Reviewerly/exHarmony huggingface dataset:
BIOFEE: Biomedical Framework for Enhanced Experimentation

Biomedical Framework for Enhanced Experimentation, based on Firebase Real-Time Database, helps in designing and testing various multimodal solutions for patients suffering from progressive diseases. It allows interacting with objects and robots to test various multimodal solutions, such as touch, voice, gesture... to perform tasks according to the patient's pathology. We tested BIOFEE with Unity3D and Webots virtual worlds to compare different ways to interact for particular tasks (switch lamps, pick and place...).
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic

We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. Specifically, we show that planning for PPLTL goals can be encoded into classical planning with minimal overhead, introducing only a number of new fluents that is at most linear in the PPLTL goal and no spurious additional actions. Based on these results, we implemented a system called Plan4Past, which can be used along with state-of-the-art classical planners, such as LAMA. An empirical analysis demonstrates the practical effectiveness of Plan4Past, showing that a classical planner generally performs better with our compilation than with other existing compilations for LTLf goals over the considered benchmarks.
PMSNet: Parallel Multi-Scale Network for Accurate Low-Light Light-Field Image Enhancement

Current low-light light-field (LF) image enhancement algorithms tend to produce blurry results, for (1) loss of spatial details during enhancement and (2) inefficient exploitation of angular correlations, which helps to recover spatial details. Therefore, in this paper, we propose a parallel multi-scale network (PMSNet), which attempts to (1) process features of different scales in parallel to aggregate the different contributions of multi-scale features at each layer, thus fully preserve spatial details, and (2) integrate multi-resolution 3D convolution streams to efficiently utilize angular correlations. Specifically, PMSNet consists of three stages: Stage-I employs multi-scale modules (MSMs) to generate local understanding with the aid of adjacent views. Notably, MSM retains high-resolution feature extraction to minimize loss of spatial details. Stage-II processes all views to encode global information. Based on the above extracted local and global information, Stage-III utilizes 3D multi-scale modules (3D-MSMs) to efficiently exploit angular correlations. To validate our idea, we comprehensively evaluate the performance of PMSNet on three publicly available datasets. Experimental results show that our method is superior to the current state-of-the-art methods, achieving an average PSNR of 24.76 dB.
Top 10 application scenarios in Metaverse

&lt;p&gt;The editorial focuses on two topics: one is to explore the development of the Metaverse industry, and the other is to improve people’s understanding of the Metaverse.&lt;/p&gt;
An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets

Host load prediction is essential for dynamic re-source scaling and job scheduling in a cloud computing environment. In this context, workload prediction is challenging because of several issues. First, it must be accurate to enable precise scheduling decisions. Second, it must be fast to schedule at the right time. Third, a model must be able to account for new patterns of workloads so it can perform well on the latest and old patterns. Not being able to make an accurate and fast prediction or the inability to predict new usage patterns can result in severe outcomes such as service level agreement (SLA) misses. Our research trains a fast model with the ability of online adaptation based on the gated recurrent unit (GRU) to mitigate the mentioned issues. We use a multivariate approach using several features, such as memory usage, CPU usage, disk I/O usage, and disk space, to perform the predictions accurately. Moreover, we predict multiple steps ahead, which is essential for making scheduling decisions in advance. Furthermore, we use two pruning methods: L1 norm and random, to produce a sparse model for faster forecasts. Finally, online learning is used to create a model that can adapt over time to new workload patterns.
The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
Support the education of your future employees

Companies have long relied on academic programs to provide trained employees. When trained with relevant, modern skills, graduates of these programs can minimize costs to businesses, including recruiting and on-the-job training expenses. Particularly in technical roles, such as optics, a pipeline from academia to industry may be created through partnerships among the academic program and the regional industries. However, graduates can only be as informed as the academic program that fosters their education. When industry members find ways to influence this educational process, graduates earn useful, in-demand skills, while industry members see applicants with the right mix of these skills apply for their job openings. This paper discusses some ways industry can impact the educational process to ensure that their applicant pool is loaded with potential.
Rogue Scholar full-text search improvements

Two weeks ago I added a first version of full-text search to the Rogue Scholar blog archive. This was a good start, as blogs typically only have the timeline, tags, and metadata like titles and authors to help readers find relevant content. Today I launched an improved version of full-text search with these improvements: Sorting of search results by relevanceSupport for fuzzy search or approximate string matchingAdded the backend for an updated search interface supporting faceted search and/or...
Helping Visually Impaired People Take Better Quality Pictures

