Reviewerly/exHarmony huggingface dataset:
Meta-learned ID Embeddings for Online Inductive Recommendation

Learning accurate user and item ID embeddings from user-item historical records has shown great success for recommender systems. Most of these embedding learning models are transductive and work well for users that appear in the training stage. However, in the model serving stage, new users continue to join the system. It’s important to quickly adapt to new users’ preferences for online inductive recommendation scenarios. Some previous works adopted embedding retraining or fed content data for new user ID embedding learning, these models either suffered from slow convergence or relied on auxiliary data. In this paper, we propose a meta-learned ID embedding framework for new users without using any side information in online inductive recommendation scenarios. Our key idea is that, we treat each user’s ID embedding learning as a separate task, and propose to meta-learn the initial embedding by modeling the global knowledge from all users (tasks). Each user’s embedding is initialized by the learned global knowledge instead of randomly initialization. Therefore, we could quickly adapt to a new user’s ID embedding based on a few updates from her online records, which can facilitate fast online recommendation. Moreover, our main technical contribution lies in how to learn the global prior knowledge for informative ID embedding initialization without any side information. Finally, extensive experimental results on three real-world datasets clearly show both the efficiency and effectiveness of the meta-learned ID embeddings for inductive recommendation.
D-REX: Static Detection of Relevant Runtime Exceptions with Location Aware Transformer

Runtime exceptions are inevitable parts of software systems. While developers often write exception handling code to avoid the severe outcomes of these exceptions, such code is most effective if accompanied by accurate runtime exception types. Predicting the runtime exceptions that may occur in a program, however, is difficult as the situations that lead to these exceptions are complex. We propose D-REX (Deep Runtime EXception detector), as an approach for predicting runtime exceptions of Java methods based on the static properties of code.The core of D-REX is a machine learning model that leverages the representation learning ability of neural networks to infer a set of signals from code to predict the related runtime exception types. This model, which we call Location Aware Transformer, adapts a state-of-the-art language model, Transformer, to provide accurate predictions for the exception types, as well as interpretable recommendations for the exception prone elements of code. We curate a benchmark dataset of 200,000 Java projects from GitHub to train and evaluate D-REX. Experiments demonstrate that D-REX predicts runtime exception types with 81% of Top 1 accuracy, outperforming multiple non-Transformer baselines by a margin of at least 12%. Furthermore, it can predict the exception prone elements of code with 75% Top 1 precision.
A Logic-Based Approach to Incremental Reasoning on Multi-agent Systems

We introduce the notion of strongly distributed multi-agent systems and present a uniform approach to incremental automated reasoning on them. The approach is based on systematic use of two logical reduction techniques: Feferman-Vaught reductions and syntactically defined translation schemes. The distributed systems are presented as logical structures \(\mathcal{A}\)’s. We propose a uniform template for methods, which allow for certain cost evaluation of formulae of logic \({\mathcal L}\) over \(\mathcal{A}\) from values of formulae over its components and values of formulae over the index structure \(\mathcal{I}\). Given logic \(\mathcal{L}\), structure \(\mathcal{A}\) as a composition of structures \(\mathcal{A}_i, i \in I\), index structure \(\mathcal{I}\) and formula \(\phi \) of the logic to be evaluated on \(\mathcal{A}\), the question is: what is the reduction sequence for \(\phi \) if any. We show that if we may prove preservation theorems for \(\mathcal{L}\) as well as if \(\mathcal{A}\) is a strongly distributed composition of its components then the corresponding reduction sequence for \(\mathcal{A}\) may be effectively computed. We show that the approach works for lots of extensions of FOL but not all. The considered extensions of FOL are suitable candidates for modeling languages for components and services, used in incremental automated reasoning, data mining, decision making, planning and scheduling. A short complexity analysis of the method is also provided.
Practical Quantum Communication and Processing

Nanophotonics offers the platform for large-scale implementation of quantum processors. A number of groundbreaking experiments of quantum information processing, based on bosonic interference, have recently been realized. The most important issues are the practical scalability of quantum circuits, the ability to perform quantum logic gates with error rates below the fault-tolerant threshold, and the incorporation into large-scale quantum circuits with realistic physical resources and single-photon detectors packed on an optical chip.
Intelligent Legal Tech to Empower Self-Represented Litigants

