Reviewerly/exHarmony huggingface dataset:
AI Virtual Mouse Using Hand Gesture Recognition

Abstract: The PC mouse is one of the wondrous developments of people in the field of Human-Computer Interaction (HCI) innovation. In new age of innovation, remote mouse or a contact less mouse actually utilizes gadgets and isn't liberated from gadgets completely, since it utilizes power from the gadget or might be from outside power sources like battery and gain space and electric power, likewise during COVID pandemic it is encouraged to make social separating and keep away from to contact things which gave by various people groups. Inside the projected AI virtual mouse utilizing hand signal framework, this constraint might be resolve by involving advanced camera or sacred camera for perceive the hand motions and fingers recognition abuse PC machine vision. The algorithmic rule used in the framework utilizes the man-made consciousness and AI algorithmic rule. Upheld the hand signals, the gadget might be controlled pretty much and might do left click, right snap, looking over capacities, and PC gadget pointer perform while not the utilization of the genuine mouse. Index Terms: deep learning base computer vision, real time mouse system, Media-pipe , HCI
GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm

Under an internet background involving artificial intelligence and big data—unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific business scenarios. One problem that urgently needs to be solved in the industry involves how to perform feature extractions, transformations, and operations in graph-structured data to solve downstream tasks, such as node classifications and graph classifications in actual business scenarios. Therefore, this paper proposes a gated recursion-based graph neural network (GR-GNN) algorithm to solve tasks such as node depth-dependent feature extractions and node classifications for graph-structured data. The GRU neural network unit was used to complete the node classification task and, thereby, construct the GR-GNN model. In order to verify the accuracy, effectiveness, and superiority of the algorithm on the open datasets Cora, CiteseerX, and PubMed, the algorithm was used to compare the operation results with the classical graph neural network baseline algorithms GCN, GAT, and GraphSAGE, respectively. The experimental results show that, on the validation set, the accuracy and target loss of the GR-GNN algorithm are better than or equal to other baseline algorithms; in terms of algorithm convergence speed, the performance of the GR-GNN algorithm is comparable to that of the GCN algorithm, which is higher than other algorithms. The research results show that the GR-GNN algorithm proposed in this paper has high accuracy and computational efficiency, and very wide application significance.
Pedagogical framework for poly multicultural education of foreign language students seeking a degree in teaching

The research aims to explore and establish the ways of creating pedagogical framework for (i) popularizing the idea of poly-/multiculturalism, and (ii) forming relevant students' skills and abilities in foreign language instruction; to determine the extent to which the engaging mentoring teacher in the educating role may influence their poly-/multicultural competence. To this end, poly-/multicultural competence is seen prerequisite of a foreign language teacher's professional competence. Experiment is the main method of the research. Assessment of the experiment effectiveness was carried out with the involvement of surveys/questionnaires, conducted in stages, along with observation as a method applied. The obtained data confirmed the key research hypothesis: the pedagogical framework for the formation of poly-/ multicultural competence with students seeking their bachelor’s degree to become teachers of a foreign language connects with mastering two or more foreign languages while intensifying the poly-/multicultural component of education. The main findings result in the contemporary need for formation of a FL teacher’s poly-/multicultural competence regarded as the improvement in foreign language proficiency, basic communication skills in a poly-/multiethnic environment, following the proactive positioning of such teacher.
On Usefulness of Outlier Elimination in Classification Tasks

Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5%) on average.
Digital Flipbook as a Learning Media to Improve Visual Literacy for 4th Grade Students at SDN Abdi Negara

The current development of technology and information has an effect on the advancement of learning media innovation. One of them is the digital-based flipbook innovation. This research was conducted in an effort to utilize flipbook media in digital form to improve visual literacy of grade 4 elementary school students. The method used is quantitative method. Subjects in this study were students at SD Negeri 1 Abdi Negara. To obtain research data used a survey by filling out a questionnaire. Based on these data, it was found that students were easy to use digital flipbooks, easy to read the text, understood the material presented, felt interested and fun when using digital based flipbooks, and understood the material that was being tested. Based on the results of data analysis, it can be said that this digital-based flipbook can be used as a learning media.
Speculative Materialities, Indigenous Worldings and Decolonial Futures in Computing &amp; Design

