Reviewerly/exHarmony huggingface dataset:
The (Mobile) Driving Experience Lab: Bridging Research and Knowledge Transfer to the General Public

In interdisciplinary human-computer interaction (HCI) research, user studies are essential. Due to easy attainability, flexible schedules, etc., these studies are primarily conducted with students. Depending on the application, however, this often does not yield results that are representative of the respective target group(s). Getting these to the, e.g., driving simulator is tedious, time-consuming, and costly. To address this issue, we have integrated a driving simulator mockup into a trailer to bring the lab directly to the target groups. It allows researchers to diversify their samples for research studies. In addition, the lab can also be used for a broad knowledge transfer to society. We hypothesize that academics and practitioners will benefit from having easy access to the general public as well as specific target groups by reducing the barrier to study participation. In this paper, we outline the development steps for this lab, present its core features, and discuss potential applications.
VariTex: Variational Neural Face Textures

Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more. Code and models are available for research on this https URL.
Accurate recognition of ancient handwritten Tamil characters from palm prints for the Siddha medicine systems

The ancient Tamil characters recognition is the complex task because there is no sufficient training information is available. Various researchers attempted to perform accurate recognition of ancient Tamil characters. In our preceding work, hybrid multi-neural learning based prediction and recognition system (HMNL-PRS) is introduced for the prediction process which lacks from inaccurate recognition. In this proposed research work, this is overcome by proposing the Brahmi character prediction and conversion system (BC-PCS) methodology. Here, the modified graph based segmentation algorithm (MGSA) is used to segment the characters. And then the statistical and structural features are extracted based on which classification is done using hybridised support vector machine based fuzzy neural network. In the MATLAB simulation environment, the proposed research work is implemented and it is confirmed that the proposed research work direct to give the excellent result compared to the preceding research methodology in terms of recognition rate.
Framework for Social Distance Monitoring with Computer Vision and YOLOV3

Corona virus has become an international pandemic affecting over 219 countries around world with 169 million cases, which lead to approximately 3.5 million deaths as on May 28th, 2021. This virus is first reported in Wuhan, China. Consequently, it has been spread across various countries and many different variants have been discovered. In absence of effective treatment, World Health Organization (WHO) provided some helpful measures like facemask, hand wash, and most importantly social distancing which has been proven very effective in this crisis. We are proposing a framework using deep learning to automatically monitor and detect the social distance violations. This framework utilizes You Only Look Once version 3 (YOLO v3) object detection method for identifying the people in the video and draw anchor boxes for each detected person. After detecting the people and drawing anchor boxes on them we will find distance between people detected by computing Euclidian Distance between centroid for all anchor boxes. We will compare the distance calculated with the Minimum distance criteria to find the distance violations, for all violating anchor boxes we will change color for them to red and otherwise remains green. We will also show the total count of violations at particular moment in the video at bottom of the screen.
NATCAT: Weakly Supervised Text Classification with Naturally Annotated Resources

We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit. NatCat consists of document-category pairs derived from manual curation that occurs naturally within online communities. To demonstrate its usefulness, we build general purpose text classifiers by training on NatCat and evaluate them on a suite of 11 text classification tasks (CatEval), reporting large improvements compared to prior work. We benchmark different modeling choices and resource combinations and show how tasks benefit from particular NatCat data sources.
The future direction and challenges of English education in the post-COVID-19 era. English Teaching

The purpose of this study was to predict educational transformation in the post pandemic era and address a new agenda for participants in both education and academia of Korean EFL. First of all, this article calls attention to rapid societal changes due to technology for the last few decades, particularly in facets of language use, knowledge building, and learning paradigm. Second, this study attempted to articulate unprecedented challenges of English education in Korea for adapting to educational reform in the digital society: 1) defining digital literacy from the second language (L2) perspective, 2) relocating English curriculum within digital learning ecology, and 3) achieving normalization of Korean school English curriculum. For successful transition, this paper finally suggests that a united voice is needed from participants and that new governance should be established in English education to connect educators, researchers, and policy makers to each other empowered by major academic associations such as KATE.
Application of Engine Technology and in 3D Animation Production

