Reviewerly/exHarmony huggingface dataset:
A recommender model based on strong and weak social Ties: A Long-tail distribution perspective

A collaborative filtering recommender model for E-commerce users (CF-S&WLT) is proposed that incorporates an improved PageRank algorithm, strong and weak social ties and long-tail distribution items. The main design feature of the CF-S&WLT model is the introduction of incentive coefficients for long-tail items based on triadic closure regulation. It was compared to three alternative models in terms of accuracy (precision and F-measure), diversity, and novelty of returned Top-N items. Results reveal that CF-S&WLT improved diversity and novelty by 20.1% and 3.8% respectively, but reduced precision by 3.7% based on a first dataset (DS1), and enhanced diversity (14.2%) and novelty (18.3%) while reducing precision cost by 4.6% using dataset DS2. Overall, the proposed CF-S&WLT model performs better than other models with respect to diversity and novelty while maintaining acceptable levels of accuracy.
Higher Rates and Information-Theoretic Analysis for the RLWE Channel

The <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Errors</i> (LWE) problem is considered to be a hard problem and lies the foundation of various cryptographic algorithms. Several cryptosystems based on the closely related <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ring</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Errors</i> (RLWE) problem have been proposed within the NIST PQC standardization process, e.g., the systems LAC and NewHope. The combination of encryption and decryption for these kinds of algorithms can be interpreted as data transmission over noisy channels. To the best of our knowledge this paper is the first work that analyzes the capacity of this channel. We extend this channel from binary to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -ary alphabets and show that this does not compromise the security of the related RLWE-based schemes if appropriate error correcting codes are used to prevent the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decryption</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">failure</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rate</i> (DFR) from increasing. We give a lower bound on the capacity of this channel showing that the achievable asymptotic rates are substantially (5.7 times for LAC and 10.7 times for NewHope) higher than the currently deployed ones for the finite length regime. Furthermore, under the assumption of stochastically independent coefficient failures, we show that substantially higher rates can also be achieved in the finite length setting by using the Gilbert-Varshamov bound. Moreover, we give explicit code constructions increasing the achievable rate by a factor of 2 for LAC and a factor of 7 for NewHope without increasing the DFR for the respective parameter sets achieving a security level equivalent to AES256.
Speech/Pause Segmentation Method Based on Teager Energy Operator and Short-Time Energy Analysis

The article presents a method for speech/pause segmentation based on energy analysis of speech signal fragments using the Teager Energy Operator and Short-Time Energy. The nature of segmentation is a linear division of a speech signal into fragments, calculation of energy characteristic of fragments using the Teager Energy Operator, calculation of Short-Time Energy values for fragments of energy characteristic, and determining `speech/pause' state of fragments based on the calculated threshold values of Short-Time Energy. The research on the proposed method was carried out, and the effectiveness of speech/pause segmentation was assessed in comparison with the method based on energy analysis using Short-Time Energy. In accordance with the obtained research results, it was concluded that due to the good susceptibility of the Teager Energy Operator to changes in signal amplitude and frequency, the proposed method provides the increased efficiency of speech/pause segmentation by 5.26 % and 5.51 % for the first and second kind errors, respectively. The proposed speech/pause segmentation method can be used for signal processing in real-time speech applications.
A Deep Learning Technique for Image Inpainting with GANs

An image is identical to a thousand words. Each image pulls the watcher in, to an alternate time and spot making him experience a heap of feelings. They ll in as a path through a world of fond memories. However, consider the possibility that these photographs were harmed or have undesirable objects. To re-establish these photographs by elim-inating damages such as scratches, haziness and overlaid content or il-lustrations, we can utilize a procedure called Image Inpainting. Image inpainting is the procedure of reestablishing the harmed and missing pieces of a picture with the objective of introducing the picture as it was initially envisioned. The extent of our strategy ranges from expulsion of undesirable articles from the picture to reproducing the deteriorated and obscured out parts of the picture. Further, it could be utilized to improve quality of the pictures (for example, the ones capturing criminal activ-ities and their perpetrators). In our paper, we present a profound deep learning procedure to accomplish the above objectives. A pix2pix Gen-erative Adversarial Network is being utilized here with various encoders and decoders which extract the essential highlights of the picture and afterwards recreate it without any fuss.
GPFS: A Graph-based Human Pose Forecasting System for Smart Home with Online Learning

Forecasting human poses given a sequence of historical pose frames has several important applications, especially in the domain of smart home safety. Recently, computer vision-based human pose forecasting has made a breakthrough using deep learning technology. However, to implement a practical system deployed on an IoT edge environment, there are still two issues to be addressed. First, existing methods on pose forecasting fail to model the coherent structural information of connected human joints and thus cannot achieve satisfactory prediction accuracy, especially for long-term predictions. Second, a general and static pre-trained prediction model may not perform well in the deployment environment due to the visual domain shift problem. In this article, we propose a hybrid cloud-edge system called GPFS to solve those issues. Specifically, we first introduce a novel graph convolutional neural network (GCN)-based sequence-to-sequence learning method, which enhances the sequence encoder by using a graph to represent both the spatial and temporal connections of the human joints in the input frames. The GCN improves the forecasting accuracy by capturing the motion pattern of each joint as well as the correlations among different human joints over time. Second, to address the domain shift issue and protect data privacy, we extend the system to perform online learning on the IoT edge to adapt the cloud trained general model with online collected on-site domain data. Extensive evaluation on Human 3.6M and Penn Action datasets demonstrates the superiority of our proposed system.
A Full- and No-Reference Metrics Accuracy Analysis for Volumetric Media Streaming

