Reviewerly/exHarmony huggingface dataset:
Design and Analysis of MEMS Microphone Using Helical Spring

This paper shows an improved version of microphone that is having good sensitivity towards sound pressure, so that any small sound pressure can be observed and converted to useful signal. It uses a very emerging technology in the field of electronics i.e. MEMS (Micro Electro Mechanical Systems).It also feasible to fabricate these microphones because they can be fabricated in the same manner as the integrated circuit (ICs) are. The sensitivity improvement is established using helical spring type structures connected with its diaphragms that is made of steel which have good tensile strength and resistant to break on applying high frequency or pressure of sound signals. It shows quite good improvement over other microphones made by other conventional method.
Design and research of fire safety simulation based on computer simulation

The frequent occurrence of major fire accidents makes the fire safety education become the key work of the current social and school safety education. The traditional fire safety education mainly adopts the plane and warning education mode, and lacks the three-dimensional and immersive safety teaching mode. Computer simulation technology can create an experience and interactive system by simulating the virtual fire environment, and "move" the traditional media fire publicity and fire drill to the simulation environment in an interactive, immersive and imaginative way. It is proposed to design simulation teaching and simulated escape based on the construction of fire-fighting facilities, the construction and Simulation of real environment, particle special effects and interactive realization, so as to transform fire safety education into effective action force and simulation interaction in the simulation environment. Optimize the existing safety education mode. Reduce the risk of safety drill and improve the immersion of safety education. Improve the efficiency of safety education in the form of vivid and three-dimensional simulation.
Failure Prediction Approach in Agile Software Development

Software failure prediction is an important activity during agile software development as it can help managers to identify the failure modules. Thus, it can reduce the test time, cost and assign testing resources efficiently. RapidMiner Studio9.4 has been used to perform all the required steps from preparing the primary data to visualizing the results and evaluating the outputs, as well as verifying and improving them in a unified environment. Two datasets are used in this work, the results for the first one indicate that the percentage of failure to predict the time used in the test is for all 181 rows, for all test times recorded, is 3% for Mean time between failures (MTBF). Whereas, SVM achieved a 97% success in predicting compared to previous work whose results indicated that the use of Administrative Delay Time (ADT) achieved a statistically significant overall success rate of 93.5%. At the same time, the second dataset result indicates that the percentage of failure to predict the time used is 1.5% for MTBF, SVM achieved 98.5% prediction.
Enhancing Low-Energy Facial Recognition Devices and Abilities To Improve Security: A Practical Study

As facial recognition (FR) continues to advance, so too has the need for its applications, especially in the realm of security. However, despite FR’s sophistication, issues remain: accuracy, the amount of energy needed to run FR technology, the need for FR on mobile devices, and the speed and efficiency of FR, especially when applied to the identification and tracking of cyber criminals and terrorists. In this paper, we address these concerns and present an approach to detect and identify a human face by tracking an image and comparing it with other image data. We used the adaptable, easy-to-use, and inexpensive Raspberry Pi Zero, a device that can scrape data off the web quickly and efficiently, and then identify a specifically targeted individual. We believe our approach can greatly enhance the effectiveness of FR for applications such as border security, providing on-the-ground agents with real-time data and mobile devices that are low cost, energy efficient, and convenient.
Identification of Implicit Subject Categories Responsible for Academic Test Scores Using Multiple Linear Regression

In this work, multiple linear regression technique is employed to study the implicit relation between fundamental categories of a academic topic to the performance of the student. Such a problem is important in the development of personalised learning system. Initially a matrix representation of test questions with its fundamental categories is discussed. Subsequently the responses of the test questions are related to the fundamental categories using a system of linear equations. Multiple Linear Regression (MLR) technique is employed to study the effect of each fundamental categories on the test scores. A synthesised data-set of known probability distributions is used during evaluations. It has been found that the MLR using F-Statistics can identify the complexity of fundamental categories and useful for recommending questions in a personalised learning system.
A Hypotheses-Driven Framework for Human-Machine Expertise Process