This intra-view follows a round-table discussion that took place during the New Materialist Informatics conference on 25 March 2021. The discussants – Indigenous researcher and game designer Outi Laiti, artists and researchers Luiza Prado de O. Martins, Femke Snelting and Caroline Ward – start with their own artistic, academic, and creative practices and discuss how these practices relate to otherwise-worldings in computing that engage materialist, anti-racist, decolonial, Indigenous, and trans*feminist thinking and doing. This discussion, facilitated by artist Ren Loren Britton and researcher Goda Klumbytė, brings up questions of collaboration and infrastructures needed to support otherwise practices in computing and design.
Deceptive Infusion of Data: A Novel Data Masking Paradigm for High-Valued Systems

This work addresses how analysts of a high-valued system (e.g., nuclear reactor, aircraft turbine designs) can extract findable, accessible, interoperable, and reusable scientific data for public dissemination to artificial intelligence and machine-learning (AI/ML) researchers in a manner that cannot be reverse-engineered, potentially compromising sensitive or proprietary information. State-of-the-art methods address this problem through data masking techniques, which allow access to a subset of the information while obfuscating private and potentially identifying information (e.g., personally identifying medical data). These methods are unsuitable for industrial engineering processes, where AI/ML tools need explicit access to all the data available to draw the best inference about the system to help optimize its performance and identify its vulnerabilities, etc. Our novel deceptive infusion of data paradigm provides a solution to this conundrum by developing a mathematical approach capable of concealing the identity of the system while providing full access to all the features employed by AI/ML tools to ensure their optimal performance.
Estimation of the computational cost of the CSIDH algorithm on supersingular twisted and quadratic Edwards curves

The properties of twisted and quadratic supersingular Edwards curves that form pairs of quadratic torsion with order p+1 over a prime field Fp are considered. A modification of the CSIDH algorithm based on the isogenies of these curves instead of the traditional arithmetic of curves in the Montgomery form is presented. The parameters of these two classes of supersingular Edwards curves for p=239 are calculated and tabulated. An example of the isogenies of these curves in the implementation of the CSIDH algorithm as a non-interactive secret sharing scheme based on the secret and public keys of Alice and Bob is given. It is shown that the sequences of parameters ±d(i) of isogeny chains for quadratic and twisted supersingular Edwards curves, respectively, have a reverse nature on the period of the sequence. A recurrent algorithm for calculating the coordinates of points that form the kernels of isogenies of odd degrees is proposed, and its implementation in various coordinate systems is considered. A comparative analysis of the cost of calculating the parameter d´ of the isogenic curve E´ using the Farashakhi-Hosseini (W : Z) - coordinates and classical projective coordinates (X : Y : Z) is given. It is noted that all calculations in the CSIDH algorithm necessary to calculate the shared secret dAB are reduced only to the calculation of the isogenic curve E´ parameter d´ and are performed by field operations and the scalar multiplication of the point. The controversial issue of refusal to calculate the isogenic function ϕ(R) of a curve point R in the CSIDH algorithm is discussed.
Rethinking and Refining the Distinct Metric

Distinct-n score (Li et al., 2016) is a widely used automatic metric for evaluating diversity in language generation tasks.However, we observed that the original approach for calculating distinct scores has evident biases that tend to assign higher penalties to longer sequences.We refine the calculation of distinct scores by scaling the number of distinct tokens based on their expectations.We provide both empirical and theoretical evidence to show that our method effectively removes the biases existing in the original distinct score.Our experiments show that our proposed metric, Expectation-Adjusted Distinct (EAD), correlates better with human judgment in evaluating response diversity.To foster future research, we provide an example implementation at https://github.com/lsy641/Expectation-Adjusted-Distinct.
Stereo Neural Vernier Caliper