Volumetric media streaming will be one of the fundamental technologies to enable near future immersive multimedia experiences.In it, objects represented as sets of points (i.e.point-clouds), are presented to remote users wearing Head-Mounted Displays (HMDs).Due to the stringent bandwidth and latency requirements of such applications, small changes in the network can affect the user in unexpected manners (physical discomfort, lack of concentration, etc.).Therefore, there is a need for assessing the perceived quality of this type of applications in real-time, i.e, the Quality of Experience (QoE).Given that subjective evaluations are not feasible for (near) realtime applications, objectively measuring this quality will be a must.While traditional objective metrics could potentially be used to fulfill the task, it is still unclear how accurate they are to assess volumetric media.To this end, this paper presents a thorough correlation analysis of both Full Reference (FR) and No Reference (NR) objective metrics to subjective Mean Opinion Scores (MOS) for different volumetric streaming scenarios.To enhance the accuracy, multiple Region-Of-Interest (ROI) selection and weighting procedures have been applied and their influence on the results have been investigated.Our results show that the classical video quality metric Video Multimethod Assessment Fusion (VMAF) is well-suited as an objective benchmark for volumetric media streaming in terms of correlation to subjective scores, while a combination of NR features could provide a suitable real-time assessment.Finally, ROI selection proves to widen the range of objective metrics, which is an important issue to apply traditional objective metrics to volumetric media.
CoInGP

We investigate the use of Genetic Programming (GP) as a convolutional predictor for missing pixels in images. The training phase is performed by sweeping a sliding window over an image, where the pixels on the border represent the inputs of a GP tree. The output of the tree is taken as the predicted value for the central pixel. We consider two topologies for the sliding window, namely the Moore and the Von Neumann neighborhood. The best GP tree scoring the lowest prediction error over the training set is then used to predict the pixels in the test set. We experimentally assess our approach through two experiments. In the first one, we train a GP tree over a subset of 1000 complete images from the MNIST dataset. The results show that GP can learn the distribution of the pixels with respect to a simple baseline predictor, with no significant differences observed between the two neighborhoods. In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20% of their pixels. In this case, we observe that the Moore neighborhood works better, although the Von Neumann neighborhood allows for a larger training set.
Study on the Influence of Driving Simulator S3D Visual Display on Distance Judgment Accuracy and User Experience in Following Task

In order to study the influence of S3D visual display in driving simulator on the accuracy of distance judgment and the user experience under the vehicle following task, in this paper, ten people were called to carry out the following experiment based on the real vehicle test, and then the reference following distance can be collected. The driving simulator experiment was carried out by adopting the 2×4 within-subjects design experiment, and the difference ΔD between the following distance of the driving simulator and the real vehicle was taken as the analysis index to characterize the following simulation fidelity in the simulator. The results show that there is a significant difference in the following distance between S3D and 2D display modes. Among them, the following distance of S3D mode is closer to the actual following distance, which has an accuracy advantage of 2.5 m (95% CI: 1.11-3.85 m) compared with 2D mode. Generally speaking, S3D plays a positive role in improving distance perception and judgment in driving simulator, and the results have not witnessed any discomfort feeling caused by S3D.
Climber Behavior Modeling and Recommendation

Sport climbing is becoming more and more popular, even among non-specialists. While new routes are built each year, both indoor and outdoor, there is no effective tool for supporting climbers to choose the most appropriate routes, either for training or simply enjoying. Route recommendation is hard and risky because a reliable evaluation of the climber’s capabilities, status and subjective difficulty perception is necessary. This can be achieved only with the exploitation of Internet of Things (IoT) sensors for the automatic recording of climbers’ activity. In this research, we want to further extend the still young research subject of activity recognition in sport climbing and combine this with new recommender systems (RSs) techniques for route suggestion. We have developed an initial solution for unobtrusively and automatically detecting climbers’ activities in a gym, and we are now connecting this information with the manual inserted diary data of climbers by means of a mobile application. We present the design and the open research questions for a system that leverages sensor data and explicit feedback to generate a climber’s profile and recommend suitable routes.
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations

Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans.However, it is non-trivial to collect large-scale dialogues that are naturally grounded on the background documents, which hinders the effective and adequate training of knowledge selection and response matching.To overcome the challenge, we consider decomposing the training of the knowledge-grounded response selection into three tasks including: 1) query-passage matching task; 2) query-dialogue history matching task; 3) multi-turn response matching task, and joint learning all these tasks in a unified pre-trained language model.The former two tasks could help the model in knowledge selection and comprehension, while the last task is designed for matching the proper response with the given query and background knowledge (dialogue history).By this means, the model can be learned to select relevant knowledge and distinguish proper response, with the help of ad-hoc retrieval corpora and a large number of ungrounded multi-turn dialogues.Experimental results on two benchmarks of knowledge-grounded response selection indicate that our model can achieve comparable performance with several existing methods that rely on crowd-sourced data for training.
Run-time Reasoning from Uncertain Observations with Subjective Logic in Multi-Agent Self-Adaptive Cyber-Physical Systems