Hypotheses-driven methodology is a widely used approach in expertise process to solve problems under conditions of limited knowledge and understanding. Although this approach has been standardized, it lacks tools to assist experts in exploring and tracking all possible causes of a problem or to capture their reasoning process and knowledge. In order to acquire, share and reuse experts’ knowledge applied during this process while assisting them in finding the causes of a problem, we introduce in this paper a framework that formalizes experts’ knowledge and methodology for resolving problems collectively using hypotheses. The framework is based on Hypothesis Theory extended with qualitative doubt and a systematic reasoning process over hypotheses exploratory graph. On the one hand, the proposed approach allows experts’ collaboration and their interaction with machine and on the other hand, it will be easier to model expertise process with very complex activities while the knowledge and steps followed during the expertise are stored. Furthermore, an experiment conducted on a case of expertise process verifies the feasibility and effectiveness of the approach.
Three N-grams Based Language Model for Auto-correction of Speech Recognition Errors

Some applications of Automatic Speech Recognition (ASR) can cause errors, especially when an environment of speech contains noise. This is one of the major barriers to the widespread adoption of speech recognition, especially for applications with a large dictionary. The persistence of ASR errors has increased the need for new techniques to automatically correct these errors. In this paper, a novel three n-grams based language model that uses Google n-gram files was designed. The proposed method performed auto-detection and correction of errors in ASR output text. It consists of two parts. The first part explains the steps for designing and building a language model database using data from Google n-gram files, while the second part shows how to use the context information of the first part in ASR troubleshooting. In addition, this method includes multi-pass filtering which leads to faster processing time and improved computation efficiency. Each pass acts as an error filter. Experimental results showed the success of using this method in reducing the rate of speech errors in the ASR text. It achieved a 15.71% relative decrease against the best Word Error Rate for the comparative methods.
Face Mask Detection In The Covid-19 Pandemic Era by Implementing Convolutional Neural Network and Pre-Trained CNN Models

The Coronavirus or Covid-19 has spread widely throughout the world since the beginning of 2020. WHO provides basic guidance in preventing the spread of the virus that can be done by the community. One of them is the use of masks when doing activities outside the home. Lack of awareness in mask usage become the obstacle in the process of efforts to prevent the spread of covid 19. The aim of this research is to develop a face mask detection model by implementing the convolutional neural network and pre trained CNN algorithm. The accuracy of the proposed models in training process, the accuracy of CNN, VGG16, and VGG19 are 97.79%, 99.87% and 100%, respectively. The proposed models evaluated using confusion matrix using testing datasets given.
Computer Vision Based Pre-processing of Microscopic Images

Preparing the microscopic images for analyses is a crucial step for any kind of image processing task. In this paper, the methods for analyzing and processing of biomedical images are presented. The main task is to select the appropriate object images of interest for further measurement of their morphological parameters. In the article, several methods are presented such as contrast adjustment, intensity adjustment, histogram equalization, morphological operation and background subtraction that can be used in pre-processing of microscopic images.
Developing an Implementation Framework for Automated Customer Support Service in Collaborative Customer Relationship Management Systems

Collaborative Customer Relationship Management (CCRM) has developed Automated Customer Support Services (ACSS), where it focuses on providing more efficient and immediate customer service. Through chatbots, virtual customers, internet routing, and automated responses, technology has evolved to aid the customer support sector through automations trained by Artificial Intelligence (AI), Machine Learning (ML), and other advancements in technology. However, ACSS is relatively new with various implementation frameworks in choosing ACSS platforms developed by CRM experts for organizations. The study aims to cover the research gaps of integrating the customer perspective in terms of behavioral trends, data security issues, engagement and responses, and proper maintenance and evaluation of the ACSS performance based on the customer relationships and experience, through the development of a new implementation framework for ACSS in an organization. Through a rating-questionnaire answered by CRM experts on three (3) different ACSS based on different frameworks and the developed one by the study, the findings show that the developed framework enhances customer relationships and experiences more than the existing frameworks, thereby validating the effectiveness of the implementation framework in the study.
Obtaining Labels for In-the-Wild Studies: Using Visual Cues and Recall