This article is based on the optimization of 3D game engine technology and 3D graphics architecture to make 3D graphics animation. First of all, this article describes the research status of traditional 3D animation and analyzes the current development trend of 3D animation. Then, the application features of the current mainstream 3D graphics engine are introduced, and the architecture and system design of the 3D engine animation system are completed. Finally, the use of the designed engine system and architecture optimization for 3D animation production has a certain impetus to the realization of the 3D animation algorithm.
Software Quality Enhancement Using Hybrid Model of DevOps

The software engineering field is alive due to its evolving development process. This development process has covered a long journey from waterfall model to agile development. During the course, test-driven development proved to be a beneficial approach for traditional models. Currently, the software engineering industry is being benefited with the latest development process, DevOps which is not only increasing the productivity of software but also enhancing its efficiency. In this research, hybrid model of software engineering is proposed which comprises a test-driven development approach in conjunction with DevOps process. To validate the model, scaling of TDD approach to DevOps system is performed, that evaluates the quality metrics along with complexity of software. Further to corroborate the results, comparative analysis is done with existing processes on the basis of defined metrics which confirms the enhanced overall quality and better maintainability of software.
Learning One Representation to Optimize All Rewards

We introduce the forward-backward (FB) representation of the dynamics of a reward-free Markov decision process. It provides explicit near-optimal policies for any reward specified a posteriori. During an unsupervised phase, we use reward-free interactions with the environment to learn two representations via off-the-shelf deep learning methods and temporal difference (TD) learning. In the test phase, a reward representation is estimated either from observations or an explicit reward description (e.g., a target state). The optimal policy for that reward is directly obtained from these representations, with no planning. We assume access to an exploration scheme or replay buffer for the first phase. The unsupervised FB loss is well-principled: if training is perfect, the policies obtained are provably optimal for any reward function. With imperfect training, the sub-optimality is proportional to the unsupervised approximation error. The FB representation learns long-range relationships between states and actions, via a predictive occupancy map, without having to synthesize states as in model-based approaches. This is a step towards learning controllable agents in arbitrary black-box stochastic environments. This approach compares well to goal-oriented RL algorithms on discrete and continuous mazes, pixel-based MsPacman, and the FetchReach virtual robot arm. We also illustrate how the agent can immediately adapt to new tasks beyond goal-oriented RL.
Blue Ocean or Dry Desert? Blockchain and Bitcoin Impact on Tourism Industry

The technological innovation has yielded great opportunities to expand and innovate the scope of services offered. This is particularly true for tourism, which has always been characterized by extensive innovativeness. The pace at which technological innovations diffuse within a specific sector depends on a variety of factors, including the influence of peers and relevant actors. The present study explores the level of interest about these new technologies on social media with the aim to explore the dynamics of reciprocal networking influence in technological diffusion. To capture the attitudes expressed in the industry, the study analyzes the ongoing discussions on Twitter as a proxy for the actors’ interest. Through a social network analysis of the flow of tweets, the analysis maps the current online discourse about new technologies diffusion. Moreover, the sentiment analysis draws the current perceptions toward the diffusion of new technologies. While previous research is focused on the users’ perception toward the development of new technologies in tourism, the aim of this study is to investigate the dynamics underneath the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research. Thus, the paper contributes to the literature on new technology diffusion by intercepting current social media debate and literature studies focused on the diffusion of new technologies in tourism. The paper also highlights the role of knowledge broker in influencing this public debate.
Interpretable Visual Reasoning via Induced Symbolic Space

