Reviewerly/exHarmony huggingface dataset:
Blend in or Pop Out? Designing an Embedded Interface for a Historical Cemetery

Historical cemeteries are under-explored design spaces within Human-Computer Interaction (HCI), even though they are visited for many reasons nowadays. We present our strategy to embed interfaces into this sensitive environment and report on interim results and the potential of such interfaces to support these places’ preservation. We followed an iterative design approach by (1) brainstorming various ideas and (2) creating four low-fidelity prototypes to (3) discuss them in two expert interviews. This led to a final prototype resembling a birdhouse, a reoccurring and tolerated object in the cemetery. We (4) tested our prototype in a field study, asking passers-by about their interaction and perception. Our results show that our design approach is suitable and unobtrusive for the culturally-sensitive target environment. Further, embedded interfaces seem to facilitate and are more inviting to visitors to inform themselves about the place’s characteristics and historical meaning, supporting its preservation.
Application of Noise Reduction Techniques to Improve Speaker Verification to Multi-Speaker Text-to-Speech Input

Text-to-speech is a very common implementation in the modern world. Its use is everywhere, from hearing aid to a virtual assistant. But the development of voice models for TTS involves a lot of sample speech from professional speakers. Voice cloning can reduce this development cost by generating artificial voices from small speech samples. Speaker verification to multi-speaker text-to-speech (SV2TTS) makes this possible with its three individual neural networks and a lot of speech data. But it is still not possible to use it casually because of the noises around us. Noise creates garbage data while being trained and that makes the output less desirable. We propose to add a noise reduction system to the recorder of SV2TTS to reduce noise from speech data and create a more desirable output from SV2TTS. We compared six noise reduction algorithms and applied the best-performing one to the SV2TTS. We intend to expand this research to implement SV2TTS for the Bengali language.
Temporal Optical Neurons For Serial Deep Learning

We propose a three-layer serial optical neural network based on the time-domain Talbot effect for the two-class recognition of English letters “u” & “c” and “a” & “s”, and the recognition accuracy rate reached 100% theoretically.
Learners’ Efficiency Prediction Using Facial Behavior Analysis

In the e-learning context, how much the learner is concentrated and engaged, or the learners’ efficiency, is essential for providing adaptive and flexible materials, timely suggestions, etc., which can lead to efficient learning. In this work, we explore to predict learners’ efficiency with a realistic configuration, in which we use a webcam or a laptop PC’s built-in camera. Specifically, we first provide a feasible definition of the learners’ efficiency, and based on this definition, we predict one’s efficiency from facial behavior. We predict the learners’ efficiency using various convolutional neural networks. Results are discussed using different evaluation metrics.
Sentiment Analysis of Customer Feedback on Restaurant Reviews

Sentiment analysis is a huge volume increasing at a humongous rate everyday which has made it almost impossible to evaluate the data manually. In Social media, twitter, restaurant site people share their opinion as in a huge number of their prevalence. In order to make the process of analyzing the text automatic there are various machine learning techniques that could be applied. The data set is for those enthusiasts who are willing to play with text data and perform sentiment analysis or text classification. The huge quantity of data in textual is generated every day has no value unless processed. The text data problem can be resolute by a choose to take up data mining technique. By using classifier it helps to predict the text data using na√Øve bayes classifier. This data set consists of actual reviews from real people. So this data set will give a real time experience as to how to deal with textual data.
Computational LEGO technic design

research-article Computational LEGO technic design Share on Authors: Hao Xu View Profile , Ka-Hei Hui View Profile , Chi-Wing Fu View Profile , Hao Zhang View Profile Authors Info & Claims ACM Transactions on GraphicsVolume 38Issue 6December 2019 Article No.: 196https://doi.org/10.1145/3355089.3356504Online:27 August 2021Publication History 6citation11DownloadsMetricsTotal Citations6Total Downloads11Last 12 Months11Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Application and Development of Cloud Computing Technology in Computer Data Processing

