Reviewerly/exHarmony huggingface dataset:
PhysiCL: An OpenCL-Accelerated Python Physics Simulator

Numerical methods of physics analysis require specialized forms of programming as well as attention to issues of implementation. PhysiCL is a Python package that aims to provide general-purpose tools for performing OpenCL-accelerated physics simulations with ease. PhysiCL contains a Numpy-based code units system, a set of generic simulation tools, built-in tools for photon scattering, tools for measuring light behavior, and tools for writing new OpenCL-based simulation features. This package can be installed via PyPI using pip install physicl, and found on GitHub with source code and examples at https://github.com/bcwarner/physicl.
Model for the Diagnosis of Autism Based on Neutrosophic Cognitive Maps

Autism spectrum disorders are diverse conditions characterized by some degree of difficulty in social interaction and communication, atypical patterns of activity and behavior, and unusual reactions to sensations. ASDs do not have a single known cause, scientists do not know the exact causes of their development, and it is believed that they are associated with a combination of several factors. The present study was carried out with 42 cases diagnosed with any of the subclasses of autism established by the DSM-V and included patients between 18 months and four years old, from 4 provinces of Ecuador, to determine the main elements to which they were exposed to establish possible common groups of causal factors of the disease. The experts carried out an analysis and determined three predominant casual factors in the group, and some recommendations derived from the analysis are included.
한맹시파(韓孟詩派) 연구시(聯句詩)의 특징

The Lian Ju Shi(聯句詩 or Joint Sentences Poem) that began with the Han Dynasty Bai Liang Ti was created in earnest at the Zhong Tang, and among them, the Lian Ju Shi(聯句詩 or Joint Sentences Poem) of the Han Meng School of poetry(韓孟詩派) are considered the highest peak of Lian Ju Shi(聯句詩 or Joint Sentences Poem). Lian Ju Shi(聯句詩 or Joint Sentences Poem) in which several people create one piece of work, not only has an entertaining function, but also competes for talents among participants, therefore creating a strong sense of competition for each other. The Lian Ju Shi(聯句詩 or Joint Sentences Poem) of the Han Meng School of poetry(韓孟詩派) used contents that were not covered in the previous Lian Ju Shi(聯句詩 or Joint Sentences Poem) while having the basic characteristics of the Lian Ju Shi(聯句詩 or Joint Sentences Poem), and various changes were made in the formal aspect of the Lian Ju Shi(聯句詩 or Joint Sentences Poem). This study examines the characteristics of the Lian Ju Shi(聯句詩 or Joint Sentences Poem) created by the Han Meng School of poetry(韓孟詩派), comparison with the Lian Ju Shi(聯句詩 or Joint Sentences Poem) created by Bai Ju Yi and Liu Yu Xi, who were active in the same period. I will examine the characteristics of the Lian Ju Shi(聯句詩 or Joint Sentences Poem) of the Han Meng School of poetry(韓孟詩派), and analyze the means of the Lian Ju Shi(聯句詩 or Joint Sentences Poem) used by the Han Meng School of poetry(韓孟詩派) through these features.
Eksplorasi Teknologi Pendukung Data Science untuk Industri Unggulan di Wilayah Kota Surabaya dan Sekitarnya

Data science competence is still quite uncommon for most people, even though this competence is very important in managing business development strategies in various leading industrial fields. Through data science, companies can find patterns, perform clustering, calculate predictions more accurately to make decisions that have an impact on company excellence. Lack of knowledge about data science, researchers seek firstly in the industry to explore tools that are mostly used, convenience, challenges, measuring success tools and advantages. In this study, the researchers conducted a Focus Group Discussion by interviewing resource persons who worked as Data Scientists. Interview method to discuss aspects of Demographics, aspects of Technology-Task Fitness, and aspects of the Technology Acceptance Model. The results of this study conclude that the tools that are the mainstay of the industry in Surabaya and its surroundings area are Jupyter Notebook, Python, and Tableau. The success in using these tools still depends on the user's abilities, the results of user simulations, team discussions, and the evaluation process.
Comparing Rewinding and Fine-tuning in Neural Network Pruning for Reproducibility Challenge 2021

