Reviewerly/exHarmony huggingface dataset:
Scalable Privacy Preservation in Big Data

In the realm of data analytics, big data has welcomed a revolution. Data that was abandoned a few years ago is now seen as a valuable resource. Big data is increasingly widely employed across all aspects of society for knowledge extraction. It is generated by practically all digitization, and it is saved and transmitted over the internet. This dependence on the web approach for massive data raises severe security problems. Due to its vast volume, diversity, and volume, traditional security measures cannot be used to big data. Privacy is also a huge security nightmare since a large dataset appears to contain confidential details. Traditional privacy preservation approaches are utilized to solve privacy difficulties in Big Data, and K-anonymity is the most often used strategy for protecting privacy for data disclosure.
Augmented Reality Based Interior Design Application

Augmented Reality (AR) is an advanced version of the real-world visual acuity, acquired through the use of digital visuals, sounds, or other technically motivated sensors. This is a growing trend among companies, especially those involved in mobile computing and business applications. Simply put, it create a virtual object and display it in 2-d dimension. Our report is based on Augmented reality came into picture when there is need of furniture and you cannot decide how it may look like when you bought this at home .Our app is very simple to use, it is user friendly. It will create a object in phone similar to the furniture. And display it in your phone. User may experience real time virtual Object and minimize the problem of returning, ones bought.
Implementation of Social Media Site for College

In today’s life, social networking sites has become one of the most popular Internet applications and has a large number of users. The public social networking systems have meet the communication demand of people, there is insufficient assistance to get connected to the users learning, professional life, and cultural life on campus with their real life activities. We have seen in the pandemic that college struggled lot to get connect with their students, so this website will give solution if something like pandemic ever happen again. Also colleges always rely on the platform such as whatsapp to connect with their student online but on such platform only limited featurs are given. Therefore, this project design made a new campus social networking site for users, which is very closely connected to the user requirements of real environment of campus and users real activities. It will also give a platform to college where college can add as many features as they want according to the requirement. In this paper, a design and implementation of social media site for college campus is achieved. This website enables interaction of all the users like students, teachers, professors etc. It has all the features the user can expect plus more exciting and useful additional features which will help users in their college life. It has been implemented using ReactJs, MongoDb, CSS, Nodejs. Details of the implementation are below in this paper.
Benchmark Library Metrics and Trends

Per the vendor, Benchmark: Library Metrics and Trends “provides libraries with data visualizations that facilitate comparisons with customized peer groups, Carnegie classes, and nationwide data.” Navigating this database can be somewhat difficult, as it has no starting point that will be obvious to the user. It takes some time to click on the various tabs and determine what functionality is available and how to use it. Therefore, a new user might benefit from instruction in the use of this database from either the vendor or someone else familiar with it. Despite these complexities, this resource offers its users a broad and varied range of data on the institutions covered therein. This includes total expenditures, circulation, information services, FTE staff, collections institutional repositories, Interlibrary Loan, group presentations, hours opens per week, weeks effected by COVID 19, student enrollment, gate-count, and materials and operations expenditures. The last of these includes total salary expenditures for all FTE librarians and other paid staff, as well as student assistants, but doesn't include salary information for specific positions.There are two levels of access to this database, the first of which is free! It allows academic libraries to view and complete open ACRL surveys and see their responses to previous surveys, among other things. The second level of access is via subscription. It gives libraries access to all of the free features, plus a custom report builder, interactive data dashboards, and the means to generate custom peer groups. The cost of the subscription portion of this database depends on whether the library subscribing is an academic or company/corporate one, and/or is part of a consortia. There are also discounts available for ACRL members and libraries that have participated in the database's 2020 survey. Consequently, the subscription price can range from an annual cost of $3,000 (for consortia) to $428 (for academic libraries taking advantage of all available discounts). Overall, most libraries will find this pricing quite reasonable. However, it's advisable that potential subscribers contact the vendor for a price quote specific to their institution. The licensing agreement for this database isn't particularly long and is in many ways average in its composition, so it presents little reason for concern.
Dynamic Gaussian Embedding of Authors