We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; and achieve an interpretable model via working on the induced symbolic concept space. To this end, we first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features. Then, we come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects' visual features and question words. Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space. Our model design makes this an easy adaption via first predicting the concepts of objects and relations and then projecting the predicted concepts back to the visual feature space so the compositional reasoning module can process normally. Experiments on the CLEVR and GQA datasets demonstrate: 1) our OCCAM achieves a new state of the art without human-annotated functional programs; 2) our induced concepts are both accurate and sufficient as OCCAM achieves an on-par performance on objects represented either in visual features or in the induced symbolic concept space.
A Parallel Framework for Scene Understanding

Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective framework based on the theory of parallel vision to combine different tasks for scene understanding. With the maintained artificial scene throughout time from the proposed framework, we can understand the scene in an elegant way. To explain our proposed framework, a case study is given.
Implementation of Cyclic Redundancy Check in Data Recovery

Cyclic Redundancy Check (CRC) technique can be widely used in data communication and storage devices in order to detect the sudden errors present in the data. The main motivation of this research was to detect the sudden, random or burst errors present in the transmission channels. This technique was very simple and easy to implement. It can be useful for the burst error detection and also to find the sequences of incorrect data present in the message signals. This technique was also good at detection of errors which were caused by the noise present in the transmission channels. The Conventional CRC Encoder and Decoder for 3 bits (101) and 4 bits (1100) was presented here. The proposed CRC Encoder and CRC Decoder were performed by using the binary division process and xor gate. Here, the generator or CRC polynomial or divisor was taken as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x^{8}+x^{2}+x^{1}+1$</tex> . It can be written as 100000111 which can be referred to 9 bits of polynomial. This polynomial was kept as constant in the whole binary division process for all random data bits. The novelty of this proposed work was the implementation of both CRC Encoder and CRC Decoder for input random bits such as 5 bits (10101), 8 bits (11000011) and 10 bits (1100110011). The CRC Encoder, Decoder and Test Bench for all these random data bits were simulated, compared and verified by using Verilog coding in Xilinx ISE 14.7 tool. This proposed CRC technique was implemented in Data Recovery application. This technique can be extended to various increased data bit sizes and different generator polynomial expressions as a future scope of this research. In this work, the simulation results were limited up to 5,8 and 10 random data bits.
Clustering-Based Adaptive Self-Organizing Map

We propose an improvement of the Self-Organizing Map (SOM). In our version of SOM, the neighborhood widths of the Best Matching Units (BMUs) are computed on the basis of the data density and scattering in the input data space. The density and scattering are expressed by the values of the inner-cluster variances, which are obtained after the preliminary input data clustering. The experiments conducted on the two real datasets evaluated the proposed approach on the basis of a comparison with the three reference data visualization methods. By reporting the superiority of our technique over the other tested algorithms, we confirmed the effectiveness and accuracy of the introduced solution.
Group Form Of Organization Of Independent Work Of Disabled Students

Social and economic changes taking place in society focus on the problem of the formation of an active, creative personality. In the development of a person capable of self-development and self-realization the following qualities are of great importance as the ability to independently learn, find the necessary information, apply the gained knowledge into practice, and make decisions independently. The article considers the methodological features of group training as one of the forms of organizing the independent work of students with disabilities in the process of studying mathematics on the example of studying the course “Probability theory and mathematical statistics”.In the course of the research, didactic materials for classes and lectures and methodological recommendations for their use were developed. The theoretical basis of the study was the theory of individualization and differentiation of education of students with disabilities. On the basis of questionnaires of students and teachers, on the comparison of the volumes of studied educational material in classes, on the number of certified students, it was established that the use of a group form of work for students with health limitations allows increasing their communication, independence, contributes to the development of memory, thinking, speech, and as a result, improves the grades of students with disabilities.
Information Technology Services in Preparing for Industrial Era 4.0