We propose a new object-centric framework for learning-based stereo 3D object detection. Previous studies build scene-centric representations that do not consider the significant variation among outdoor instances and thus lack the flexibility and functionalities that an instance-level model can offer. We build such an instance-level model by formulating and tackling a local update problem, i.e., how to predict a refined update given an initial 3D cuboid guess. We demonstrate how solving this problem can complement scene-centric approaches in (i) building a coarse-to-fine multi-resolution system, (ii) performing model-agnostic object location refinement, and (iii) conducting stereo 3D tracking-by-detection. Extensive experiments demonstrate the effectiveness of our approach, which achieves state-of-the-art performance on the KITTI benchmark. Code and pre-trained models are available at https://github.com/Nicholasli1995/SNVC.
Integralno planiranje kadrova u sferi održivog razvoja

The basic characteristic of the concept of sustainable development is the intertwining of social, economic, environmental and political factors.All member states of the United Nations have adopted the 2030 Agenda with 17 Sustainable Development Goals, pledging to work for a more prosperous, equitable and sustainable world on behalf of all citizens.In this way, they will interconnect all the goals of sustainable development with the achievement of social well-being.The main goal of this paper is to present the achievement of sustainable development goals (SDG) in relation to one of the most vulnerable and marginalized groups of the population in the Republic of Serbia -the population living in Roma settlements.The paper uses official SDG indicators to present the situation, as well as other indicators that enable a better insight into certain aspects of life.The results of the research presented in the paper show that the Roma, as a large minority in the Republic of Serbia, face serious problems of socio-economic nature, so that over time they become one of the most vulnerable social groups.Roma continue to be on the margins of society, discriminated against in many spheres of social life.The causes of many problems in this area stem from the decades-long exclusion of Roma children from the education system, as well as from ingrained prejudices about Roma.In order for the Republic of Serbia to achieve sustainable development, it is necessary in the first place to reduce poverty and the inclusion of the Roma population in life and in education and work.
RCMVis: A Visual Analytics System for Route Choice Modeling

We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers’ route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.
Preparing Middle Grades Candidates for edTPA in Uncertain Times

Teacher candidates in North Carolina must earn a passing score on the edTPA assessment to get certified. The middle grades education program at Western Carolina University integrates aspects of the edTPA assessment throughout pre-student teaching coursework and field experiences to prepare candidates for this high-stakes assessment. Some of the edTPA practice assignments serve as key assessments that help the middle grades program faculty evaluate the program and make decisions about curriculum. The pivot to remote and blended learning formats on campus and in partner middle level schools affected the implementation of the edTPA-related assignments. The authors share some of the challenges of implementing edTPA practice portfolios during the pandemic as well as insights gleaned from their assessment of the data.
Integration of Metal Materials and Semarang Batik Motifs into the Design of Tableware

Technological progress goes hand in hand with the development of human civilization.One of the results of human civilization is tableware, namely coasters.This article will describe the design of coasters with the Semarang Batik motifs application, namely Warak Ngendog, as a result of acculturation of Javanese, Chinese, and Arabic cultures through the depiction of mythological animals as symbols of harmony according to Semarang people beliefs.The primary material used is copper metal combined with wood.Combining this material was chosen because it can fulfill the value of the function and aesthetics.The research method is descriptive experimental through laser cutting and forging techniques on copper metal, identification, and structured documentation.The result of this experiment is a set of coasters with the Warak Ngendog motif.
Serverless Computing

The first thing we would like you to think about now is what serverless computing actually is and why we like it enough to write an entire book about it. The term serverless computing might seem contradictory, as software that is accessible in the cloud always has some kind of server involved somewhere. And of course, serverless computing is a misnomer as it does not mean that there is no server involved in running your solution, but rather that you do not control the server yourself.
Design and implementation of adaptive neuro-fuzzy inference system for the control of an uncertain ball and beam apparatus