Modern society has become increasingly reliant on the omnipresent cyber-physical systems (CPSs), making it paramount that the contemporary autonomous and decentralized CPSs (e.g., robots, drones and self-driving cars) act reliably despite their exposure to a variety of run-time uncertainties. The sources of uncertainties could be internal, i. e., originating from the systems themselves, or external-unpredictable environments. Self-adaptive CPSs (SACPSs) modify their behavior or structure at run-time in response to the uncertainties mentioned above. The adaptation relies on gained knowledge from the observations that the SACPSs make during their operation. As a result, to build the knowledge, the need for run-time observations aggregation and reasoning emerges since the observations made by decentralized CPSs are uncertain, partial, and potentially conflicting. In response, in this paper, we propose a novel methodological approach for deriving or aggregating knowledge from uncertain observations in SACPSs utilizing the Subjective Logic. The effectiveness of the proposed approach is demonstrated through extensive evaluation on an in-house, multi-agent system from the robotics domain.
Behavioural Preorders via Graded Monads

Like notions of process equivalence, behavioural preorders on processes come in many flavours, ranging from fine-grained comparisons such as ready simulation to coarse-grained ones such as trace inclusion. Often, such behavioural preorders are characterized in terms of theory inclusion in dedicated characteristic logics; e.g. simulation is characterized by theory inclusion in the positive fragment of Hennessy-Milner logic. We introduce a unified semantic framework for behavioural preorders and their characteristic logics in which we parametrize the system type as a functor on the category Pos of partially ordered sets following the paradigm of universal coalgebra, while behavioural preorders are captured as graded monads on Pos, in generalization of a previous approach to notions of process equivalence. We show that graded monads on Pos are induced by a form of graded inequational theories that we introduce here. Moreover, we provide a general notion of modal logic compatible with a given graded behavioural preorder, along with a criterion for expressiveness, in the indicated sense of characterization of the behavioural preorder by theory inclusion. We illustrate our main result on various behavioural preorders on labelled transition systems and probabilistic transition systems.
NewsLink: Empowering Intuitive News Search with Knowledge Graphs

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph embeddings into search. First, they enrich the search context, leading to robust results. Second, the relationship paths linking entities inter and intra news documents can help users better understand and digest the results for the given query. Through both human and automatic evaluations, we verify that NEWSLINK can help users understand the result-to-query relatedness, while its search quality is robust and outperforms many established search approaches, including Apache Lucene and a KG-powered query expansion approach, as well as popular deep learning models, Sentence-BERT (SBERT) and DOC2VEC.
BR-NS

As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex real-world problems will require the exploration to operate in higher dimensional Behavior Spaces which will not necessarily be Euclidean. Novelty Search traditionally relies on k-nearest neighbours search and an archive of previously visited behavior descriptors which are assumed to live in a Euclidean space. This is problematic because of a number of issues. On one hand, Euclidean distance and Nearest-neighbour search are known to behave differently and become less meaningful in high dimensional spaces. On the other hand, the archive has to be bounded since, memory considerations aside, the computational complexity of finding nearest neighbours in that archive grows linearithmically with its size. A sub-optimal bound can result in "cycling" in the behavior space, which inhibits the progress of the exploration. Furthermore, the performance of NS depends on a number of algorithmic choices and hyperparameters, such as the strategies to add or remove elements to the archive and the number of neighbours to use in k-nn search. In this paper, we discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS), which does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search. We conduct experiments to gain insight into its feasibility and dynamics as well as potential advantages over archive-based NS in terms of time complexity.
How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?

Incorrectly labeled examples, or label noise, is common in real-world computer vision datasets. While the impact of label noise on learning in deep neural networks has been studied in prior work, these studies have exclusively focused on homogeneous label noise, i.e., the degree of label noise is the same across all categories. However, in the real-world, label noise is often heterogeneous, with some categories being affected to a greater extent than others. Here, we address this gap in the literature. We hypothesized that heterogeneous label noise would only affect the classes that had label noise unless there was transfer from those classes to the classes without label noise. To test this hypothesis, we designed a series of computer vision studies using MNIST, CIFAR-10, CIFAR-100, and MS-COCO where we imposed heterogeneous label noise during the training of multi-class, multi-task, and multi-label systems. Our results provide evidence in support of our hypothesis: label noise only affects the class affected by it unless there is transfer.
Hybrid Thresholding Lifting Dual Tree Complex Wavelet Transform with Wiener filter for quality assurance of medical image

The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. In this research, we propose a denoising approach basing on Lifting Dual Tree Complex Wavelet Transform (LDTCWT) using Hybrid Thresholding with Wiener filter to enhance the quality image. We describe the LDTCWT, a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.