Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Based Asymmetrical DNN Modularization for Optimal Loading

Latency of DNN (Deep Neural Network) based prediction is the summation of model loading latency and inference latency. Model loading latency affects the first response from the applications, whereas inference latency affects the subsequent responses. As model loading latency is directly proportional to the model size, this work aims at improving the response time of an intelligent app by reducing the loading latency. The speedup is gained by asymmetrically modularizing the given DNN model among several small child models and loading them in parallel. The decision about number of feasible child models and their corresponding split positions are taken care by reinforcement learning unit (RLU). RLU takes into account the available hardware resources on-device and provides the best splitting index k and their positions p→ specific to the DNN model and device, where p→=(p1,p2,...,pk) and pi is the end position of ith child: Mi. The proposed method has shown significant loading improvement (up to 7X) on popular DNNs, used for camera use-case. The proposed method can be used to speed up the app response. Along with that RLU driven approach facilitates for On-device personalization by separating one module only with trainable layers and loading that particular module while training on-device.
Effects of jet angle on harmonic structure of sound radiating from the flute

For an isolated flute head joint, the effects of jet angle on harmonic structure of a single note are investigated within the practical range for human players. The mechanisms of these effects are discussed on the basis of both the radiated sound and the flow field measured with a hot-wire anemometer. The blowing parameters, viz., jet angle (angle between jet direction and window), jet offset (relative height of jet direction from the edge), lip-to-edge distance, and flow rate, were varied independently by using an artificial blowing device based on measured conditions for a human player, where the jet direction is defined as that measured without the head joint. The radiated sound revealed that jet angle varied the differential sound pressure level of the second to third harmonic (ΔSPL) less than jet offset, however, as much as flow rate and more than lip-to-edge distance. The spatial distribution of jet fluctuation center showed that, with increasing jet angle (the jet direction approaches vertical to the window), the jet deflected more inside, so that the actual jet offset was estimated to be further inside. The variation of ∆SPL with jet angle seems to be caused mainly by this shift in the actual jet offset.
Implementing Elearning System For General Information Technology Course At Van Lang University, Period 2017 - 2020

Van Lang University's General Information Technology course has been implementing eLearning from 2017 to 2020. The course is divided into 3 phases and from level 2 to level 4 of the eLearning training program (Dung, 2020). At every phase, there are improvements in education methods, supporting functions,  and above all, system optimization, from functions that support distance learning to course management and others. It ensures the best learning environment for students and helps teachers teach and post results more efficiently. During the eLearning program's developmental phase, different education methods have been used to suit different needs and constitute 20% and up to 100% of the time. During the whole time, the program is receptive to teachers' and students' feedback to improve and has already received encouraging results.
LibGuides: Keep Calm and Study On: Technology Help

LibGuide serves as a platform to support Butler University students, faculty and staff during remote learning through a variety of resources to engage students academically and socially.
Polarized Classification Tree Models: Theory and Computational Aspects

Abstract In this paper, a new approach in classification models, called Polarized Classification Tree model, is introduced. From a methodological perspective, a new index of polarization to measure the goodness of splits in the growth of a classification tree is proposed. The new introduced measure tackles weaknesses of the classical ones used in classification trees (Gini and Information Gain), because it does not only measure the impurity but it also reflects the distribution of each covariate in the node, i.e., employing more discriminating covariates to split the data at each node. From a computational prospective, a new algorithm is proposed and implemented employing the new proposed measure in the growth of a tree. In order to show how our proposal works, a simulation exercise has been carried out. The results obtained in the simulation framework suggest that our proposal significantly outperforms impurity measures commonly adopted in classification tree modeling. Moreover, the empirical evidence on real data shows that Polarized Classification Tree models are competitive and sometimes better with respect to classical classification tree models.
Research on the Relationship between Solid Physics and Quantum Mechanics Based on Computer

Abstract Solid state physics is the basis of quantum mechanics to study the microstructure and macro properties of crystal materials. The combination of the two can promote the further improvement and development of the structure and properties of solid materials. Under the background of computer application, the development of quantum mechanics is inseparable from the effective support of solid-state physics. It can be seen that the relationship between solid-state physics and quantum mechanics is mutual promotion and correlation. Based on this, this paper first studies the key points of quantum mechanics and its relationship with solid state physics, and then analyses the concrete relationship between quantum mechanics and solid state physics based on computer.
Intra-layer Neural Architecture Search

