Reviewerly/exHarmony huggingface dataset:
Digitizing Dissection: A Case Study on Augmented Reality and Animation in Engineering Education

Abstract Advancing virtual education through technology is an important step for engineering education. This has been made evident by the educational difficulties associated with the 2020 Covid-19 pandemic. Maintaining educational standards while using virtual learning is something possibly solved through researching new educational technologies. A potential technology that can enhance virtual education is Augmented Reality, since it can show information that would otherwise not be easily experienced or obtained. Traditional learning tools fail to offer the ability to control objects and explore numerous perspectives the way augmented reality can. Augmented reality can be even further enhanced through the addition of animation. Animation could add the ability to see motion, increasing overall understanding as well as increasing the motivation to learn. When motion is not visualized, it must be perceived, which can increase cognitive load and cause the limitations of working memory to be met. Reaching the limits of working memory has been shown to negatively affect learning. Therefore, the purpose of this study was to identify the impact of digitizing product dissection on engineering student learning and cognitive load. Specifically, we sought to identify the impact of Augmented Reality and Animations through a full factorial experiment with 61 engineering students. The results of the study show that the virtual condition with animation exhibited increased effectiveness as a learning tool. It also showed that augmented reality is not significantly different than a virtual environment in the context of product dissection. The results of this study are used to explore future uses of augmented reality and animation in education, as well as lay the groundwork for future work to further explore these technologies.
Self-orthogonal Codes over Fq + uFq and Fq + uFq + u 2Fq

In this paper, we establish a mass formula for Euclidean and Hermitian self-orthogonal codes over the finite ring Fq + uFq, where Fq is the finite field of order q and u2 = 0. We also establish a mass formula for Euclidean self-orthogonal codes over the finite ring Fq + uFq + u2Fq, with u3 = 0 and characteristic of Fq is odd. These mass formulas are used to give a classification of Euclidean and Hermitian self-orthogonal codes over F2 + uF2 and F3 + uF3 of small lengths.
EAdam Optimizer: How $\epsilon$ Impact Adam

Many adaptive optimization methods have been proposed and used in deep learning, in which Adam is regarded as the default algorithm and widely used in many deep learning frameworks. Recently, many variants of Adam, such as Adabound, RAdam and Adabelief, have been proposed and show better performance than Adam. However, these variants mainly focus on changing the stepsize by making differences on the gradient or the square of it. Motivated by the fact that suitable damping is important for the success of powerful second-order optimizers, we discuss the impact of the constant $\epsilon$ for Adam in this paper. Surprisingly, we can obtain better performance than Adam simply changing the position of $\epsilon$. Based on this finding, we propose a new variant of Adam called EAdam, which doesn't need extra hyper-parameters or computational costs. We also discuss the relationships and differences between our method and Adam. Finally, we conduct extensive experiments on various popular tasks and models. Experimental results show that our method can bring significant improvement compared with Adam. Our code is available at this https URL.
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions

Many existing approaches for estimating feature importance are problematic because they ignore or hide dependencies among features. A causal graph, which encodes the relationships among input variables, can aid in assigning feature importance. However, current approaches that assign credit to nodes in the causal graph fail to explain the entire graph. In light of these limitations, we propose Shapley Flow, a novel approach to interpreting machine learning models. It considers the entire causal graph, and assigns credit to \textit{edges} instead of treating nodes as the fundamental unit of credit assignment. Shapley Flow is the unique solution to a generalization of the Shapley value axioms to directed acyclic graphs. We demonstrate the benefit of using Shapley Flow to reason about the impact of a model's input on its output. In addition to maintaining insights from existing approaches, Shapley Flow extends the flat, set-based, view prevalent in game theory based explanation methods to a deeper, \textit{graph-based}, view. This graph-based view enables users to understand the flow of importance through a system, and reason about potential interventions.
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over $14000$ models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties encouraged by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered disentangled and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.
A software maintenance-focused process and supporting toolset for academic environments

