Reviewerly/exHarmony huggingface dataset:
Multi-objective Path Optimization Method in Terminal Building Based on Improved Genetic Algorithm

In view of the existing route planning methods in airport terminals, factors such as real-time passenger flow changes and land-side traffic delay time are not taken into consideration. A route planning method for traffic transfer and navigation in the terminal building based on improved genetic algorithm is proposed. This paper builds a two-way network based on the spatial characteristics of the terminal building and environmental changes. Taking the path distance, time cost and the pedestrian density as the weight, and established a multi-objective path optimization model in the terminal. Finally, the genetic algorithm fused with BFS is used to achieve multi-objective path optimization in the terminal building in the multilayer road network established in this paper, and generate the optimal path considering path distance, congestion, and time cost. The experiment result shows that the multi-objective path optimization method can recommend three single-objective optimal paths with the smallest path distance, the lowest congestion and the least time cost.
Use, Perceptions, and Awareness of LibGuides among Undergraduate and Graduate Health Professions Students

Abstract Objective – This study investigated usage, perceptions, and awareness of library research guides created using Springshare’s LibGuides among undergraduate and graduate health professions students. Methods – The researchers recruited 100 health professions students in April 2017 from Hunter College, a senior college within the City University of New York system. Participants were asked to complete a paper survey to ascertain their use, perceptions, and awareness of Springhare’s LibGuides. Results – Nearly two-thirds of study participants were not aware of library-created LibGuides and 68% had never used this tool. Compared to undergraduates, graduate students were more likely to be aware of LibGuides. The use of LibGuides was higher among graduate respondents (43%) than their undergraduate counterparts (30%). The study found low awareness and use of LibGuides among health professions students overall, regardless of age, gender, academic level, and health sciences concentration. Physical therapy students were more likely to use and be familiar with LibGuides than nursing, medical laboratory sciences, and speech-language pathology and audiology students. Participants reported using general subject guides more than course-specific guides, and the most commonly used page was the Databases guide. Of those participants who had used LibGuides, the vast majority (97%) said they found them useful in their studies. Conclusion – This study demonstrates low usage and awareness of LibGuides among health professions students at a large urban public college. Findings suggest a need for academic libraries serving such students to develop and implement strategies to promote awareness and increase usage of online research guides. The researchers recommend instructing with LibGuides during information literacy sessions and demonstrating their usefulness during reference consultations. Additional strategies include linking LibGuides to course sites through learning management systems such as Blackboard and collaborating with faculty members to better inform students about the guides.
Patterns, predictions, and actions: A story about machine learning.

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning. A chapter on datasets as benchmarks examines their histories and scientific bases. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences. Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.
embedGAN: A Method to Embed Images in GAN Latent Space

Abstract GAN is an efficient generative model. By performing a latent walk in GAN, the generation result can be adjusted. However, the latent walk cannot start from a selected image. The embedGAN is proposed to embed selected images into GAN and remain the generation effect. It contains an embedded network and a generative network. Application cases of residential interior design are given in the article. With advantages of a low computing cost and short training time, embedGAN shows its potential. The embedGAN algorithm framework can be applied to various GANs.
A pseudo Karnaugh mapping approach for datasets imbalance

The problem of dataset imbalance has raised a wide concern in many machine learning areas, but not in non-intrusive load monitoring, or load disaggregation. In this study, a pictorial evaluation method is proposed to representation the imbalance class distribution in datasets. We colored a Karnaugh maps according to the quantities of different variables combination to offer a visual impact to the whole dataset. After utilizing this method on a public dataset and its testing result, a clear imbalanced abundance in the dataset and an exciting performance have been found. A preliminary Python package to realize this mapping method has been uploaded on GitHub a .
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to generate a coherent sentence describing an everyday scenario using these concepts (e.g., "a man throws a frisbee and his dog catches it"). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 79k commonsense descriptions over 35k unique concept-sets. Experiments show that there is a large gap between state-of-the-art text generation models (e.g., T5) and human performance. Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
AACP: Model Compression by Accurate and Automatic Channel Pruning

Channel pruning is formulated as a neural architecture search (NAS) problem recently. However, existing NAS-based methods are challenged by huge computational cost and inflexibility of applications. How to deal with multiple sparsity constraints simultaneously and speed up NAS-based channel pruning are still open challenges. In this paper, we propose a novel Accurate and Automatic Channel Pruning (AACP) method to address these problems. Firstly, AACP represents the structure of a model as a structure vector and introduces a pruning step vector to control the compressing granularity of each layer. Secondly, AACP utilizes Pruned Structure Accuracy Estimator (PSAE) to speed up the performance estimation process. Thirdly, AACP proposes Improved Differential Evolution (IDE) algorithm to search the optimal structure vector effectively. Because of IDE, AACP can deal with FLOPs constraint and model size constraint simultaneously and efficiently. Our method can be easily applied to various tasks and achieve state of the art performance. On CIFAR10, our method reduces $65\%$ FLOPs of ResNet110 with an improvement of $0.26\%$ top-1 accuracy. On ImageNet, we reduce $42\%$ FLOPs of ResNet50 with a small loss of $0.18\%$ top-1 accuracy and reduce $30\%$ FLOPs of MobileNetV2 with a small loss of $0.7\%$ top-1 accuracy. The source code will be released after publication.
Statistical Decision-Making by Distance Measures

