Reviewerly/exHarmony huggingface dataset:
Discovering Multi-label Actor-Action Association in a Weakly Supervised Setting

Since collecting and annotating data for spatio-temporal action detection is very expensive, there is a need to learn approaches with less supervision. Weakly supervised approaches do not require any bounding box annotations and can be trained only from labels that indicate whether an action occurs in a video clip. Current approaches, however, cannot handle the case when there are multiple persons in a video that perform multiple actions at the same time. In this work, we address this very challenging task for the first time. We propose a baseline based on multi-instance and multi-label learning. Furthermore, we propose a novel approach that uses sets of actions as representation instead of modeling individual action classes. Since computing the probabilities for the full power set becomes intractable as the number of action classes increases, we assign an action set to each detected person under the constraint that the assignment is consistent with the annotation of the video clip. We evaluate the proposed approach on the challenging AVA dataset where the proposed approach outperforms the MIML baseline and is competitive to fully supervised approaches.
On End-to-End Chinese Speech Synthesis Based on World-Tacotron

In recent years, there have been many attempts at Chinese speech synthesis with Tacotron, an end-to-end speech synthesis model. However, Tacotron adopts Griffin-Lim algorithm which requires acoustic features with high dimensionality and synthesizes speech with low quality. Google launched Tacotron 2 which adopted WaveNet instead of Griffin-Lim and greatly enhanced the quality of speech synthesis, however, WaveNet should be trained with mass data and has slow synthesis speed. With regard to the above-mentioned issues, we propose World-Tacotron, which combines World vocoder with the Tacotron model optimized by Tacotron 2. Our scheme reduces the dimensionality of original Tacotron by 98% (from 2129 to 38) while improving the synthesis quality. In addition, the articulation at the end of the speech synthesized by World-Tacotron is incomplete. Therefore, the paper optimizes the articulation by deleting the trim in preprocessing, which however will result in a longer convergence period.
Application of FP_Growth Algorithm of Sequential Pattern Mining on Container Maintenance Components Association

Using the available historical maintenance data to reveal the patterns and laws behind the frequent damages would provide a possible solution to the maintenance issues. This study collected 9 years container maintenances record data from China Railway Container Transport Corp, Ltd (CRCT). Based on those data, establishes a maintenance component chain for the maintenance parts of the maintenance operation, and converts it into a sparse matrix format data set according to the algorithm requirements. FP_growth (Frequent Pattern) algorithm is used to analyze the data set. According to the actual situation, the value of the confidence and lifting degree are set, and the 178 strong association rules are obtained. The results indicate that the components of the doors are most frequently damaged parts and there are strong relationships among the damages of components of rear end and doors.
Intelligent detection of fetal hydrocephalus

This paper describes an enhanced process able to attain righteous classification of morphological malformation in foetal head ultrasound images. These anomalies can be detected approximately 20–22 weeks. In effect, the experts rely on manual analysis by extracting typical biometric measures from head region to interpret pregnancy evolution. The contribution of this work presents a totally computerised method of cerebral defect-recognition based on conventional neural network (CNN) in order to supply quantitative appraisal of hydrocephalus (HD) or healthy (HL) topics. The obtained results have achieved an important classification (Accuracy = 97.20%, sensitivity = 97.95% and specificity = 98.23%) when applying the CNN. The proposed methodology enables us to assess the anomalous cases in premature period within a reduced processing time. Experimental results prove the success of the proposed rating system for a suitable diagnostic of foetal head hydrocephalus when compared to previous work.
Research on infrared signal processing circuit of large size infrared touch screen with interference rejection

Infrared touch screen realizes touch operation by locating touch position on display screen with occlusion of infrared light. Infrared transmission diodes and photodiodes are placed in the frame of the screen to form an equivalent infrared optical network on the surface of the screen. Large size infrared touch screen will increase the number of transmission diodes and photodiodes, as well as the scanning speed and transmission distance of infrared light, it causes signal interference increases and signal-to-noise ratio decreases. This paper presents an infrared signal processing circuit with interference rejection, which improve the signal-to-noise ratio while ensuring the signal processing speed. The infrared light is transmitted in switch mode at a fixed frequency. The infrared signal processing circuit uses resonant circuit to restore the transmitted infrared signal which has the same resonance frequency. The output of the infrared processing circuit intensity extracted by band-pass filter and switch lock-in amplifier. The experiment results show that proposed circuit has 150$\mu$s fast response time, the output voltage varies with the ambient light illuminance by 1.5 percent per 10000 lux, and the circuit has well characteristic of frequency selection which limited noise bandwidth. The interference rejection is improved effectively.
Multi-Agent Online Coordination via Bayesian Inverse Planning

