Reviewerly/exHarmony huggingface dataset:
Accelerating Constraint-Based Causal Discovery by Shifting Speed Bottleneck

Causal discovery is a technique to find the causal relationship between variables using data. This technique has many applications in data mining and knowledge discovery. However, the high data dimensionality results in a significant computational efficiency problem. A common speed bottleneck in conventional causal discovery methods is the execution of conditional independence (CI) tests. This paper proposes, analyzes, and evaluates a novel acceleration strategy for causal discovery, which has low communication costs and can effectively exploit FPGA on-chip memory and parallelism. First, we propose an algorithmic method to shift the speed bottleneck from CI test execution to CI test generation. Second, we design a hardware accelerator for CI test generation on FPGAs. Third, we evaluate the proposed approach by comparing the accuracy-speed trade-off against four state-of-the-art accelerated causal discovery tools on CPUs and GPUs. Our accelerated implementation running on an Intel Arria 10 GX FPGA shows a superior accuracy-speed trade-off in 12 causal discovery problems. The implementation achieves up to 8.8 times speedup over the cuPC software running on an NVIDIA GeForce RTX 2080 Ti GPU. It also achieves up to 155.7 times speedup over the stable.fast software running on an Intel Xeon Silver 4110 octa-core CPU. To the best of our knowledge, the proposed approach is the first FPGA-based acceleration approach for constraint-based causal discovery.
Research on Teaching Application and Innovative Teaching Practice in Colleges Based on Digital Intelligence

Purpose &#8211; The purpose of this paper is proving that mobile phones can also be used in class and become a good teaching tool and means to serve teaching. Therefore, mobile Internet will become a major opportunity and future development trend of educational informatization. Design/Methodology/Approach &#8211; Mobile information-based teaching is a new information-based teaching mode that uses students’ Own mobile terminals (BYOD, Bring You Own Device) to carry out realtime interactive feedback under the mobile Internet environment. Mobile and personalized teaching can be realized anytime and anywhere, and it is the best information-based supporting environment for schools to carry out micro-lesson teaching and flipped classroom teaching. It is a great opportunity to solve the “last kilometer” of information teaching. Findings &#8211; This paper found that the traditional teaching method is still the main teaching method in colleges and universities, but at the same time, the mobile Internet teaching method has injected new vitality into college teaching. At present, colleges and universities should adhere to the dominant position of traditional teaching, actively participate in the innovation of teaching methods, and give play to the important role of mobile teaching methods. Research Implications &#8211; It is a combination of intelligent terminal equipment, communication technology and Internet technology development. As all three continue to grow, there is a huge opportunity for mobile cloud services.
Tire Wear Detection for Accident Avoidance Employing Convolutional Neural Networks

Tires are one of the most essential components of a vehicle, as they actively contribute to driving dynamics. However, they are often among the most overlooked when it comes to proper scrutiny and maintenance. More often than not, the general masses are found to be negligent of the condition of their tires. Treadwear and sidewall damage occur in abundance, and not tending to these problems can have devastating consequences in the long run. There is an innumerable number of road accident cases reported which were found to have been caused due to use of damaged and worn-out tires, and these occurrences are more prevalent in highways and during the rainy season. Despite being a widespread issue, many people are unable to identify good usable tires from worn-out ones, increasing their likelihood of using dangerous unsafe tires on roads. This paper introduces a model that can differentiate between good and worn-out tires, which has been implemented using Image Processing. The model takes external pictures of tires provided by the user as input and provides a verdict on its condition after comparing them with the model’s dataset using the machine learning algorithms DenseNet and MobileNet. This model has been made keeping in mind that it can be further used with appropriate hardware for implementing in real-life applications. By enforcing said implementation by the concerned regulatory bodies, tire-related accidents can be sharply reduced and damage to human life and property can be prevented on public roads.
Question-oriented cross-modal co-attention networks for visual question answering

