Reviewerly/exHarmony huggingface dataset:
Measurement Error Correlation in Processing of Overlapping Blocks in FMCW Radar

The paper investigates measurement error correlation caused by the processing of overlapping blocks in FMCW (Frequency-Modulated Continuous-Wave) radar. The problem is analyzed theoretically by discussing the impact of measurement error correlation on tracking and mathematical evaluation of the effect. The results are confronted with simulation and data from an real experiment. The paper describes predicting measurement error correlation to allow for proper addressing the issue in the tracking subsystem.
The Application of Data Imputation and Deep Learning Network in the Papillary Thyroid Carcinoma Classification

Missing values, diversified data types and insufficient sample size have become obstacles to clinical data analysis. In this work, we proposed a machine learning pipeline to analyze the metastasis rate of papillary thyroid carcinoma using B mode ultrasound images and incomplete clinical features simultaneously. Missing values in the clinical features were imputed by the multiple imputation algorithm and US images were analyzed by a deep transfer learning network. We applied the support vector machine to concatenate two types of features and made final predictions. Our proposed method achieves an AUC of 0.76, a sensitivity of 0.67, a specificity of 0.75 and an accuracy of 0.72 under 10-fold cross validation. These results are better than the transfer learning network based on US images (AUC=0.74) and the SVM method based on the clinical features (AUC=0.73).
Is Everything in Order? A Simple Way to Order Sentences

The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine’s understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional text-to-marker generation problem. We present Reorder-BART (Re-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text. Re-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall’s tau. We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform several experiments to understand the functioning and limitations of our framework.
Research on Web Data Mining Strategy Based on Cloud Computing

With the comprehensive development of Internet technology, web data mining technology has achieved rapid development and is widely used in various industries. Cloud computing is a new method of sharing infrastructure, which is based on open standards and services and centered on the Internet, providing safe, fast and convenient data storage and network computing services. Cloud computing has strong information storage capacity, high security and high level of data processing and analysis. As an indispensable part of storage technology, the application level of cloud computing technology has been improved, effectively ensuring the quality and efficiency of data preservation. By analyzing the characteristics of Web data mining technology in cloud computing environment, we can realize the breakthrough of cloud computing in data storage and improve the storage capacity and security. This paper analyzes the Web data mining algorithm in the cloud computing environment, improves the accuracy and effectiveness of data mining, and uses the cloud computing environment to improve the Web data mining strategy, thus laying the foundation for the healthy development of China's Internet.
Application of Rain Classroom in Teaching Practice

Rain Classroom is the embodiment of the application of information technology in the field of education. Different from traditional teaching, Rain Classroom presents a brand new teaching experience, prompting teachers to transform from classroom managers and knowledge imitators to guides and promoters of learning knowledge. Rain Classroom is easy to use and powerful, and have been achieved good teaching results in teaching practice.
Objects as Extreme Points

Object detection can be regarded as a pixel clustering task, and its boundary is determined by four extreme points (leftmost, top, rightmost, and bottom). However, most studies focus on the center or corner points of the object, which are conditional results of the extreme points. In this paper, we present an Extreme-Point-Prediction-Based object detector (EPP-Net), which directly regresses the relative displacement vector between each pixel and the four extreme points. We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss. Moreover, we propose a novel branch to predict the EIoU between the ground-truth and the prediction results, and take it as the localization confidence to filter out poor detection results. On the MS-COCO dataset, our method achieves an average precision (AP) of 44.0% with ResNet-50 and an AP of 50.3% with ResNeXt-101-DCN. The proposed EPP-Net provides a new method to detect objects and achieves very competitive performance among the state-of-the-art anchor-free detectors.
Fractional Calculus for improving Edge-Based Active Contour Models

In this paper, an attention is given to edge-stop function (ESF) in active contour models which are based on a gradient calculus. Usually this kind of algorithms uses the gradient of a smoothed image by a Gaussian kernel. In this work, a fractional calculus is introduced to the edge-stop function formulation. The regular gradient in the ESF formulation has been substituted by a fractional one. The Grunwald-Letnikov definition has been used. The proposed method has been tested on MRI database. Obtained results are good enough compared to existing methods in literature.
Automatic Generation of Dynamic Inference Architecture for Deep Neural Networks

