Reviewerly/exHarmony huggingface dataset:
Noise Estimation and Automatic Filter Design Based on Silent Slot Prior

The optimal parameter of a filter is usually dependent on the degree and the style of the noise. Therefore, noise estimation plays a critical role in filter design. In this study, we propose a new method of noise estimation in the time-plane. We apply the fact that natural signals without noise must have a sparse time-frequency distribution. That is, their energy must be concentrated in some regions of the time-frequency plane. By contrast, the time-frequency distribution of the background noise is usually much more uniform. For a noise-interfered signal, if there are regions in the time-frequency plane that do not have a concentrated distribution and the average energy in the region is relatively smaller, the region is probable to be the noise part. We call it the silent slot. After performing the weighted Fourier transform on the silent slot, the degree of the noise is well estimated. With the estimation results, the optimal filter is automatically designed, and it is unnecessary to adjust the parameter for each signal manually. The proposed algorithm is helpful for adaptive and real-time filter design.
Research on Intelligent Clustering Model of Undergraduate English Achievement Based on K-means Algorithm

The K-means clustering algorithm is used to cluster the students’ scores. After data preprocessing, the clustering is carried out and a visual class diagram is generated to understand the weaknesses of each class of students in English. The results show that this teaching model enables teachers to carry out targeted teaching according to the learning characteristics of undergraduates and effectively improve their English scores.
Feasibility Analysis of Document Legalization Importing Blockchain Technology

Document verification is time-consuming and labor-intensive. In this study, we use a classic document verifying procedure for literature review, data collection, analysis, and interpretation. We use blockchain and NFT technology to analyze the feasibility of document legalization importing blockchain and NFT technology.
Quantization Method Based on Weight Compression and Format Simplification

With the combination of artificial intelligence and the Internet of Things, related technology applications become more diversified. Artificial intelligence is no longer only used in cloud servers as in the past but is available in specific fields. Therefore, many artificial intelligence applications are currently imported into the embedded system architecture. The embedded system has storage space, energy consumption, and computing performance limitations. If the training model is directly embedded in the system, the embedded system does not operate normally. Therefore, we propose a novel quantization algorithm to simplify the data format in the model. At the same time, the location with a lower density is found by using the normal distribution detection in statistics to determine the weight distribution, and the value of the adjacent location replaces that of the location. The results show that even if the data format is modified, the feature does not disappear. In the case of using fewer testing resources, there are prominent features that increase the identification accuracy.
Weighted Inverse Short-Time Fourier Transform and Denoising Filters in the Time-Frequency Plane

The short-time Fourier transform (STFT) is a popular time-frequency analysis method and widely used in acoustics biomedical signal processing and communication. However, since the STFT is a two-dimensional distribution but the original signal is one dimension, the inverse STFT is not a one-to-one operation, which makes the filter in the STFT domain hard to design, especially for the signal whose instantaneous frequency varies fast with time. In this work, we propose a way that applies the weighted sum of the inverse Fourier transform along different vertical lines in the STFT domain to determine the inverse STFT. With the proposed method, the original signal is well reconstructed even ifwe apply the filter in the STFT domain. The proposed algorithm is robust to the variation of instantaneous frequency and the choice of the window. It will be useful for noise removal, signal decomposition, and demodulation.
Intelligent Evaluation Model for Postgraduate English Teaching Effectiveness Based on PSO Algorithm

Based on the analysis and evaluation of the effectiveness of postgraduate English learning, an intelligent evaluation model is proposed. This model applies the PSO algorithm to the achievement analysis system of graduate students. This algorithm analyzes student achievement data in the educational administration system to find out the internal relationship between courses. Based on the result, the educational administration staff can arrange teaching work scientifically. The results show that the method has the best effect on the evaluation of postgraduate English teaching with an average evaluation accuracy of 90%, a short evaluation time, and a test time shorter than 10 ms. This model improves the accuracy and efficiency of the evaluation of postgraduate English teaching and meets the requirements of teaching.
Research on Intelligent Construction of China English Network New Words Database Based on Adjacent Entropy Recognition Algorithm

With the development and popularization of the network, the network plays an important role in people’s daily life, work, and the dissemination of social hot information. Most of the online Chinese English neologisms are widely disseminated through the Internet platform and are known and used by people. New word recognition plays an important role in Chinese word segmentation and information retrieval. With the emergence of a large number of new words in China English, the lack of a China English neologism database has become a major obstacle to the study of China English. New word recognition is a major technical issue in building a corpus. To solve the problem that the internal cohesion of words in the existing point mutual information and low adjacency entropy of new word recognition algorithms, a new word recognition algorithm of China English is proposed. The algorithm also solves the problem of the single threshold setting of point mutual information of the threshold invalid phrases, and the low threshold of new word groups by using point mutual information to identify new words. The experimental results show that, under the same data and experimental environment, the method improves the accuracy, recall, and F values, which is effective and feasible for corpus construction.
Real-time pedestrian detection independent of illumination dedicated for automotive applications