Abstract Multi-agent coordination is a classical applied engineering problem and has been widely used in daily life. To efficiently coordinate all agents in the environment, most traditional models rely on a robust communication system between agents. However, in the scenario of autonomous vehicles, building a reliable communication system is extremely expensive and is very difficult to achieve in the foreseeable future. In this paper, we propose an online multi-agent coordination mechanism for each agent in the absence of communication and explore the benefits of Bayesian inverse planning in achieving the Nash equilibrium which provides an optimal coordination solution for each participant in multi-agent environment. By deliberate design, the proposed Bayesian inverse planning enables each independent agent to infer goals and predict future actions of other agents only by simply observing the past actions of them. To make a timely response to the dynamic environment, the inferences and predictions can be flexibly adjusted accordingly with the continuously updated action information of agents. Based on future action predictions, agents take coordinated actions to achieve Nash equilibrium coordination. We designed several mixed driving scenarios in the real world to test our model, and simulation results show that our model is efficient and flexible, compared with an optimal model and a heuristic model.
A Love Affair Between Bias Amplifiers and Broken Noise Sources

In this paper, we extend the concept of bias amplifiers and show how they can be used to detect badly broken noise sources both in the production and service phases of a true random number generator. We also develop a theoretical framework that supports the experimental results obtained in this paper.
Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity

Recently, using graph neural networks to model the hidden features of natural language has achieved success. In this paper, a novel sentence modeling method named TextSimGNN based on graphical representation is proposed to measure the semantic textual similarity. For embedding sentences into a graphical structure, we first construct a semantic textual graph which combines textual structure information and semantic information together. Then an end-to-end graph neural network is used to measure the similarity between graph pairs. The experiments show that our method has achieved good performance in semantic textual similarity task, which proves the advantage and effectiveness of graphical representation on natural language sentence modeling.
Performance Comparison of Qur’anic Search Engines

The trend of information retrieval related to Islamic scriptures is on the rise. One of the most popular and regularly read Islamic scripture is the Digital Quran. Digital Quran is written in Arabic and involve the use of diacritics/symbols. These diacritics assist the user in reading the Quranic verses properly and interpret the meaning correctly. However, these diacritics reduce the accuracy of Quranic verse retrieval. There are numerous Quranic websites available on the internet from where users can find and retrieve any Quranic verse. This paper investigates the performance of most popular Quranic search engines with respect to the accuracy of verse retrieval based on different observations. The findings will help in developing a more efficient search engine for Digital Quran.
Low-Complexity Neural Network Design for Super-Resolution Imaging

Super-resolution imaging is a widely used technology in consumer electronics, such as 4K TV and portable media players. The convolution neural network (CNN) has confirmed the high quality for super-resolution (SR) imaging. However, in order to pursue better imaging quality and higher PSNR/SSIM, recent researches usually involve high complexity architecture and lead to a large amount of memory usage. To overcome these drawbacks, we propose a low-complexity neural network architecture, which has a significant reduction in resource consumption. Furthermore, it also can serve 2X, 3X, and 4X SR images at the same time with a single architecture. Compared with similar less complicated architectures [1]-[2], the results show that our architecture can achieve the best imaging quality (PSNR and SSIM) with the lowest parameter usage.
Automated Identification and Classification of Usability Aspect of Stack Overflow Constraints