The computational cost of deep neural network(DNN) model can be reduced dramatically by applying different architectures based on the difficulties of each sample, which is named dynamic inference tech. Manually designed dynamic inference framework is hard to be optimal for the dependency on human experience, which is also time-consuming and labor-intensive. In this paper, we provide an auto-designed AB-Net based on the popular dynamic framework BranchyNet, which is inspired by neural architecture search (NAS). To further accelerate the search procedure, we also develop several specific techniques. Firstly, the search space is optimized by the pre-selection of candidate architectures. Then, a neighborhood greedy search algorithm is developed to efficiently find the optimal architecture in the improved search space. Moreover, our scheme can be extended to the multiple-branch cases to further enhance the performance of the AB-Net. We apply the AB-Net on multiple mainstream models and evaluate them on datasets CIFAR10/100. Compared to the handcrafted BranchyNet, the proposed AB-Net is able to achieve 1.57× computational cost reduction at least even with slight accuracy improvement on CIFAR100. Moreover, the AB-Net also significantly outperforms the S2DNAS on accuracy with similar cost reduction, which is the state-of-the-art automatic dynamic inference architecture.
Optimization of CSO algorithm based on adaptive inertia weight coefficient

Abstract The traditional CSO algorithm is easy to fall into local extremum in optimization. In this paper, a CSO algorithm based on weight coefficient is proposed. In the CSO algorithm, the inertia weight coefficient is introduced into the hen position formula, and the learning factor influenced by the rooster is added to the chick position formula. Finally, using the idea of heredity, individuals with excellent fitness value are selected for crossover and mutation with a certain probability. Through the simulation comparison of five typical test functions, the simulation results show that the improved CSO algorithm can avoid local optimization, strengthen the global extreme value search ability, and improve the convergence speed and accuracy range of the algorithm.
ADROIT: An Adaptive Dynamic Refresh Optimization Framework for DRAM Energy Saving In DNN Training

To achieve high accuracy, DNN training usually consumes and generates myriads of data, which requires a large DRAM for efficient processing. The refresh power consumption in large DRAM has become a severe problem. Previous refresh energy saving methods have drawbacks on usability, flexibility or training supporting. We propose ADROIT, an adaptive dynamic refresh optimization framework for various DNNs and processing platforms. ADROIT dynamically adjusts the refresh rates for different types of data according to runtime loss feedback in DNN training. Data idle time, lifetime and size are taken into consideration to reduce the search space of refresh rate and remove most refresh operations. Experimental results show that ADROIT can reduce the refresh energy and total DRAM energy in DNN training by up to 98.9% and 24.7% respectively, while maintaining the accuracy. Moreover, ADROIT can automatically apply to different DNNs and hardware platforms without tedious manual configuration.
User Emotion Direction for Recommendation Systems-A Decade Review

Recommendation systems are rapidly gaining popularity in software development, including e-commerce, news, advertising, social networking, and entertainment. It filters appropriate information for user decisions. The most popular approaches of recommendation systems are content-based and collaborative filtering-based, which are created as a model by using user preferences and providing recommendations. Additionally, the hybrid approach is proposed to improve recommendations typically by combining the advantages of two techniques to increase efficiency and prediction performance. However, general recommendation systems typically abandon users’ contextual preferences such as culture, emotions, and other details in different situations. Researchers are attempting to apply knowledge from other scientific fields to improve the performance of their recommendation systems. Psychology is one of approaches that can be applied to understand and explain humanity and shows that emotions influence decision-driven, efficient, and predictable. This paper reviews relevant research analyzes state-of-arts, gaps, and further recommendation system research based on emotion. We find that most of the selected research use sentiment data extracted from open data sources and social networks. As for the data extraction and data analysis depend on data sciences and statistics theory, and Cold start is still a challenge for researchers. However, we find that the data from social media reaction can compare with the emotional wheel in psychology and present emotion as more complex than sentiment. Future research on an individual recommendation system will bring the complexity of psychological emotion into improving the system.
A Supervised Approach for Providing Contextual Information to User Behavior in IoT Smart Home Environment

Smart devices are becoming popular with time and we are on the cusp of having smart homes all around the globe. With the adoption of more and more smart devices and their personalized use by the users, it becomes critical to provide personalized home automation recommendations to the users. Our recommendation system provides these relevant recommendations to the users so that they can create home automations in a single tap. However, these automation recommendations currently lack the context. We present the work in this paper to provide a relevant context to the user's automation recommendations. Specifically, we provide the context to the user's end of the day activities and start of the day activities. This work is a foundational building block for more sophisticated context building for home automations.
Compound Nouns of Subordinate Type in the Hungarian Language

