Reviewerly/exHarmony huggingface dataset:
Research on Calibration Method Based on Structured Light Vision Measurement System

The visual measurement system based on structured light has the advantages of large scanning range, high measurement accuracy, simple structure, flexible and convenient use. It is widely used in many fields. The technical specifications for metrological traceability of this type of equipment have not yet been formulated. Therefore, on the basis of expounding the structural composition and working principle of the measuring system, this paper explores its main error sources and mathematical models, and then summarizes the metrological characteristics of the structured light vision measuring system. Study the calibration of measurement systems using standard instruments that meet the requirements of traceability of quantities.
Pipeline Detection and Reconstruction Based on Local Features

Aiming at the difficulty of detecting and reconstructing large-area and high-similar texture regions in pipelines, a local feature stereo matching method combined with the principle of region growing is proposed, and dense reconstruction is realized by reprojection matrix. First, features are extracted from the preprocessed image and matched, and then the exact match point pair is obtained through screening. After that, we use the obtained matching pairs as seed pairs. According to the principle of parallax continuity and the criterion of stereo vision limit constraint, the search area is delimited. For the obtained parallax, we use the WSL filter to filter the parallax. Finally, the depth and 3D coordinates are calculated according to the parallax to reconstruct the 3D model of the pipeline. Experimental results show that the proposed method has good adaptability to texture features in pipeline detection and reconstruction. Therefore, it can provide a feasible basis for pipeline inspection and maintenance.
Fire alarm model based on AHP and improved BP neural network

Establishing a reliable fire warning system is of great significance to national property and life safety. Based on data processing, this paper uses analytic hierarchy process to set different weights for each technical index, so as to evaluate and analyze the classified and summarized data. Secondly, Select the component name, fire frequency, failure rate of different components, reliability and other parameters to establish a grey correlation model, so as to obtain the correlation analysis between the occurrence of fire and other indicators. At the same time, adopt the l-BFGD algorithm improved neural network and Softmax classifier model for fitting analysis. Take the practical application background of a certain group as an example. Evaluate the authenticity of the alarm signals sent by different components and get the probability that each alarm signal is a real fire.
A Cross-Modal Alignment for Zero-Shot Image Classification

Different from major classification methods based on large amounts of annotation data, we introduce a cross-modal alignment for zero-shot image classification.The key is utilizing the query of text attribute learned from the seen classes to guide local feature responses in unseen classes. First, an encoder is used to align semantic matching between visual features and their corresponding text attribute. Second, an attention module is used to get response maps through feature maps activated by the query of text attribute. Finally, the cosine distance metric is used to measure the matching degree of the text attribute and its corresponding feature response. The experiment results show that the method get better performance than existing Zero-shot Learning in embedding-based methods as well as other generative methods in CUB-200-2011 dataset.
Computationally Efficient Scaled Clustering for Perceptually Invisible Image Intensity Tuning

Quality and information content of an image is a vital aspect affecting the performance of any artificial intelligence-related task. Improving the quality of an image while maintaining its color and naturalness in a very small time frame is a challenge. In this article, a computationally efficient scaled clustering-based contrast enhancement method is proposed for enhancing perceptually invisible images and preserving color and naturalness. Pixels in the image are grouped in clusters using the fuzzy c-means algorithm and are assigned membership to different clusters. Membership values of the pixels are modified and scaled up using the cluster centers. Based on these modified and scaled cluster memberships, the intensity level of the pixels is modified. The membership-based proportional modification of the intensity values in the scaled domain improves the color and information content of an image. It is scaled down to the original range of intensity levels, using linear scaling to obtain an enhanced image with better contrast and color information. The generated image is free from artifacts and is natural in appearance. The proposed method is simulated and tested on frequently used standard datasets. The simulation results and the average computation time of the proposed method are compared to some of the well-established conventional and latest techniques.
(Retracted) Simulating human visual perception behaviors for video quality assessment

