Reviewerly/exHarmony huggingface dataset:
A Research Study to Increase Usage of PVs in Residential Areas

Self-generation of energy by residential houses has been met with many obstacles. When PV Solar energy technology is considered, the barriers manifest in problems related to the location, slope, strength, and shade exposure of house roofs are the most common. Therefore, it is not possible to meet daily energy needs from PV panels placed on the existing roofs of many houses. Solar Tracking Systems keep PV panels perpendicular to the Sun throughout the day, providing a significant increase in their efficiency. But the application of these systems on the roofs or houses is not suitable for many reasons, especially in terms of aesthetic appearance. This article is aimed at effectively showing how the slope and direction inconsistencies in the existing roofs of houses in residential areas cause great losses in the performance of PVs; also a research and design study is presented to find a solution to the application of Sun tracking systems in residential areas without creating aesthetic appearance problem. As a solution, combining a dual axis Sun tracking system with an aesthetic looking Gazebo has been considered. A design study was carried out for the targeted system, and the dimensions of a movable platform/roof such a system should have in order to meet the electricity needs of a house from the Sun throughout the year was investigated. How much energy the PV panels can collect annually is determined by a simulation program called “PV performance tool”.
Constructing the Sequential Event Graph for Event Prediction towards Cyber-Physical Systems

One of the primary goals of cyber-physical system is to deeply integrate cyberspace and the physical world to realize intelligent interaction of the system. Event prediction technique is a powerful means to fulfill this goal. Recently, a novel knowledge graph, the event graph, is widely studied in the field of event analysis due to its excellent ability in event relationship modeling. Therefore, this paper proposes constructing the event graph to model the sequential event evolution in the physical world for event prediction. To this end, the sequential event graph construction method and related event prediction mechanism for CPSs are proposed. First, a flexible and universal paradigm is designed to assist in extracting event instances from the data generated by physical devices. Then, an automatic event graph construction method based on frequent episode mining is proposed. Finally, the related prediction mechanism is designed, including the identification of contextual information a nd the prediction of subsequent events. A case study on car usage illustrates the feasibility of our approach. The flexibility and support for complexity are demonstrated by a comparative discussion.
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents’ communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05\(^{\circ }\)/2.21 mm, 8.62\(^{\circ }\)/2.36 mm and 5.93\(^{\circ }\)/0.89 mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size.
Stochastic Quantum Circuit Simulation Using Decision Diagrams

Recent years have seen unprecedented advance in the design and control of quantum computers. Nonetheless, their applicability is still restricted and access remains expensive. Therefore, a substantial amount of quantum algorithms research still relies on simulating quantum circuits on classical hardware. However, due to the sheer complexity of simulating real quantum computers, many simulators unrealistically simplify the problem and instead simulate perfect quantum hardware, i.e., they do not consider errors caused by the fragile nature of quantum systems. Stochastic quantum simulation provides a conceptually suitable solution to this problem: physically motivated errors are applied in a probabilistic fashion throughout the simulation. In this work, we propose to use decision diagrams, as well as concurrent executions, to substantially reduce resource-requirements-which are still daunting—for stochastic quantum circuit simulation. Backed up by rigorous theory, empirical studies show that this approach allows for a substantially faster and much more scalable simulation for certain quantum circuits.
Confident Semantic Ranking Loss for Part Parsing

Part parsing is taken as a dense prediction task, assigning each pixel a semantic part label. Some previous methods tried to model the human-known relationships among different parts (inter-part). However, these methods are hard to be used for multi-object part parsing since the given relationships are highly dependent on human priors which require the special model to learn. In addition, pixels in the same part (intra-part) are always assumed equally important. In fact, even they belong to the same part, some pixels are quite uncertain for their predictions while some are with high confidence, but theoretically they are representing the same feature. In this paper, we study the inequality and uncertainty of intra-part and inter-part pixels and propose the confident-semantic-ranking (CO-Rank) loss function, which maximizes the similarities of different groups of pixels to alleviate the uncertainty and models the pixel relationships among intra-/inter-parts. In addition, previous feature maps lost some of part-level relationships due to simply using the global max/average pooling, hence, we propose a new Global Object Pooling layer (GOP) to encode the abundant global information while preserving the geometry details. The experimental results show that our proposed method achieves new state-of-the-art performance on multi-class part parsing benchmark Pascal-Part dataset.
Test Data Reuse for the Evaluation of Continuously Evolving Classification Algorithms Using the Area under the Receiver Operating Characteristic Curve