Perception-based image analysis technologies can be used to help visually impaired people take better quality pictures by providing automated guidance, thereby empowering them to interact more confidently on social media. The photographs taken by visually impaired users often suffer from one or both of two kinds of quality issues: technical quality (distortions), and semantic quality, such as framing and aesthetic composition. Here we develop tools to help them minimize occurrences of common technical distortions, such as blur, poor exposure, and noise. We do not address the complementary problems of semantic quality, leaving that aspect for future work. The problem of assessing, and providing actionable feedback on the technical quality of pictures captured by visually impaired users is hard enough, owing to the severe, commingled distortions that often occur. To advance progress on the problem of analyzing and measuring the technical quality of visually impaired user-generated content (VI-UGC), we built a very large and unique subjective image quality and distortion dataset. This new perceptual resource, which we call the LIVE-Meta VI-UGC Database, contains 40K real-world distorted VI-UGC images and 40K patches, on which we recorded 2.7M human perceptual quality judgments and 2.7M distortion labels. Using this psychometric resource we also created an automatic blind picture quality and distortion predictor that learns local-to-global spatial quality relationships, achieving state-of-the-art prediction performance on VI-UGC pictures, significantly outperforming existing picture quality models on this unique class of distorted picture data. We also created a prototype feedback system that helps to guide users to mitigate quality issues and take better quality pictures, by creating a multi-task learning framework.
Toward Projected Clustering With Aggregated Mapping

Projected clustering is the foundation of deep clustering models. Aiming at catching the essence of deep clustering, we propose a novel projected clustering framework by summarizing the core properties of prevalent powerful models, especially deep models. At first, we introduce the aggregated mapping, consisting of projection learning and neighbor estimation, to obtain clustering-friendly representation. Importantly, we theoretically prove that the simple clustering-friendly representation learning may suffer from severe degeneration, which can be regarded as over-fitting. Roughly speaking, the well-trained model would group neighboring points into plenty of sub-clusters. These small sub-clusters may scatter randomly due to no connection between them. The degeneration may occur more frequently with the increasing of model capacity. We accordingly develop a self-evolution mechanism that implicitly aggregates the sub-clusters and the proposed method can alleviate the potential risk of over-fitting and obtain prominent improvement. The ablation experiments support the theoretical analysis and verify the effectiveness of the neighbor-aggregation mechanism. Finally, we show how to choose the unsupervised projection function through two specific examples, including a linear method (namely locality analysis) and a non-linear model.
ABC-Norm Regularization for Fine-Grained and Long-Tailed Image Classification

Image classification for real-world applications often involves complicated data distributions such as fine-grained and long-tailed. To address the two challenging issues simultaneously, we propose a new regularization technique that yields an adversarial loss to strengthen the model learning. Specifically, for each training batch, we construct an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive batch prediction</i> (ABP) matrix and establish its corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive batch confusion norm</i> (ABC-Norm). The ABP matrix is a composition of two parts, including an adaptive component to class-wise encode the imbalanced data distribution, and the other component to batch-wise assess the softmax predictions. The ABC-Norm leads to a norm-based regularization loss, which can be theoretically shown to be an upper bound for an objective function closely related to rank minimization. By coupling with the conventional cross-entropy loss, the ABC-Norm regularization could introduce adaptive classification confusion and thus trigger adversarial learning to improve the effectiveness of model learning. Different from most of state-of-the-art techniques in solving either fine-grained or long-tailed problems, our method is characterized with its simple and efficient design, and most distinctively, provides a unified solution. In the experiments, we compare ABC-Norm with relevant techniques and demonstrate its efficacy on several benchmark datasets, including (CUB-LT, iNaturalist2018); (CUB, CAR, AIR); and (ImageNet-LT), which respectively correspond to the real-world, fine-grained, and long-tailed scenarios.
Using Artificial Intelligence Tools in Teaching a Foreign Language in Higher Technical Institutions

Technological developments that take place nowadays and the accompanying process of digitalization in every area entail serious challenges for the whole society and for the system of education as its integral part.As a result, the teaching process nowadays, including foreign language teaching, involves using a wide range of technical and technological facilities.This paper analyses the possibilities of using digital intelligent technologies in university academic process with the focus on teaching a foreign language to students of higher education institutions specialising in the technical field.The work gives a review of existing online study courses, educational platforms, and artificial intelligence (AI) technologies.The key features of several intelligent applications that are in common use are also outlined.The paper presents a description of a pedagogical experiment carried out in a technical university and aimed at estimating the effectiveness of integration of digital tools into the teaching and learning process.The outcomes of the experiment are evidence that using digital technologies enhances the learning activity and motivation of the students, with a positive impact on their lexical skills and overall performance, which as a result leads to achieving the ultimate goal of language learning, the communicative competence.The article also offers specific recommendations on the choice of electronic educational resources and outlines a number of problems encountered while using digital software.
Sentiment Analysis og Goride Services on Twitter Social Media Using Naive Bayes Algorithm

