Reviewerly/exHarmony huggingface dataset:
Inform, Explain, or Control: Techniques to Adjust End-User Performance Expectations for a Conversational Agent Facilitating Group Chat Discussions

A conversational agent (CA) effectively facilitates online group discussions at scale. However, users may have expectations about how well the CA would perform that do not match with the actual performance, compromising technology acceptance. We built a facilitator CA that detects a member who has low contribution during a synchronous group chat discussion and asks the person to participate more. We designed three techniques to set end-user expectations about how accurately the CA identifies an under-contributing member: 1)information: explicitly communicating the accuracy of the detection algorithm, 2)explanation: providing an overview of the algorithm and the data used for the detection, and 3)adjustment: enabling users to gain a feeling of control over the algorithm. We conducted an online experiment with 163 crowdworkers in which each group completed a collaborative decision-making task and experienced one of the techniques. Through surveys and interviews, we found that the explanation technique was the most effective strategy overall as it reduced user embarrassment, increased the perceived intelligence of the CA, and helped users better understand the detection algorithm. In contrast, the information technique reduced members' contributions and the adjustment technique led to a more negative perceived discussion experience. We also discovered that the interactions with other team members diluted the effects of the techniques on users' performance expectations and acceptance of the CA. We discuss implications for better designing expectation-setting techniques for AI-team collaboration such as ways to improve collaborative decision outcomes and quality of contributions.
Framework for Assessment of Bitcoin Price Prediction Using Ensembling Machine Learning Approach

As crypto market knowledge is not widely available to new investors; this problem may lead to wrong investments and financial losses. Inadequate understanding of the elements that have a significant impact on the price of bitcoin might potentially result in poor investing decisions. Now, there are more than $230 billion worth of openly traded cryptocurrencies on the market. The main purpose of Bitcoin, the most valuable cryptocurrency, which also has the best price predictability, is to act as a digital store of wealth. Bitcoin is a digital currency that is used all over the world for advanced payments or mostly for speculation. For instance, Bitcoin is decentralized because no one owns it. Bitcoin exchanges are easy since they are not tied to any one country. This research study employs a variety of machine learning approaches to predict Bitcoin values. Accurate price prediction is crucial for making wise investing decisions nowadays because of its high volatility feature. The price of bitcoin is initially divided into daily and high-frequency categories in this study. A collection of high-dimensional characteristics and fundamental trading features are used, respectively, for its daily and price forecast. Next, we see that while sophisticated machine learning algorithms like SVM accurately forecast pricing, statistical approaches like linear regression do not. The significance of sample dimensions in machine learning techniques is acknowledged in this study on predicting Bitcoin values.
Hybrid Model to Address Software Controlled Arduino Robocar Using Advanced Computing Technique

The cost of fuel is increasing day by day and there is the possibility of depletion of fuel sources in the future. Also, there is an increase in pollution day by day, so the major objective of this project is to manufacture this Arduino Robocar for material handling in various industries. In this study, a robotic vehicle is manufactured which runs like a car. When the bot is loaded with some goods or another material.
Age, Gender and Emotion Recognition by Speech Spectrograms Using Feature Learning

Speech signals are complex and dynamic signals that carry important linguistic information and ideas. The origins of human speech date back to around 300,000 to 500,000 years ago, coinciding with the emergence of anatomically modern humans. Despite the importance of speech in human communication, the accurate classification of the human voice in digital media remains a challenging task in pragmatic applications. To address the previously mentioned challenge, this research paper presents a state-of-the-art technique for identifying various speaker characteristics, including age, gender, and emotion, by analyzing sampled audio. For age and gender classification, eight different algorithms were experimented with to achieve maximum accuracy, where K-nearest neighbor (KNN) performed with the highest accuracy, and for emotion recognition, a simple five-layered CNN module was developed. Relevant features were extracted from the audio samples using different techniques to identify and classify the different speaker characteristics. Multiple datasets were utilized to perform various tasks, including the Common Voice dataset for age and gender classification, and the combination of CREMA-D and RAVDESS datasets for emotion recognition. The system's accuracy is evaluated using test samples, resulting in 79.57% and 93.26% accuracy for age and gender classification respectively, and 98% accuracy for emotion recognition. Overall, the proposed technique aims to contribute to the development of a novel and valuable tool for the audio-based analysis of human characteristics. The system has potential applications in various fields, including healthcare, psychology, and security.
Privacy Preservation in Federated Learning: its Attacks and Defenses