With the rising popularity of autonomous cars, research is exploring the boundaries of what is possible with taking in consideration safety. However, with having a vision based detection system, we arrive at the same limitation of the human eye, which is lighting conditions. Taking that in consideration, an expanded vision spectrum from visible to infrared will allow the detection of the thermal signature of the human body even in low light. In this paper, our contribution is establishing a real time detection of pedestrians both on visible and thermal images by training multiple YOLO models with modified KAIST dataset and comparing their performance for real time applications. Out results concluded that YOLO v5 had the best compromise between speed 31 FPS for 640×40 input image and a missrate of 32.5.
The Impact of Corporate Social Responsibility (CSR) on Firm Performance During the COVID-19 Pandemic

This study investigates the impact of corporate social responsibility's features as predictor variables of firm performance with a sample of firms belonging to financial and industrial sectors in the Muscat Stock Exchange (MSX) in the Sultanate of Oman. This study analysed data for 75 financial and industrial firms. It used annual reports for the physical years 2019 and 2020 to analyze the impact of CSR features on firm performance during COVID-19 pandemic. The present study tested the hypotheses and utilised variables via utilizing the Smart-PLS for data analysis. The results found that CSR has a positive impact on firm performance in the financial and industrial sectors in the Sultanate of Oman.
ANALISIS MATURITY SISTEM INFORMASI MANAJEMEN RUMAH SAKIT DOMAIN SERVICE OPERATION FRAMEWORK ITIL V3

At present the world has entered the 4.0 era where information technology is a major factor in the advancement of agency services and hospitals are one of them. XYZ Hospital is a health agency that provides health services to the community and has a duty to provide comfort and quality in its services. Hospital Management Information System (SIMRS) is a system or information technology that is useful for supporting services in hospitals. In connection with the use of information technology in services at XYZ Hospital, management is needed so that the process of using information technology can be measured. The measurement was carried out to find out how much the SIMRS readiness was in supporting the existing services at the hospital. In this study, analysis of information technology management uses the Information Technology Infrastructure Library (ITIL) version 3 framework in the Service Operation domain. The results of this study are a maturity level value of 3.17 which is at level 3, namely Defined which is obtained from the results of the questionnaire as an instrument and recommendations from measurements that have been carried out and focus on reaching level 4, namely Managed
Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>±</mo> </math> standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real-time decisions.
A Small Target Pedestrian Detection Model Based on Autonomous Driving

Since small-target pedestrians account for a small proportion of pixels in images and lack texture features, the feature information of small-target pedestrians is often ignored in the feature extraction process, leading to reduced accuracy and poor robustness. To improve the accuracy of small-target pedestrian detection and the anti-interference ability of the model, a small-target pedestrian detection model that fuses residual networks and feature pyramids is proposed. First, a residual block with a discard layer is constructed to replace the standard residual block in the residual network structure to reduce the complexity of the model computation process and solve the problems of gradient disappearance and explosion in the deep network. Then, feature selection and feature alignment modules are added to the lateral connection part of the feature pyramid to enhance important pedestrian features in the input image, and the multiscale feature fusion capability of the model is enhanced for small-target pedestrians, thereby improving the detection accuracy of small-target pedestrians and solving the problems of feature misalignment and ignored multiscale features in the feature pyramid network. Finally, a cascaded autofocus query module is proposed to increase the inference speed of the feature pyramid network through focusing and querying, thus improving the performance and efficiency of small-target pedestrian detection. The experimental results show that the proposed model achieves better detection results than previous models.
A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.
Revisiting semiconductor bulk hamiltonians using quantum computers

With the advent of near-term quantum computers, the simulation of properties of solids using quantum algorithms becomes possible. By an adequate description of the system's Hamiltonian, variational methods enable to fetch the band structure and other fundamental properties as transition probabilities. Here, we use k$\cdot$p Hamiltonians to describe semiconductor structures of the III-V family and obtain their band structures using a state vector solver, a probabilistic simulator, and a real noisy-device simulator. The resulting band structures are in good agreement with the ones obtained by direct diagonalization of the Hamiltonian. Simulation times depend on the optimizer, circuit depth, and simulator used. Finally, with the optimized eigenstates, we convey the inter-band absorption probability, demonstrating the possibility of analyzing the fundamental properties of crystalline systems using quantum computers.
Shape-aware fine-grained classification of erythroid cells

Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC .
Afterpulse effects in quantum key distribution without monitoring signal disturbance

The round-robin differential phase shift (RRDPS) quantum key distribution (QKD) protocol is the only one that does not require monitoring of signal disturbance. Moreover, it has been proven that RRDPS has excellent performance of resistance to finite-key effects and high error rate tolerance. However, the existing theories and experiments do not take the afterpulse effects into account, which cannot be neglected in high-speed QKD systems. Here, we propose a tight finite-key analysis with afterpulse effects. The results show that the non-Markovian afterpulse RRDPS model optimizes the system performance considering afterpulse effects. The advantage of RRDPS over decoy-state BB84 under short-time communication still holds at typical values of afterpulse.
Futuristic Approach of 5G in Marketing and Branding Through Entertainment

The fifth-generation wireless technology (5G) is gradually making its presence felt (Petroch, 2021). Though there is a long way to go before it consolidates, yet it definitely brings in a promise for the Marketing and Branding space as it offers a plethora of avenues in discovering a host of advanced creative formats, exploring mobile video capabilities, tracking strategic alliances and evaluating data collection trends and requirements. Brand Managers and Marketing experts therefore look at 5G as an opportunity to transform entertainment with a very futuristic spread that can be ahead of its times with reference to the way entertaining content is created, disseminated, and consumed. Through contemporary practices and use cases, this chapter presents a perspective on how 5G can usher in a revolution in branding and marketing content giving way to hi-tech immersive and interactive consumer experiences.
Electronic and Digital Media; Accountability in the Age of AI and Digitalization: An Indian Perspective

Embracing democracy as a form of governance has opened the doors for peoples’ participation in government. The three well-established constitutional pillars, i.e., legislature, executive, and judiciary, play a pivotal role in a democratic setup. Another pillar, though not included in the constitutional setup, has made its presence felt in the governance of the country. Media is the fourth pillar of democracy which acts as a mechanism of check and balance in the democratic setup. The facets of media, from print, electronic and digital, have acted as the voice and mediator between the administrative machinery and the public at large. A robust media is a sine qua non for a fair, transparent, and accountable democracy. The journey of media in India has witnessed changing dimensions over several decades, from being a mode of conveying information to channelizing mass movements. The impact of media has reached a whole new level with the digital revolution, by reaching out to every household and individual. The infusion of artificial intelligence in the field of digitalization and electronic media has raised a different set of questions. The expanding impact has also raised several questions that need to be taken into consideration i.e., fake news, paid news, and several others to be named. Can media escape accountability on the ground of freedom of speech and expression? The author(s) of the chapter aims to analyze the electronic media regulation applicable in India, with an intent to examine the effectiveness of these regulations. The objective of this research paper is to examine the following research questions: firstly, is the self-regulating mechanism prevalent in India sufficient to make the media accountable?; secondly, is there a need for effective legislation having command of the sovereign to regulate electronic media?; and thirdly, whether regulating media by legislative means will hamper its freedom and credibility? The authors intend to draw a conclusion based on the discussion, taking into consideration the Indian perspective.
Concept of hidden classes in pattern classification

Abstract Our paper presents a novel approach to pattern classification. The general disadvantage of a traditional classifier is in too different behaviour and optimal parameter settings during training on a given pattern set and the following cross-validation. We describe the term critical sensitivity, which means the lowest reached sensitivity for an individual class. This approach ensures a uniform classification quality for individual class classification. Therefore, it prevents outlier classes with terrible results. We focus on the evaluation of critical sensitivity, as a quality criterion. Our proposed classifier eliminates this disadvantage in many cases. Our aim is to present that easily formed hidden classes can significantly contribute to improving the quality of a classifier. Therefore, we decided to propose classifier will have a relatively simple structure. The proposed classifier structure consists of three layers. The first is linear, used for dimensionality reduction. The second layer serves for clustering and forms hidden classes. The third one is the output layer for optimal cluster unioning. For verification of the proposed system results, we use standard datasets. Cross-validation performed on standard datasets showed that our critical sensitivity-based classifier provides comparable sensitivity to reference classifiers.
Small area estimation of average compositions under multivariate nested error regression models

Abstract This paper investigates the small area estimation of population averages of unit-level compositional data. The new methodology transforms the compositions into vectors of $$R^m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:math> and assumes that the vectors follow a multivariate nested error regression model. Empirical best predictors of domain indicators are derived from the fitted model, and their mean squared errors are estimated by parametric bootstrap. The empirical analysis of the behavior of the introduced predictors is investigated by means of simulation experiments. An application to real data from the Spanish household budget survey is given. The target is to estimate the average of proportions of annual household expenditures on food, housing and others, by Spanish provinces.