Reviewerly/exHarmony huggingface dataset:
A Joint Dynamic Ranking System with DNN and Vector-based Clustering Bandit

The ad-ranking module is the core of the advertising recommender system. Existing ad-ranking modules are mainly based on the deep neural network click-through rate prediction model. Recently an innovative ad-ranking paradigm called DNN-MAB has been introduced to address DNN-only paradigms’ weakness in perceiving highly dynamic user intent over time. We introduce the DNN-MAB paradigm into our ad-ranking system to alleviate the Matthew effect that harms the user experience. Due to data sparsity, however, the actual performance of DNN-MAB is lower than expected. In this paper, we propose an innovative ad-ranking paradigm called DNN-VMAB to solve these problems. Based on vectorization and clustering, it utilizes latent collaborative information in user behavior data to find a set of ads with higher relativity and diversity. As an integration of the essences of classical collaborative filtering, deep click-through rate prediction model, and contextual multi-armed bandit, it can improve platform revenue and user experience. Both offline and online experiments show the advantage of our new algorithm over DNN-MAB and some other existing algorithms.
Large‐Scale Personalized Categorization of Financial Transactions

A major part of financial accounting involves organizing business transactions using a customizable filing system that accountants call a “chart of accounts.” This task must be carried out for every financial transaction, and hence automation is of significant value to the users of accounting software. In this article we present a large‐scale recommendation system used by millions of small businesses in the USA, UK, Australia, Canada, India, and France to organize billions of financial transactions each year. The system uses machine learning to combine fragments of information from millions of users in a manner that allows us to accurately recommend chart‐of‐accounts categories even when users have created their own or named them using abbreviations or in foreign languages. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.
Imaginarios y desafíos actuales en la educación media superior en Querétaro, México

Actually, teacher’s practice is full of great challenges, especially if we talk about teaching at high school level. The context of both, student and teachers are basic elements to take into account during the process. The teacher must face a great diversity of students' imaginaries and practices regarding their education, their study habits and their actions inside and outside of the classroom. In addition to this, an extremely important element is technology; which facilitates the process and, at the same time, makes it too difficult. The objective of this article was to discuss how we could generate significant processes in the field of education in this context.
Algorithm for real-time binary classification of adenomas and norms images obtained by confocal microscopy

Diseases of the thyroid and parathyroid gland today are a common and urgent problem. Diagnosis of these diseases is currently carried out by medical professionals by invasive intervention in order to take biopsy samples. Simplification and automation of this process by analyzing images of confocal microscopy using artificial neural networks can improve the accuracy of diagnosis and get rid of the need for invasive intervention, which makes research in this area relevant. The authors analyze various approaches and propose an optimal algorithm for solving the problem of recognition and classification of thyroid images obtained using confocal microscopy in real time.
Design and Application of a Production Accident Early Warning and Analysis System

In view of the common occurrence of potential accidents in iron and steel industry at present, but only post-accident analysis of production accidents, according to the analysis of production environment, data communication and data traceability requirements of production site, a pre-defined accident information is proposed to alert production site accidents, and the pre-defined accident information can self-improve the accident early warning analysis framework after confirming the architecture, event flow, data acquisition, acousto-optic control and data real-time are studied in depth, which makes the accident early warning analysis architecture modular, visual, scalable, robust and real-time, and effectively prevent or reduce accidents. Through the early warning information of the acousto-optic device, the production site takes timely preventive measures to effectively eliminate and optimize the hidden danger of the accident, realize the early warning of the accident, solve the problem that the accident is only analyzed after the event, which is of great significance to the reduction of the accident rate, the improvement of the production efficiency and the improvement of the intelligent production.
KLN: A Deep Neural Network Architecture for Keypoint Localization

Localization of keypoints on pixel-level precision is an essential step for stitching panoramic images because the keypoints are matching, and their locations are used for computing stitching transformation. We recall the main standard computer vision techniques for keypoint localization and focus on the precise localization. We design a neural network architecture containing an encoder, a latent representation handler, and a decoder, where the encoder is motivated by SIFT. In contrast to domain-agnostic neural network architectures, the developed encoder reflects the scale-space construction as well as the difference of Gaussians estimation used in SIFT. In the benchmark, we show that our architecture has a higher number of keypoints localized with pixel-level precision than other standard and neural network-based approaches.
A Breast Image Classification Method based on Deep Convolutional Neural Network

