Reviewerly/exHarmony huggingface dataset:
Comparison between the backpropagation and single exponential smoothing method in sugar production forecasting case

Sugar is a strategic commodity, because it is one of the basic needs consumed by the community. The decline in sugar production causes turmoil both economically and politically. So companies must be able to balance sugar production in accordance with market demand. Because, if the sugar production exceeds market demand, it will cause accumulation and expenditure costs that exceed the limit. Conversely, if the sugar production is too little, then the company is unable to meet demand. This study was comparing the Single Exponential Smoothing method and the Backpropagation method in predicting the amount of sugar production. The data used was based on production in 2013-2017, with predictions every month starting in the fifth month. The alpha parameter in Single Exponential Smoothing was 0.84, while in Backpropagation used 5 input nodes and 5 hidden layer nodes. From the results of tests that have been carried out, it can be seen that the Backpropagation method produces the smallest MSE and MAPE, amounting to 57187817.49 and MAPE of 1.012% compared to the Single Exponential Smoothing method with MSE of 83602989.43 and MAPE of 1.46%.
Move Your Body: Engaging Museum Visitors with Human-Data Interaction

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user (mode type) next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

We categorize meta-learning evaluation into two settings: $\textit{in-distribution}$ [ID], in which the train and test tasks are sampled $\textit{iid}$ from the same underlying task distribution, and $\textit{out-of-distribution}$ [OOD], in which they are not. While most meta-learning theory and some FSL applications follow the ID setting, we identify that most existing few-shot classification benchmarks instead reflect OOD evaluation, as they use disjoint sets of train (base) and test (novel) classes for task generation. This discrepancy is problematic because -- as we show on numerous benchmarks -- meta-learning methods that perform better on existing OOD datasets may perform significantly worse in the ID setting. In addition, in the OOD setting, even though current FSL benchmarks seem befitting, our study highlights concerns in 1) reliably performing model selection for a given meta-learning method, and 2) consistently comparing the performance of different methods. To address these concerns, we provide suggestions on how to construct FSL benchmarks to allow for ID evaluation as well as more reliable OOD evaluation. Our work aims to inform the meta-learning community about the importance and distinction of ID vs. OOD evaluation, as well as the subtleties of OOD evaluation with current benchmarks.
Fuzzy Multi-Criteria Decision-Making: Example of an Explainable Classification Framework

Explanation, or system interpretability, has always been important in applications where critical decisions need to be made, for example in the justice system or biomedical applications. In artificial intelligence and machine learning, there is an ever increasing need for system interpretability. This paper investigates a Fuzzy Multi-Criteria Decision-Making (MCDM) model as the basis for an interpretable framework for explainable classification. The proposed framework includes a Fuzzy Inference System paired with a modified MCDM-based model for data-driven classification. The modular nature of MCDM allows for the development of a model-based layer capable of generating factual and counterfactual explanations. Results on a ‘Titanic’ survivors’ dataset classification, which illustrates a minimal trade-off in predictive performance while gaining textual and graphical explanation, autonomously provided by the proposed model-based MCDM framework.
Mobile Augmented Reality Application In Medical Education In The 2010s

In the recent years, medical education is facing various difficulties. Students are struggling to visualize the internal organs and bones, it is insufficient cadaver for dissection demonstration, there are lack of equipment for training, and not enough opportunity for hands-on training. Technologies have been utilized to supplement teaching in medical education to overcome the mentioned difficulties. Augmented Reality in particular mobile Augmented Reality has shown a rise in the application of medical education in this decade. This paper aims to do a narrative review to study the overall progress and development of mobile Augmented Reality application in medical education from year 2010 to 2020. From the review done, we found that the application of mobile Augmented Reality in medical education is feasible, it is well accepted by medical students. We also identified current research gaps and potential future development of mobile Augmented Reality in medical education. It was found that there is lack of research done to study long term effect on the users. Interaction between virtual object and real-world object was lacking in some applications. More refinement and update are needed on flow, media and design of Augmented Reality application. There is also essential to study the influence of marker size or surface material.
On Large-Cohort Training for Federated Learning

Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each round (the cohort size) impacts the quality of the learned model and the training dynamics of federated learning algorithms. Our work poses three fundamental questions. First, what challenges arise when trying to scale federated learning to larger cohorts? Second, what parallels exist between cohort sizes in federated learning and batch sizes in centralized learning? Last, how can we design federated learning methods that effectively utilize larger cohort sizes? We give partial answers to these questions based on extensive empirical evaluation. Our work highlights a number of challenges stemming from the use of larger cohorts. While some of these (such as generalization issues and diminishing returns) are analogs of large-batch training challenges, others (including training failures and fairness concerns) are unique to federated learning.
The Effect of Psychological Contract Fulfillment on Employees’ Outcomes: Employee Commitment and Job Satisfaction in Education sector