In the context of artificial intelligence, the traditional centralized server-based machine learning model training approaches have significant security and privacy issues. The federated learning (FL) approach was created to address these problems. FL uses a privacy-by-design architecture, in which several devices work together to develop any machine learning system that does not reveal users' personal information under the control of a single server. In federated learning, privacy preservation is achieved through several techniques that involve adding noise, secure aggregation, local training, and model compression. These techniques aid in making sure that raw data is not transmitted over the network and that only the required information is shared to train a global model. This research study has discussed about the federated learning-related privacy threats and defenses. The existing research works are divided into two types as attack methods and corresponding defense mechanism and further analyzed the relevant research papers from the last few years, and then further discussed the research challenges, and finally concluded the research study.
Smart Proctoring System Using AI

From past two years all human activities are affected by COVID19 but where the knowledge gaining and education for students aren't stopped. Due to covid-19 affect many of the educational institutions are providing online degrees and most of the students are actively taking part in advanced online courses. Conducting online examination and proctoring all the students to stop malpractice by a single proctor is a major challenging task for educational institutions. Day by day as the technology is enlarging, with the advanced AI technology we can proctor all the students with less human effort. In this project we lively monitor the activity of test taker like Head pose estimation, object detection and voice recognition. This AI integrated system will prevent the cheating in examination without physical presence of the human proctor.
Improving Library Document Resource Management and Data Sharing Through Semantic Web Technology

This research focuses on the limitations of the original association rule and presents a method for addressing these limitations by dividing the dataset into event sequences. The original association rule has a limited ability to identify references related to a keyword, only focusing on the closest ones, which can lead to incomplete and inaccurate results. The proposed method overcomes this limitation by categorizing different types of references based on the research subject, which enables the identification of relevant references that are not immediately adjacent. The reliability of different categories is calculated based on the event table, and candidate records undergo procedures such as aggregation and screening to eliminate less reliable records. The feasibility of this method is evaluated by comparing it with numerous association rule processes in the marketplace. The results show that the proposed method has a significant advantage over other algorithms, with a data correlation rate that remains stable at 80%. Moreover, this study emphasizes the importance of Linked Data technology in publishing and sharing data online. Linked Data provides a lightweight and incremental data transfer model, which makes it an ideal way to integrate resources into the information flow continuously. Libraries can use this technology to become part of the internet's information resources, thereby facilitating the addition and interoperability of further web resources. The development of Semantic Web technologies allows for easier communication between humans and computers, sharing information perception specifications that greatly facilitate communication between the two parties.
A Tour of a Technical and Educational Campus Using Virtual Reality

Today, in this technical world, where all subjects are changing from traditional approach to digital approach, extended reality (ER) has played an important role in supporting this change. One of the efficient parts of ER is virtual reality (VR). VR can be defined as bringing a virtual world or fictitious world into real life where a user can explore the world in his/her own home space without any interference. VR can be well applied in various fields such as education like viewing solar space in school using VRHeadset in immersive way so that students can understand the concept clearly, construction and industrial regions like designing the building in virtual space before starting the actual development process so that the engineer have proper idea for development without much loss, simulations including training regions, gaming industries for entertainment, tourism, virtual tour, etc. Considering virtual tour as one of the test cases of VR, creating a virtual tour of St. John College of Engineering and Management (SJCEM) campus would be much preferable for visitors so that students can not only get an immersive experience of the campus but also can explore different sections of the campus without any interruptions using VR at their own home space. This study aims to provide a virtual tour of SJCEM campus to a user whose can not only explore the campus but also take an in and out immersive experience.
Study of Natural Language Processing for Sentiment Analysis