Breast cancer is a serious threat to women's physical and mental health. Computer-based breast cancer assistant diagnosis system can help radiologists reduce workload, reduce missed diagnosis rate, and improve accuracy to a certain extent, but the performance still needs to be further improved to meet clinical needs. Aided diagnosis systems based on traditional machine learning and pattern recognition methods require manual design and feature extraction, which not only requires rich medical expertise, but also difficult to extract deep features of the image, causing a bottleneck in system performance. In this paper, a model based on deep convolutional neural network is designed and used for breast cancer assistant diagnosis. Experimental results show that the area under the ROC curve can reach 0.896, which has achieved good performance.
$$\textsf {GQA}_{\textsf {RDF}}$$: A Graph-Based Approach Towards Efficient SPARQL Query Answering

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. In graph-based RDF data management solution, SPARQL queries are translated into subgraph patterns and evaluated over RDF graphs via graph matching. However, answering SPARQL queries requires handing RDF reasoning to model implicit triples in RDF data, which is largely overlooked by existing graph-based solutions. In this paper, we investigate to equip graph-based solution with the important RDF reasoning feature for supporting SPARQL query answering. (1) We propose an on-demand saturation strategy, which only selects an RDF fragment that may be potentially affected by the query. (2) We provide a filtering-and-verification framework to efficiently compute the answers of a given query. The framework groups the equivalent entity vertices in the RDF graph to form semantic abstracted graph as index, and further computes the matches according to the multi-grade pruning supported by the index. (3) In addition, we show that the semantic abstracted graph and the graph saturation can be efficiently updated upon the changes to the data graph, enabling the framework to cope with dynamic RDF graphs. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach.
Jazz Rhythm

When we think about the musical features most characteristic of jazz, those that particularize its style and distinguish it from other kinds of music, we almost always think of rhythm first. There are other important features, to be sure – the centrality of improvisation, for example, or the blues foundation of jazz melodic practice. But rhythm has typically been the feature addressed first in most writings on jazz since its origins early in the twentieth century, pride of place signaling its significance to jazz fans, critics, and historians.
Shape Grammars as the Decoder of Cultural DNA of Archaeological Artifacts

This paper evaluates shape grammars as a methodology to decode the cultural DNA of a set of fragmentary archaeological artefacts for parallel and precedent analysis and the definition of typologies of artefacts and buildings. Three historical grammars—one symbolic and grammar two shape grammars—and a recent shape grammar of a whole building type are reviewed. The paper draws conclusions on the contribution of shape grammars for archaeological typologies as well as the challenges and limitations of shape grammars in archaeological research.
The Implementation of Simple Smart Contract Language and Its Compiler Based on Ethereum Platform

Compiling technology is an important technology in computer science. It is of great practical significance to create a language and implement its compiler. At the same time, with the rise of blockchain technology in recent years, smart contracts reappear in people's vision, and the combination of the two has great potential. The typical smart Contract languages, such as Solidity and LLL, have complex syntax and are not easy for beginners to learn and use. This paper introduces the process of making a simple smart contract language that is easy for beginners to learn. The whole process includes from language design to intermediate code execution. The compiler uses Python as the programming language, and the target running platform is Ethereum virtual machine. At the end of this paper, a demonstration example is shown.
Research on Verification Techniques of Common Glass Measuring Capacity Based on Image Processing

Traditional glass measuring instrument verification scheme is usually performed manually, the verification process and result will have deviations. This article introduces the camera to collect the liquid level image of the measuring device, use the image processing method to determine the liquid level, and proposes a method of horizontally photographing the liquid level, and cooperates with the electronic scale to perform the measuring device verification, making the measuring device verification more convenient, accurate and intelligent.
Construction of Intelligent Library Self-service and Management System of College