Scope of reproducibility: We are reproducing Comparing Rewinding andFine-tuning in Neural Networks from arXiv:2003.02389. In this work the authorscompare three different approaches to retraining neural networks after pruning:1) fine-tuning, 2) rewinding weights as in arXiv:1803.03635 and 3) a new,original method involving learning rate rewinding, building upon Lottery TicketHypothesis. We reproduce the results of all three approaches, but we focus onverifying their approach, learning rate rewinding, since it is newly proposedand is described as a universal alternative to other methods. We used CIFAR10 for most reproductions along with additional experiments onthe larger CIFAR100, which extends the results originally provided by theauthors. We have also extended the list of tested network architectures toinclude Wide ResNets. The new experiments led us to discover the limitations oflearning rate rewinding which can worsen pruning results on largearchitectures. Results: We were able to reproduce the exact results reported by the authorsin all originally reported scenarios. However, extended results on larger WideResidual Networks have demonstrated the limitations of the newly proposedlearning rate rewinding -- we observed a previously unreported accuracydegradation for low sparsity ranges. Nevertheless, the general conclusion ofthe paper still holds and was indeed reproduced.
Disentangling what and where for 3D object-centric representations through active inference

Although modern object detection and classification models achieve high accuracy, these are typically constrained in advance on a fixed train set and are therefore not flexible to deal with novel, unseen object categories. Moreover, these models most often operate on a single frame, which may yield incorrect classifications in case of ambiguous viewpoints. In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object categories over time. Drawing inspiration from the human brain, we build object-centric generative models composed of two information streams, a what- and a where-stream. The what-stream predicts whether the observed object belongs to a specific category, while the where-stream is responsible for representing the object in its internal 3D reference frame. We show that our agent (i) is able to learn representations for many object categories in an unsupervised way, (ii) achieves state-of-the-art classification accuracies, actively resolving ambiguity when required and (iii) identifies novel object categories. Furthermore, we validate our system in an end-to-end fashion where the agent is able to search for an object at a given pose from a pixel-based rendering. We believe that this is a first step towards building modular, intelligent systems that can be used for a wide range of tasks involving three dimensional objects.
Re-imagining Pedagogy for Early Childhood Education Pre-service Curriculum in the Face of the COVID 19 Pandemic

COVID-19 has caused a change in the demography of the Early Childhood Education (ECE) teaching fraternity. This paper problematizes the ECE curriculum delivery gap created by the influence of COVID-19. Central to this paper is the assumption that online learning might limit the stimulation of children’s holistic development. Reflecting on their experiences, the authors examine the influence of COVID-19 on pedagogy for ECE pre-service and kindergarten curriculum through the lens of design thinking theory. The reflections are based on one open and distance learning institution in South Africa and one kindergarten program in Canada. The findings reveal challenges, such as digital inequality brought on by socio-economic imbalances and opportunities. The authors suggest the digital literacy skills needed to mitigate the influence of COVID-19 in the ECE pre-service and kindergarten curriculum delivery.
Improvement of Speaker Number Estimation by Applying an Overlapped Speech Detector

The efficiency of modern automatic meeting transcription suffers from the problem of speaker diarization during overlapping speech segments. The problem can be tackled if each segment of a recording could be marked with the number of active speakers. However, overlapped speech recordings with more than two simultaneous speakers serve as a weak point for speaker number estimation. The problem becomes even more complicated if the speaker number estimation system tends to far-field recordings of multiple speakers acquired by a distant microphone. In this paper we propose an improvement for speaker number estimation by combining it with an overlapped speech detector. In our approach we apply different configurations of speaker number estimation and overlapped speech detector models trained and evaluated on the AMI and LibriSpeech datasets with several types of signal representation. Experimental evaluation based on fusion of models yields an improvement of speaker number estimation performance of up to 10% based on the F1-score metric compared with base speaker number estimation model.
Removal Of Random Noise Based On Convolutional Neural Network With Optimization Technique

Noise corrupted the digital image because of environmental factor. A new convolution neural network for removing images is presents. The proposed system comprises of convolution neural network with optimization techniques. At first, the digital image is applied into the Batch normalization process for better normalization process. Secondly, the image is given into the Convolution Neural Network (CNN) with Leaky rectified linear unit where the feature is extracted. The CNN process reconstructs the attributes of the recovered image. Finally, to learn and refine the extracted features, the resolved image is introduced in the MSE loss function and the Adam optimization approach. The effectiveness of the proposed framework is tested across a variety of noise levels and compared with various pads and strides. The suggested framework's results show that it outperforms previous denoising algorithms in terms of peak signal-to-noise ratio (PSNR), and it produces the best results.
Entity-Based Knowledge Conflicts in Question Answering

Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4%-7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e., time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.
Cryptocurrencies as an Example of Innovation in Finance

The paper contains theoretical considerations on cryptocurrencies in the context of innovation and specifically focuses on the most popular cryptocurrency – bitcoin (BTC). Based on critical analysis of domestic and foreign writings, bitcoin was classified as a modular innovation despite some of its features suggesting the continuous and breakthrough nature of the solution. The conclusion helped positively validate the research hypothesis proposed in this paper according to which cryptocurrencies, despite their characteristics of an incremental and breakthrough innovation, can be treated as modular innovations.
Relaxed Marginal Consistency for Differentially Private Query Answering

Many differentially private algorithms for answering database queries involve a step that reconstructs a discrete data distribution from noisy measurements. This provides consistent query answers and reduces error, but often requires space that grows exponentially with dimension. Private-PGM is a recent approach that uses graphical models to represent the data distribution, with complexity proportional to that of exact marginal inference in a graphical model with structure determined by the co-occurrence of variables in the noisy measurements. Private-PGM is highly scalable for sparse measurements, but may fail to run in high dimensions with dense measurements. We overcome the main scalability limitation of Private-PGM through a principled approach that relaxes consistency constraints in the estimation objective. Our new approach works with many existing private query answering algorithms and improves scalability or accuracy with no privacy cost.
STAR: Distributed Secret Sharing for Private Threshold Aggregation Reporting.

In practice and research, threshold aggregation systems~ -- ~that attempt to preserve client K-anonymity during data analysis~ -- ~incur significant implementation hurdles, including: i) use of trusted servers; ii) complex and expensive coordination between clients to ensure records are not identifying; and iii) protocols that are expensive in terms of computation, network use, or both. In this work, we present STAR (Distributed Secret Sharing for Threshold Aggregation Reporting): a performant solution allowing untrusted threshold aggregation of collected data, using techniques taken from $\kappa$-out-of-$\eta$ threshold secret sharing. Server-side aggregation in our protocol takes only 21 seconds for data received from 1,000,000 clients. Furthermore, it requires only a single message sent from each client to the untrusted aggregation server (at most 129 bytes), and no interaction between clients. Additionally, STAR is extensible in that clients can send additional data with their measurement, which is only revealed if the threshold check is satisfied. The STAR protocol supports multiple deployment scenarios. For instance, when client inputs come from a highly entropic input distribution STAR requires only a single untrusted server. When such guarantees cannot be made, STAR can be deployed using a second non-colluding randomness server, to provide clients with an outsourced method for generating shared randomness. Finally, this work makes novel cryptographic contributions in defining and constructing puncturable partially oblivious PRF (PPOPRF) protocols, which we expect to be useful in applications beyond STAR.
Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes

The development of Covid-19 cases in Indonesia continues to increase. with the continued increase in these cases causing panic among the public regarding the presence or absence of this corona virus, in the midst of this condition an effective and efficient communication pattern is needed in providing education and information about this corona virus, for example with social media Youtube. Many people's responses to this news are expressed in the comments column. Therefore, a sentiment analysis model is needed to classify public comments into Positive, Negative and neutral. In this study, the Naive bayes method is used to build a sentiment analysis model for public responses about the news on the development of the Covid-19 case on the Youtube page, precisely on the KompasTV Chanel. Accuracy is 74% with the number of comments Positive 361, Negative 800 and neutral 490.
Research on the Stability of the Grade Structure of a University Title

The evaluation of college teachers' title is an extremely important task in personnel management, and it is also the focus of college teachers and other professional and technical personnel. The work is policy-oriented and involves a wide range of issues. Whether it is scientific, fair and reasonable is related to whether it can mobilize the enthusiasm and creativity of teachers. It is related to the construction of the teaching staff and the adjustment of the academic echelon, which is of great significance for promoting the sustainable development of higher education.
Mediation Effects of outcome expectation and self-monitoring on the Relationship Between Learning Environment Perceived and grit in Physical Education Class