Abstract In recent years, cloud computing technology has been extensively employed in various industries of society. Many enterprises generally use cloud computing technology to store data in computer hard disk and mobile hard disk. Cloud computing technology has grown the pace of local computer and data processing, and put forward the function of distributed storage for data storage, which speeds up the transformation of computer Internet mode. It is an instant service. Through the unique fixed matching mode, different client data and resources are sorted out and transmitted to the cloud to realize the calculation of big message and ensure the accuracy of the consequences. Cloud computing has its own characteristics, its computing power is not limited by the device itself, for example, through the Internet will need to provide services to request, through cloud computing will be presented in the mobile device terminal. Therefore, cloud technology has caused great changes to national life and production mode, and has greatly improved national level of scientific and technological development. At present, there are still some loopholes in computer data processing, so in order to further improve the perfection of data processing, it is necessary for relevant personnel to adopt reasonable technology for cloud computing application, perfect cloud computing technology analysis and research work, and then promote the quality of computer data processing, make sure the safety of computer information data, and ensure the scope and effectiveness of cloud computing in computer data progression.
esearch on Credit Card Fraud Classification Based on GA-SVM

Credit card transaction data belongs to imbalanced data. It is necessary to identify whether the credit card is at risk of fraud by analyzing the previous transaction data, so as to deal with the risk in time. Aiming at the problem that a traditional classifier cannot achieve good results in dealing with imbalanced data classification, a Support Vector Machine (SVM) model optimized by Genetic Algorithm (GA) is proposed in this paper. First, cluster centroids sampling is used to make the dataset relatively balanced. Second, we employ GA to optimize the parameters of the SVM and select optimal features of the data to improve the classification performance. The Area Under Receiver Operating Characteristic (ROC) Curve (AUC) and accuracy serve as classification evaluation indicators. Compared with traditional classifiers, accuracy and AUC value have been improved by GA-SVM. Compared to Random Forest, Logistic Regression and Naive Bayes classification, GA-SVM has improved the accuracy by 2.03%, 5.14% and 6.89%, respectively. On the AUC value, it is increased by 2.00%, 5.36% and 6.93%, respectively. These results show that GA-SVM can improve the overall accuracy and optimize the performance of the classifier on the credit card fraud recognition.
Towards All-optical Circuit-switched Datacenter Network Cores

All-optical circuit switched network core is the holy grail for the next-generation datacenter architectures, as electrical packet switches are struggling to cope up with increasing challenges posed by the end of Moore's law. However, traffic skewness is the biggest enemy of such all-optical network cores comprising of a simple round-robin circuit-scheduling abstraction. Even though valiant load balancing can theoretically solve the problem, it falls short in most of the practical scenarios. In this paper, we point towards a new research direction to address the skewness problem: why not resolve most of the skewness at the network edge while keeping the optical core simple? This approach is fundamentally different and can potentially enable the all-optical network core to achieve good performance in practice. We discuss relevant strategies and envision that a holistic system design is necessary considering all these strategies together.
A Semi-Automated HTTP Traffic Analysis for Online Payments for Empowering Security, Forensics and Privacy Analysis

The paper discusses means to identify potential impacts of data flows on customers’ security, and privacy during online payments. The main objectives of our research are looking into the evolution of cybercrime new trends of online payments and detection, more precisely the usage of mobile phones, and describing methodologies for digital trace identification in data flows for potential online payment fraud. The paper aims to identify potential actions for identity theft while conducting the Reconnaissance step of the kill chain, and documenting a forensic methodology for guidance and further data collection for law enforcement bodies. Moreover, a secondary objective of the paper is to identify, from a user’s perspective, transparency issues of data sharing among involved parties for online payments. We thus declare the transparency analysis as the incident triggering a forensic examination. Hence, we devise a semi-automated traffic analysis approach, based on previous work, to examine data flows, and data exchanged among parties in online payments. For this, the main steps are segmenting traffic generated by the process payment, and other sources, subsequently, identifying data streams in the process. We conduct three tests which include three different payment gateways: PayPal, Klarna-sofort, and Amazon Pay. The experiment setup requires circumventing TLS encryption for the correct identification of forensic data types in TCP/IP traffic, and potential data leaks. However, it requires no extensive expertise in mobile security for its installation. In the results, we identified some important security vulnerabilities from some payment APIs that pose financial and privacy risks to the marketplace’s customers.
Consequences of Heuristic Distortions on SHM-Based Decision