A collection of constraints on stack overflow can be used as material for evaluating the quality of software so that developers can improve the quality of the software utilizing text mining. the study aims to determine the usability aspects of software based on the classification of questions on stack overflow to ease the task of developers in evaluating the quality of the software. This research has several processes, namely: first, we do preprocess data, then data that has been preprocessed, is classified to get data that reflects the usability attribute, data that includes usability attributes will be calculated inverse document frequency and sorting to get the 20 highest term scores which are an aspect of the usability attribute. This study succeeded in getting 20 aspects of the usability of the results of the question extraction process using the classification process by comparing the five classification methods, namely: Naive Bayes, Support Vector Machine, Neural Networks, Logistic Regression, and Random Forest. The best accuracy results are obtained when using the Naive Bayes method with a value of 70 percent, a usability grade precision of 71 percent, and a recall value of 67%. In the Non-Usability class, the values of precision and recall are 70 percent and 74 percent, respectively.
Research on Public Opinion Hot Spot Acquisition Method Based on Reverse Relation in Social Network

The processing and application of public opinion are getting more and more attention all over the world with increasing technologies and systems (software) one after another, however, rare of them focus on the technology of reverse relation. In this paper, it takes the classic association algorithms Apriori and FP-Tree as its basis, presents a process design of reverse relation on public opinion, and realizes the acquisition and query of hot public opinions in social networks.
Popular Content Prediction Based on Web Visitor Data With Data Mining Approach

Abstract A quality website has five parameters that must be considered are: information, security, convenience, comfort, quality of service. But of course, the fifth parameter does not always guarantee the amount through its Web page will increase, from that problem. So research is conducted to predict website content based on visitor data with a data mining approach, this research aims to improve the quality of content on target according to the interest of website visitors. Evaluation of random forest algorithm has the value the accuracy of classification of 71 percent by value of Kappa 0.712 whereas the k-NN algorithm has higher accuracy values of random Forest algorithm i.e. worth 84.88 percent and kappa values of the mean 0.847 the k-NN algorithm performs data processing process predictions against data of web content more effectively than the random forest
An Incremental Boolean Algorithm for Computing Minimal Hitting Sets

Deriving all minimal hitting sets (MHSs) for a family of conflict sets is a classical problem in model-based diagnosis. An incremental Boolean algorithm for computing MHSs was proposed. According to the traditional Boolean algorithm, after selecting the element, we decomposed the whole family of conflict sets into left and right branches (parts), and incidentally, the left one was a subset of the right one. Therefore, after we got the solution of the left branch, we could directly obtain the solution of the right branch in an incremental way. Experimental results showed that our incremental approach was more efficient than the traditional Boolean algorithm and some other related algorithms, in many cases.
A New Ensemble Learning Method Embedded with PCA-ReliefF Algorithm

Improving the generalization ability of ensemble learning is very important. This paper proposes a new ensemble learning method embedded with PCA-ReliefF Algorithm. It can efficiently remove the influence of noise features, and improve the classification accuracy of ensemble learning. Experiments on UCI dataset show that the method in this paper is feasible and effective, and provides a new research approach for feature classification in pattern recognition.
On the problem of predicting meantime between failures on the basis of data from automated testing of on-board software components

Abstract The article considers the problem of reducing costs for the development and maintenance of software components. The possibilities of reusing software components and as the result the need for their testing are considered. A method of the repeated testing is proposed. It allows achieving the required level of testability of software components that reduces time costs. This method is a more detailed investigation of the neighborhood of the identified failures and the elimination of already verified areas. The software implementation of the repeated testing method in the framework of the Integrated Development Environment developed by the authors is considered. The presented tool allows designers to automate the re-testing procedure partially. It is possible to predict the time of occurrence of the next error based on the data on the detected errors while testing. It is essentially the time between failures. A de-eutrophication model for predicting the mean time between failures and a software tool that implements the given model are proposed. The results obtained by testing on the known data set are presented. The models and approaches described in the article help to solve the problem that is confirmed by the results obtained using the developed software tools.
The College Student Credit Evaluation Based on Characteristic Data