We propose an efficient neural architecture search (NAS) algorithm with a flexible search space that encompasses layer operations down to individual weights. This work addresses NAS challenges in a search space of weight connections within layers, specifically the large number of architecture variations compared to a high-level search space with predetermined layer types. Our algorithm continuously evolves network architecture by adding new candidate parameters (weights and biases) using a first-order estimation based on their gradients at 0. Training is decoupled into alternating steps: adjusting network weights holding architecture constant, and adjusting network architecture holding weights constant. We explore additional applications by extend this method for multi-task learning with shared parameters. On the CIFAR-10 dataset, our evolved network achieves an accuracy of 97.42\% with 5M parameters, and 93.75\% with 500K parameters. On the ImageNet dataset, we achieve 76.6\% top-1 and 92.5\% top-5 accuracy with a search restriction of 8.5M parameters.
Robust Temporal Ensembling

Successful training of deep neural networks with noisy labels is an essential capability as most real-world datasets contain some amount of mislabeled data. Left unmitigated, label noise can sharply degrade typical supervised learning approaches. In this paper, we present robust temporal ensembling (RTE), a simple supervised learning approach which combines robust task loss, temporal pseudo-labeling, and a new ensemble consistency regularization term to achieve noise-robust learning. We demonstrate that RTE achieves state-of-the-art performance across the CIFAR-10, CIFAR-100, and ImageNet datasets, while forgoing the recent trend of label filtering/fixing. In particular, RTE achieves 93.64% accuracy on CIFAR-10 and 66.43% accuracy on CIFAR-100 under 80% label corruption, and achieves 74.79% accuracy on ImageNet under 40% corruption. These are substantial gains over previous state-of-the-art accuracies of 86.6%, 60.2%, and 71.31%, respectively, achieved using three distinct methods. Finally, we show that RTE retains competitive corruption robustness to unforeseen input noise using CIFAR-10-C, obtaining a mean corruption error (mCE) of 13.50% even in the presence of an 80% noise ratio, versus 26.9% mCE with standard methods on clean data.
Research on the Interactive Mode of Data News Content Presentation

Based on the analysis of multiple interactive data journalism cases, this article delves into the multiple interaction modes of news content presentation, summarizes the current data visualization, human-computer interaction, and participation sharing modes, and analyzes data news interaction the profound impact of the model on the dissemination of news information, and provides reference suggestions for the improvement of the data journalism interaction model. Finally, the model and development of the data journalism interaction are summarized and prospected.
Security Class Actions: Leading Indicators and Covariates

This report provides contingency tables (crosstab) analysis of security class actions (SCAs) and flags from Corporate Watchdog Reports. Watchdog Research assigns red and yellow flags to disclosures made by publicly traded firms according to their proprietary classification system. We find the following flags to be leading indicators of security class actions: red flags for financial revisions and CFO changes; and yellow flags for the Beneish M-Score, cybersecurity, and non-audit fees. We also find that a security class action in one year increases the probability of another security class action in the following year, i.e. security class actions are serially correlated over time. Additionally, we find that several flags are correlated with security class actions. The presence of a correlated flag increases the probability that a security class action will occur in the same calendar year it was awarded. The analysis is based on a panel data set of NYSE and NASDAQ companies from 2014 to 2018.
Domain Adaptation via Anaomaly Detection

Domain shift in finetuning from pre-training can significantly impact the performance of deep neural networks. In NLP, this led to domain specific models such as SciBERT, BioBERT, ClinicalBERT, and FinBERT; each pre-trained on a different, manually curated, domain-specific corpus. In this work, we present a novel domain-adaptation framework to tailor pre-training so as to reap the benefits of domain specific pre-training even if we do not have access to large domain specific pre-training corpus. The need for such a method is clear as it is infeasible to collect a large pre-training corpus for every possible domain. Our method is completely unsupervised and unlike related methods, works well in the setting where the target domain data is limited in size. We draw a connection between the task of adapting a large corpus to a target domain and that of anomaly detection, resulting in a scalable and efficient domain adaptation framework. We evaluate our framework and various baselines on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews. Our framework outperforms all the baseline methods and yields an average gain of 1.07% in performance. We also evaluate it on one of the GLUE task, sentiment analysis and achieve an improvement of 0.4% in accuracy.
Unbiased Learning with State-Conditioned Rewards in Adversarial Imitation Learning

