Reviewerly/exHarmony huggingface dataset:
Online Projected Gradient Descent for Stochastic Optimization With Decision-Dependent Distributions

This letter investigates the problem of tracking solutions of stochastic optimization problems with time-varying costs that depend on random variables with decision-dependent distributions. In this context, we propose the use of an online stochastic gradient descent method to solve the optimization, and we provide explicit bounds in expectation and in high probability for the distance between the optimizers and the points generated by the algorithm. In particular, we show that when the gradient error due to sampling is modeled as a sub-Weibull random variable, then the tracking error is ultimately bounded in expectation and in high probability. The theoretical findings are validated via numerical simulations in the context of charging optimization of a fleet of electric vehicles.
Graphonomics for the e-citizens: e-health, e-society and e-education

The Kinematic Theory of rapid movements, and its associated Sigma-Lognormal, model 2D spatiotemporal trajectories. It is constructed mainly as a temporal overlap of curves between virtual target points. Specifically, it uses an arc and a lognormal as primitives for the representation of the trajectory and velocity, respectively. This paper proposes developing this model, in what we call the Kinematic Theory Transform, which establishes a mathematical framework that allows further primitives to be used. Mainly, we evaluate Euler curves to link virtual target points and Gaussian, Beta, Gamma, Double-bounded lognormal, and Generalized Extreme Value functions to model the bell-shaped velocity profile. Using these primitives, we report reconstruction results with spatiotemporal trajectories executed by human beings, animals, and anthropomorphic robots.
FUTURE APPLICATION OF MULTISENSORY MIXED REALITY IN THE HUMAN CYBER—PHYSICAL SYSTEM

Mixed reality as an emerging technology can improve users’ experience. Using this technology, people can interact between virtual objects and the real world. Mixed reality has enormous potential for enhancing the human cyber — physical system for different manufacturing functions: planning, designing, production, monitoring, quality control, training, and maintenance. This study aims to understand the existing development of mixed reality technology in manufacturing by analysing patent publications from the InnovationQ-Plus database. Evaluations of trends in this developing technology have focused on qualitative literature reviews and insufficiently on patent technology analytics. Patents connected to mixed reality will be mapped to give technology experts a better grasp of present progress and insights for future technological development in the industry. Thus 709 patent publications are systematically identified and analysed to discover the technological trends. In addition, we map existing patent publications with manufacturing functions, and illustrate this with a technology function matrix. Finally, we identify future research in human cyber — physical system development by enhancing different human senses to give users more sensations while interacting with virtual objects. We provide insight into this human cyber — physical system development in three industries: automotive, food and beverage, and textiles.
Lossless coding of HDR color images in a floating point format using block-adaptive prediction with exponent equalization

This paper describes an efficient lossless coding method for HDR color images stored in a floating point format called radiance RGBE. In this method, three mantissa parts of RGB components as well as a common exponent part, each of which is represented in 8-bit depth, are encoded by the block-adaptive prediction technique. In order to improve the prediction accuracy, mantissa parts of RGB components used in the prediction are adjusted so that their exponent parts can be regarded as same. Moreover, not only the same color but also already encoded other color components are used in the prediction to exploit inter-color correlations. Simulation results indicate that introduction of the above exponent equalization as well as inter-color prediction can considerably improve the coding efficiency.
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments

Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: given a trained classifier, can we evaluate its accuracy on varying unlabeled test sets? In this work, we train semantic classification and rotation prediction in a multi-task way. On a series of datasets, we report an interesting finding, i.e., the semantic classification accuracy exhibits a strong linear relationship with the accuracy of the rotation prediction task (Pearson's Correlation r > 0.88). This finding allows us to utilize linear regression to estimate classifier performance from the accuracy of rotation prediction which can be obtained on the test set through the freely generated rotation labels.
Robust and Active Learning for Deep Neural Network Regression.

We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an capable of providing real-valued supervision (a regression target) for samples. For example, the oracle could be a numerical solver which, operationally, is much slower than the DNN. Given a discovered set of local error maximizers, the DNN is either fine-tuned or retrained in the manner of active learning.
Exploring the State of the Art in Legal QA Systems

Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. Question answering (QA) systems are designed to generate answers to questions asked in human languages. QA uses natural language processing to understand questions and search through information to find relevant answers. QA has various practical applications, including customer service, education, research, and cross-lingual communication. However, QA faces challenges such as improving natural language understanding and handling complex and ambiguous questions. Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. Moreover, we have established a public GitHub repository where we regularly upload the most recent articles, open data, and source code. The repository is available at: \url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}.
The quantum algorithm for graph isomorphism problem

The graph isomorphism (GI) problem is the computational problem of finding a permutation of vertices of a given graph $G_1$ that transforms $G_1$ to another given graph $G_2$ and preserves the adjacency. In this work, we propose a quantum algorithm to determine whether there exists such a permutation. To find such a permutation, we introduce isomorphic equivalent graphs of the given graphs to be tested. We proof that the GI problem of the equivalent graphs is equivalent to the GI problem of the given graphs. The idea of the algorithm is to determine whether there exists a permutation can transform the eigenvectors of the adjacency matrix of the equivalent graphs each other. The cost time of the algorithm is polynomial.
Secure Linear Regression Algorithms: A Comparison