The increasing demand for high-tech talents has placed additional significance on the academic achievement for college students. Consequent to this demand, academic misconducts have become widespread in college and university. It has become apparent that an evaluation of college students’ academic and social integrity is necessary in face of such circumstance. This paper focuses on providing a new characteristic data to detailing the college students’ credit evaluation, using data sourced from academic related behavior such as trends in occupying library seating. Analytic Hierarchy Process (AHP) algorithm is the model chosen to evaluate student credit. As a supplement, an Area Under Curve (AUC) evaluation model was produced for a comparison against the Sesame Credit Score of Alipay to produce a 52.7% accuracy reading of our model. The results show that the credit score of academic behavior data is consistent with the credit score generated from the economic data used in the Sesame system. For that reason, our method can provide an alternative means for estimating economic credit rating as academic behavior data of college students can be evaluated to serve as a reference. This study supports the Education Office and the universities in their effort to improve the moral correctness of college students through rewarding academic honesty and punishment of dishonest behavior. Furthermore, the model proposed in this study can be invaluable to the field of psychological education, as data generated can serve as basis for future analysis in psychological counseling.
Definitions, disambiguation and differentiation of related fields

As one of the themes highlighted within this book, digital evidence is a rapidly evolving and changing field. The field can also be confusing with a wide range of terms used to describe what are essentially the same functions, and, often, one term has different meanings to different people. This chapter attempts to provide some clarity and distinction between the terms and the meanings.
Indonesia: Covid-19 and E-Learning in Student Attendance Method

When the Covid 19 pandemic situation, suddenly almost all countries apply learning done from home. Various systems in the field of education have to change. Not all are ready for distance learning, both in terms of systems, methods, resources, management, and students themselves. But all forced. One of the problems is in recording student attendance. This study discusses various alternatives for recording student attendance during distance learning, along with the phenomenon of scheduling lecture attendance. The method used is a literature review and interviews with lecturers from multiple universities. The results of this study are methods of recording student attendance, application, time recording, dataset, and reporting. The results of this study are one of the bases in developing eLearning systems, especially in recording student attendance, especially for university management and application developers.
A Weighted Distance Learning Method for Graph Classification

A weighted distance-learning method is used, in this paper, to optimize the accuracy of Nearest-Neighbour classifier for a graph-based data set. This algorithm tunes the weights of the training instances to minimize an analytical approximation of the classification-error on the training data. With this method, not only the distance function, which has an effective role in the performance of classifier, is tuned, but also a prototype reduction is achieved as a side effect to reduce both of time and memory complexity of the classifier. This efficiency is more important in the case of having graph instances, because computing the distance of a query graph with each prototype graph is a time-consuming process due to isomorphism. By applying the proposed method on IAM, a benchmark graph dataset, the uniformly good behaviour of the proposed method is presented having results competitive with the related methods.
Visually Impaired Assistance with Arabic Speech Recognition on GPS

People who have impaired vision regularly need a guide to assist in obstacle avoidance. Several electronic devices are currently used to provide guidance for a remote location. One of the latest trends in technology is Automatic Speech Recognition (ASR) which has become a primary communication tool for special needs people such as visually impaired and blind people. Nowadays, these people in Saudi Arabia could not find public places offering services such as braille readings on menus and flyers, sound facilities, ease of movements, health care and so on. Our application (Ayn) provides a database of several locations and all the information needed. This project investigated the suitability of a user-centered and client-server approach for the development of a talking GPS planned to fill a niche for outdoor wayfinding. We highlight the importance of having more places serving blind people in public places such as restaurants, centers, hospitals, parks ... etc. The developed application used a speech-recognition speech-synthesis interface. The prototype solution incorporates a custom web application that accesses the Google Maps API. The system is intended to be scalable and extensible with additional features. The quality of Arabic speech recognition is improved over Google Speech Recognition API for Arabic using one of the machine learning algorithms: Artificial Neural Network (ANN).
Deep Convolutional Neural Network for Object Classification

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.
An Introduction to Query Understanding

This book aims to present a systematic study of practices and theories for query understanding of search engines. The studies in this book can be categorized into three major classes. One class is to figure out what the searcher wants by extracting semantic meaning from the searcher’s keywords, such as query classification, query tagging, and query intent understanding. Another class is to analyze search queries and then translate them into an enhanced query that can produce better search results, such as query spelling correction, query rewriting. The third class is to assist users to refine or suggest queries so as to reduce users’ search effort and satisfy their information needs, such as query auto-completion and query suggestion. This chapter discusses organization, audience, and further reading for this book.