This research is inspired from the assumption that higher education institutions must prepare themselves for industrial era 4.0. IAIN Bukittinggi as one of these institutions must also prepare itself in order to maintain its existence in such era. In this paper, the writer attempts to explore the readiness of information technology services at IAIN Bukittinggi in preparing for industrial era 4.0. The research method used is quantitative through a survey with questionnaire distributed to 227 students from six study programs in the faculty of tarbiyah and teacher training. The overall findings show that 5% of respondents feel that information technology services at IAIN Bukittinggi are inadequate; 22% of them feel that information technology services at IAIN Bukittinggi are somewhat adequate; 39% of them feel that information technology services at IAIN Bukittinggi are adequate, and 34% of them feel that information technology services at IAIN Bukittinggi are very adequate . Based on these findings, it can be concluded that IAIN Bukittinggi must be able to improve it information technology services in line with the today technological needs, developments, and advances. It also needs to increase access, facilities, and infrastructure to support them.
Counting polynomials over finite fields with prescribed leading coefficients and linear factors

We count the number of polynomials over finite fields with prescribed leading coefficients and a given number of linear factors. This is equivalent to counting codewords in Reed-Solomon codes which are at a certain distance from a received word. We first apply the generating function approach, which is recently developed by the author and collaborators, to derive expressions for the number of monic polynomials with prescribed leading coefficients and linear factors. We then apply Li and Wan's sieve formula to simplify the expressions in some special cases. Our results extend and improve some recent results by Li and Wan, and Zhou, Wang and Wang.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-Life 3D Category Reconstruction

Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data. Our main goal is to facilitate advances in this field by collecting real-world data in a magnitude similar to the existing synthetic counterparts. The principal contribution of this work is thus a large-scale dataset, called Common Objects in 3D, with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such, it is significantly larger than alternatives both in terms of the number of categories and objects. We exploit this new dataset to conduct one of the first large-scale in-the-wild evaluations of several new-view-synthesis and category-centric 3D reconstruction methods. Finally, we contribute NerFormer - a novel neural rendering method that leverages the powerful Transformer to reconstruct an object given a small number of its views. The CO3D dataset is available at this https URL .
MyOPE: Malicious SecuritY for Oblivious Polynomial Evaluation

Oblivious Polynomial Evaluation (OPE) schemes are interactive protocols between a sender with a private polynomial and a receiver with a private evaluation point where the receiver learns the evaluation of the polynomial in their point and no additional information. In this work, we introduce MyOPE, a “short-sighted” non-interactive polynomial evaluation scheme with a poly-logarithmic communication complexity in the presence of malicious senders. In addition to strong privacy guarantees, MyOPE enforces honest sender behavior and consistency by adding verifiability to the calculations. The main building block for this new verifiable OPE is an inner product argument (IPA) over rings that guarantees an inner product relation holds between committed vectors. Our IPA works for vectors with elements from generic rings of polynomials and has constant-size proofs that consist in one commitment only while the verification, once the validity of the vector-commitments has been checked, consists is one quadratic equation only. We further demonstrate the applications of our IPA for verifiable OPE using Fully Homomorphic Encryption (FHE) over rings of polynomials: we prove the correctness of an inner product between the vector of powers of the evaluation point and the vector of polynomial coefficients, along with other inner-products necessary in this application’s proof. MyOPE builds on generic secure encoding techniques for succinct commitments, that allow real-world FHE parameters and Residue Number System (RNS) optimizations, suitable for high-degree polynomials.
A Unified Objective for Novel Class Discovery

In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled set containing different, but related classes. Existing approaches tackle this problem by considering multiple objective functions, usually involving specialized loss terms for the labeled and the unlabeled samples respectively, and often requiring auxiliary regularization terms. In this paper, we depart from this traditional scheme and introduce a UNified Objective function (UNO) for discovering novel classes, with the explicit purpose of favoring synergy between supervised and unsupervised learning. Using a multi-view self-labeling strategy, we generate pseudo-labels that can be treated homogeneously with ground truth labels. This leads to a single classification objective operating on both known and unknown classes. Despite its simplicity, UNO outperforms the state of the art by a significant margin on several benchmarks (~+10% on CIFAR-100 and +8% on ImageNet). The project page is available at: \url{this https URL}.