The study of opinions, sentiments, assessments, perspectives, and feelings as they are expressed in written language is the focus of sentiment analysis and evaluation mining. It is commonly the subject of text mining, information mining, and web mining because it is one of the most active study fields in natural language processing. Sentiment analysis has been applied in many different contexts, including the study of how events affect unofficial groups and the exploration of presumptions about goods and services. The importance of sentiment analysis is increasing at the same time as virtual entertainment such as surveys, group chats, web blogs, micro sites, Twitter, and interpersonal organizations. In this work, we provide the results of our preliminary studies on twitter sentiment analysis. This study seeks to distinguish subjects from sentiment in tweets. It recognizes the sentiment that refers to the particular problem using Natural Language Processing techniques. Our analysis consists of three main processes, subjective grouping, semantic affiliation, and extreme order, to characterize sentiment. By describing the syntactic relationship between sentiment dictionaries and subject, the study makes use of sentiment vocabularies. Exploratory results demonstrate that the proposed framework performs better than existing message sentiment analysis tools because tweets don't have the same architecture as traditional messages.
Violence Prediction System Using Bi-LSTM

Usage of video surveillance to monitor public places has been rapidly increasing. Traditionally, people use human labor to monitor the provinces. To decrease human intervention, one can use computer vision and machine learning techniques for safety monitoring. Generally, the intensity of an action involving violence can be measured from the accuracy of the system used. This work implements an architecture for classifying a video into violent or non-violent, and identifying the violent scenes using MobileNet V2 and bi-directional LSTM. A dataset of 2000 videos are used to train the model, combining hockey fights, movie fights, and real-time fights. The model achieved an accuracy of up to 95%.
Deep Reinforcement Learning with Pedestrian Trajectory Prediction Model for Service Robot Navigation in Crowded Environments

Safety and effectiveness are crucial considerations in the navigation of robots within crowded environments. Previous research in this field has predominantly focused on an omniscient approach, designing environments with exceptional obstacle avoidance capabilities. However, this approach fails to address the constraints imposed by real-world applications’ limited field of view. Additionally, constructing a robot with a complete field of view can be prohibitively expensive. To tackle these challenges, this paper presents a novel approach that combines a social spatial-temporal graph convolutional neural network (Social-STGCN) with reinforcement learning (RL) to enhance crowd avoidance navigation. This approach offers a more practical and cost-effective solution. Simulations conducted to evaluate our proposed method demonstrate its effectiveness by achieving higher success rates and lower collision rates compared to existing approaches. These results confirm the efficiency and safety of our algorithm for crowd avoidance navigation. In practical applications, we employ the Intel RealSense L515 camera to capture depth and RGB image data, enabling the utilization of the Deep SORT algorithm for pedestrian tracking and recognition. This facilitates the acquisition of accurate position and speed information. To validate the performance of our system, we conducted robot experiments in real-world scenarios, including indoor corridors and elevator lobbies. The results obtained from these experiments clearly indicate that our proposed system successfully accomplishes navigation tasks with improved safety and effectiveness. In summary, this paper introduces an effective crowd avoidance navigation algorithm. By leveraging the Social-STGCN and RL techniques, our algorithm enables robots to navigate tasks with enhanced safety and efficiency, even when their field of view is limited.
Missing Children Face Identification Using Deep Learning Algorithm

A toddler dies in India every 20 seconds; the majority of them are female and come from low socioeconomic backgrounds. The Supreme Court found that no one seemed to care about the children who are not in their homes, citing statistics that show that only thirty percent of them are fortunate to reach their homes. Herein lies the irony. Many of these unruly teenagers sadly find themselves being trafficked into a foreign and dangerous environment that is occasionally located far from home, where they end up working as infants, begging, staying in shelter homes, or being forced into trading. With the active support of the civil community, it is possible to ensure baby safety and address the issue of missing children. The techniques used by parents to report their missing children in a web application with manual and automatic location with resources that lost children rejoin with their families were discussed in the current study. Unlike typical deep learning applications, our technique only uses a convolutional network as high-level feature extraction and lets a trained SVM classifier perform the child recognition. After selecting the most effective Haar classification algorithm model for face recognition and properly training it, a deep learning model that is robust to noise, contrast, image pose, child age, and previous face recognition missing child identification techniques. and the police case of child death is also produced.
Measuring User-Moderator Alignment on r/ChangeMyView