Aiming at the problem that the existing cross-modal co-attention models in visual question answering (VQA) task dealing with text and image information lacks focus, a question-oriented cross-modal co-attention network is proposed. The network consists of a multimodal feature extraction module, a question-oriented cross-modal co-attention module, feature fusion module and classifier. The extracted image and the question features are respectively output with weighted attention features after passing through layers of attention. After the linear fusion of features, it is fed into the SoftMax classifier to obtain the predictive answer to the question; Finally, combined with the counting module, the counting ability of the model is improved. The results show that the model performs well on the public data set VQA v2.0, and obtains an overall classification accuracy of 70.71 % and 70.78% on the test_dev and test_std, respectively, which shows some advantages compared with most advanced models.
Quality Assurance of a German COVID-19 Question Answering Systems using Component-based Microbenchmarking

Question Answering (QA) has become an often used method to retrieve data as part of chatbots and other natural-language user interfaces. In particular, QA systems of official institutions have high expectations regarding the answers computed by the system, as the provided information might be critical. In this demonstration, we use the official COVID-19 QA system that was developed together with the German Federal government to provide German citizens access to data regarding incident values, number of deaths, etc. To ensure high quality, a component-based approach was used that enables exchanging data between QA components using RDF and validating the functionality of the QA system using SPARQL. Here, we will demonstrate how our solution enables developers of QA systems to use a descriptive approach to validate the quality of their implementation before the system's deployment and also within a live environment.
Federated Learning Model for Healthchain System

Global pandemic has emphasized the need for healthcare information processing capabilities even while pushing data privacy, openness, and consent to the forefront. The pandemic has revealed the limitations of employing existing digital healthcare technology to handle public health emergencies. As a result of the pandemic scenario, research institutes and governments have been compelled to reconsider healthcare delivery methods in order to maintain service continuity. Researchers have been looking at ways to improve the digital health workflow with disruptions such as decentralized artificial system. Disintermediate blockchain technology and federated learning are powerful solutions that can address these problems. The federated learning technique which enables data to be shared for better predictions while maintaining data openness based on authentication. Thus, to attain high accuracy in prediction and to enhance the security and privacy in healthcare, a disintermediate intelligent system is investigated with advanced learning model called federated learning model.
Time Series Analysis and Forecasting on Crime Data

The main objective of this work is to employ and style prophetic additive model to predict and forecast by quantifying the crime activities. Building the forecast model is aided by the detailed and systematic Exploratory Data analysis on crime data of city Sacremento. The dataset is the real time dataset from the police department of Sacremento over the period of January 2018–April 2020. The FB Prophet algorithm along with extrapolation of US Holidays has been used to forecast the growth of crime data. The experimental results obtained using Time series analysis algorithm forecasts the crime situation for the next one month with certainty and also displays that there is a gradual dip in the criminal activities as the year goes. The built forecast model shows 62% of accuracy, and the results captures weekly, yearly, and holidays seasonal trends and non-seasonal trends.
DEVELOPMENT OF THE 2P FIFTH-DEGREE INTERPOLATION CONVOLUTIONAL KERNEL

The first part of the paper describes the parameterization of the fifth-degree polynomial convolutional interpolation kernel. In this way, a two-parameter interpolation kernel is created. The second part of the paper describes an experiment in which the interpolation of some test images, using the one-parameter and the proposed two-parameter kernel, is performed. Some standard Test images, which are frequently used in digital image processing, were used in the experiment. Interpolation errors were determined using MSE. After that, the results of the experiment are presented using graphs and tables. Finally, a detailed comparative analysis, which shows the higher precision of the interpolation with the proposed two-parameter fifth-degree interpolation kernel, is presented.
Hole-robust Wireframe Detection