The construction of intelligent library is a new direction and hotspot of library construction, and the construction of intelligent library includes many factors, such as intelligent technology, equipment, person, intelligent management and intelligent system; among them, the smart library self-service and management system is the core infrastructure of intelligent library construction. This paper overviews the research of intelligent library management system, from fine management system, seat management system, conference management system, seminar room management system, self-service text printing system, self-service copy system, self-service printing system, automated scanning system, electronic newspaper reading system, library multimedia information publishing system, electronic reading room management information system, self-service borrowing 12 aspects to discuss intelligent library self-service and construction management system of college.
Protection of Patients’ Data Privacy by Tamper Detection and Localization in Watermarked Medical Images

Sharing medical documents among different specialists in different hospitals has become popular as a result of modern communication technologies. Accordingly, protecting patients’ data and authenticity against any unauthorized access or modification is a must. Watermarking technique is one of the solutions to protect patients’ information against any signal processing or geometric attacks. This paper uses a tamper detection and localization watermarking technique. It embeds a Quick Response (QR) code generated from the patient’s information into a medical image. In the extraction step, it can detect if the QR image is attached or not. The proposed approach detects signal processing and geometric attacks and localizes the tamper resulting from text addition, content removal and copy and paste attacks.
Facial expression animation through action units transfer in latent space

Automatic animation synthesis has attracted much attention from the community. As most existing methods take a small number of discrete expressions rather than continuous expressions, their integrity and reality of the facial expressions is often compromised. In addition, the easy manipulation with simple inputs and unsupervised processing, although being important to the automatic facial expression animation applications, is relatively less concerned. To address these issues, we propose an unsupervised continuous automatic facial expression animation approach through action units (AU) transfer in the latent space of generative adversarial networks. The expression descriptor which is depicted with AU vector is transferred into the input image without the need of labeled pairs of images and even without their expressions and further network training. We also propose a new approach to quickly generate input image's latent code and cluster the boundaries of different AU attributes with their latent codes. Two latent code operators, vector addition and continuous interpolation, are leveraged for facial expression animation simulating align with the boundaries in the latent space. Experiments have shown that the proposed approach is effective on facial expression translation and animation synthesis.
A Hybrid Classification Approach using Topic Modeling and Graph Convolution Networks

Text classification has become a key operation in various natural language processing tasks. The efficiency of most classification algorithms predominantly confide in the quality of input features. In this work, we propose a novel multi-class text classification technique that harvests features from two distinct feature extraction methods. Firstly, a structured heterogeneous text graph built based on document-word relations and word co-occurrences is leveraged using a Graph Convolution Network (GCN). Secondly, the documents are topic modeled to use the document-topic score as features into the classification model. The concerned graph is constructed using Point-Wise Mutual Information (PMI) between pair of word co-occurrences and Term Frequency-Inverse Document Frequency (TF-IDF) score for words in the documents for word co-occurrences. Experimentation reveals that our text classification model outperforms the existing techniques for five benchmark text classification data sets.
The Study of Argumentative Relations in Popular Science Discourse

The paper presents an approach to modeling and study of argumentation found in popular science literature. The study of argumentation is performed by means of comparative analysis of discourse structures. Different types of argumentative structure are considered and the co-occurrence of arguments “from Expert opinion” with other types of argumentative reasoning typical of the popular science genre is analyzed. With the view of automatic extraction of argumentative relations, the analysis of correlation between rhetorical and argumentative annotations was carried out. The experiment was conducted on a corpus of 11 popular science articles from the Ru-RSTreebank.
Who Would Bob Blame? Factors in Blame Attribution in Cyberattacks Among the Non-Adopting Population in the Context of 2FA

This study focuses on identifying the factors contributing to a sense of personal responsibility that could improve understanding of insecure cybersecurity behavior and guide research toward more effective messaging targeting non-adopting populations. Towards that, we ran a 2(account type) x2(usage scenario) x2(message type) between-group study with 237 United States adult participants on Amazon MTurk, and investigated how the non-adopting population allocates blame, and under what circumstances they blame the end user among the parties who hold responsibility: the software companies holding data, the attackers exposing data, and others. We find users primarily hold service providers accountable for breaches but they feel the same companies should not enforce stronger security policies on users. Results indicate that people do hold end users accountable for their behavior in the event of a breach, especially when the users' behavior affects others. Implications of our findings in risk communication is discussed in the paper.
Examination Paper Image Segmentation with Adversarial Network