Legal technologies, or “legal tech,” are disrupting the practice of law and providing efficiencies for businesses around the globe. Indeed, legal tech often conjures up notions around billion-dollar businesses and highly sophisticated parties. However, one branch of legal tech that holds particular promise for less sophisticated parties is expanding access to justice (A2J) through the use of online dispute resolution (ODR). This is because ODR uses technology to allow for online claim diagnosis, negotiation, and mediation without the time, money, and stress of traditional court processes. Indeed, courts are now moving traffic ticket, condominium, landlord/tenant, personal injury, debt collection, and even divorce claims online. The hope is that legal tech such as online triage and dispute resolution systems will provide means for obtaining remedies for self-represented litigants (SRLs) and those who cannot otherwise afford traditional litigation. Meanwhile, the Covid-19 pandemic has accelerated the growth of online processes, including court and administrative processes that traditionally occurred in person. Nonetheless, these online processes seem focused on mainly case management and communication, neglecting the need for more imaginative and innovative uses of technology. Accordingly, this Article proposes a six-module process framework for ODR programs and identify gaps in development – where new technologies are needed to advance A2J. Indeed, there is great room for development of Artificial Intelligence (AI) and data analytics to assist SRLs and others in pursuit of remedies, and justice.
An Improved Method of Non-local Means Denoising Based on Histogram

Non-local means (NLM) filter based on similarity structure index (SSIM) has the disadvantages of large amount of computation and long operation time. This paper proposes a method of NLM filter based on histogram to improve similarity measurement of similarity windows. By comparative experiments, this method can greatly shorten the time while keeping the filtering effect approximately unchanged, compared with the improved NLM filter based on SSIM.
A Detail Process for CAD Based Breast Cancer Detection

Breast cancer is known to cause high mortality unless detected in time. Early detection during the onset of the disease can prevent mortality. Early detection can prevent the spreading of the disease thus providing a healthy life to senior citizens. Mammographic screening and surgical biopsy will yield huge number of images to be deciphered by radiologists and pathologists respectively. MIAS dataset is sufficiently large to conduct experimental analysis. Moreover, the dataset contains 322 mammogram images of different size, shape and morphology. This paper discussed about breast cancer detection and diagnosis process. (Word count -91 words).
Analysis and Prospect of Disciplinary Situation of Mine Safety Field Based on Research Data

In the entire life cycle of the academic research ecology, subject research is a sustainable, cyclical, interactive, and continuously improving process. We use research data sets to study the process, results and main points of academic research, and analyze the process of academic research. Stages, development, and the future, especially the frontier analysis of academic research and the outlook of the subject situation, can guide the development of the industry, guide the frontier development of the academic field and track future trends. This article mainly focuses on research data in a broad sense, and conducts basic data and structural data such as field discipline development, expert research and judgment, patent navigation, competitiveness analysis, etc., and reveals the field research power, contribution and influence of the mining safety discipline and the frontier of the engineering field.
FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation

Pre-training can alleviate the requirement of labeling data for a new task. However, Pre-training as a sequential learning typically suffers in fact from forgetting the older tasks. Especially in complex medical image segmentation tasks, this problem is more prominent. To solve above problem, we propose a network structure based on feature space transformation (FS-Net) for data expansion of medical image segmentation. FS-Net share parameters during training to help exploiting regularities present across tasks and improving the performance by constraining the learned representation. In the experiment, we use M&Ms as the extended dataset of HVSMR, these two tasks have the same segmentation target (heart). The segmentation accuracy of FS-Net is up to 7.12% higher than the baseline network, which is significantly better than Pre-training. In addition, we use Brats2019 as expansion dataset on WMH, and the segmentation accuracy is improved by 0.77% compared with the baseline network. And Brats2019 (glioma) and WMH (white matter hyperintensities) have different segmentation targets.
Discussion on the Application of the Internet of Things Technology in English Teaching