Performance evaluation of continuously evolving machine learning algorithms presents new challenges, especially for high-risk application domains such as medicine. In principle, to obtain performance measures that generalize to a target population, a new independent test dataset randomly drawn from the target population should be used each time a new performance evaluation is required. However, test datasets of sufficient quality are often hard to acquire, and it is tempting to utilize a previously used test dataset for a new performance evaluation. With extensive experiments on simulated and real data we illustrate how such a "naive" approach to test data reuse can inadvertently result in overfitting the algorithm to the test data, resulting in a generalization loss and overly optimistic conclusions about the algorithm performance. We investigate the use of a modified version of the reusable holdout mechanism of Dwork et al. [Science, 349 (2015), pp. 636--638], which allows for repeated reuse of the same test dataset. We extend their approach to the use of AUC, the area under the receiver operating characteristic curve, as the reported performance metric. Theoretical guarantees for our method are proven to hold in extremely data-rich scenarios. However, our empirical results indicate promising performance of the proposed technique even on small data. With extensive simulation studies and experiments on real medical imaging data we show that our procedure indeed substantially reduces the problem of overfitting to the test data, even when the test dataset is small, at the cost of a mild additional uncertainty on the reported test performance.
Performance Analysis of Deep Learning Architectures for Super Resolution

Abstract Super resolution (SR) being one of the computer vision tasks with increasing applications in modern scenarios, several challenging factors are still prominent despite the numerous breakthroughs achieved in this field in recent years. Introduction of deep convolutional neural networks has brought a booming development to the existing SR techniques tackling many unsolved challenges. As an attempt to perform a relative analysis between currently used methods, this paper explores and establish the capability of Enhanced and Wide super resolution networks. These models are encompassed with improved residual networks with an aim to achieve a higher accuracy with reduced memory usage. The models trained with DIV2K dataset are evaluated using the T91 dataset and found to be showcasing a reliable performance in comparison with other cutting-edge methods devised for super resolution.
A framework to enhance generalization of deep metric learning methods using general discriminative feature learning and class adversarial neural networks

Deep Metric Learning (DML) methods automatically extract features from data and learn a non-linear transformation from the input to a semantically embedding space. Many DML methods focused to enhance the discrimination power of the learned metric by proposing novel sampling strategies or loss functions. This approach is very helpful when both the training and test examples are selected from the same set of categories. However, it is less effective in many applications of DML such as image retrieval and person-reidentification. Here, the DML should learn general semantic concepts from observed classes and employ them to rank or identify objects from unseen categories. Neglecting the generalization ability of the learned representation and just emphasizing to learn a more discriminative embedding on the observed classes may lead to the overfitting problem. To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network. To learn a general representation, we employ feature maps of intermediate layers in a deep neural network and enhance their discrimination power through an attention mechanism. Besides, a class adversarial network is utilized to force the deep model to seek class invariant features. We evaluate our work on widely used machine vision datasets in a ZSL setting. Extensive experimental results confirm that our framework can improve the generalization of existing DML methods, and it consistently outperforms baseline DML algorithms on unseen classes.
Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&amp;R-CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health &amp; Medical” category. The experimental results confirm the conclusion that RR&amp;R-CNN has a better effect compared to a traditional method.
A Language Prior Based Focal Loss for Visual Question Answering

According to current research, one of the major challenges in Visual Question Answering (VQA) models is the overdependence on language priors (and neglect of the visual modality). VQA models tend to predict answers only based on superficial correlations between the first few words in question and frequency of related answer candidates. To address this issue, we propose a novel Language Prior based Focal Loss (LP-Focal Loss) by rescaling the standard cross entropy loss. Specifically, we employ a question-only branch to capture the language biases for each answer candidate based on the corresponding question input. Then, the LP-Focal Loss dynamically assigns lower weights to biased answers when computing the training loss, thereby reducing the contribution of more-biased instances in the train split. Extensive experiments show that the LP-Focal Loss can be generally applied to common baseline VQA models, and achieves significantly better performance on the VQA-CP v2 dataset, with an overall 18% accuracy boost over benchmark models.
Integrating Design Thinking in Freshmen-Level Engineering Curriculum

This work presents embedding a design thinking teaching module within an existing engineering undergraduate lab course. The lab, which is freshmen level, gradually builds up critical skills beyond the common mathematical and analytical skills. Such skills are becoming mandatory by the fourth industrial revolution and jobs of future skillset. It gradually and solidly introduces design thinking concepts through a number of experiments where students are fully exposed to the stages of design thinking: empathize, define, ideate, prototype and test. Such skills are needed to address problems, common in modern technological advances, which are not well defined and require out of the box innovation. The lab, which was primarily offered on-line due the COVID-19 pandemic, was taken by more than 100 freshmen level students, required a final design project. The student learning outcomes were acquired and analyzed.
Cooperative classification method for multi-fault diagnosis of machinery based on parameterized wavelet kernel extreme learning and sparse representation