This work aims to study the effect of psychological contract fulfilment on an employee outcome of satisfaction and commitment in the Education sector. The universities that put additional effort in maintaining and achieving upper level of fulfillment of psychological contract between employer, employees, and universities have satisfied and committed employees. The study had been conducted in Private Universities of National Capital Region. Descriptive reasearch had been conducted to study the effect of psychological contract fulfilment on employee outcomes of employee commitment and job satisfaction. This quantitative study based upon data collection through questionnaire. The data was collected from 50 employees involved in teaching in Private Universities of National Capital Region. The study proves that there is a positive and significant relationship between psychological contract and employee outcomes of commitment and satisfaction after analysis of data. Hence, psychological contract fulfillment has a significant effect on employee outcomes of commitment and satisfaction.
Adversarial Scrubbing of Demographic Information for Text Classification.

Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In this paper, we present an adversarial learning framework “Adversarial Scrubber” (AdS), to debias contextual representations. We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions. We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing. Experimental evaluations on 8 datasets show that AdS generates representations with minimal information about demographic attributes while being maximally informative about the target task.
Blockchain Operational Environment, Its Applications and Issues

In recent years, Blockchain technology is gaining huge attention of the researchers. The main reason behind this attention is its various attractive features like decentralization, immutability, auditability, security and wider application areas. Blockchain is a distributed ledger that securely keeps all the transactions on a decentralized peer-to-peer network. The Blockchain technology itself is very complex in nature as it involves several complex sub concepts and its implementation also demands good expertise [1-2]. This is one of the primary reasons behind low adoption of Blockchain. The objective of this paper is to present the basic structure of Blockchain and its key features, classification of Blockchain and then disclosing its vital role in various sectors like health, supply chain, government, education and IoT (Internet of Things). In addition, this study is also explaining precisely the working of various consensus algorithms such as proof of work, proof of stake, proof of authority, proof of burn and their issues. To achieve the objectives, research studies published in reputed journals have been reviewed. This study uncovers several open issues of the Blockchain technology like scalability, high energy consumption and low transaction rate. The implications of this study is that it will surely help the scholars, academicians and practitioners to understand the phenomena of Blockchain and gives useful insights for improvements by using Blockchain with other technologies such as artificial intelligence, internet of things (IoT), big data and cloud computing. Satoshi Nakamoto was first introduced the concept of blockchain technology. Blockchain is a decentralized and immutable ledger, which works on peer to peer network. This technology is used in combination with other technologies like cryptography, encryption, consensus protocol and peer-to-peer (P2P) networks. The consensus mechanism is used by the Blockchain that allows everyone to mutually agree on the creation of a new block [3-4]. In Blockchain, every block is cryptographically connected to the previous block except the genesis block as it is the first block of the entire chain. Every single block holds transactions up to 1 MB typically on a bit coin protocol and it may vary from protocol to protocol. Despite of its several benefits, the Blockchain technology itself is very complex in nature as it involves several complex sub concepts and its implementation also demands good expertise in several related tools.
Learning Universal Authorship Representations.

Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
Hitting your MARQ: Multimodal ARgument Quality Assessment in Long Debate Video

The combination of gestures, intonations, and textual content plays a key role in argument delivery.However, the current literature mostly considers textual content while assessing the quality of an argument, and is limited to datasets containing short sequences (18-48 words).In this paper, we study argument quality assessment in a multimodal context, and experiment on DBATES, a publicly available dataset of long debate videos.First, we propose a set of interpretable debate-centric features such as clarity, content variation, body movement cues, and pauses, inspired by theories of argumentation quality.Second, we design the Multimodal ARgument Quality assessor (MARQ) -a hierarchical neural network model that summarizes the multimodal signals on long sequences and enriches the multimodal embedding with debate-centric features.Our proposed MARQ model achieves an accuracy of 81.91% on the argument quality prediction task and outperforms established baseline models with an error rate reduction of 22.7%.Through ablation studies, we demonstrate the importance of multimodal cues in modeling argument quality.
Automatic Bilingual Markup Transfer.

We describe the task of bilingual markup transfer, which involves placing markup tags from a source sentence into a fixed target translation. This task arises in practice when a human translator generates the target translation without markup, and then the system infers the placement of markup tags. This task contrasts from previous work in which markup transfer is performed jointly with machine translation. We propose two novel metrics and evaluate several approaches based on unsupervised word alignments as well as a supervised neural sequence-to-sequence model. Our best approach achieves an average accuracy of 94.7% across six language pairs, indicating its potential usefulness for real-world localization tasks.
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community