The observer effect found in laboratory studies has long posed a problem for researchers.In-the-wild studies reduce the observer effect, but have problems with gathering accurately labeled data usable for training algorithms.Manual labeling is time-consuming, obtrusive, and unfeasible, and if done by the researchers, it potentially violates the privacy of the participants.In this article, we present a labeling workflow based on an in-the-wild study that investigated cognitive state changes through eye-gaze in naturalistic settings.We contribute a setup that enables participants to label their data unobtrusively and quickly.We use J!NS MEME electrooculography glasses, Narrative Clip 2 wearable cameras, and a proprietary data tagging software package.Our setup is reproducible for field studies, preserves data integrity, and maintains participant privacy.This workflow can be extended to other studies in pervasive and ubiquitous computing and is especially suitable for deployment in the pandemic and postpandemic world.
TreasAR Hunt – Location-Based Treasure Hunting Application in Augmented Reality for Mobile Devices

TreasAR Hunt is a mobile application that blends together Augmented Reality and location tracking methods.It addresses to both technical and non-technical users.The application comes to enhance the learning process by creating an immersive environment that keeps the user interested and engaged.Thanks to the type of game it models, a treasure hunt, and to the fact that it involves interaction between the user and its surroundings, the application can also be used in tourism, as a city guide.In addition to that, it can be seen as a team-building tool, that dares players to collaborate.This paper describes the project's objectives, its use cases, core functionalities and development phases.User testing results are provided and interpreted at the end, so to conclude how does the application fit in the stated scopes.
A Tentative Exploration for the Design of Intelligent Household Furniture

Nowadays, the intelligent technology is widely developed. As a major category of industrial design products, the design and manufacture of furniture has gradually innovated from the traditional type to the direction of intelligence. Based on researching the existing intelligent products, the paper briefly introduced and classified the concept to the intelligent furniture and its industry status. This paper mainly summarizes the composition, technical support, design principles and the development of intelligent furniture. Through this study, the author can provide new design ideas for the designers of intelligent furniture.
Combining Pretrained and Graph Models for Text Classification

Large-scale pretrained models have led to a series of breakthroughs in Text classification. However, Lack of global structure information limits the performance of pertrained models. In this paper, we propose a novel network named BertCA, which employs Bert, Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to handle the task of text classification simultaneously. It aims to learn a rich sentence representation involved semantic representation, global structure information and neighborhood nodes features. In this way, we are able to leverage the complementary strengths of pretrained models and graph models. Experimental results on R8, R52, Ohsumed and MR benchmark datasets show that our model obtains significant performance improvement and achieves the state-of-the-art results in four benchmark datasets.
Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.
Radio monitoring knowledge acquisition based on rules

The radio monitoring knowledge acquisition is one of the important theoretical foundations of knowledge-driven radio regulatory. Adopting the idea of edge computing, seven kinds of radio monitoring knowledge acquisition algorithms based on spectrum engineering and rules were proposed in this paper. By means of studying the method which acquiring multi-dimensional data of radio signals with spectrum sensors, the electromagnetic environment, radio services application status, quantity changes of radio signals in designated directions and other radio monitoring knowledge were obtained. It is very conducive for building the complete radio regulatory knowledge system and is of great practical significance for intelligent radio regulation exploring.
Detection of Trajectory Outliers in Intelligent Transportation Systems

In this paper, we provide a technique for identifying outliers based on embedding trajectory deviation points and deep clustering. We begin by constructing the network topology and the neighbors of the nodes to create a structural embedding while capturing the interactions of the nodes. We then develop a strategy to determine the hidden representation of distraction points in the road network topology. To create a collection of sequences from a hierarchical multilayer network, a biased random walk is used. This sequence is used to fine tune the embedding of the nodes. The trip embedding was then determined by averaging the node embedding values. Finally, the embeddings are clustered using an LSTM-based pairwise classification strategy based on similarity metrics. The experimental results show that compared to the generic techniques Node2Vec and Struct2Vec, the proposed embedding learning trajectory captures the structural identity and improves the F-measure by 5.06% and 2.4%, respectively.
Image stitching method based on image edge detection and SIFT algorithm