Adversarial imitation learning has emerged as a general and scalable framework for automatic reward acquisition. However, we point out that previous methods commonly exploited occupancy-dependent reward learning formulation—which hinders the reconstruction of optimal decision as an energy-based model. Despite the theoretical justification, the occupancy measures tend to cause issues in practice because of high variance and low vulnerability to domain shifts. Another reported problem is termination biases induced by provided rewarding and regularization schemes around terminal states. In order to deal with these issues, this work presents a novel algorithm called causal adversarial inverse reinforcement learning. Our formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy. In experiments, we demonstrate that our approach outperforms prior methods in challenging continuous control tasks, even under significant variation in the environments.
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN DENGAN KETERAMPILAN MENULIS TEKS EKSPLANASI SISWA KELAS XI SMA NEGERI 14 PADANG

ABSTRACT This article reveals the correlation of reading comprehension skills with writing skills of explanatory texts grade students XI SMA Negeri Padang 14. There are three purposes of this study. First, to describe the reading comprehension skills of class XI SMA Negeri 14 Padang. Second, to describe the explanatory text writing skills of class XI SMA Negeri 14 Padang . Third, to describe the correlation of reading comprehension skills with explanatory text writing skills in class XI SMA Negeri 14 Padang. This type of research is quantitative with descriptive method. The design used in this study is correlation design. The population of this study is was XI SMA Negeri 14 who were enrolled in the 2018/2019 school year, which was 273 students. The sample in this study was determined by proportional random sampling of 15% of the population which is 40 students. The research data is in the form of scores on the result of explanatory writing skills. The istrument of this study is an objective test to measure comprehension reading skills and performance tests to measure explanatory writing skills. The results of this study are three. First, the reading comprehension skills of students XI SMA Negeri 14 Padang are in good qualification (B). Second, explanatory text writing skills of students XI SMA Negeri 14 Padang are in good qualification (B). Third, reading comprehension skills have a significant correlation with explanatory text writing skills of the eleventh grade students XI SMA Negeri 14 Padang of at 76%, while remaining 24% is influenced by other factors not examined in this study. Kata kunci: Korelasi, Keterampilan, Membaca Pemahaman, Menulis, Teks Eksplanasi
Strategies for the Construction of Business English Teaching Model in the Internet Era

In today’s rapidly changing science and technology society, Internet technology is becoming more and more popular. Human society faces the development of globalization. In order to adapt to social progress, the globalization of language has become important. Under the international trend, the English learning market is surging. Many training institutions regard business English as their main training program. Business English is aimed at helping people to satisfy the language requirements of the workplace, and the content involves all aspects of business. In order to adapt to the development of the times and cultivate the talents needed by society, the business English teaching model should be combined with Internet technology, and continuous exploration, reform and innovation are needed to improve the quality of teaching. This article mainly introduces the status quo of business English teaching, improvement strategies and the application of the Internet in business English teaching.
Visual Intelligent Monitoring Technology of Data Center Based on SSD Algorithm

As a new digital infrastructure, data center is the key to the transformation and upgrading of power grid to energy Internet. With the continuous expansion of data center scale and the increase of operation and maintenance personnel, the traditional video monitoring mode relying on manual observation is difficult to meet the increasingly complex operation and maintenance needs. This paper mainly studies the application of artificial intelligence technology in the field of intelligent security. It uses the deep learning model to identify the operation and maintenance personnel in the data center, and optimizes the network layers and window parameters of SSD (Single Shot Multi-Box Detector) algorithm to improve the operation efficiency and intelligent level of video monitoring. The experimental results show that the visual intelligent monitoring technology of the data center constructed in this paper has good target recognition performance, and effectively improves the security level of the daily management of the data center.
Learning with Unpaired Data

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Many learning tasks can be summarized as learning a mapping from a structured input to a structured output, such as machine translation, image captioning, and image dehazing. Such mappings are usually learned on paired training data, where an input sample and its corresponding output are both provided. Collecting paired training data often involves expensive human annotation, and the scale of paired training data is therefore often limited. As a result, the generalization ability of models trained on paired data is also limited. One way to mitigate this issue is learning with unpaired data, which is far less expensive to collect. Taking machine translation as an example, the unpaired training data can be collected separately from newspapers in the source language and target language without any annotation. The challenge of unpaired learning turns into how to align the unpaired data. With carefully designed objectives, unpaired learning has achieved remarkable progress on several tasks. This talk will cover the data collection and training methods of several unpaired learning tasks to illustrate the power of learning with unpaired data.
SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations

With the excessive use of neural networks in safety critical domains the need for understandable explanations of their predictions is rising. Several methods were developed which identify the most relevant inputs, such as sensitivity analysis and most prominently layerwise relevance propagation (LRP). It has been shown that the noise in the explanations from the sensitivity analysis can be heavily reduced by averaging over noisy versions of the input image, a method referred to as SmoothGrad. We investigate the application of the same principle to LRP and find that it smooths the resulting relevance function leading to improved explanations for state-of-the-art LRP rules. The method, that we refer to as SmoothLRP, even produces good explanations on poorly trained neural networks, where former methods show unsatisfactory results. Interestingly, we observed, that SmoothLRP can also be applied to the identification of adversarial examples.
Disentanglement, Visualization and Analysis of Complex Features in DNNs

This paper aims to define, visualize, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method disentangles and visualizes feature components of different complexity orders from the feature. The disentanglement of feature components enables us to evaluate the reliability, the effectiveness, and the significance of over-fitting of these feature components. Furthermore, such analysis helps to improve the performance of DNNs. As a generic method, the feature complexity also provides new insights into existing deep-learning techniques, such as network compression and knowledge distillation. We will release the code when the paper is accepted.
PENGARUH TEKNIK MIND MAPPING BERBASIS MODEL DISCOVERY LEARNING TERHADAP KETERAMPILAN MENULIS TEKS LAPORAN HASIL OBSERVASI SISWA KELAS VII SMP NEGERI 12 PADANG

ABSTRACT The purpose of this study is three, namely, first, describing the writing skills of the report text of the observation of class VII students of SMP Negeri 12 Padang before using mind mapping techniques based on discovery learning models. Second, describe the writing skills of the report report observation text of grade VII students at SMP Negeri 12 Padang after using mind mapping techniques based on discovery learning models. Third, describing the effect of mind mapping techniques based on discovery learning models on the writing skills of the report's observation text in grade VII students at SMP Negeri 12 Padang. There are two data of this study, namely the score of the results of the test writing skills in the report text of the seventh grade students of SMP Negeri 12 before and after using the mind mapping technique based on the discovery learning model. Based on the results of the study, it was concluded three things, namely first, the writing skills of the report report observation text of class VII Padang 12 Public Middle School before using the mind mapping technique based on discovery learning models in the Enough qualifications (C) with an average value of 59.51. Second, the report writing skills of the observation results of the seventh grade students of SMP Negeri 12 Padang after using the mind mapping technique based on the discovery learning model are more than adequate (LdC) with an average value of 71.74. Third, there is a significant effect in the use of mind mapping techniques based on discovery learning models on the report writing skills of the observation results of class VII students at SMP Negeri 12 Padang because of the tcount&gt; t table (4.91&gt; 1.70). Kata kunci: Pengaruh, Teknik Mind Mapping, Model Discovery Learning, Laporan Hasil Observasi.
On the Transformation of Chemical Subject Knowledge to the Core Accomplishment of Chemical Subject

At present, the formation of core accomplishment of chemistry has become a hot spot of education research.Chemistry course teaching is no longer the knowledge in the textbook, but to cultivate and develop students, chemistry core accomplishment through the course teaching. This paper discusses the relationship between the subject knowledge and the subject core accomplishment, and explains how to build an efficient chemistry classroom teaching from the perspective of teachers, students and teaching activities, so as to realize the transformation from the subject knowledge to the subject core accomplishment.
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics

Humans are capable of reasoning about physical phenomena by inferring laws of physics from a very limited set of observations. The inferred laws can potentially depend on unobserved properties, such as mass, texture, charge, etc. This sample-efficient physical reasoning is considered a core domain of human common-sense knowledge and hints at the existence of a physics engine in the head. In this paper, we propose a Bayesian symbolic framework for learning sample-efficient models of physical reasoning and prediction, which are of special interests in the field of intuitive physics. In our framework, the environment is represented by a top-down generative model with a collection of entities with some known and unknown properties as latent variables to capture uncertainty. The physics engine depends on physical laws which are modeled as interpretable symbolic expressions and are assumed to be functions of the latent properties of the entities interacting under simple Newtonian physics. As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties. These inference and regression steps are performed in an iterative manner following the expectation–maximization algorithm to infer the unknown properties and use them to learn the laws from a very small set of observations. We demonstrate that on three physics learning tasks that compared to the existing methods of learning physics, our proposed framework is more data-efficient, accurate and makes joint reasoning and learning possible.
Collecting Dutch archaeological grey literature reports

Describe how DANS encourages researchers to make their digital research data available on the e-Depot for Dutch Archaeology.