Participating in a software engineering process is an important experience for students in a project-based software engineering course. Popular agile software engineering processes like Scrum were designed for use in environments that have significant differences from those in the classroom. These differences include the relative importance of student learning and growth with respect to that of producing a product, and the varying levels of experience, availability, and motivation of developers in the classroom. In curriculums that only have a single software engineering course or capstone course, this course may also be tasked with teaching students about various software engineering concepts and activities. A software maintenance-focused process designed for use in these types of environments can accommodate the large set of learning outcomes these courses are meant to provide. However, understanding and adhering to a process while learning how to work and communicate with other developers on a relatively large and unfamiliar software project is a daunting set of tasks for many novice developers. The importance of tool support for agile processes has been recognized, but little such support has been designed for these types of environments. This paper aims to articulate two primary contributions: 1) a software maintenance-focused process suitable for use in a software engineering classroom, and 2) a toolset to support this process and the environment in which it operates.
Research on the Innovation of College Mathematics Teaching Based on Computer Aided Technology under the Thought of Mathematical Modeling

Abstract Mathematical application plays an irreplaceable role in various scientific fields, especially in engineering technology and economic management. Mathematics is the basic subject of nine-year compulsory education, which occupies a large proportion on quality education in China. Mathematics learning is undoubtedly very important for engineering college students. If the university mathematics teaching integrates the mathematical modeling thought, the student will be more convenient to study the abstruse mathematics question. This method can effectively improve the teaching efficiency and students’ interest. At the same time, with the development of science and technology, computer technology has provided a lot of convenience for our study work, and the active use of computer-assisted technology in teaching can greatly improve the efficiency of education. Based on this, this article discusses the function of mathematical modeling and uses computer-aided technology to analyze the existing problems in mathematical teaching. Finally, the paper puts forward the innovative reform method of mathematics teaching.
Style Variation as a Vantage Point for Code-Switching

Code-Switching (CS) is a common phenomenon observed in several bilingual and multilingual communities, thereby attaining prevalence in digital and social media platforms. This increasing prominence demands the need to model CS languages for critical downstream tasks. A major problem in this domain is the dearth of annotated data and a substantial corpora to train large scale neural models. Generating vast amounts of quality text assists several down stream tasks that heavily rely on language modeling such as speech recognition, text-to-speech synthesis etc,. We present a novel vantage point of CS to be style variations between both the participating languages. Our approach does not need any external annotations such as lexical language ids. It mainly relies on easily obtainable monolingual corpora without any parallel alignment and a limited set of naturally CS sentences. We propose a two-stage generative adversarial training approach where the first stage generates competitive negative examples for CS and the second stage generates more realistic CS sentences. We present our experiments on the following pairs of languages: Spanish-English, Mandarin-English, Hindi-English and Arabic-French. We show that the trends in metrics for generated CS move closer to real CS data in each of the above language pairs through the dual stage training process. We believe this viewpoint of CS as style variations opens new perspectives for modeling various tasks in CS text.
No more 996: Understanding Deep Learning Inference Serving with an Automatic Benchmarking System.

Deep learning (DL) models have become core modules for many applications. However, deploying these models without careful performance benchmarking that considers both hardware and software's impact often leads to poor service and costly operational expenditure. To facilitate DL models' deployment, we implement an automatic and comprehensive benchmark system for DL developers. To accomplish benchmark-related tasks, the developers only need to prepare a configuration file consisting of a few lines of code. Our system, deployed to a leader server in DL clusters, will dispatch users' benchmark jobs to follower workers. Next, the corresponding requests, workload, and even models can be generated automatically by the system to conduct DL serving benchmarks. Finally, developers can leverage many analysis tools and models in our system to gain insights into the trade-offs of different system configurations. In addition, a two-tier scheduler is incorporated to avoid unnecessary interference and improve average job compilation time by up to 1.43x (equivalent of 30\% reduction). Our system design follows the best practice in DL clusters operations to expedite day-to-day DL service evaluation efforts by the developers. We conduct many benchmark experiments to provide in-depth and comprehensive evaluations. We believe these results are of great values as guidelines for DL service configuration and resource allocation.
Model-based Generation of Web Application Programming Interfaces to Access Open Data