In response to the problem of the image matching algorithm during image splicing, the problem of poor real-time performance is proposed, and an image splicing algorithm based on image edge detection and SIFT algorithm is proposed. The algorithm first uses the image edge detection algorithm to weaken the pixel point of the area of the region in the image, the pixel points of the image in the image, and enhance the feature point of the matching degree, and use the SIFT algorithm to search on the strong feature point area to find out the image. Match feature points and achieve rapid accurate registration. Then, the splicing between images having overlapping regions is achieved by performing geometric correction and image fusion. The three sets of splicing images were carried out by using the proposed method. The experimental results show that the improvement method of this paper overcomes the problem of high computational complexity in image splicing compared to standard splicing methods, with a certain value of application in actual image stitching.
Recommendation Systems Based on Textual Document Analysis

IN general, recommendation systems are defined as techniques used to predict an individual assessment that will provide an item or social entities. These items can be books, movies, restaurants, and things to which individuals have different preferences. These systems help users to decide on the appropriate items and facilitate the task of finding a favorite collection items. The aim of this project is to create a system of recommendation based on textual documents, exactly will create an application making the recommendation of the mentor and the committee automatically based on the proposal of the topic and documents that are in the archive database. The algorithms that we have used are Rake which used to extract the keyword through textual documents and Leveshtein distance algorithm. This algorithm used to derive the similarity between documents based on keyword extraction that have the textual documents. Initially, the student must apply the system to be registered on the database, after applying the system extract the keywords from the document applied and compares with the proposals of the topics that are in the archive of data base and in the end brings us automatically appropriate mentor for the topic and the commission.
Elevators as media objects manipulating information in time

We investigate elevators as media. Our central argument is that elevators manipulate information in time. Time manipulation of elevators (movement data + genetic algorithms) produces temporal order. Elevators have become media objects because they produce data that are digitally manipulated to optimize movement. We conducted an empirical study in a multinational corporation that manufactures elevators, including 4 months of field research at multiple locations and interviewed 64 people. We show how time manipulation changes with the information architecture: first, time manipulation took place inside and during the movement of elevators by pushing the buttons. Second, time manipulation took place in the cloud by statistical mathematics. The latest development is toward decentralized social application where elevators as independent media objects manipulate time using genetic algorithms and communicate with each other. We reveal how largely hidden media affects our temporality and argue that media theory should study its implications in contemporary society.
The Propaganda Machine: Generating Biased Reports about Risk Games

In this work we present a system that generates reports for a game of Risk. These reports, far from being “neutral”, aim instead at mimicking propaganda, and try to influence the opinions of the readers about the performance of a Risk player. The system, while limited in scope, was able to persuade some test subjects in a qualitative evaluation, hinting at the abilities that a more sophisticated system might have.
Exploring Effective Speech Representation via ASR for High-Quality End-to-End Multispeaker TTS

The quality of multispeaker text-to-speech (TTS) is composed of speech naturalness and speaker similarity. The current multispeaker TTS based on speaker embeddings extracted by speaker verification (SV) or speaker recognition (SR) models has made significant progress in speaker similarity of synthesized speech. SV/SR tasks build the speaker space based on the differences between speakers in the training set and thus extract speaker embeddings that can improve speaker similarity; however, they deteriorate the naturalness of synthetic speech since such embeddings lost speech dynamics to some extent. Unlike SV/SR-based systems, the automatic speech recognition (ASR) encoder outputs contain relatively complete speech information, such as speaker information, timbre, and prosody. Therefore, we propose an ASR-based synthesis framework to extract speech embeddings using an ASR encoder to improve multispeaker TTS quality, especially for speech naturalness. To enable the ASR system to learn the speaker characteristics better, we explicitly feed the speaker-id to the training label. The experimental results show that the speech embeddings extracted by the proposed method have good speaker characteristics and beneficial acoustic information for speech naturalness. The proposed method significantly improves the naturalness and similarity of multispeaker TTS.