This paper puts forward a new idea for the present situation of English procedural evaluation based on the Internet of Things. With the help of this technology, the intelligent data can be obtained and the flexibility of teaching can be enhanced. Through the data mining and the data analysis in the process of procedural evaluation, the procedural evaluation of teachers in English teaching is inferred. English teaching is linked with the Internet of Things ecosystem to improve students’ enthusiasm, interaction and participation in English learning. English teaching is bound to rely more and more on technology, but policymakers need to actively consider digital technology as a teaching tool and work extensively with educators and technical experts to develop innovative educational strategies.
Measuring Hilbert-Schmidt Independence Criterion with Different Kernels

Hilbert-Schmidt independence criterion (HSIC) which is a kernel-based method for testing statistical dependence between two random variables. It is widely applied in a variety of areas. However, this approach comes with a question of the selection of kernel functions. In this paper, we conduct an experiment using the forest fire data from UCI in the context of independence test, contrasting four commonly used kernel functions: Linear kernels, Gaussian kernels, Brownian kernels, Matern kernels. Through comparing p-value and rejection rate of hypothesis test we constructed; it is shown that the different choices in associated kernel function of HSIC give comparable performance on results.
Collecting a Dataset of Gestures for Skill Assessment in the Field: a beach volleyball serves case study

Activity and gesture recognition from wearable sensors data can be used for skill assessment in order to gauge the capability of a user at performing a task. As many other problem of automatic classification, gesture recognition relies on annotated data for the training of the classification system and to gather a set of gestures for the assessment. The collection of a multi-sensors dataset for this goal can be challenging, especially when it is performed in the field rather than in a more controlled environment such as a laboratory. In this paper, we present the collection of a beach volleyball gestures dataset in the field. The resulting dataset is made publicly available to the community and it includes 585 annotated gestures, collected by 10 users, with 4 wearable inertial sensors per user. In addition, we also provide a list of lessons learnt, suggestions and guidelines to improve future data collections in the field.
3D Modeling Techniques for the Characterization and Sound Synthesis of Timbila Bars

Timbilas are wooden xylophones finely manufactured and tuned by Chopi people from Mozambique. In the context of a research project concerned with historical Timbilas, we developed a workflow for the characterization and sound synthesis of Timbila bars using 3D scanning technology, reverse engineering and sound synthesis techniques. If the interest in 3D scanning is evident from the perspective of cultural heritage for documenting valuable objects without the need to make mechanical contact, the 3D data could also be used as inputs to computational methods for vibration and acoustic analysis. In this paper, we detail and apply the methodological framework to a real-life nine-bar Timbila, starting with the acquisition of the 3D data, moving to the finite element modeling and the modal computations, and ending with the building of a sound synthesis model based on physical modeling techniques. The proposed approach provides a non-invasive way of assessing the tuning of the bars and the reference pitch of the instrument, and also offers realistic synthetic sounds that can be exploited in combination with the produced high resolution rendering 3D model for applications in the fields of multimedia and virtual reality.
Fast Arbitrary Shaped Scene Text Detection via Text Discriminator

Abstract Robust scene text detection is one of the difficult and significant challenges in the computer vision community. Most previous methods detect arbitrary-shaped text using complicated post-processing steps. In this paper, we propose a trainable fast arbitrary-shaped text detection network by using the text discriminator, sharing visual information among the two complementary tasks. Specifically, we extend PSENet [1] by adding a text discriminator to fuse multiple predictions for each text instance, rather than using complicated post-processing steps which are time consuming. The text discriminator shares visual information with text detection network, and thus can achieve much faster detection speed compared with PSENet, while maintaining a similar accuracy reported in PSENet. Furthermore, our text discriminator can reduce the false alarms effectively. Experiments on ICDAR 2017 MLT, ICDAR 2015, and ICDAR 2019 ART datasets demonstrate that the proposed approach can achieve nearly realtime detection speed while keeping state-of-the-art detection accuracy.
Personalized advertisement push method based on semantic similarity and data mining

Personalized advertisement push method based on semantic similarity and data mining is studied in this paper. According to existing research, it is difficult to use one of them alone to make the feature comparison between images sufficient and also comprehensive. In addition, only performing feature extraction and fusion on the global and local scales can hardly break through the bottleneck in retrieval accuracy. Therefore, this research work utilizes the calculation model to extract the relevant attribute information of the product from the forwarded information, and decide whether to include it in the system's recommendation database or exclude it from the sentiment attitude. The designed model is simulated on the existing data sets to verify the general performance. It is reported that the pushing performance is much well improved.
Aspects of the mathematical education of engineering students at the HTW Dresden - University of Applied Sciences and at the Samara State Transport University