Multimedia video quality evaluation system plays an important role in modern Internet applications, such as WebRTC-based video conference. However, video quality may be affected during transmission because of noise or network congestion. Constructing an end-to-end intelligent video transmission monitoring system is of great significance. We design a system based on no-reference quality assessment, which can be used to evaluate the video-based object quality evaluation. More specifically, we first leverage Gabor filter for video segmentation. Then we conduct image smoothness estimation to measure the correlation between two adjacent frames. Subsequently, we consider two states of video distortion: compression and video transmission. We model these two states, respectively. We compare our VQA algorithm with a set of well-known NR-IQA. The reported VQA accuracies have clearly shown the superiority of our proposed video quality predictor.
Implications of Tokenizers in BERT Model for Low-Resource Indian Language

For any deep learning language model, the initial tokens are prepared as a part of the text preparation process, Tokenization. Important de facto models like BERT and GPT de facto utilize WordPiece and Byte Pair Encoding (BPE) as approaches. Tokenization may have a distinct impact on models for low-resource languages, such as the south Indian Dravidian languages, where many words may be produced by adding prefixes and suffixes. In this paper, four tokenizers are compared at various granularity levels, i.e., their outputs range from the tiniest individual letters to words in their most basic form. Using the BERT pretraining process on the Tamil text, these tokenizers as well as the language models are trained. The model is then fine-tuned with numerous parameters adjusted for the improved performance for a subsequent job in Tamil text categorization. The custom-built tokenizer for Tamil text is created and trained with BPE, WordPiece Vocabulary, Unigram, and WordLevel mechanisms and the compared results are presented after the downstream task of Tamil text categorization is performed using the BERT algorithm.
On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings

Dynamic networks reflect temporal changes occurring to the graph’s structure and are used to model a wide variety of problems in many application fields. We investigate the design space of dynamic graph visualization along two major dimensions: the network structural and temporal representation. Significant research has been conducted evaluating the benefits and drawbacks of different structural representations for static graphs, however, few extend this comparison to a dynamic network setting. We conduct a study where we assess the participants’ response times, accuracy, and preferences for different combinations of the graph’s structural and temporal representations on typical dynamic network exploration tasks, with and without support of common interaction methods. Our results suggest that matrices provide better support for tasks on lower-level entities and basic interactions require longer response times while increasing accuracy. Node-link with auto animation proved to be the quickest and most accurate combination overall, while animation with playback control the most preferred temporal encoding.
Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images

Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring.
Hierarchical Aggregation for Numerical Data under Local Differential Privacy

The proposal of local differential privacy solves the problem that the data collector must be trusted in centralized differential privacy models. The statistical analysis of numerical data under local differential privacy has been widely studied by many scholars. However, in real-world scenarios, numerical data from the same category but in different ranges frequently require different levels of privacy protection. We propose a hierarchical aggregation framework for numerical data under local differential privacy. In this framework, the privacy data in different ranges are assigned different privacy levels and then disturbed hierarchically and locally. After receiving users' data, the aggregator perturbs the privacy data again to convert the low-level data into high-level data to increase the privacy data at each privacy level so as to improve the accuracy of the statistical analysis. Through theoretical analysis, it was proved that this framework meets the requirements of local differential privacy and that its final mean estimation result is unbiased. The proposed framework is combined with mini-batch stochastic gradient descent to complete the linear regression task. Sufficient experiments both on synthetic datasets and real datasets show that the framework has a higher accuracy than the existing methods in both mean estimation and mini-batch stochastic gradient descent experiments.
ANALYSIS OF CHARACTER EDUCATION OF MAIN CHARACTERS IN THE NOVEL DEAR NATHAN BY ERISCA FEBRIANI

This research is based on the fact that character education is important. Novels as a literary medium are good for teaching character education because they consist of a fairly long storyline and describe the development of characters in sufficient detail. This study aims to describe the character education of the main character in the novel Dear Nathan by Erica Febriani. This study uses a descriptive method and a descriptive approach. The data source in this research is the novel Dear Nathan. The data collection procedure was reading and note-taking techniques. The researchers used reading techniques by reading the novel Dear Nathan by Erisca Febriani repeatedly to identify which sentences were included in the value of character education. The results of this study can be concluded that the character education of the main character in the Dear Nathan novel by Erisca Febriani is honest, patient, responsible, willing to take risks, never give up, willing to sacrifice, critical, curious, friendly, cheerful, persistent, likes to help, independent, hardworking and friendly.
Leading with Spiritual Energy