Authors publish documents in a dynamic manner. Their topic of interest and writing style might shift over time. Tasks such as author classification, author identification or link prediction are difficult to solve in such complex data settings. We propose a new representation learning model, DGEA (for Dynamic Gaussian Embedding of Authors), that is more suited to solve these tasks by capturing this temporal evolution. We formulate a general embedding framework: author representation at time t is a Gaussian distribution that leverages pre-trained document vectors, and that depends on the publications observed until t. The representations should retain some form of multi-topic information and temporal smoothness. We propose two models that fit into this framework. The first one, K-DGEA, uses a first order Markov model optimized with an Expectation Maximization Algorithm with Kalman Equations. The second, R-DGEA, makes use of a Recurrent Neural Network to model the time dependence. We evaluate our method on several quantitative tasks: author identification, classification, and co-authorship prediction, on two datasets written in English. In addition, our model is language agnostic since it only requires pre-trained document embeddings. It outperforms existing baselines by up to 18% on an author classification task on a news articles dataset.
Irregularity Index for Vector-Valued Morphological Operators

Mathematical morphology is a valuable theory of nonlinear operators widely used for image processing and analysis. Although initially conceived for binary images, mathematical morphology has been successfully extended to vector-valued images using several approaches. Vector-valued morphological operators based on total orders are particularly promising because they circumvent the problem of false colors. On the downside, they often introduce irregularities in the output image. This paper proposes measuring the irregularity of a vector-valued morphological operator by the relative gap between the generalized sum of pixel-wise distances and the Wasserstein metric. Apart from introducing a measure of the irregularity, referred to as the irregularity index, this paper also addresses its computational implementation. Precisely, we distinguish between the ideal global and the practical local irregularity indexes. The local irregularity index, which can be computed more quickly by aggregating values of local windows, yields a lower bound for the global irregularity index. Computational experiments with natural images illustrate the effectiveness of the proposed irregularity indexes.
“Gamer Citizens”: Emojis as Civic Duty in a Circuit of Visual Culture

This paper is a practical and theoretical look at the notion of “emojis as civic duty.” I frame the discussion in terms of an “emoji code” that goes beyond an evolution of natural languages to integrate more fundamentally into specific experiences, particular communities, and a networked regime of images. I introduce Stuart Hall’s “circuit of culture” as an alternative theoretical frame to prevailing linguistic research that investigates only the form and function of emojis. Hall endures because he helps us to recognize a politics of the image beyond form and function, signaling an interplay between consumption and production through live-streaming media. I make theoretical connections to others who have thought about media as essentially political communication. Finally, I offer a preliminary analysis of “emojis as civic duty” in the case of an esports champion. I conclude by suggesting where future study is necessary for a more robust notion of a “Gamer Citizen” to take shape.
Human Pose Estimation in 3D using heatmaps

3D human pose estimation involves estimating human joint locations in 3D directly from 2D camera images. The estimation model would have to estimate the depth information directly from the 2D images. We explore two methods in this paper both of which represent human pose as a heatmap. The first one follows (Newell et al. [6]) and (Martinez et al. [7]) where we predict 2D poses and then lift these 2D poses to 3D. The second approach is inspired by (Pavlakos et al. [8]) and involves learning 3D pose directly from the 2D images. We observe that while both these approaches work well, the mean of both their predictions gives us the best mean per-joint prediction error (MPJPE) score.
Automatic Video Surveillance System for Pedestrian Crossing Using Machine Learning

Nowadays accidents became very common in this world and the majority of accidents are from the crosswalks. The accidents are caused due to the absence of traffic lights to lead the traffic structure and people. Even though the traffic flow is huge by the traditional traffic accessories has a very particular warning capabilities which is incapable of to satisfy the safety requirements. Like a driver is unable to divine there will be pedestrian crossing the pathway forward, and pedestrians lack of traffic security alertness, which may easily cause of accidents. So, to overcome this issue the video surveillance helps the crippled persons to cross the pedestrians in a riskless way and it helps for independent vehicles. The work recommended an emerging technology to accommodate the crippled persons to cross the pathway just about the traffic signal and it also supports to someone who walks at a leisurely pace with the help of video surveillance. Here the image processing analysis algorithm takes a very crucial role to trace the motion of the object in the certain pedestrian crossing which can accommodate or serve the crippled persons or any senior citizens. They are numerous morphological filtering operations will improve the distinction of the motion of the person in the video. The proposed work improves more accuracy than the previous accuracy methods and also in addition of this we are implementing the crowd detection feature on the pedestrian, whenever the crowd was more in pedestrians.
Renewable energy trading mechanism based on blockchain technology