"Wireframe" is a line segment based representation designed to well capture large-scale visual properties of regular, structural shaped man-made scenes surrounding us. Unlike the wireframes, conventional edges or line segments focus on all visible edges and lines without particularly distinguishing which of them are more salient to man-made structural information. Existing wireframe detection models rely on supervising the annotated data but do not explicitly pay attention to understand how to compose the structural shapes of the scene. In addition, we often face that many foreground objects occluding the background scene interfere with proper inference of the full scene structure behind them. To resolve these problems, we first time in the field, propose new conditional data generation and training that help the model understand how to ignore occlusion indicated by holes, such as foreground object regions masked out on the image. In addition, we first time combine GAN in the model to let the model better predict underlying scene structure even beyond large holes. We also introduce pseudo labeling to further enlarge the model capacity to overcome small-scale labeled data. We show qualitatively and quantitatively that our approach significantly outperforms previous works unable to handle holes, as well as improves ordinary detection without holes given.
An Uncertainty Principle for Lowband Graph Signals

In this article, we introduce a novel lower bound on the support size of lowband graph signals. This result allows the deduction of an optimality criterion for the lowband and sparse decomposition of any graph signal, establishing the uniqueness of well behaving solutions. A comparison of the new bound with previously introduced results is performed, showing the improvements brought by the present work. An illustration on a practical denoising usecase on a real graph is also provided.
A Study on the Relationship among the Students’ Interest, Satisfaction, Educational Effect and Continuation of Class in VR Sports Classroom

Purpose: The purpose of this study is to investigate the relationship among the factors of fun, satisfaction, educational effect, and the intention of participating in the class with elementary school students who had experienced physical education in VR sports classroom.<BR> Method: A total of 440 5th and 6th-grade students who visited two VR sports classroom installation schools in Seoul and Chungcheongbuk-do and received physical education classes were selected. A total of 423 subjects, excluding 17 questionnaires that were partially missing or double-written, responded with a self-administered questionnaire and the completed questionnaires were collected. The data were processed using the SPSS 21.0 program to perform exploratory factor analysis, reliability test, descriptive statistical analysis, one-way ANOVA, correlation analysis, and multiple regression analysis.<BR> Results: The autonomy, achievement and teacher attraction among the factors of class fun after the physical education class in VR sports class recognized by elementary school students had a significant effect on class satisfaction, and competence was rejected. In addition, class satisfaction had a significant effect on all educational effects such as cognitive, affective, and psychomotor effects, and cognitive and psychomotor effects had a positive effect on the intention of participating in class, but no affective effect.<BR> Conclusion: Therefore, it is necessary to supplement the competence of the class fun factor and to strengthen the cognitive and mental effects because the affective effect does not affect the participation in class continuation, and improves the problems of the affective effects.
On the Importance of Appearance and Interaction Feature Representations for Person Re-identification

In recent person re-identification (Re-ID) approaches, combining global and local appearance-based features has been shown to increase performance effectively. These types of models are often characterized by multiple branches that act as experts for specific local regions or global high-level semantic features. We argue that attention mechanisms can be useful for multi-branch Re-ID models by creating more robust representations based on the interaction of informative image features. In this paper, we investigate this idea and propose a novel multi-branch architecture with experts that learn distinct representations based on (i) the global image appearance and (ii) the interaction between features. Unlike former methods with local experts acting on partitions that are fixed a-priori, our feature interaction expert uses a novel attention-based pooling to automatically extract semantically-rich and discriminative features from different regions of a person image. Compared with existing attention-based algorithms, our method maintains the feature interaction information separately in order to discriminate between identities. Our approach achieves state-of-the-art performance across three popular benchmarks - CUHK03, Market1501 and MSMT17. Furthermore, saliency visualizations show that appearance and interaction experts learn complementary representations that attend to multiple discriminant regions, leading to improved classification ability.
High-Utility Itemset Mining

High-utility itemset mining (HUIM) is an emerging data science approach that comprises discovered itemsets having huge significance in the database. HUIM Please check and approve all the edits made. is the identification of highly profitable items considering the unit profit of the item in the huge transaction database. HUIM is an essential subject with broad applications in recent years. HUIM paves the way for knowing the profitable items utilizing factors such as quantity and profit, which generalizes the issue of frequent itemset mining (FIM). Until today, abundant algorithms have been found to mine HUIs and they are entirely different from the conventional algorithms. Most of the utility mining algorithms are designed to handle complexities in finding HUIs when processing a database holding distinctive items of huge size. In recent times, HUIM has adopted diversified optimization and artificial intelligence techniques. In this article, HUIM algorithms and their significance are elaborated.
Designing an Augmented Reality Experience