Online motorcycle taxi is an application-based transportation technology innovation. Online motorcycles offer relatively low prices and offer discount features. However, the existence of online motorcycles creates congestion problems and conflicts between conventional transports. Various speculations arose in the midst of the public against Goride. So it makes the public opine and wants to judge an object openly through social media, one of which is Twitter. An opinion given by society is a textual opinion that can be analyzed. Sentiment analysis is used to detect opinions in the form of a person's judgment, evaluation, attitude, and emotion. The textual classification algorithm used in this study was Naive Bayes. This research aims to find out the public sentiment towards Goride's service as an online motorcycle taxi in positive and negative categories and to find out the accuracy results of the Naive Bayes algorithm against Goride's service. The data used in this study are secondary data. Data obtained by crawling using an API provided by Twitter developer. Analysis techniques are performed by text preprodeing, data labelling, word weighting, classification, then performance evaluation of classification. The results of the positive category sentiment classification are 698 data, while the negative category sentiment is 517 data. Which means more positive sentiment than negative sentiment. The accuracy performance of the Naive Bayes algorithm results in an accuracy rate of 77.78%.
On a Population Sizing Model for Evolution Strategies Optimizing the Highly Multimodal Rastrigin Function

A model is presented that allows for the calculation of the success probability by which a vanilla Evolution Strategy converges to the global optimizer of the Rastrigin test function. As a result a population size scaling formula will be derived that allows for an estimation of the population size needed to ensure a high convergence security depending on the search space dimensionality.
Efficient Vision Transformer via Token Merger

Vision Transformers (ViTs) split an image into fixed-size patches as tokens. This strategy has succeeded in computer vision tasks, but introduces considerable tokens similar in semantics and appearances. This work proposes Token Merger to spot redundant tokens and merge them into a compact representation to accelerate ViTs. For each forward inference, the Token Merger first identifies meta tokens to represent meaningful cues of the image content, then adaptively merges similar tokens into a uniform one referring to meta tokens. To pursue a reasonable tradeoff between accuracy and efficiency, we further introduce learnable gates to adaptively decide the token merge ratios of different layers. As a generalizable module, Token Merger can be easily plugged into different layers of ViTs to boost their efficiency. Visualizations show that Token Merger progressively merges tokens and finally learns a compact set of tokens representing clear semantics. Compared with token pruning methods, Token Merger is more effective in preserving meaning contextual cues, thus performs and generalizes substantially better in different vision tasks. Extensive experiments and comparisons with other state-of-the-art downsampling methods also demonstrate its promising performance. For instance, it reduces 95% tokens and accelerates the inference speed by 62%. Meanwhile, the ImageNet classification accuracy only drops by 0.4%. The code will be available.
Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning

The development of deep learning based image representation learning (IRL) methods has attracted great attention for various image understanding problems. Most of these methods require the availability of a set of high quantity and quality of annotated training images, which can be time-consuming, complex and costly to gather. To reduce labeling costs, crowdsourced data, automatic labeling procedures or citizen science projects can be considered. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed as in most of the existing methods. This leads to sub-optimal learning procedures, and thus inaccurate characterization of images. To address this issue, in this paper, we introduce a generative reasoning integrated label noise robust deep representation learning (GRID) approach. The proposed GRID approach aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a supervised variational autoencoder. This allows the proposed GRID approach to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of the other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels during training independently from the IRL method being selected. Thus, unlike the existing label noise robust methods, GRID does not depend on the type of annotation, label noise, neural network architecture, loss function or learning task, and thus can be directly utilized for various image understanding problems. Experimental results show the effectiveness of the proposed GRID approach compared to the state-of-the-art methods. The code of the proposed approach is publicly available at https://github.com/gencersumbul/GRID.
An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features

This study focuses on predicting the popularity of a product based on its overall rating score. Unlike previous studies that focus on predicting the review rating based on sentiment analysis and the polarity of the reviews, in this research, the effect of product features in determining its popularity is directly measured and analyzed. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to three different deep learning (DL) models: deep feedforward neural network (DFFNN), probabilistic neural network (PNN), and radial basis function neural network (RBFNN). Phase 2 identifies other features that customers care about the most by applying the named entity recognition (NER) algorithm to the customer online reviews. Finally, Phase 3 feeds the combination of the general and custom features to the same DL models to predict the overall rating score of the product. The experimental results on a dataset of laptop products indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features in the inputs of the DL algorithm. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today’s competitive markets.
Two-mode squeezing over deployed fiber coexisting with conventional communications

Squeezed light is a crucial resource for continuous-variable (CV) quantum information science. Distributed multi-mode squeezing is critical for enabling CV quantum networks and distributed quantum sensing. To date, multi-mode squeezing measured by homodyne detection has been limited to single-room experiments without coexisting classical signals, i.e., on “dark” fiber. Here, after distribution through separate fiber spools (5 km), −0.9 ± 0.1-dB coexistent two-mode squeezing is measured. Moreover, after distribution through separate deployed campus fibers (about 250 m and 1.2 km), −0.5 ± 0.1-dB coexistent two-mode squeezing is measured. Prior to distribution, the squeezed modes are each frequency multiplexed with several classical signals—including the local oscillator and conventional network signals—demonstrating that the squeezed modes do not need dedicated dark fiber. After distribution, joint two-mode squeezing is measured and recorded for post-processing using triggered homodyne detection in separate locations. This demonstration enables future applications in quantum networks and quantum sensing that rely on distributed multi-mode squeezing.