices. Experiments show that our method can synthesize high-quality Mongolian emotional speech.
An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography

This paper proposes an improved ResU-Net framework for automatic liver CT segmentation. By employing a new loss function and data augmentation strategy, the accuracy of liver segmentation is improved, and the performance is verified on two public datasets LiTS17 and SLiver07. Firstly, to speed up the convergence of the model, the residual module is used to replace the original convolution module of U-Net. Secondly, to suppress the problem of pixel imbalance, the opposite number of Dice is proposed to replace the cross-entropy loss function, and the morphological method is introduced to weigh the pixels. Finally, to improve the generalization ability of the model, random affine transformation and random elastic deformation are employed for data augmentation. From 20 training datasets of Sliver07, 16 sets were selected as the training set, two sets were used for verification, and two sets were used for the test; meanwhile, from 131 training datasets of LiTS2017, eight sets were selected as the test set. In the experiment, four evaluation metrics, including DICE global, DICE per case, VOE, and RVD, were calculated, with the accuracies of 94.28, 94.24 ± 2.07, 10.83 ± 3.70, and -0.25 ± 2.74, respectively. Compared with U-Net and ResU-Net, the performance of the proposed method is significantly improved. The experimental results show that, although the method's complexity is high, it has a faster convergence speed and stronger generalization ability. The segmentation effect on the 2D image is significantly improved, and the scalability on 3D data is also robust. In addition, the proposed method performs well in the case of low-contrast neighboring organs, which proves the robustness of the proposed method.
Where Should I Go? A Deep Learning Approach to Personalize Type-Based Facet Ranking for POI Suggestion

In a faceted search system, type-based facets (t-facets) represent the categories of the resources being searched. Ranking algorithms are needed to select and promote the most relevant t-facets. However, as these are extracted from large multi-level taxonomies, they are impossible to show entirely to the user. Facet ranking is usually employed to filter out irrelevant facets for the users. Existing facet ranking methods neglect both the hierarchical structure of t-facets and the user historical preferences. This research introduces a personalized t-facet ranking that addresses both issues. During a first step, a Deep Neural Network (DNN) model is trained to assign a relevance score to each t-facet based on three groups of relevance features. The score reflects the t-facet relevance to the user, the input query, and its general importance in the dataset. Subsequently, these scores are aggregated and the t-facets are re-organised into a smaller sub-tree to be presented to the user. Our approach aims at minimizing the effort required by the user to reach their intended search target. This is measured in terms of number of clicks the user has to perform on the t-facet tree to reach a relevant resource. The approach is applied to a Point-Of-Interest suggestion task. We solve the problem by ranking the categories of the venues as t-facets. The evaluation compares our DNN-based approach with other existing baselines and investigates the individual contribution of each group of features. Our experiment has demonstrated that the proposed personalized deep learning model leads to better t-facet rankings and minimized user effort.
On the Impact of Viewing Distance on Perceived Video Quality

Due to the growing importance of optimizing the quality and efficiency of video streaming delivery, accurate assessment of user-perceived video quality becomes increasingly important. However, due to the wide range of viewing distances encountered in real-world viewing settings, the perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality. A subjective experiment was conducted with full HD sequences at three different fixed viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice. Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when the accurate prediction of perceived video quality in real-world environments is required.
Detecting Attackers during Quantum Key Distribution in IoT Networks using Neural Networks

Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers, while considering the use case of sensors deployed in railroad networks. Neural Network (NN) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. The results show that the proposed techniques can reach 99% accuracy.
Safe Fakes: Evaluating Face Anonymizers for Face Detectors

Since the introduction of the GDPR and CCPA privacy legislation, both public and private facial image datasets are increasingly scrutinized. Several datasets have been taken offline completely and some have been anonymized. However, it is unclear how anonymization impacts face detection performance. To our knowledge, this paper presents the first empirical study on the effect of image anonymization on supervised training of face detectors. We compare conventional face anonymiz-ers with three state-of-the-art Generative Adversarial Network-based (GAN) methods, by training an off-the-shelf face detector on anonymized data. Our experiments investigate the suitability of anonymization methods for maintaining face detector performance, the effect of detectors overtraining on anonymization artefacts, dataset size for training an anonymizer, and the effect of training time of anonymization GANs. A final experiment investigates the correlation between common GAN evaluation metrics and the performance of a trained face detector. Although all tested anonymization methods lower the performance of trained face detectors, faces anonymized using GANs cause far smaller performance degradation than conventional methods. As the most important finding, the best-performing GAN, DeepPrivacy, removes identifiable faces for a face detector trained on anonymized data, resulting in a modest decrease from 91.0 to 88.3 mAP. In the last few years, there have been rapid improvements in realism of GAN-generated faces. We expect that further progression in GAN research will allow the use of Deep Fake technology for privacy-preserving Safe Fakes, without any performance degradation for training face detectors.
An improved asymptotic test for the Jaccard similarity index for binary data

For paired binary data, we propose a new asymptotic test of independence for the Jaccard index. As demonstrated, the test offers marked improvements in maintaining nominal Type I error rates, and exhibits higher power when these error rates are comparable.
A general phase ambiguity suppression algorithm combining geometric constraints and temporal phase unwrapping

In fringe projection profilometry, various sophisticated temporal phase unwrapping (TPU) methods are conceived to recover a continuous absolute phase map. However, phase ambiguity is a thorny problem that affects measurement accuracy, which is deeply troubled by researchers. Dealing with the challenge, a universal algorithm (G-TPU) uniting geometric constraints phase unwrapping (GCPU) and TPU is proposed to comb out the phase ambiguity. Firstly, The invalid phase map or continuous relative phase map is unwrapped by GCPU with the minimum artifact. Then the unwrapped phas map is divided into several object regions, and the effective phase range (EPR) and invalid phase range (IPR) of each object region are determined by the proposed edge determination and image filling algorithm. Finally, through the statistical analysis of the decoded codewords, the relative orders are added to each EPR and IPR, and the absolute phase map is obtained. The proposed method inherits the complementary advantages of absolute position determination and strong robustness in TPU and GCPU respectively. Since GCPU does not need additional fringe patterns, it can be perfectly integrated into all TPU methods. The experimental results demonstrate the success of the proposed method combining GCPU and TPU.
ArchGAN: A Generative Adversarial Network for Architectural Distortion Abnormalities in Digital Mammograms

Applying deep learning (DL) models to medical image processing has yielded impressive performances. Generative adversarial networks (GAN) are examples of DL models proposed to mediate the challenge of insufficient image data by synthesizing data similar to the real sample images. However, we observed that benchmark datasets with the region of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography have only limited image samples with architectural distortion abnormalities. Images with architectural distortion abnormality in digital mammograms are sparsely distributed across all publicly available datasets. This paper proposes a GAN model named ArchGAN, which is aimed at synthesizing ROI-based samples with architectural distortion abnormality for training convolutional neural networks (CNNs). Our approach involves the design of the GAN model, consisting of both generator and discriminator, to learn a hierarchy of representation in digital mammograms. The proposed GAN model was applied to Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. In addition, the quality of images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, and FID. Results obtained showed that ArchGAN performed very well. The outcome of this study is a model for augmenting CNN models with ROI-centric images with architectural distortion abnormalities in digital mammography.
Beyond NED

Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique for doing this is to apply named entity disambiguation (NED) on the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.