Reviewerly/exHarmony huggingface dataset:
Research on Singular Value Decomposition Recommendation Algorithm Based on Data Filling

In the era of big data, the problem of information overload has become increasingly prominent. Recommendation systems are widely studied due to the problem. Due to the sparseness of data, the recommendation effect is not always ideal. To alleviate the problem of data sparsity, a singular value decomposition recommendation algorithm based on data filling is proposed. First, an improved Tanimoto similarity coefficient calculation method is proposed to calculate the similarity, and effective interpolation data is generated for the singular value decomposition model according to the proposed prediction formula. The experimental results show that when using the same dataset MovieLens100K, compared with several commonly used recommendation algorithms, the improved algorithm improves the prediction accuracy of the model, In the best case, RMSE is 10.1% lower than KNNBasic, 7.8% lower than Slope One algorithm, 6.9% lower than SVD algorithm, and 4.8% lower than SVD++ algorithm, verifying that this method can improve the recommendation quality.
Using perceptual classes to dream policies in open-ended learning robotics

Achieving Lifelong Open-ended Learning Autonomy (LOLA) is a key challenge in the field of robotics to advance to a new level of intelligent response. Robots should be capable of discovering goals and learn skills in specific domains that permit achieving the general objectives the designer establishes for them. In addition, robots should reuse previously learnt knowledge in different domains to facilitate learning and adaptation in new ones. To this end, cognitive architectures have arisen which encompass different components to support LOLA. A key feature of these architectures is to implement a proper balance between deliberative and reactive processes that allows for efficient real time operation and knowledge acquisition, but this is still an open issue. First, objectives must be defined in a domain-independent representation that allows for the autonomous determination of domain-dependent goals. Second, as no explicit reward function is available, a method to determine expected utility must also be developed. Finally, policy learning may happen in an internal deliberative scale (dreaming), so it is necessary to provide an efficient way to infer relevant and reliable data for dreaming to be meaningful. The first two aspects have already been addressed in the realm of the e-MDB cognitive architecture. For the third one, this work proposes Perceptual Classes (P-nodes) as a metacognitive structure that permits generating relevant “dreamt” data points that allow creating “imagined” trajectories for deliberative policy learning in a very efficient way. The proposed structure has been tested by means of an experiment with a real robot in LOLA settings, where it has been shown how policy dreaming is possible in such a challenging realm.
Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered “close” to the user for whom the prediction is being generated have given to the same product or service. In general, “close” users for some user u correspond to users that have rated items similarly to u and these users are termed as “near neighbors”. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset’s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method’s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.
Academic mobility of university employees. prerequisites for the transition to a new type of housing

The globalization of the modern world imposes new requirements on the academic mobility of teachers. Academic mobility provides unlimited opportunities for professional and personal growth, access to various educational systems and environments. It contributes to improving the quality of education, its internationalization, and the development of international research cooperation.&#x0D; However, along with the positive aspects, there are many limitations. In particular, the issue of organizing the accommodation of teachers is especially relevant. The article will consider the main aspects that affect the organization of teachers' accommodation and current trends.
An adaptive cross‐scale transformer based on graph signal processing for person re‐identification

Abstract Extracting robust feature representation is one of the key challenges for person re‐identification (ReID) task. Although convolution neural network (CNN)‐based methods have achieved great success, they still cannot handle the part occlusion and misalignment caused by limited receptive field. Recently, pure transformer models have shown its power in the person ReID task. However, current transformer models adopt patches of equal‐scale as input, and cannot solve the problem of cross‐scale interaction properly. To overcome this problem, an adaptive cross‐scale transformer from a perspective of the graph signal, named ACSFormer, is proposed. Specifically, the self‐attention module is first treated as an undirected fully connected graph. And then, “node variation” is introduced as an indicator to adaptively merge neighbourhood tokens. To the best of the authors’ knowledge, their ACSFormer is the first work to attempt to combine pure transformers and graph signal processing in the field of person ReID. Extensive evaluations are conducted on three person ReID datasets to validate the performance of ACSFormer. Experiments demonstrate that this ACSFormer performs on par with state‐of‐the‐art CNN‐based methods and consistently improves transformer‐based baseline, for example, surpassing ViT‐baseline by 2.5%, 2.7% and 4.8% mAP on Market1501, DukeMTMC‐reID and MSMT17, respectively.
Batik Nitik 960 Dataset for Classification, Retrieval, and Generator