Owing to complicated manual design and feature extraction processes of mechanical multi-fault classification and recognition methods, single-class classifiers do not adapt well to the high dimensionality and high self-similarity among mechanical vibration signals with nonlinear characteristics, resulting in insufficient classification accuracy. Combining the adaptive learning and feature extraction capabilities of the wavelet kernel-based extreme learning machine (WKELM) with the sparse representation classifier (SRC), this paper proposes a hybrid artificial bee colony (ABC)-optimized Morlet wavelet kernel-based extreme learning machine (ABC-MWKELM) with SRC. The two-level classifier is capable of cooperatively classifying multiple mechanical faults. First, a Morlet wavelet kernel-based extreme learning machine (MWKELM) model that matches the kernel function construction condition is presented. An ABC optimization algorithm is adopted to optimize critical parameters of the MWKELM model, thereby improving the accuracy and generalization ability of the model. In addition, the ABC algorithm is used to optimize the MWKELM model as a pre-classifier for classifying test samples. According to the posterior probability of each training sample category output, the threshold decision method selects candidate categories with a high confidence coefficient. When the preset meets the threshold, the classification result of the ABC-optimized MWKELM is directly adopted. Otherwise, the candidate training samples are used to construct a dictionary and the SRC is used to classify the test samples further. The experimental results demonstrate that compared with the ELM, WKELM, (ELM-SRC), and radial basis function (RBF)-KELM and SRC (RBFKELM-SRC), the method presented in this paper has significant advantages in terms of accuracy of mechanical multi-fault classification. The validity and applicability of this method in mechanical multi-fault classification were verified.
Learning Ising models from one or multiple samples

There have been two main lines of work on estimating Ising models: (1) estimating them from multiple independent samples under minimal assumptions about the model's interaction matrix ; and (2) estimating them from one sample in restrictive settings. We propose a unified framework that smoothly interpolates between these two settings, enabling significantly richer estimation guarantees from one, a few, or many samples.
A Systematic review of wearable devices for tracking physiological indicators of Dementia related difficulties

We present a systematic review of wearable devices used for predicting or identifying dementia-related agitation. In the review, six named and described devices were found in the literature and each was evaluated and reviewed to identify their strengths and weaknesses, with all possessing at least one weakness which made it not ideal for future research on the prediction or detection of dementia-related agitation. Only two of the wearables contained all three of the desired sensing modalities, with one of those devices being prohibitively expensive for use in many applications and studies and the other being untested and unevaluated as well as not being available commercially or for use. Future work should focus on the development of a device which contains the three desired sensing modalities while remaining inexpensive and usable enough to be accessible for use in a wide variety of studies and applications.
A Combinatorial Design for Cascaded Coded Distributed Computing on General Networks

Coding theoretic approaches have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li et al. can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li et al. for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.
Image Compression Method Based on Analysis of Energy Features

Currently, many information processing and control systems are based on computer vision technologies. Among the many problems that need to be solved in such systems, it is important to reduce the redundancy of information in the image. To solve this problem, various methods of image compression are used, including those based on the wavelet transform. This paper is devoted to one of these methods. The proposed method is based on the calculation and analysis of energy features, which allow us to assess the significance of the detailing coefficients for the representation of the image in a compressed form. As a result of its application, a matrix of approximating coefficients of the initial level, significance maps and sets of significant detailing coefficients of various levels of the wavelet transform are formed. To improve the compression quality, significant detailing coefficients are normalized. In addition, you can apply entropy coding for each level of data. The described method is simple to implement and provides flexible parameter settings to obtain the required image compression quality indicators.
Target Detection of Forward Vehicle Based on Improved SSD

Vehicle detection plays a vital role in vehicle assisted driving. Aiming at the low accuracy and missed detection problems of SSD algorithm for vehicle detection, an improved SSD algorithm is proposed. In order to extract more vehicle feature information, we propose a SSD network with ResNet50 as the backbone. Aiming at the problem of low recognition rate of small target vehicles, the Feature Fusion Model was designed by fusing the position information of shallow features with the semantic information of deep features; in order to improve the performance of the model, we inserted SE block in the feature extraction layer to analyze different features. The importance of the channel is recalibrated. The experimental results show that the improved method has an average accuracy of 83.09% on the self-made vehicle data set, which is 3.23% higher than the accuracy of the previous algorithm.
On Polymorphic Sessions and Functions

This work exploits the logical foundation of session types to determine what kind of type discipline for the Λ-calculus can exactly capture, and is captured by, Λ-calculus behaviours. Leveraging the proof theoretic content of the soundness and completeness of sequent calculus and natural deduction presentations of linear logic, we develop the first mutually inverse and fully abstract processes-as-functions and functions-as-processes encodings between a polymorphic session π-calculus and a linear formulation of System F. We are then able to derive results of the session calculus from the theory of the Λ-calculus: (1) we obtain a characterisation of inductive and coinductive session types via their algebraic representations in System F; and (2) we extend our results to account for value and process passing, entailing strong normalisation.