There are times when inspiration strikes. There are times when the iron is hot. When inspiration strikes and the iron is hot, that is usually when you have the greatest of momentum, and that is when you go all in. Right?
Between AI and Learning Science: The Evolution and Commercialization of Intelligent Tutoring Systems

This article traces the history of a class of AI systems known as “intelligent tutoring systems” (ITSes). An ITS is structured like a tutor: A student engages with the system on some educational task (such as creating a geometry proof) and the system can calculate the student's learning and provide interventions that help the student develop a certain skill. The article shows that ITS researchers have been able to navigate the twists and turns in AI patronage as the field fell into its booms and busts. They did so by carefully crafting new identities—most notably as “learning scientists”—while also holding on to their AI credentials. To successfully commercialize ITSes for adoption in schools, they had to reconceive the systems themselves: from standalone aids for students to learning aids for both teachers and students.
Speech Recognition Technology of English Translation System Based on DTW Algorithm

Translation is an ancient and highly practical subject. With the development of the times, people pay more and more attention to language. As one of the most important tools in international communication, English has become a topic of common concern for people all over the world. Under the modern language system, it has become very necessary to study multiple strategies to replace traditional sentence patterns or morphological words. At the same time, in the era of big data, translation technology is one of the important components. Among them, multi-strategy translation uses certain technical means to translate the original text into the target language in different language environments. It can enable the target reader to quickly understand and accept the intended information or intention, and can also quickly and accurately express the original content and obtain accurate results. The DTW algorithm is a shortest path method based on the minimum non-random search method. It has strong robustness when solving multi-target translation problems and can well overcome many shortcomings in traditional classical grammatical analysis. This article adopts experimental analysis and data analysis methods to explore the multi-strategy English translation system by combining the research of DTW algorithm, so as to achieve high-quality and high-efficiency translation of the target language. According to the experimental results, the recognition time of the system tested in this experiment is relatively short, and the recognition rate can be as high as 98.3%, and the work efficiency is relatively high.
Promoting Open Science Practices: What Can (Should) Graduate Programs Do?

The training that applied linguists receive in graduate school can have a long-lasting impact on their career and research practices. Collectively, the field must therefore consider how to best educate upcoming generations of researchers to see Open Science practices as the standard rather than as the exception. In this chapter, we describe what we are eager to see in graduate programs that not only advocate for Open Science, but also proactively equip students with knowledge and skill sets to embrace the future research environment. We propose a more systematic integration of Open Science into the training of research methods, both within individual courses and across the program at large. We conclude by pointing readers to a short survey quiz for graduate students and program directors to self-assess their readiness to welcome an Open Science future in applied linguistics.
On salience-based theories of demonstratives

In this paper, we examine a number of ways in which the notion of salience has been or might be used to fix the reference of demonstrative expressions. Although we find the idea generally attractive, we conclude that the prospects for a theory of demonstrative reference based on salience are not, in fact, very good. We conclude by considering how certain aspects of these salience-based views might be productively integrated into alternative theories of demonstrative reference—and, indeed, theories of meaning more broadly.
Understanding (Mis)Behavior on the EOSIO Blockchain

EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many decentralized applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly $2.6 million) in total.
Traffic sign target detection method based on deep learning

With the development of autonomous driving technology, traffic sign recognition technology requirements are further improved. This paper conducts training and testing on the public data set of traffic sign recognition and performs filtering and data enhancement on the original data set to strengthen the detection of small targets. Research on common target detection algorithms such as Faster R-CNN, SSD (Single Shot MultiBox Detector), RetinaNet, yolov3 (You Only Look Once), yolov5, and optimize their network parameters. Experimental results show that the test accuracy of Faster RCNN, RetinaNet, and YOLO algorithms all reach more than 98.2%. The SSD algorithm has relatively low test accuracy due to the shallow number of layers, but it performs well in training and detection speed.