Social media sites like Reddit, Discord, and Clubhouse utilize a community-reliant approach to content moderation. Under this model, volunteer moderators are tasked with setting and enforcing content rules within the platforms' sub-communities. However, few mechanisms exist to ensure that the rules set by moderators reflect the values of their community. Misalignments between users and moderators can be detrimental to community health. Yet little quantitative work has been done to evaluate the prevalence or nature of user-moderator misalignment. Through a survey of 798 users on r/ChangeMyView, we evaluate user-moderator alignment at the level of policy-awareness (does users know what the rules are?), practice-awareness (do users know how the rules are applied?) and policy-/practice-support (do users agree with the rules and how they are applied?). We find that policy-support is high, while practice-support is low -- using a hierarchical Bayesian model we estimate the correlation between community opinion and moderator decisions to range from .14 to .45 across subreddit rules. Surprisingly, these correlations were only slightly higher when users were asked to predict moderator actions, demonstrating low awareness of moderation practices. Our findings demonstrate the need for careful analysis of user-moderator alignment at multiple levels. We argue that future work should focus on building tools to empower communities to conduct these analyses themselves.
About Regular Graphs

Abstract In this article regular graphs, both directed and undirected, are formalized in the Mizar system [7], [2], based on the formalization of graphs as described in [10]. The handshaking lemma is also proven.
Advanced deflectometry methods for industrial application

Deflectometry is a versatile optical testing tool used in various fields, from astronomy to industrial applications, due to its non-null testing capability which facilitates precise measurement despite challenging optical surfaces and system layout constraints. In this manuscript, we present novel variational advancements to traditional deflectometry, towards universal functionality and system friendliness. Traditional dark-field illumination is an inspection technique that is sometimes used to detect particles on a specular surface. Problems arise in its repeatability, as an intensity-based measurement is vulnerably dependent on the testing conditions of time, limiting its ability to be used in automated fashion. The first advancement leverages phase algorithms commonly seen in deflectometry; by adding a secondary light source (normal to the surface) and modulating each source's intensity with a time-varying sinusoid. The phase-based information has a higher sensitivity to the light scattered from a defect producing a more robust computational image process method that is now insensitive to the environment. The second advancement is an alignment method to obtain lower-order shape. While deflectometry proves effective in measuring mid-to-high frequency surface shape, it faces challenges when assessing low-order shape measurements like power, astigmatism, and coma due to relative position and alignment error between the unit under test (UUT) and the deflectometry system. To avert the necessity of additional instruments like a coordinate measuring machine, laser trackers, or interferometers, we leveraged computational fiducials and sensitivity matrices to identify and address misalignments effectively. With enhanced capabilities and system-friendly features, our advanced deflectometry techniques provide powerful options in optical testing. By addressing the challenges in low-order shape measurements and incorporating dark field testing, our approaches extend the potential of deflectometry as a valuable tool in optical metrology across a broad spectrum of industries and scientific endeavors.
Glove-Based Classification of Hand Gestures for Arabic Sign Language Using Faster-CNN

Recently, American Sign Language has been widely researched to help disabled people to communicate with others. However; the Arabic Sign Language “ASL” has received much less attention. This paper has proposed a smart glove which has been designed using flex sensors to collect a dataset about hand gestures applying ASL. The dataset is composed of resistance and voltage measurements for the bending of the fingers to represent alpha-numeric characters. The measurements are manipulated using normalization and zero referencing methods to create the dataset. A Convolutional Neural Network ‘CNN’ composed of twenty-one layers is proposed. The dataset is used to train the CNN, and the Accuracy and Loss parameters are used to characterize its success. The dataset is classified with an average success rate of 95% based on the classification accuracy. Loss has decreased from 3 to less than 0.5. The proposed CNN layers have classified ASL characters with a reasonable degree of accuracy.
Computed tomography images preliminary processing for their analysis by artificial intelligence methods

In this paper we consider method, proposed by the authors, for pre-processing X-ray images based on the convolutional neural network. Multilayer computed tomography scans of the lungs are ubiquitous, used to detect pathology, such as covid-19 diseases. The proposed method consists of computed tomography lung image segmentation, the volume of interest localization at these scans, distinguishing between denser structures, such as bones, and less dense ones, such as blood vessels. A comparison of the pathological formations classification quality was made on images with different degrees of preprocessing on large data sets. Image Classifier software was developed for demonstration of x-rays preliminary processing opportunities in the context of their class association forecasting. The developed software showed quite good results. Maximum value of the average probability reached 77.5%. The number of epochs required to achieve the specified quality level is four.