Abstract While some despair of the dystopia that technology will unleash, others believe that technology will finally provide time to pay attention to things that matter. In the Age of Destruction and Distraction, the new vision of corporate leaders with external and internal sensibilities will function as a kind of yogi and represent hope for the future of humanity. This ‘yogi’ at work is a warrior-monk, unafraid to take on new risks and unyielding in the face of continuous disruption. These leaders are interested in honing the superpower of staying focused at work for long periods without getting distracted. In these leaders is a heightened sensitivity able to connect to the work and the ecosystem of colleagues to provide purpose and energy. The ‘corporate yogi’ may be the future of human work.
Distraction Is the New Addiction

Abstract Distraction is big business. This is the Age of Distraction, and there is no end in sight. Technology-driven waves of connection and information constantly wash over almost every aspect of life. As change rides those waves, it brings opportunity, access, and creative innovation that have disrupted business, organisations, the workforce, and lifestyle. The bright side of this brave new world is that these changes have the prospect of affecting equity, climate, poverty, and global relationships that humans have struggled with for millenniums. The dark side is buried in the curious human psyche where we are prone to focus on whatever new curiosity is presented. The human mind is easily distracted. In an age where distraction is everywhere, it is having a heyday. The result of humans’ innovative nature is a system built to use distraction to garner attention and thereby gain access to billions of minds. Distraction has been weaponised and is pervasive. It is highly corrosive to individuals, societies, economics, politics, and businesses. As the intensity of distraction continues to increase, its impact becomes more evident. It becomes highly corrosive and threatens to damage some of the basic systems and sectors that sit at the foundation of society. The process, however, is completely reversible. Humans can reverse the effects of distraction. It involves reaching back into the basics of our cognitive facilities and applying the initiative of self-discipline.
Distraction Versus Traction

Abstract The Industrial Age is over. Some argue that we are entering the Fourth Industrial Revolution, while others call it the Information Age or the Digital Age. It may be that the right name for our times is ‘The Distraction Age’. In 2011 the top six global companies, in market capitalisation, were dominated by oil companies; by the end of 2017 the top ten global companies by market cap had six companies that make money from distraction. Distraction is the new oil, and it is made more potent using algorithms that track the large volume of user data and trick the users into addiction. The sources of this distraction are pervasive, beginning with open offices, emails, social media, and, smartphones. This continuous distraction is corrosive to personal productivity as well as for the economy at large. There are, however, ways to reclaim the attention and increase the depth of engagement.
Student-Teacher Anomaly Detection Considering Knowledge Consistency between Layer Groups

Student-teacher networks have been widely used for anomaly detection, which is often addressed as a one-class classification task. The mainstream idea is to calculate the loss of multiple feature maps between the student network and the teacher network independently without considering their relevance to detect anomalies. In this paper, we introduce a knowledge consistency loss into the student-teacher framework for further improving the performance based on the observation that anomaly scores obtained between adjacent layer groups should be spatially consistent. Evaluational experiments on a publicly available benchmark confirmed that our proposal can improve pixel-level anomaly detection when the anomaly score map is calculated from the feature map in the highest resolution.
Quick Screen View Rotation based on Device Rotation Prediction by SVM

Smartphones have the function of automatic screen rotation. The function’s policy and implementation have been kept similar without large modification for a decade. Its policy is pessimistic and operating systems do not quickly rotate their screen view and this delayed rotation may decline user experience. In our previous work, we proposed a method for early detecting a device rotation and achieved quick rotation. However, the method was optimistic and sometimes incorrectly rotated the screen. In this paper, we propose a method for detecting the device rotation using a support vector machine. The method extracts feature values from the acceleration obtained from the accelerometer in a smartphone and predicts device rotation in the near future. We then evaluate the proposed method using an actual Android device and show that the proposed method can predict device rotation more accurately than the existing method. In the case of shaking a device, the accuracy was improved by more than three times.
Digital Signage System Using a Directional Speaker and Displaying a Target Image