Batik is one of the traditional heritages of Indonesia, with each motif of batik having a profound cultural and philosophical significance. This article introduces Batik Nitik 960 dataset from Yogyakarta, Indonesia. The dataset was extracted from a piece of fabric with 60 Nitik patterns. The dataset was supplied by the Paguyuban Pecinta Batik Indonesia (PPBI) Sekar Jagad Yogyakarta collection of Winotosasto Batik and the data were extracted from the APIPS Gallery. Each of the 60 categories in the collection contains 16 photographs, for a total of 960 images. The photographs were acquired with a Sony Alpha a6400, illuminated with a Godox SK II 400, and the data were compressed using the jpg file format. Each category contains four motifs rotated by 90, 180, and 270 degrees. Thus, the total number of images per motif is 16. Each class has a specific philosophical significance associated with the motif’s origins. This dataset aims to enable the training and evaluation of machine learning models for classification, retrieval, or generation of a new batik pattern using a generative adversarial network. To our knowledge, this study is the first to present a Batik Nitik dataset equipped with philosophical significance that is freely accessible.
Quantum first detection of a quantum walker on a perturbed ring

The problem of quantum first detection time has been extensively investigated in recent years. Employing the stroboscopic measurement protocol, we consider such a monitored quantum walk on sequentially or periodically perturbed rings and focus on the statistics of first detected return time, namely, the time it takes a particle to return to the initial state for the first time. Using time-independent perturbation theory, we obtain the general form of the eigenvalues and eigenvectors of the Hamiltonian. For the case of a sequentially perturbed ring system, we find steplike behaviors of ${\ensuremath{\sum}}_{n=1}^{\mathcal{N}}n{F}_{n}$ ($\ensuremath{\rightarrow}\ensuremath{\langle}n\ensuremath{\rangle}$ as $\mathcal{N}\ensuremath{\rightarrow}\ensuremath{\infty}$) when $\mathcal{N}$ increases, with two plateaus corresponding to integers, where ${F}_{n}$ is the first detected return probability at the $n\mathrm{th}$ detection attempt. Meanwhile, if the initial condition preserves the reflection symmetry, the mean return time is the same as the unperturbed system. For the periodically perturbed system, similar results can also appear in the case where the symmetry is preserved; however, the size of the ring, the interval between adjacent perturbations, and the initial position may change the mean return time in most cases. In addition, we find that the decay rate of the first detection probability ${F}_{n}$ decreases with the increase in perturbation amplitude. More profoundly, the symmetry-preserving setup of the initial conditions leads to the coincidence of ${F}_{n}$. The symmetry of the physical systems under investigation is deeply reflected in the quantum detection time statistics.
A Focused Event Crawler with Temporal Intent

Temporal intent is an important component of events. It plays an important role in collecting them from the web with focused crawlers. However, traditionally focused crawlers usually only consider factors such as topic keywords, web page content, and anchor text, ignoring the relationship between web pages and the temporal intent of events. This leads to their poor crawling performance. This paper aims to understand the temporal intent of events and apply it within focused crawlers. First, a new temporal intent identification method is proposed based on Google Trends data. The method can automatically identify the start time of an event and quantify the temporal distribution of the event. Then, a new focused event crawler with temporal intent is proposed. The crawler incorporates the start time of the event into the similarity calculation module, and a new URL (Uniform Resource Locator) priority assignment method is developed using the quantified temporal distribution of temporal intent as the independent variable of a natural exponential function. Experimental results show that our method is effective in identifying the start time of events at the month level and quantifying the temporal distribution of events. Furthermore, compared to the traditional best-first crawling method, the precision of our method improves by an average of 10.28%, and a maximum of 25.21%. These results indicate that our method performs better in retrieving relevant pages and assigning URL priority. This also illustrates the importance of the relationship between web pages and the temporal intent of events.
Modeling the Transition between Localized and Extended Deposition in Flow Networks through Packings of Glass Beads