Abstract Examination paper analysis is important for students to improve their learning efficiency. Traditional paper examination papers are difficult to sort out, which makes collecting mistakes for future review both time-consuming and laborious, either by handwriting or existed software tools. To easy the process, we propose a layout analysis method combined with the conditional generative adversarial network (CGAN). The traditional semantic segmentation structure is improved and used as a generator in the network, while a discriminator is designed to make the segmentation results more accurate. The motivation is that the discriminator can judge the authenticity of the image, so it can help reduce the unreasonable phenomenon in the semantic segmentation results of the generator. The experimental results show that by this method the examination paper image can be welly split into various components, which provides convenience for further sorting and analysis of the examination paper images.
Helping companies to evaluate their status quo in information security with a serious gaming-based economical quantification approach

The number of cyber-attacks on small and medium enterprises (SMEs) is constantly increasing. SMEs do not recognize the attacks until the damage has occurred. Only then, they fight with measures to increase IT-security and IT-safety. Many studies come to the point that this refers to a lack of budget, expertise and awareness of the need for IT-security. There are many compendia with recommendations for action, but they are too comprehensive and unspecific to the individual needs of SMEs. In this paper, we present the results of a research activity on the gaps that address the challenges faced by SMEs. In addition, we develop a concept for a serious gaming approach that includes an economic perspective on IT-security measures and shows how SMEs can derive their own IT-security target state.
Pushdown Automata Versioning Framework for Web-Based Developmental Surveillance Systems

The development of existing software system, including web-based developmental surveillance programs, adopt linear software versioning systems wherein all files associated to a particular software version is pre-packed together and distributed to clients. In this paper, the two inconveniences incurred by linear software versioning onto web-based software development have been highlighted in terms of the software system update and rollback infrastructure. The use of a new Pushdown Automata (PDA) based versioning framework has been proposed to resolve these issues, by reducing update file storage requirements on the master server, and by simplifying the update files fetching process and version update/rollback installation process. Based on the proposed PDA-based versioning framework, a description is provided on how branched software versioning systems can be implemented to facilitate the development of a light-weight central repository server which allows access to different online language screening tools for all respective needs.
A Rapid Method of Monocular Image Measurement Based on Rectangle Information

Abstract A method of rapid measurement of monocular image based on rectangle information is proposed in this paper. Based on the pinhole imaging model, a fast method of calculating the object size in monocular image is derived by fitting the straight line and then finding the intersection point of the straight line. The algorithm can not only measure the size and absolute depth of the object in monocular image, but also meet the conditions of the specific pinhole imaging model with a distance far greater than the focal length. The complexity of the algorithm is low, and it can meet the requirements of real-time calculation. It provides a new idea for the use of small and micro-computing platform.
A Deep Autoencoder Based Outlier Detection for Time Series

Time series outlier detection is an important topic in data mining, having significant applications in reality. Due to the complexity and dynamics of time series, it is quite difficult to detect outlier in time series. Particularly, influenced by outside factors, time series are usually unpredictable, accompanied with concept drift. Recently, recurrent neural network has been used to identify time series outlier, and demonstrated great potential. However, RNN usually uses deterministic state transition structure, which cannot characterize the variability of high-dimensional time series. This paper proposes to incorporate latent variables into RNN, aiming to catch the time series variability as much as possible. In particular, our method combines RNN and variation auto-encoder framework. We evaluate our method with several real datasets, and demonstrate that our method has superior detecting performance.
SuperQuery: Single Query Access Technique for Heterogeneous DBMS

With the increasing interest in machine learning, data management has been receiving significant attention. We propose SuperQuery, a big data virtualization technique that abstracts the physical elements of heterogeneous DBMS in various systems and integrates data into a single access channel. SuperQuery can integrate and collect data regardless of the location, shape, and structure of the data and enable rapid data analysis.