The main purpose of structural health monitoring (SHM) is to provide accurate and real-time information about the state of a structure, which can be used as objective inputs for decision-making regarding its management. However, SHM and decision-making occur at various stages. SHM assesses the state of a structure based on the acquisition and interpretation of data, which is usually provided by sensors. Conversely, decision-making helps us to identify the optimal management action to undertake. Generally, the research community recognizes people tend to use irrational methods for their interpretation of monitoring data, instead of rational algorithms such as Bayesian inference. People use heuristics as efficient rules to simplify complex problems and overcome the limits in rationality and computation of the human brain. Even though the results are typically satisfactory, they can differ from those derived from a rational process. Many heuristic behaviors have been studied and demonstrated, with applications in various fields such as psychology, cognitive science, economics, and finance, but not yet in SHM-based decision. SHM-based decision-making is particularly susceptible to the representativeness heuristic, where simplified rules for updating probabilities can distort the decision maker’s perception of risk. In this paper, we examine how representativeness affects the interpretation of data, providing a deeper understanding of the differences between a heuristic method affected by cognitive biases and the classical approach. Our study is conducted both theoretically through comparison with formal Bayesian methods as well as empirically through the application to a real-life case study about the evaluation of a bridge safety.
Personalized Face Authentication Based On Few-Shot Meta-Learning

Existing face authentication methods rely heavily on the general feature extractor trained with face verification algorithms without considering the target identity. No consideration of the target identity causes inefficiency in that the face authentication works on a specific target identity. Personalized face authentication systems that integrate target identity information is demanded to yield an efficient and superior performance than current methods. With the help of fast adaptation in few-shot meta-learning, we propose a novel personalized Face Authentication based on few-shot Meta-Learning (FAML). FAML introduces a unique person-specific binary classification task and trains an initialization model that can evolve into the personalized face authentication model for any target identity with few data and a small number of iterations. We evaluate the FAML on two public datasets, and it outperforms the baselines by a large margin and demonstrates the practicability as a security application.
Daily rainfall modeling using Neural Network

Abstract In the early 2020, Indonesia experienced flooding in several areas. This disaster caused a lot of damage and losses. One of the causes of flooding in Indonesia is due to high rainfall. This was not anticipated beforehand so there was a flood. Therefore, research on rainfall in Indonesia is very important to anticipate floods. If it is predicted that rainfall is very high and conditions do not allow it to accommodate, the government can prepare watersheds so that rainwater can flow and not be trapped. In this research, the rainfall data were obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia), then the analysis of rainfall data in Indonesia was carried out. There are several statistical methods that can be used. There are ARIMA and Neural Network. In this research, the results of ARIMA model are used as input variables in the Neural Network model. Then there are several numbers of hidden layer in the Neural Network model that are compared. The results of ARIMA model and Neural Network model showed that Neural Network model is better than ARIMA model, because the mean square error (MSE) value of Neural Network model is smaller than ARIMA model.
Differentiable Dynamic Channel Association for Knowledge Distillation

Knowledge distillation is an effective model compression technology, which encourages a small student model to mimic the features or probabilistic outputs of a large teacher model. Existing feature-based distillation methods mainly focus on formulating enriched representations, while naively address the channel dimension gap and adopt the handcrafted channel association strategy between teacher and student for distillation. This not only introduces more parameters and computational cost, but may transfer irrelevant information to student. In this paper, we present a differentiable and efficient Dynamic Channel Association (DCA) mechanism, which automatically associates proper teacher channels for each student channel. DCA also enables each student channel to distill knowledge from multiple teacher channels in a weighted manner. Extensive experiments on classification task, with various combinations of network architectures for teacher and student models, well demonstrate the effectiveness of our proposed approach.
Automated ceramic plate defect detection using ScaledYOLOv4-large

Automated visual inspection has become a popular topic of research in the last couple of decades, as computation power available grew exponentially. Judging by the fact that visual inspection is a critical task for the quality of the products, it would be highly recommended that people only supervise the system. This paper proposes a low cost, rapid development defect detection system based on the Scaled-YOLOv4 object detection model. The original model achieves almost state of the art detection mAP on the COCO dataset with a mAP(mean average precision) of 56.0 for the largest variant, named YOLOv4-P7. Our version is derived from the ScaledYOLOv4-P5 model and is trained on ceramic plate defects and achieves 87.4 mAP at a intersection of union of 0.5, while comfortably processing a frame in 20ms on a consumer RTX3070 GPU. Thus, the real time constraint for the manufacturing system is fulfilled. Hence, the critical aspects of the development process are the: quick development process, fast training, rapid deployment on the factory floor, quick validation and feedback, using images acquired in the lab - not on the factory floor for first iteration and overall low cost of the automated inspection system.
Preserving and conserving culture