In order to accelerate the establishment of a market system and long-term mechanism to promote renewable energy production and consumption, renewable energy trading (RET) has been proposed. In this study, we consider a blockchainbased renewable energy trading scheme and try to analyze the trading mechanism. Blockchain may guarantee the security of propagated information and help with the rapid and automatic trading matching through smart contract. In summary, blockchain-based RET owns the properties of decentralization, immutability, traceability and automation compare to traditional RET. In addition, results of cases show that the rule that switching the trading mechanism when ratio of supply and demand (RSD) exceeds 1.0 should be added in smart contract in order to get more profit.
Chassis dynamometer system based on driving robot

Abstract. Aiming at the problems of low automation level and single test object of traditional chassis dynamometer, this paper designs the driving robot system and introduces its working process to meet the performance requirements of chassis dynamometer driving robot, so as to realize the test vehicle under the given target test conditions. of autonomous driving. Taking automobiles as the main test objects, while taking into account new energy vehicles and agricultural tractors, a digital vehicle chassis dynamometer system is constructed based on rapid prototypes. The test shows that the sliding distance error between the chassis dynamometer test based on the driving robot and the actual road sliding test is 0.7%, which meets the test requirements and verifies the rationality of the digital chassis dynamometer system constructed in this paper and the necessity of driving robot.
Didactic Word Games As a Means of Developing Communicative Universal Educational Activities in Younger Schoolchildren

The article discusses the problem of implementing the requirements of the Federal State Educational Standard of Primary General Education for the formation of universal educational activities for younger students. The influence of didactic word games on the formation of the ability of younger students to communicate productively with peers is considered: to raise questions, discuss joint actions, take into account the characteristics and interests of partners in the game. Examples of didactic word games are given that contribute to the formation of three types of communication: interaction with a partner; taking into account the position of the interlocutor or partner in activities; communication aimed at ensuring cooperation.
An Analysis of Semantically-Aligned Speech-Text Embeddings

Embeddings play an important role in end-to-end solutions for multi-modal language processing problems. Although there has been some effort to understand the properties of single-modality embedding spaces, particularly that of text, their cross-modal counterparts are less understood. In this work, we study some intrinsic properties of a joint speech-text embedding space, constructed by minimizing the distance between paired utterance and transcription inputs in a teacher-student model setup, that are informative for several prominent use cases. We found that incorporating automatic speech recognition through both pretraining and multitask scenarios aid semantic alignment significantly, resulting in more tightly coupled embeddings. To analyse cross-modal embeddings we utilise a quantitative retrieval accuracy metric for semantic alignment, zero-shot classification for generalisability, and probing of the encoders to observe the extent of knowledge transfer from one modality to another.
Recognizing defects in surface layer of bearings by using fractal analysis of eddy current signal

Bearing manufacturing requires strict adherence to the technological standards and quality control of products at all stages of processing. One of the most accurate and environmentally friendly quality monitoring methods in bearing production is the eddy current method. When using this method, a large array of numerical data is generated that reflects information on the physical and mechanical conditions of the surface. Thus, defect detection process is reduced to defining the task of data segmentation, that is, selection of parts of an array with different dynamics, which is rather difficult for an operator. To solve this problem, a pattern recognition system is proposed, the elements &#x0D; of which are an alphabet of classes (defects of the surface layer), a dictionary of features (fractal dimension of a data array), rules for decision making on recognition (method of standards). Fractal analysis is applied to a two-dimensional array consisting of several data vectors, which will increase the degree of automation of the process &#x0D; of searching and identifying sensor readings indicating a defect, since the defect affects the surface layer of the product not only in the area of its greatest prevalence. Three methods of calculating the fractal dimension of a two-dimensional array are presented. Application of the reference method of the theory of pattern recognition is substantiated, an analytical expression that reflects the method is presented. According to the proposed solutions, it is possible to classify the defect of the rolling surface of the bearing rings by preliminary calculating the fractal dimension for each defect. Implementation of the software developed by using this method can improve the efficiency of quality control in bearing manufacturing and update the technological process.
Analysis of factors influencing college students' satisfaction with their departments using logistic regression analysis method and random forest method.