We use a theoretical model to explore how fluid dynamics, in particular, the pressure gradient and wall shear stress in a channel, affect the deposition of particles flowing in a microfluidic network. Experiments on transport of colloidal particles in pressure-driven systems of packed beads have shown that at lower pressure drop, particles deposit locally at the inlet, while at higher pressure drop, they deposit uniformly along the direction of flow. We develop a mathematical model and use agent-based simulations to capture these essential qualitative features observed in experiments. We explore the deposition profile over a two-dimensional phase diagram defined in terms of the pressure and shear stress threshold, and show that two distinct phases exist. We explain this apparent phase transition by drawing an analogy to simple one-dimensional mass-aggregation models in which the phase transition is calculated analytically.
STUDENTS PERCEIVED CONFIDENCE IN WRITING PUBLISHABLE ARTICLES

There has been an increasing demand for undergraduate students to do their scholarly research and publish the finding in the form of articles in their final year of study. The importance of writing publishable articles is a repeated and step-by-step process to maintain self-confidence in writing articles. In other words, many students struggle to publish their articles because the rate of successful articles can be affected by the common mistake made. Therefore, this qualitative study aimed to find how undergraduate students perceived confidence in writing publishable articles since they may still have difficulties in writing articles. The researcher used thematic data analysis to summarize data points. This study applied criterion-based sampling as a technique to determine the sample. The total number of participants was 8 students. This study focused on English Language Education Study Program (ELESP) students in semester 8 at Sanata Dharma University who once joined a conference and published their articles. Data were collected from interviews, Focus Group Discussions (FGD), and document analysis. The findings showed that most ELESP students faced some challenges in writing articles. The challenges experienced frequently by students were the use of grammar and vocabulary. Nonetheless, the results showed that ELESP students utilized some online tools and benefited from feedback from other parties to increase their confidence in writing articles.
Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.
Stretching Deep Architectures: A Deep Learning Method without Back-Propagation Optimization

In recent years, researchers have proposed many deep learning algorithms for data representation learning. However, most deep networks require extensive training data and a lot of training time to obtain good results. In this paper, we propose a novel deep learning method based on stretching deep architectures that are composed of stacked feature learning models. Hence, the method is called “stretching deep architectures” (SDA). In the feedforward propagation of SDA, feature learning models are firstly stacked and learned layer by layer, and then the stretching technique is applied to map the last layer of the features to a high-dimensional space. Since the feature learning models are optimized effectively, and the stretching technique can be easily calculated, the training of SDA is very fast. More importantly, the learning of SDA does not need back-propagation optimization, which is quite different from most of the existing deep learning models. We have tested SDA in visual texture perception, handwritten text recognition, and natural image classification applications. Extensive experiments demonstrate the advantages of SDA over traditional feature learning models and related deep learning models.
Unsupervised Depth Completion Guided by Visual Inertial System and Confidence

This paper solves the problem of depth completion learning from sparse depth maps and RGB images. Specifically, a real-time unsupervised depth completion method in dynamic scenes guided by visual inertial system and confidence is described. The problems such as occlusion (dynamic scenes), limited computational resources and unlabeled training samples can be better solved in our method. The core of our method is a new compact network, which uses images, pose and confidence guidance to perform depth completion. Since visual-inertial information is considered as the only source of supervision, the loss function of confidence guidance is creatively designed. Especially for the problem of pixel mismatch caused by object motion and occlusion in dynamic scenes, we divide the images into static, dynamic and occluded regions, and design loss functions to match each region. Our experimental results in dynamic datasets and real dynamic scenes show that this regularization alone is sufficient to train depth completion models. Our depth completion network exceeds the accuracy achieved in prior work for unsupervised depth completion, and only requires a small number of parameters.
New Horizons or Business as Usual? New Zealand’s Medico-Legal Response to Digital Harm