The paper aims to identify the most productive word-formation models for compound nouns of subordinate type in the Hungarian language. The main focus of attention lies in the consideration of different parts of speech as the first component. The scientific originality of the research consists in the thorough analysis of compound nouns of subordinate type in the Hungarian language on the material of the dictionary “Magyar-orosz szótár = Hungarian-Russian Dictionary”. As a result, it has been revealed that any part of speech can be the first component, while the second component is presented by a noun, which determines the belonging of compound words to this part of speech. The examples provided prove that the formation of compound nouns based on the subordinate relationship is a productive way of word-formation.
Computational Intelligence for Image Caption Generation

The problem of generating a description just by seeing an image has been of great interest to the developers. In recent years due to the advancements in the fields of deep learning and natural language processing (NLP), we are able to solve this problem. This problem can be divided into two sub problems. First, to retrieve the image feature vectors to create a vocabulary of all the objects in the image and second, to generate a meaningful caption by using those objects. We have used convolutional neural networks (CNN) and recurrent neural network (RNN) to achieve the solution to these problems. CNN architecture helps to extract features from the images and then these features are further passed to a RNN which uses long short-term memory (LSTM) units which generates an accurate and meaningful description for that image. The dataset used for training and testing of this model is Flickr8k, and the evaluation of the generated caption is done using the BLEU metric. The BLEU metric is an algorithm used for the evaluation of a machine translated text by comparing it with natural language texts for the same image.
Design and Implementation of Intelligent Assignment Management System Application For Distance Learning

The COVID-19 pandemic has changed the way people do activities, including learning and teaching activities. The learning and teaching process during the pandemic is carried out online where teachers and students need certain technologies such as the WhatsApp application, one of the conversation applications is the choice of teachers and students in the learning process because it is considered effective and easy to use in discussing, sending materials, and sending student answers. However, it is not uncommon for photos sent via WhatsApp to have a low resolution, making it difficult for teachers to correct and students to understand the material. From these problems, an intelligent assignment management system will be made that integrates the feature of collecting tasks collected in the form of photos with handwriting text detection into digital text. This application utilizes the text conversion process with Handwriting Text Recognition. In Handwriting Text Recognition, there are six stages that must be carried out starting from preprocessing, segmentation, normalization, feature extraction, classification, and transcription. With the development of this CEBAN (Cek dan Koreksi Jawaban / Check and Correction Answers) Android application, handwriting on images or photos will be easily converted into digital text which is then used by teachers or correctors easily in reading student answers who collect assignments, especially pictures. This of course will shorten the correction time because the text in digital form is no longer in the form of photos or images which most likely have different resolutions, different types of handwriting, and image clarity which will make it difficult for teachers to correct answers.
Real Time Crowd Surveillance using Machine Learning and IoT

COVID-19 is an acute, sometimes severe, respiratory illness caused by the novel coronavirus. In 2019 First discovered in Wuhan, China, the virus has spread all over the globe, where places like Europe, India are facing the second wave of COVID19 pandemic in 2021. Now that the pandemic is nowhere to be seen to take an end, efforts must be made to mitigate the spread of the Virus. Organizations like Shopping malls, schools, Banks have turned out to be places where crowd gatherings happen the most, and here the chance of being infected also increases. So, to stop the spread of the virus, we have proposed a system that provides smart surveillance in crowded areas. In the proposed model we have done Face Mask detection, temperature Monitoring along with continuous tracking of crowd density and to integrate all these modules we have used Multiple Regression Mode. For Mask detection, we have used the OpenCV library called Dlib, which accurately extracts the face feature and with specified conditions, it can be identified if a person is wearing a mask or not. For monitoring of temperature, MLX90641 is used, with a pi-cam to get the video footage, along with keeping track of people entering. All the Required Computation is done on the Raspberry Pi and according to the specified condition, the Raspberry pi signals the servo motor to close and open the entry door.
Time-efficient Bayesian Inference for a (Skewed) Von Mises Distribution on the Torus in a Deep Probabilistic Programming Language