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro- Fuzzy Inference System) using an uncertain Ball and Beam System. The custom- designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle.
An adaptive multi-hop routing with IoT abstraction for minimizing delay-node capacity trade-offs in mobile ad-hoc network

&lt;span&gt;Delay and node capacity are incompatible mobile ad hoc constraints because of the network's versatility and self-disciplined design. &lt;/span&gt;&lt;span&gt;It is a challenging problem to maximize the trade-off between the above mobility correlation factors. &lt;/span&gt;&lt;span&gt;This manuscript proposes an adaptive multi-hop routing (A.M.R.) for mobile ad-hoc network (MANET) to minimize the trade-off by integrating the internet of things (IoT). IoT nodes' smart computing and offloading abilities are extended to ad-hoc nodes to improve routing and transmission. Dor MANET nodes in route exploration, neighbor selection, and data transmission, the beneficial features of IoT include enhanced decision making. The traditional routing protocols use IoT at the time of the neighbor discovery process in updating the routing table and localization. &lt;/span&gt;&lt;span&gt;The harmonizing technologies with their extended support improve the performance of MANETs has been estimated. The proposed method achieves better throughput (14.16 Mbps), delay (0.118), packet drop (126), and overhead (36 packets) when compared to existing methods.&lt;/span&gt;
Evaluation of Interleukin 10 Levels among Pulmonary Tuberculosis Patients Attending Aminu Kano Teaching Hospital, Kano

Despite medical advances, tuberculosis is responsible for over one billion deaths in the last 200 years. This study was carried out to determine interleukin 10 levels in serum of tuberculosis patients attending Aminu Kano Specialist Hospital, Kano (AKTH). A total of 330 subjects were enlisted for this study. The subjects were grouped into: Group A (30 control) and Group B (300 subjects). DNA sequences specific for Mycobacterium tuberculosis and rifampicin resistance were detected using Gene Xpert MTB/RIF assay. 5mls of blood were aseptically collected from subjects using standard venipuncture procedure into plain tubes for HIV and Interleukin 10 assay. TB patients co-infected with HIV were excluded from this study. The Interleukin 10 level was measured using commercial Sandwich ELISA kits. The result revealed that there was significant difference (p&lt;0.05) between the interleukin 10 levels of the test (11.30±3.79) and control group (9.38±4.39). Furthermore, the result showed that there was no significant difference (p&gt;0.05) between the interleukin-10 levels of rifampicin resistant (11.80±3.87) and non-rifampicin resistant patients (11.65±3.55). Interleukin 10 was also found to be higher among patients aged 30-39 (12.33±3.31) and female patients (11.95±3.50) than in male patients (11.45±3.65). Furthermore, Interleukin 10 level was found to be higher in TB patients before anti TB treatment (12.54±3.82) than in patients after treatment (9.86±3.59). The findings of the study showed that interleukin 10 is a viable biomarker in the pathogenesis of TB infection and can be used as prognostic marker for management of TB patients.
Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback

In this paper, we consider robot navigation problems in environments populated by humans. The goal is to determine safe trajectories that also maximize human satisfaction. In practice, human satisfaction is subjective and hard to describe mathematically. As a result, the planning problem we consider in this paper may lack important contextual information. To address this challenge, we propose a semi-supervised Bayesian Optimization (BO) method to design globally optimal robot trajectories using bandit human feedback, in the form of complaints or satisfaction ratings, that expresses how desirable a trajectory is. We demonstrate the efficiency of our proposed trajectory planning method in a simulated scenario where humans have diversified and unknown demands.
Combining Machine Learning With Inductive Logic Learning To Detect Deviations From Daily Routines In Ambient Intelligent Environments

The availability of sensor technology in home and building automation offers new opportunities for AI applications. Machine learning (ML) methods can recognize device patterns and profiles based on sensor data and make energy-relevant predictions. At a higher level, the detected patterns can in turn be used to learn activity patterns. In this paper, we present a hybrid approach to augment ML-based outputs with inductive logic-based learning techniques. We present evaluation results and illustrate an application to agent-supported critical situation detection in the assisted living domain.