In order to facilitate the reusing of open data from open data platforms’ catalogs, Web Application Programming Interfaces (APIs) are an important mechanism for reusers. However, there is a lack of suitable Web APIs to access data from open data platforms. Moreover, in most cases, the currently available APIs only allow to access catalog’s metadata or to download entire data resources (i.e. coarse-grain access to data), hampering the reuse of data. Therefore, we propose a model-based approach to automatically generate Web APIs from open data. Our generated Web APIs facilitate the access and reuse of specific data (i.e., providing fine-grain or query-level access to data), which will result in many societal and economic benefits such as transparency and innovation. With this approach we address open data publishers which will be able to include a Web API within their data, but also open data reusers in case of missing APIs. This APIfication process, which means the creation of APIs for every available dataset, is based on automatic, generic and standardised generation mechanisms. The performance and functioning of this approach is validated with different datasets, which successfully generates Web APIs that facilitate the reuse of data.
Minimax Classification with 0-1 Loss and Performance Guarantees

Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. MRCs achieve efficient learning and out-of-sample generalization by minimizing worst-case expected 0-1 loss w.r.t. uncertainty sets that are defined by linear constraints and include the true underlying distribution. In addition, MRCs' learning stage provides performance guarantees as lower and upper tight bounds for expected 0-1 loss. We also present MRCs' finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.r.t. state-of-the-art techniques using benchmark datasets.
Passport-aware Normalization for Deep Model Protection

Despite tremendous success in many application scenarios, deep learning facesserious intellectual property (IP) infringement threats. Considering the costof designing and training a good model, infringements will significantlyinfringe the interests of the original model owner. Recently, many impressiveworks have emerged for deep model IP protection. However, they either arevulnerable to ambiguity attacks, or require changes in the target networkstructure by replacing its original normalization layers and hence causesignificant performance drops. To this end, we propose a new passport-awarenormalization formulation, which is generally applicable to most existingnormalization layers and only needs to add another passport-aware branch for IPprotection. This new branch is jointly trained with the target model butdiscarded in the inference stage. Therefore it causes no structure change inthe target model. Only when the model IP is suspected to be stolen by someone,the private passport-aware branch is added back for ownership verification.Through extensive experiments, we verify its effectiveness in both image and 3Dpoint recognition models. It is demonstrated to be robust not only to commonattack techniques like fine-tuning and model compression, but also to ambiguityattacks. By further combining it with trigger-set based methods, both black-boxand white-box verification can be achieved for enhanced security of deeplearning models deployed in real systems. Code can be found athttps://github.com/ZJZAC/Passport-aware-Normalization.
Active Invariant Causal Prediction: Experiment Selection through Stability

A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data originating from different experimental environments, improves identifiability. However, the improvement depends critically on the target and nature of the interventions carried out in each experiment. Since in real applications experiments tend to be costly, there is a need to perform the right interventions such that as few as possible are required. In this work we propose a new active learning (i.e. experiment selection) framework (A-ICP) based on Invariant Causal Prediction (ICP) (Peters et al., 2016). For general structural causal models, we characterize the effect of interventions on so-called stable sets, a notion introduced by (Pfister et al., 2019). We leverage these results to propose several intervention selection policies for A-ICP which quickly reveal the direct causes of a response variable in the causal graph while maintaining the error control inherent in ICP. Empirically, we analyze the performance of the proposed policies in both population and finite-regime experiments.
Autonomous Flying of Drone Based on PPO Reinforcement Learning Algorithm

In this study, the performance of autonomous flight was analyzed by introducing the PPO method as reinforcement learning for autonomous flight of drones. A simulator based on the dynamics of a drone was produced, and the performance of autonomous flight was confirmed when reinforcement learning was applied to a drone using this simulator. After that, the possibility of autonomous flight was confirmed by applying the PPO algorithm to the actual drone. Also, a lightweight embedded PC was attached to the drone to perform independent calculations to simultaneously construct obstacle avoidance and path planning.
Needs Assessment Knowledge from Facilitator of Community Empowerment Program in the Digital Era