A socio-legal commentary, this article examines the emerging issue of digital harm in New Zealand’s health settings. There are recent cases, and an increasing number of them, demonstrating the medico-legal response to various forms of digital harm. Of these, several representative cases are considered in order to identify features of digital harm within the health context. The article questions whether this is a new type of harm, enabled by the creation of new technologies, or simply a different manifestation of conventional unprofessional or unethical behaviour. The article considers whether the existing medico-legal framework can appropriately respond to this harm and whether new legal or policy tools are required. The cases suggest that the rights and disciplinary systems in place can adequately deal with digital harm within their existing scopes, particularly when individuals have been harmed. However, gaps in the legal framework are identified, with particular reference to the actions of unregistered providers and harm to professions. Further, a future challenge for the system may be the response to COVID-19 vaccine denial and misinformation. As the legal response to digital harm in the health context is a relatively unexamined area of research, this work may guide future research.
Integrating Reconstructor and Post-Editor into Neural Machine Translation

Neural machine translation (NMT) mainly comprises the encoder and decoder. The encoder is mainly used to extract the feature vector of the source language sentence. The decoder predicts the next token according to the feature vector extracted by the encoder and the information of the current moment. In this process, there is no guarantee that the features extracted by the encoder are indistinguishable from the meaning of the sentences in the source language. There is also no guarantee that the decoder can accurately predict the corresponding character. These issues can lead to over-translation and under-translation issues in the translated results. Previous researchers alleviated this problem by calculating the gap between the reconstructed source-language sentences and the source-language sentences. Inspired by this method, we propose to integrate a reconstructor and a post-editor into NMT during the training. The reconstructor takes the translation of NMT as input to reconstruct the source sentence, and the post-editor takes the translation as input and post-edits it to predict the target sentence. Through the training of the reconstructor and the post-editor, the semantics of the translation are forced to follow the source sentence and the target sentence. Experimental results show that our approach can effectively improve the performance of NMT on multiple translation tasks.
Affective Feedback Synthesis Towards Multimodal Text and Image Data

In this article, we have defined a novel task of affective feedback synthesis that generates feedback for input text and corresponding images in a way similar to humans responding to multimodal data. A feedback synthesis system has been proposed and trained using ground-truth human comments along with image–text input. We have also constructed a large-scale dataset consisting of images, text, Twitter user comments, and the number of likes for the comments by crawling news articles through Twitter feeds. The proposed system extracts textual features using a transformer-based textual encoder. The visual features have been extracted using a Faster region-based convolutional neural networks model. The textual and visual features have been concatenated to construct multimodal features that the decoder uses to synthesize the feedback. We have compared the results of the proposed system with baseline models using quantitative and qualitative measures. The synthesized feedbacks have been analyzed using automatic and human evaluation. They have been found to be semantically similar to the ground-truth comments and relevant to the given text–image input.
Variable Selection by Domain Experts vs. Filter Algorithms for Clinical Predictive Modeling

Studies on variable selection in the medical field have focused largely on algorithms with little attention paid to domain experts in this regard. This chapter compared the performance of domain experts with filter algorithms in variable selection for clinical predictive modeling. Five clinical datasets on bacterial survival, neonatal birthweight, breast cancer, diabetes, and myocardial infarction were employed. For each dataset, fifteen domain experts were requested to rank the importance of the variables on a five-point Likert scale. The same variables were ranked using four algorithms, namely, chi-squared, Fisher score, Pearson's correlation, and varImp function. Results of classification models showed that both methods performed competitively. This means human expertise and experience are important in clinical predictive modeling and must not be mortgaged to algorithms. Further studies should focus on developing automated platforms that codify domain knowledge and experience to facilitate real-time, speedy, and seamless variable selection.