Many recent digital signage systems use sensors to switch content based on surrounding conditions dynamically. However, many people are unaware of the displayed content. Therefore, we considered a design to improve this. In this study, we developed and evaluated a digital signage system with a directional speaker and the ability to present an image of the target. We designed and evaluated a digital signage system that uses a directional speaker to call out and present an image of the subject. Our proposed system showed that the subjects had increased the impression of the advertisements.
Quality Improvement of Children’s Speech by Speaker Vector in General Purpose Vocoder

A neural vocoder is a vocoder that converts acoustic features into speech signals by applying deep learning and is capable of synthesizing high-quality speech so that it is indistinguishable from human speech. The objective of this research is to create a general-purpose vocoder that can synthesize high-quality speech for children and adults. We propose a speaker vector to improve the quality of synthesized speech of children. The quality of the synthesized speech is evaluated by comparing each model with the speaker vector and the amount of training data, and the effectiveness of the proposed method is confirmed.
Anomaly Detection for Lung CT Images Using SVDD-AE

CT (Computed Tomography) screening is becoming widespread to detect cancer at an early stage. The purpose of this study was to develop a computerized detection scheme for lesions in CT images based on an anomaly detection approach. Our database consisted of CT images for 100 abnormal cases and 300 normal cases. In this study, a novel network (SVDD-AE) for anomaly detection was constructed by introducing a support vector data description to an autoencoder. The classification accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve for cases with the proposed method were 77%, 78%, 76%, and 0.81, respectively, showing an improvement when compared with a conventional variational autoencoder (61%, 72%, 50%, and 0.58). The SVDD-AE was shown to have the potential to be a useful anomaly detection network.
Calculation of Mean Time to Failure of Infocommunication System wich Failure Rate is Cycle Changed

In the article we describe a way of computation of mean time to failure of a system that consist of unrecoverable elements with constant failure rates (elements' modes of use and storage alternate in a cycle way). We got exact and approximate formulas of mean time to failure and gave an example of computations with these formulas. We also wrote some summary about formulas usage in engineering practice.
Fast Seam Carving for Video Image Based on Cost Equalisation

In recent years, with the spread of devices with various screen sizes, the demand for resizing methods is increasing. Seam carving (SC) is one of the resizing methods. This method removes visually unimportant areas (hereinafter referred to as "seam") and resizes the image without losing its impression. SC for video images has the problem of shifting the most impressive regions because the seams in each frame are different. There are previous methods that solve this problem, but they require a huge amount of processing time. The proposed method enables SC with reduced misalignment and processing time by region segmentation and seam reuse.
Detection of Hitting Point Area Using Footwork Trajectory in Badminton Video

Video analysis is considered necessary in competitive sports, and various studies have been conducted on the subject. Particularly in badminton, shot information is essential primary data for performance analysis. In badminton singles, the base is typically near the center of the court. After releasing a shot, the player returns to the base and prepares for the opponent’s shot. This movement is considered as basic footwork in badminton. Badminton performance analysis using software utilizes player position data of a court area divided into nine sections. This study proposes a method for detecting the hitting point area using the footwork trajectory of the player and nine-division area. In a demonstration experiment using a monocular camera, the hitting point detection accuracy on the front court and backcourt is 85.9 % and 70.5 %, respectively. Thus, we confirm that the hitting point area can be detected with high accuracy from the footwork trajectory of the player.
Hardware Implementation of Multi-kernel Function for Support Vector Machine Classification Using Stochastic Logic

The support vector machine (SVM) is a supervised learning method widely used for classification and regression in the machine learning. The nonlinear kernel functions in the SVM are involved in high hardware cost in practical implementations. This paper presents a general stochastic circuit architecture for various kernel functions with an aim to reducing hardware complexity. In the proposed stochastic circuits, the unipolar format is applied for implementing linear, polynomial, hypertangent and radial basis functions. The innovation of the presented design lies in the reduction on the total number of random number generators (RNGs) with the correlation technique. The synthesized results show that the circuit area of the proposed design is improved as compared to the previous finite state machine (FSM)-based method. Experimental results of Iris flower classification problem indicate that the accuracy with the RBF and hypertangent functions are improved by 60% and 10%, respectively, over the previous design as the length of the bit-stream is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> .