Cadres of Kampung KB as facilitators of community empowerment programs are required to have good knowledge in implementingÂ  needs assessmentÂ  withÂ adaptiveÂ dataÂ managementÂ with theÂ industrialÂ eraÂ 4.0.Â TheÂ  needs assessmentÂ  technique isÂ not only doneÂ manually but can use digital technologyÂ toÂ facilitate data collection and data access.Â However, the use of technology needs to be supported by the facilitator's knowledge of the needâ€™s assessment, because it is the basis for understanding the meaning of data tracing. This study uses a quantitative approach, in the form of descriptive statistics, namely interpreting the percentage data from the answers given by the respondents. From the results of the study, it was found that the cadres' needs assessment ability was included in the â€œEnoughâ€ category, while the competence that needed to be improved was knowledge about setting priority needs. So that the researchers suggest, it is necessary to hold competency training for KB cadres or village administrators which aims to provide understanding and skills in conducting needs assessment.Â
Fairness constraints can help exact inference in structured prediction

Many inference problems in structured prediction can be modeled as maximizing a score function on a space of labels, where graphs are a natural representation to decompose the total score into a sum of unary (nodes) and pairwise (edges) scores. Given a generative model with an undirected connected graph $G$ and true vector of binary labels, it has been previously shown that when $G$ has good expansion properties, such as complete graphs or $d$-regular expanders, one can exactly recover the true labels (with high probability and in polynomial time) from a single noisy observation of each edge and node. We analyze the previously studied generative model by Globerson et al. (2015) under a notion of statistical parity. That is, given a fair binary node labeling, we ask the question whether it is possible to recover the fair assignment, with high probability and in polynomial time, from single edge and node observations. We find that, in contrast to the known trade-offs between fairness and model performance, the addition of the fairness constraint improves the probability of exact recovery. We effectively explain this phenomenon and empirically show how graphs with poor expansion properties, such as grids, are now capable to achieve exact recovery with high probability. Finally, as a byproduct of our analysis, we provide a tighter minimum-eigenvalue bound than that of Weyl's inequality.
Neural Networks Learning and Memorization with (almost) no Over-Parameterization

Many results in recent years established polynomial time learnability of various models via neural networks algorithms. However, unless the model is linear separable, or the activation is a polynomial, these results require very large networks -- much more than what is needed for the mere existence of a good predictor. In this paper we prove that SGD on depth two neural networks can memorize samples, learn polynomials with bounded weights, and learn certain kernel spaces, with near optimal network size, sample complexity, and runtime. In particular, we show that SGD on depth two network with $\tilde{O}\left(\frac{m}{d}\right)$ hidden neurons (and hence $\tilde{O}(m)$ parameters) can memorize $m$ random labeled points in $\mathbb{S}^{d-1}$.
Modeling and Optimization Trade-off in Meta-learning

By searching for shared inductive biases across tasks, meta-learning promises to accelerate learning on novel tasks, but with the cost of solving a complex bilevel optimization problem. We introduce and rigorously define the trade-off between accurate modeling and optimization ease in meta-learning. At one end, classic meta-learning algorithms account for the structure of meta-learning but solve a complex optimization problem, while at the other end domain randomized search (otherwise known as joint training) ignores the structure of meta-learning and solves a single level optimization problem. Taking MAML as the representative meta-learning algorithm, we theoretically characterize the trade-off for general non-convex risk functions as well as linear regression, for which we are able to provide explicit bounds on the errors associated with modeling and optimization. We also empirically study this trade-off for meta-reinforcement learning benchmarks.
Approximation Based Variance Reduction for Reparameterization Gradients

Flexible variational distributions improve variational inference but are harder to optimize. In this work we present a control variate that is applicable for any reparameterizable distribution with known mean and covariance matrix, e.g. Gaussians with any covariance structure. The control variate is based on a quadratic approximation of the model, and its parameters are set using a double-descent scheme by minimizing the gradient estimator's variance. We empirically show that this control variate leads to large improvements in gradient variance and optimization convergence for inference with non-factorized variational distributions.