Probabilistic programming languages (PPLs) are at the interface between statistics and the theory of programming languages. PPLs formulate statistical models as stochastic programs that enable automatic inference algorithms and optimization. Pyro [1] and its sibling NumPyro [2] are PPLs built on top of the deep learning frameworks PyTorch [3] and Jax [4], respectively. Both PPLs provide simple, highly similar interfaces for inference using efficient implementations of Hamiltonian Monte Carlo (HMC), the No-U-Turn Sampler (NUTS), and Stochastic Variational Inference (SVI). They automatically generate variational distributions from a model, automatically enumerate discrete variables, and support formulating deep probabilistic models such as variational autoencoders and deep Markov models. The Sine von Mises distribution and its skewed variant are toroidal distributions relevant to protein bioinformatics. They provide a natural way to model the dihedral angles of protein structures, which is important in protein structure prediction, simulation and analysis. We present efficient implementations of the Sine von Mises distribution and its skewing in Pyro and NumPyro, and devise a simulation method that increases efficiency with several orders of magnitude when using parallel hardware (i.e., modern CPUs, GPUs, and TPUs). We demonstrate the use of the skewed Sine von Mises distribution by modeling dihedral angles of proteins using a Bayesian mixture model inferred using NUTS, exploiting NumPyro's facilities for automatic enumeration [5].
Calculating the victory chances

The goal of this chapter is to determine stylometric features and keywords of the selected texts produced by the candidates for the 2018 Czech presidential election, and to interpret whether these may have had any impact upon the final results. The stylometric indexes researched include MATTR (moving-average type-token ratio), ATL (average token length), TC (thematic concentration), STC (secondary thematic concentration), Q (activity), and VD (verb distances); finally, a keyword analysis for two chosen candidatesʼ programmes is carried out. The outcomes of the analyses show that each candidate adopts a special strategy to influence his electorate and that this strategy can be captured via stylometric methods.
Chapter 10. CEFR-J × 28

This chapter discusses the development of multilingual pedagogical resources that make use of functional descriptors such as vocabulary and grammar items aligned to the CEFR levels in English. CEFR-J × 28 is a project that sets out to create multilingual pedagogical resources for 28 languages based upon the resources developed for the CEFR-J, a localised version of the CEFR for English language teaching in Japan. The chapter discusses the original CEFR-J project and its accompanying resources such as word/phrase lists, grammar and text profiles. It describes how to convert English resources into multiple languages using machine translation and multilingual corpora. A discussion of the advantages of this approach as well as future work is provided. The chapter discusses a series of e-learning tools developed to support learners’ vocabulary and grammar learning as well as a web-based learner corpus collection tool for multilingual spoken and written production.
Alleviate the Cybersickness in VR Teleoperation by Constructing the Reference Space in the Human-Machine Interface

The introduction of virtual reality into the teleoperation system can enhance the three-dimensional and immersive sense of visual feedback, but the serious cybersickness caused by it needs to be solved urgently. Scholars have proposed many methods proceed from the hardware or software aspect to alleviate cybersickness but increased the user's mental burden and physical exertion to some extent. Inspired by the static frame hypothesis (RFH), this research proposes a method to alleviate cybersickness by rendering virtual reference space in the virtual environment. This method aims to build a three-dimensional reference space through the rendering plane to help users establish a stable feeling on the ground of the real environment in the virtual environment, thereby alleviating cybersickness. The experiment results show that rendering the reference space in a virtual teleoperation environment can significantly alleviate the cybersickness. Specifically, the total cybersickness score (TS) of participants in the virtual environment with a reference space was significantly lower than that of a virtual environment without a reference space (0.023*), a decrease of 9%. Among them, the SSQ-D score of participants in the virtual environment with a reference space is significantly lower than the virtual environment without reference space (p < 0.001***), which is reduced by 19.7%.
Enhancing Voice Assistants: A Proactive Approach

Today the smart devices are more and more connected to each other. The people interact with these devices to execute simple and basic operations. The increase of the personal assistants has simplified these interactions thanks to the vocal commands. However they are not smart enough to adapt to the user lifestyle, forcing the user to manually configure the actions to be performed when a specific event is triggered (top-down approach). Therefore the interactions between the devices are conditioned by the user perception of its lifestyle, risking to miss some scenarios that the human does not sense. In this work we propose a proactive approach (bottom-up) to the interaction between the personal assistants and other IoT devices, that enables to modify at run-time the actions and to anticipate the user requests, self-configuring to provide more services. This approach uses a main personal assistant that analyse the user interactions to the IoT devices and other personal assistants, writes and installs the driver code to the interested devices. The interaction between the IoT devices and the main personal assistant is supported by WoX.