The article discusses aspects of the mathematical education of engineering students in Germany and Russia on the example of the HTW Dresden - University of Applied Sciences and the Samara State Transport University. The quality of an engineer’s mathematical education presupposes not only mastering the system of mathematical knowledge, but also acquiring competencies for independent assimilation of new knowledge. Having analyzed the methods of teaching, we see the presence of common didactic problems. How can students with non-maths majors (study fields where mathematics is used as a “tool”) increase their motivation to study mathematics? Which teaching methods contribute to a reliable assimilation of mathematical knowledge and a training of application competences in engineering fields? The various methods to promote independent learning, which have been tested by the authors, are designed to respond to these problems most appropriately. Particular attention is paid to the elements of digitalization of the presented methods.
The Application of Internet of Things and Online Video Recognition in the Cultivation of Intelligent Secretarial Talents

In 2012, the Ministry of education listed secretarial science as a special undergraduate major, which is a qualitative leap in the development of higher Secretarial Education. The traditional training of secretarial talents is mainly oriented to enterprises, government organs and news media, so the training program is more inclined to Chinese major. In this paper, a frame sampling framework based on Multi-Agent Reinforcement Learning is proposed, which is used to sample key frames in long video and improve the accuracy of video recognition. The method proposed in this paper can reduce the cost of computation and reduce the number of queries of non-target attacks by more than 28%.
Combining Feature Extraction Methods and Principal Component Analysis for Recognition of Vietnamese Off-Line Handwritten Uppercase Accented Characters

This paper proposes a blended model that is suitable for recognizing off-line handwritten accented characters in general and Vietnamese characters in particular. The recognition model linearly combines four extracting methods in the feature extraction period, including Zones density, Projection histogram, Contour profiles, and Haar wavelets. The set of features obtained will be applied with Principal Component Analysis (PCA) to retain useful features, reducing the recognition time. Additionally, a Support Vector Machine (SVM) is also utilised for training and recognition. The proposed model is tested on the dataset of 21174 samples with 99 Vietnamese off-line handwritten accented characters.
A novel strategy of multi-scale conditional super-resolution

Image super-resolution is an ill-posed problem, which means that one low-resolution image may correspond to multiple different high-resolution images. We proposed a novel strategy of non-blind multi-scale conditional super-resolution (MSCSR), which not only uses low-resolution image but also uses label of scale factor as the model input, and trains the network using image with the corresponding scale factor. To embed the condition into the network is to combine the condition label with the channel attention. Our model can reconstruct images with different degrees of information by inputting the same low-resolution image and different scale factors.
Dealing with Intractability

In this chapter we review different "coping strategies" that cognitive scientists have been adopting for dealing with the intractability\sind{intractability} of computational-level theories\sind{computational-level theories} and reflect on their validity and usefulness. We next turn to the question how the concepts and techniques from Chapters 2–7 can be useful tools for cognitive scientists to deal with intractability of computational-level theories of cognition in a constructive and conceptually coherent way.
Application Analysis of Image Rrecognition Technology in Artificial Intelligence

Abstract In the process of the information age, the image recognition technology in artificial intelligence has been used in various fields as a mature technology, infiltrating into our daily life, providing convenience for various work. In the field of information processing, it can process all kinds of information more quickly by using computer instead of human to identify information. Now in our interactive life with information, there is no lack of contact between image recognition technology and us. This paper will start from the meaning and principle of image recognition technology in artificial intelligence, and analyze its development status.
Producing High Quality Labels through Progressively Improving Strategy

Deep learning has made breakthrough achievements in image classification, object detection, semantic segmentation and other tasks in computer vision. The above results have already promoted the progress of related disciplines. In terms of application, due to the huge differences in the original data, deep learning cannot achieve good results in actual production. The production data is used as the initial sample for network training. With the participation of professionals, a high-precision data set is produced through continuous correction of the sample data and noise removal. Finally, the UpperNet semantic segmentation network was used to classify the area's surface coverage, and the Mean Intersection over Union (mIoU) has reached 85.3%.