The purpose of this study is to provide basic research data for college students' career guidance and policy and system establishment related to dropout prevention by analyzing major factors affecting college students' satisfaction with their departments by using machine learning analysis methods. For this purpose, 1,298 four-year college students from the 􍾧Korean Education & Employment Panel 􎟯(KEEP􎟯)' data were analyzed through logistic regression analysis and random forest analysis method, which are machine learning analysis methods. The main analysis results are as follows. First, in the year of college admission, explanatory variables related to high school enrollment period and career plan after high school graduation, in addition to variables related to college life, accounted for a significant proportion of the top 10 items of importance. In the period excluding the year of admission and the year immediately before graduation, variables related to major learning and career activities were important variables. In the year immediately before graduation, activity variables such as job preparation and education and training experience recorded high importance in both logistic regression analysis and random forest analysis results. Second, according to the two analysis methods, the agreement of the top 10 variables by grade level was 63.3%. Third, unlike logistic regression analysis, in random forest analysis, the explanatory variables answered by the survey respondents using multiple scales were included in the top 10 explanatory variables of importance in relatively many cases. This study is significant in that it attempted to compare the results by deriving common factors using two machine learning methods rather than a single analysis method for the educational panel data.
Integrating Prior Knowledge in Post-hoc Explanations

In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model. Integrating prior knowledge into such interpretability methods aims at improving the explanation understandability and allowing for personalised explanations adapted to each user. In this paper, we propose to define a cost function that explicitly integrates prior knowledge into the interpretability objectives: we present a general framework for the optimization problem of post-hoc interpretability methods, and show that user knowledge can thus be integrated to any method by adding a compatibility term in the cost function. We instantiate the proposed formalization in the case of counterfactual explanations and propose a new interpretability method called Knowledge Integration in Counterfactual Explanation (KICE) to optimize it. The paper performs an experimental study on several benchmark data sets to characterize the counterfactual instances generated by KICE, as compared to reference methods.
Automatic Rainwater Sensing Umbrella

In today's world, robotics and artificial intelligence algorithms are replacing many manual works and the entire world is becoming fully automatic and autonomous in nature. In the concept of machine learning, the data sets are initially gets trained to the microcontroller regarding the threshold value, the nature of data etc. and hence the microcontroller unit understands the input data sets and it automatically takes decision with the help of artificial intelligence decision making algorithm whenever the same kind of data that enters in to it as a input values as they are trained earlier. This type of concept is called machine learning and artificial intelligence. (Abstract)
Disambiguation of morpho-syntactic features of African American English – the case of habitual be

Recent research has highlighted that natural language processing (NLP) systems exhibit a bias against African American speakers.The bias errors are often caused by poor representation of linguistic features unique to African American English (AAE), due to the relatively low probability of occurrence of many such features in training data.We present a workflow to overcome such bias in the case of habitual "be".Habitual "be" is isomorphic, and therefore ambiguous, with other forms of "be" found in both AAE and other varieties of English.This creates a clear challenge for bias in NLP technologies.To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances.With this balanced corpus, we train unbiased machine learning classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving .65 F 1 score disambiguating habitual "be".
Personalized wearable interactions with WearSkill

WearSkill is our web-based application for personalized input with wearables that acts as a middleware to control connected devices that support Wi-Fi and WebSocket communications, e.g., control the smart lighting system with the smartwatch. WearSkill primarily addresses users with upper-body motor impairments, for whom it enables personalized recommendations for input modalities (touch, motion, and voice) and three categories of wearables (smartwatches, rings, and glasses) based on users' self-reported motor impairments.
Geological and mineralogical aspects of the Skorpion non-sulphide zinc deposit, Namibia

The Skorpion zinc deposit, which is situated in the southern part of Namibia, is characterised by its non-sulphide ore paragenesis. The ore-mineral assemblage is hosted by Late Proterozoic clastic metasediments, which have been overprinted by greenschist metamorphism. Sauconite, hemimorphite and smith-sonite make up the main ore minerals. XRF and ICP analyses show significantly different distribution patterns of major, trace and RE elements in both, ores and wall rocks.