Managing and sharing cultural heritages also in supranational and multi-literate contexts is a very hot research topic. In this paper we discuss the research we are conducting in the DigitalMaktaba project, presenting the first steps for designing an innovative workflow and tool for the automatic extraction of knowledge from documents written in multiple non-Latin languages (Arabic, Persian and Azerbaijani languages). The tool leverages different OCR, text processing techniques and linguistic corpora in order to provide both a highly accurate extracted text and a rich metadata content, overcoming typical limitations of current state-of-the-art systems; this will enable in the near future the development of an automatic cataloguer which we hope will ultimately help in better preserving and conserving culture in such a demanding scenario.
Carrier synchronization for continuous-variable measurement-device-independent quantum key distribution with a real local oscillator

We propose a carrier synchronization scheme for continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) with a real local oscillator (LO). The middle node Charlie evaluates carrier frequency offsets and phase drift values based on the pilot signal sent by the legal parties Alice and Bob and a part of their public data. Alice and Bob then calibrate their modulation data according to the evaluation parameters to achieve carrier synchronization. We simulate the entire CV-MDI-QKD protocol and verify the feasibility of this scheme. The influences of pilot segment size and public data size on the excess noise are also analyzed. Besides, we experiment to measure the actual frequency offsets and phase drifts in the channel, which gives guidance on the actual implementation. This solution can ensure the implementation of CV-MDI-QKD with a real LO, paving the way for the practical application of this high-rate QKD protocol.
Research on Interior Design Strategy Based on Optimization of Computer Renderings Production Process

Abstract Interior design is the use of visual language to disseminate information, which is often expressed on the basis of aesthetics and visual psychology. Aiming at the problems of weak modeling accuracy and low versatility, this paper uses computer technology to optimize the rendering process, that is, by optimizing the computer modeling process, changing the production steps, comprehensively considering lighting, materials, etc. for rendering, from actual needs To set out, make renderings more suitable for interior design, effectively improve the speed and quality of interior design renderings, enable students to master more practical production methods, and improve the effect of teaching.
PLNL-3DSSD: Part-Aware 3D Single Stage Detector Using Local And Non-Local Attention

3D object detection in the real crowded scene is still a challenging task due to occlusion and density change. We propose a part-aware 3D single-stage detector with local and non-local attention (PLNL-3DSSD) to fully use part information and inter-object relation. A primary part feature fusion is proposed for encoding the entire box feature vector by introducing semantic parts dividing. We develop a parallel part branch for robust and accurate object detection. We also develop 10-cal and non-local attention in set abstraction for enhancing data flow transfer between objects. Our method ranks second in single-stage 3D object detector on the KITTI 3D car detection benchmark while ensuring satisfactory efficiency.
Revisiting knowledge distillation for light-weight visual object detection

An essential element for intelligent perception in mechatronic and robotic systems (M&amp;RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&amp;RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.
Evaluation of the Medical Students' Satisfaction with Learning About Diabetes Through the Mobile App During the COVID-19 Pandemic

Background: The use of technology in distance education can help cross the borders of space and time for lifelong learning. One of the active learning methods is the use of the mobile apps with the potential to develop medical education. Objectives: The aim of this study was to evaluate the efficacy of using diabetes educational mobile app in the endocrine physiology virtual classroom during the COVID-19 pandemic. Methods: In the present study, an educational mobile app for diabetes, including the information modules, as well as educational videos made by the researchers of this study, was designed for 32 medical students of Abadan University of Medical Sciences who had endocrine physiology courses in the first semester of the academic year 2019 - 2021. A researcher-made questionnaire consisting of 10 questions about learning the educational content of diabetes through the application containing educational videos in the virtual classroom of endocrine physiology, scoring based on a five-point Likert scale, was provided. The data were analyzed by SPSS 21 using descriptive statistics. Results: The results obtained based on the highest satisfaction percentage were as follows: creativity in learning (90.625%), fast learning (87.5%), accessible learning (84.375%), practical learning (81.25%), sustainable learning (81.25%), eagerness to learn (78.78%), attractive learning (78.125%), accurate learning (78.125%), reducing the student's dependence on the teacher (75%), and feeling more confident (75%). Conclusions: Using educational applications along with other teaching methods can lead to fast, creative, practical, accurate, and lasting learning, and due to its attractiveness, it creates a desire to learn in people.