The problem of secure linear regression calculation has been widely considered in the literature. It involves multiple parties, with a private dataset each, wanting to collectively carry out linear regression on the union of their datasets but are unable to combine the data due to privacy restrictions. The solutions suggested in the literature use different methods from cryptography to securely calculate the regression parameters while keeping the parties’ data private. In this paper, we compare the different algorithms in terms of security, efficiency and accuracy.
1950~1960년대 대학교 교지 소재 번역시 연구

This study is based on the research on the type of poems Korean college students translated in the 1950s and 60s. It is important to note that university journals show the variations of keywords from time to time. The main keywords in the university journals published in the 1950s were ‘Reconstruction’, ‘Existentialism’, and ‘Romanticism.’ Meanwhile, there were active discussions on ‘humanism’, ‘symbolism’, and ‘realism’ in the 1960s. The translations of foreign poems that were published in university journals in those times were closely related to these keywords. For instance, students in the 1950s translated foreign poems that spoke of death, absurdity, and voidness into Korean, in the reflection of the tragic experience of the Korean War from 1950 to 1953. During that time, college students were interested in British and American poetry.However, the students in the 1960s were more passionate about French and German poets. Therefore, many French and German poems that had symbolism, realism, and parnassianism were translated into Korean. This reflects the agony of Korean society under military dictatorship and students’ resistance against it. (Presbyterian University and Theological Seminary, Korea)
Relative Performance of Fisher Information in Interval Estimation

Maximum likelihood estimates and corresponding confidence regions of the estimates are commonly used in statistical inference. In practice, people often construct approximate confidence regions with the Fisher information at given sample data based on the asymptotic normal distribution of the MLE (maximum likelihood estimate). Two common Fisher information matrices (FIMs, for multivariate parameters) are the observed FIM (the Hessian matrix of negative log-likelihood function) and the expected FIM (the expectation of the observed FIM). In this article, we prove that under certain conditions and with an MSE (mean-squared error) criterion, approximate confidence interval of each element of the MLE with the expected FIM is at least as accurate as that with the observed FIM.
Language-based Decisions.

In Savage's classic decision-theoretic framework, actions are formally defined as functions from states to outcomes. But where do the state space and outcome space come from? Expanding on recent work by Blume, Easley, and Halpern (BEH), we consider a language-based framework in which actions are identified with (conditional) descriptions in a simple underlying language, while states and outcomes (along with probabilities and utilities) are constructed as part of a representation theorem. Our work expands the role of language from that of BEH by using it not only for the conditions that determine which actions are taken, but also the effects. More precisely, we take the set of actions to be built from those of the form do(phi), for formulas phi in the underlying language. This presents a problem: how do we interpret the result of do(phi) when phi is underspecified (i.e., compatible with multiple states)? We answer this using tools familiar from the semantics of counterfactuals: roughly speaking, do(phi) maps each state to the closest phi-state. This notion of closest is also something we construct as part of the representation theorem; in effect, then, we prove that (under appropriate assumptions) the agent is acting as if each underspecified action is first made definite and then evaluated (i.e., by maximizing expected utility). Of course, actions in the real world are often not presented in a fully precise manner, yet agents reason about and form preferences among them all the same. Our work brings the abstract tools of decision theory into closer contact with such real-world scenarios.
Multiview Detection with Shadow Transformer (and View-Coherent Data  Augmentation)

Multiview detection incorporates multiple camera views to deal with occlusions, and its central problem is multiview aggregation. Given feature map projections from multiple views onto a common ground plane, the state-of-the-art method addresses this problem via convolution, which applies the same calculation regardless of object locations. However, such translation-invariant behaviors might not be the best choice, as object features undergo various projection distortions according to their positions and cameras. In this paper, we propose a novel multiview detector, MVDeTr, that adopts a newly introduced shadow transformer to aggregate multiview information. Unlike convolutions, shadow transformer attends differently at different positions and cameras to deal with various shadow-like distortions. We propose an effective training scheme that includes a new view-coherent data augmentation method, which applies random augmentations while maintaining multiview consistency. On two multiview detection benchmarks, we report new state-of-the-art accuracy with the proposed system. Code is available at https://github.com/hou-yz/MVDeTr.
Agile-minded Organizational Excellence: Empirical Investigation

Each paper must start off with an abstract (with the exception of case studies). The The concept of organizational agility in UAE has been investigated widely in relation to the oil and gas sector and the information technology industry in which the most recent explorations were within the digital marketing field, however, limited research studies investigated organizational agility in relation to organizational excellence in service sector specifically healthcare settings. This study is planned to empirically test the impact of organizational agility on achieving organizational excellence in healthcare services organizations in the UAE healthcare services sector. A quantitative research design, using the surveys-based methodology by adopting two questionnaires to quantify the responses and opinions of the study participants. The study found that organizational agility practices that emphasize extensive sensing agility, and response agility when considered as a comprehensive system, have a significant positive effect on organizational excellence in the UAE healthcare services sector. While agile concepts that have not been explored extensively with respect to the healthcare sector, the study findings emphasized two forms of agility applicable for healthcare settings, sensing agility, and response agility where most of the efforts can be deployed by organizations to achieve excellence. Accordingly, top management in a healthcare setting should precede organizational excellence efforts with organizational agility initiatives. That is because agile organizations are the trend and agility is the valid reason for survival in such a turbulent environment accordingly management should invest in technology, communication channels, and highly mobile resources which are considered as the main enabler of organizational agility.
Single-dataset Experts for Multi-dataset Question Answering

Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets. Prior work has addressed this goal by training one network simultaneously on multiple datasets, which works well on average but is prone to over- or under-fitting different sub-distributions and might transfer worse compared to source models with more overlap with the target dataset. Our approach is to model multi-dataset question answering with a collection of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model. We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems.
Graph neural network-based fault diagnosis: a review

Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs. Indeed, this particular representation form has led to superior performance compared to traditional FD approaches. In this review, an easy introduction to GNN, potential applications to the field of fault diagnosis, and future perspectives are given. First, the paper reviews neural network-based FD methods by focusing on their data representations, namely, time-series, images, and graphs. Second, basic principles and principal architectures of GNN are introduced, with attention to graph convolutional networks, graph attention networks, graph sample and aggregate, graph auto-encoder, and spatial-temporal graph convolutional networks. Third, the most relevant fault diagnosis methods based on GNN are validated through the detailed experiments, and conclusions are made that the GNN-based methods can achieve good fault diagnosis performance. Finally, discussions and future challenges are provided.
Generic resources are what you need: Style transfer tasks without  task-specific parallel training data

Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source-target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. In practice, we adopt a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model (BART). First, we strengthen the model's ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource. Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process. Lastly, we let our best reresulting model generate static synthetic pairs to be used in a supervised training regime. Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients

Adaptive gradient methods have shown excellent performances for solving many machine learning problems. Although multiple adaptive gradient methods were recently studied, they mainly focus on either empirical or theoretical aspects and also only work for specific problems by using some specific adaptive learning rates. Thus, it is desired to design a universal framework for practical algorithms of adaptive gradients with theoretical guarantee to solve general problems. To fill this gap, we propose a faster and universal framework of adaptive gradients (i.e., SUPER-ADAM) by introducing a universal adaptive matrix that includes most existing adaptive gradient forms. Moreover, our framework can flexibly integrate the momentum and variance reduced techniques. In particular, our novel framework provides the convergence analysis support for adaptive gradient methods under the nonconvex setting. In theoretical analysis, we prove that our SUPER-ADAM algorithm can achieve the best known gradient (i.e., stochastic first-order oracle (SFO)) complexity of $\tilde{O}(\epsilon^{-3})$ for finding an $\epsilon$-stationary point of nonconvex optimization, which matches the lower bound for stochastic smooth nonconvex optimization. In numerical experiments, we employ various deep learning tasks to validate that our algorithm consistently outperforms the existing adaptive algorithms. Code is available at https://github.com/LIJUNYI95/SuperAdam
DVFL: A Vertical Federated Learning Method for Dynamic Data

Federated learning, which solves the problem of data island by connecting multiple computational devices into a decentralized system, has become a promising paradigm for privacy-preserving machine learning. This paper studies vertical federated learning (VFL), which tackles the scenarios where collaborating organizations share the same set of users but disjoint features. Contemporary VFL methods are mainly used in static scenarios where the active party and the passive party have all the data from the beginning and will not change. However, the data in real life often changes dynamically. To alleviate this problem, we propose a new vertical federation learning method, DVFL, which adapts to dynamic data distribution changes through knowledge distillation. In DVFL, most of the computations are held locally to improve data security and model efficiency. Our extensive experimental results show that DVFL can not only obtain results close to existing VFL methods in static scenes, but also adapt to changes in data distribution in dynamic scenarios.
Peltier Air Conditioner using Peltier Effect

Abstract: As we are moving towards the future the problem of global warming is emerging as one of the most important issues which concern the whole world. In recent years the world has come to a realization that if this problem is not dealt with now it will have devastating effects later. Nowadays we are experiencing the changes in the climate right before our eyes - unexpected climate change, rainfalls, Landslides, Air pollution reaching dangerous levels, melting off glaciers, Ozone Depletions, there are many such examples which are caused due to the increasing Global warming. To combat it we are switching to green infrastructure to mitigate its influence and that’s where the Peltier steps in Conventional AC make use of harmful gasses like CFC, HCFC, Ferons, R22, R410A but in the case of Peltier AC, it makes use of the intrinsic property of the Peltier chips which utilizes the Peltier effect to provide cooling. One of the most important advantages of the Peltier modules is that It doesn’t release any harmful gasses whatsoever. Peltier AC can be considered as the future of air cooling due to its advantage over its predecessors and can play a vital role in mitigating the problem which concerns the whole world. Keywords: Peltier Effect, Seebeck effect, Air conditioner, Green infrastructure, Global Warming, Peltier AC