The purpose of this study analyzed the structural relationship between outcome Expectancy and self-monitoring grit according to learning environment in middle school physical education class, and tried to verify the mediating effect of outcome Expectancy and self-monitoring. subjects of this study were 500 middle school students in the metropolitan area, and 418 copies were used as final analysis data. data were processed using SPSS WIN Ver. 20.0 and AMOS 20.0 to perform confirmatory factor analysis, structural equation model analysis, and boot strap. Analysis result, First, the effort/challenge perceived environment and competency perceived environment of learning environment had a significant effect on outcome Expectancy, and threat perceived environment did not have a significant effect on outcome Expectancy. Second, the effort/challenge perceived environment of learning environment had a positive effect on self-monitoring. the threat perceived environment negatively affected self-monitoring. but the competence perceived environment did not have a significant effect on self-monitoring. Third, the outcome Expectancy did not have a significant effect on the grit. Fourth, self-monitoring had a significant effect on grit. Fifth, In the relationship between learning environment and grit, outcome expectation was not mediated, self-monitoring was mediated.
Video Mining for Anomaly Examining and Mishap Scrutinize

This paper portrays the effective utilization of CCTV for traffic observing and mishap identification. The framework which is structured has the ability to characterize the mishap and can give alarms when important. Presently a days we have CCTV's on the greater part of the streets, yet its abilities are being underused. There likewise doesn't exist a proficient framework to distinguish and characterize mishap on continuous. Such huge numbers of death happens as a result of undetected mishap. It is hard to recognize mishaps in remote spots and at evenings. The proposed framework can distinguish and group mishap as major and minor. It can consequently caution the specialists on the off chance that it manages a significant mishap. Utilizing this framework the reaction time on mishap can be diminished by handling the visuals of CCTV. In this framework distinctive picture preparing and AI strategies are utilized. The dataset for preparing is extricated from the visuals of as of now happened mishaps.Mishaps for the most part happen in light of imprudent driving, liquor utilization and over speeding. Another fundamental driver of death because of mishaps are the deferral in detailing mishaps since there doesn't exist any computerized frameworks. Mishaps are primarily announced by general society or by traffic specialists. We can spare numerous lives by recognizing and revealing the mishap rapidly. In this framework live video is caught from the CCTV's and it is handled to recognize mishaps. In this framework utilizing YOLOV3 calculation is utilized for object identification.Presently a days traffic observing have a more noteworthy importance. CCTV's can be utilized to distinguish mishaps since it is available in the vast majority of the streets. It is just utilized for traffic checking. Ordinarily mishaps can be delegated two classes major and minor. The proposed framework can characterize the mishap as major or minor by object location and following approaches. Each mishap needn't bother with crisis support. Just significant mishap must be taken care of rapidly. The proposed framework catches the video and experience object location calculations to distinguish the various items like vehicles and individuals. After the discovery stage the framework will attempt to extricate the highlights of the vehicles.The highlights like length, width and centroid is extricated to group the vehicle appropriately. The vehicle tally is likewise distinguished, which can be utilized for traffic clog control.
Ultrasonic Gloves for Deaf, Dumb and Blind

Sign language was the only way for the communication between disabled and normal people. Normal people felt difficulty in understanding sign language. To overcome this many devices were invented, which have certain drawbacks. In order to overcome those we are proposing a system entitled “ULTRASONIC GLOVES FOR DEAF, DUMB AND BLIND”. The scope of our project is to help disabled people in communicating with normal people. Gloves are fitted with flex sensors, gestures made will be converted into their respective word, if person fells down it will be sensed through the accelerometer and when if obstacle is sensed by the ultrasonic sensor in its range, and all these will be speak out through speaker and displayed on LCD and the same will be sent to guardian’s phone through TCP/UDP application using WI-FI module. Merits of this system is that it is cost effective, delay between gesture recognition is reduced.
Neural Network Based on the Special Potential Function for the Recognition of the Multifunctional Signal

The article suggests using elements described by a special potential function as elements of a neural network. The article describes the features and characteristics of a potential function that has an output signal close to a real physiological neuron. The description and structure of a neural network is given, the elements of which are described by a special potential function. To study the neural network and configure its elements, a mathematical apparatus of tensor calculus is proposed. The article describes the possibility of using the proposed neural network for recognizing and finding areas with specified parameters on a complex spatial surface. As an example, we describe the procedure for determining an area on a complex surface that has the specified geometric and qualitative characteristics.
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes

This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a practical and principled combination of DNNs with sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen as a special case of sparse GPs, namely mixtures of GP experts (MoE-GP), and we devise a learning algorithm that brings the derived theory into practice. In experiments from two different robotic tasks -- inverse dynamics of a manipulator and object detection on a micro-aerial vehicle (MAV) -- we show the effectiveness of our approach in terms of predictive uncertainty, improved scalability, and run-time efficiency on a Jetson TX2. We thus argue that our approach can pave the way towards reliable and fast robot learning systems with uncertainty awareness.