Drawing causal conclusions from observational real-world data is a very much desired but a challenging task. In this paper we present mixed-method analyses to investigate causal influences of publication trends and behavior on the adoption, persistence and retirement of certain research foci – methodologies, materials, and tasks that are of interest to the computational linguistics (CL) community. Our key findings highlight evidence of the transition to rapidly emerging methodologies in the research community (e.g., adoption of bidirectional LSTMs influencing the retirement of LSTMs), the persistent engagement with trending tasks and techniques (e.g., deep learning, embeddings, generative, and language models), the effect of scientist location from outside the US e.g., China on propensity of researching languages beyond English, and the potential impact of funding for large-scale research programs. We anticipate this work to provide useful insights about publication trends and behavior and raise the awareness about the potential for causal inference in the computational linguistics and a broader scientific community.
Label Noise SGD Provably Prefers Flat Global Minimizers

In overparametrized models, the noise in stochastic gradient descent (SGD) implicitly regularizes the optimization trajectory and determines which local minimum SGD converges to. Motivated by empirical studies that demonstrate that training with noisy labels improves generalization, we study the implicit regularization effect of SGD with label noise. We show that SGD with label noise converges to a stationary point of a regularized loss $L(\theta) +\lambda R(\theta)$, where $L(\theta)$ is the training loss, $\lambda$ is an effective regularization parameter depending on the step size, strength of the label noise, and the batch size, and $R(\theta)$ is an explicit regularizer that penalizes sharp minimizers. Our analysis uncovers an additional regularization effect of large learning rates beyond the linear scaling rule that penalizes large eigenvalues of the Hessian more than small ones. We also prove extensions to classification with general loss functions, SGD with momentum, and SGD with general noise covariance, significantly strengthening the prior work of Blanc et al. to global convergence and large learning rates and of HaoChen et al. to general models.
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models.

Enthymemes are defined as arguments where a premise or conclusion is left implicit. We tackle the task of generating the implicit premise in an enthymeme, which requires not only an understanding of the stated conclusion and premise but also additional inferences that could depend on commonsense knowledge. The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model. To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020). However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes. We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.
Technology and Truth. Reflections on Russia, America, and Live Not By Lies

The main thesis of the author of the essay is that in contemporary Western society the objective and subjective truth is created by the people. This is the key to what the author calls technological and technocratic society for which truth and reason are no longer the goals that the man should be striving for in his thoughts and behavior. In a technocratic society the reality itself is created and thus accordingly the term “lie” loses its meaning. Anything can potentially be viewed as a useful step in the process of creating something new and “efficient” for those who above all value the strengthening of their own control (over people, nature, etc). Subjects acting in the framework of technological society deny any identifications and technologically determine themselves what man, nature and society is, – right down to the basics of our biological existence. This approach overturns such notion as absolute truth, establishing relevant truth. In contemporary Western society subjective and objective truth is created. The author examines in detail how this approach is used by the United States in their international policy to achieve dominance. He also considers Russia to be the only great power that is prepared to choose a different line of development due to its historical development. The possibility of such choice is capable to prevent final Americanization of Europe, which, should it happen, would lead to the “mankind as a whole losing its past”, as Simone Weil predicted.
PENERAPAN METODE PQ4R UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PEMAHAMAN SISWA KELAS III SEKOLAH DASAR

This research is motivated by the lack of ability of third grade students in one of the elementary schools in Cidadap District, Bandung City of understanding a text. The purpose of this study is to describe the application of the PQ4R method to improve reading comprehension skills of third grade students. The research method used is Classroom Action Research with Kemmis and Mc Taggart models. Data collection techniques, namely: observation, testing, and study documentation. Qualitative data were analyzed using data reduction, data presentation, and data inference. Quantitative data were analyzed using averages and percentages. The results showed that the application of the PQ4R method (preview, question, read, reflect, recite, review) had been able to improve reading comprehension skill of third grade students in elementary school.
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing

Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are learned over a distributed network of heterogeneous devices; here, the need to keep data on device and perform local training makes it difficult to efficiently train and evaluate configurations. In this work, we investigate the problem of federated hyperparameter tuning. We first identify key challenges and show how standard approaches may be adapted to form baselines for the federated setting. Then, by making a novel connection to the neural architecture search technique of weight-sharing, we introduce a new method, FedEx, to accelerate federated hyperparameter tuning that is applicable to widely-used federated optimization methods such as FedAvg and recent variants. Theoretically, we show that a FedEx variant correctly tunes the on-device learning rate in the setting of online convex optimization across devices. Empirically, we show that FedEx can outperform natural baselines for federated hyperparameter tuning by several percentage points on the Shakespeare, FEMNIST, and CIFAR-10 benchmarks, obtaining higher accuracy using the same training budget.
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics

Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and model-based planners are. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce Learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning.
Smart plugs invite cyber criminals into the home

The Internet of Things (IoT) is one of the fastest-growing technology markets and has steadily picked up steam in recent years as technology improves and price points drop, with the global IoT market forecast to reach revenues of $1.1tr by 2024. 1 Fields including manufacturing, healthcare and transport have benefited hugely from the ability to create linked networks of smart devices, facilitating a growing level of operational automation and visibility.