Statistical decision-making or feature classification is the final step of a data-driven SHM strategy. This step utilizes features extracted from feature extraction algorithms for damage diagnosis including damage detection, localization, and quantification. In the context of SHM, novelty detection is an unsupervised learning approach to statistical decision-making. Among all novelty detection algorithms, distance-based methods are widely used approaches due to some advantages such as simplicity, computational efficiency, and being non-parametric. This chapter of the book is intended to evaluate some conventional statistical distance measures and then propose several new distance-based novelty detection methods via new distance measures. Depending upon the type of features in terms of being univariate or multivariate and the level of damage diagnosis (detection, localization, and quantification), the proposed methods are divided into univariate and multivariate distance algorithms. In most cases, in order to increase the reliability of damage diagnosis, the process of decision-making in SHM is carried out by threshold limits. This chapter also describes two well-known threshold estimation techniques.
Generalized net model of a biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations

In the current research work a multimodal biometric system is investigated. It combines the palm vein authentication and palm geometry recognition methods. The system will be used to manage the access control. The apparatus of generalized nets is applied to model the biometric authentication processes. The constructed generalized net model of biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations can be used for simulation of the real processes. The intuitionistic fuzzy evaluations are used to compare the user traits with the templates stored in database.
Research on recommendation algorithm based on collaborative filtering of fusion model

Abstract Based on the facts nowadays, recommendation systems are widely used, the focus of this research is how to effectively improve the accuracy and adaptability of such systems. The research proposes a collaborative filtering model algorithm based on the fusion model, which combines five classical algorithms: Singular value decomposition, Knn-Baseline, K-Means, Non-negativistic matrix factorization and SlopeOne algorithm. By combining the outputs of five models, and then carrying out regression and fusion, a collaborative filtering algorithm is obtained. The model can effectively integrate the advantages of the five models, and can carry out experimental validation on the datasets of Movielens-1M and Movielens-100k. The experimental results are more accurate than applying the five algorithms individually, and the regression model has good adaptability and predictability.
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals

We study the problem of agnostic learning under the Gaussian distribution. We develop a method for finding hard families of examples for a wide class of problems by using LP duality. For Boolean-valued concept classes, we show that the $L^1$-regression algorithm is essentially best possible, and therefore that the computational difficulty of agnostically learning a concept class is closely related to the polynomial degree required to approximate any function from the class in $L^1$-norm. Using this characterization along with additional analytic tools, we obtain optimal SQ lower bounds for agnostically learning linear threshold functions and the first non-trivial SQ lower bounds for polynomial threshold functions and intersections of halfspaces. We also develop an analogous theory for agnostically learning real-valued functions, and as an application prove near-optimal SQ lower bounds for agnostically learning ReLUs and sigmoids.
Modernization of Libraries: Use of e-resources in the academic library

This paper describes the use and use of e-resources in the academic library and analyses the status of use of e-resources.Information technology (IT) has made rapid changes in the field of education.Along with this, librarians will also have to adapt to this new development for storage and e-delivery and dissemination of information.
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records

One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure. EHR usually contains a mixture of structured (codes) and unstructured (free-text) data with sparse and irregular longitudinal features -- all of which doctors utilize when making decisions. In the deep learning regime, determining how different modality representations should be fused together is a difficult problem, which is often addressed by handcrafted modeling and intuition. In this work, we extend state-of-the-art neural architecture search (NAS) methods and propose MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across multimodal fusion strategies and modality-specific architectures for the first time. We demonstrate empirically that our MUFASA method outperforms established unimodal NAS on public EHR data with comparable computation costs. In addition, MUFASA produces architectures that outperform Transformer and Evolved Transformer. Compared with these baselines on CCS diagnosis code prediction, our discovered models improve top-5 recall from 0.88 to 0.91 and demonstrate the ability to generalize to other EHR tasks. Studying our top architecture in depth, we provide empirical evidence that MUFASA's improvements are derived from its ability to both customize modeling for each data modality and find effective fusion strategies.
Improving Accountability in Recommender Systems Research Through Reproducibility

Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving towards fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human-Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts, and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature, and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem, and facilitate progress in the field by increasing the accountability of research.
Smoothing the Curvature of Trajectory of Ground Robot in 3D Space

Purpose or research. Development of an algorithm for smoothing the trajectory of a ground robot over rough terrain, represented as a graph in three-dimensional space. Methods. This article presents the CSA (Curve Smoothing and Averaging) algorithm for smoothing local curves in the Oxy plane that make up a global curve, represented as a path on a connected graph in 3D space. The presented algorithm is based on the previously developed LRLHD-A * approach, which uses information about the vertices of the graph, their neighbors and the edges connecting them to select the area through which the smoothed curve will run. In order to avoid a broken curve at the output of the algorithm, a curve averaging method was developed, the idea of which is to shift the midpoints of local curves along the edges on which they are located. Results. An experimental comparison was made of the curvature of the trajectories obtained using the curve smoothing algorithm with curve averaging (CSA) and without it (CS). The method was carried out on a threedimensional map of the area, presented in the form of a graph with 100082 vertices. For the experiments, a sample of 10 pairs of random vertices was used, between which a path was built using the LRLHD-A * algorithm. The results of the experiments have shown that averaging the curve after smoothing reduces its curvature from 24 to 42%. Conclusion. Trajectories smoothed using the developed CSA algorithm have smoother curve bends at turns, compared to the algorithm taken as a basis. This allows the robots to move more smoothly and, as a consequence, reduce the consumption of the robot's battery.
Determining Optimal Tilt Angle to Maximize the PV Yield

The integration of renewable and clean solar energy through photovoltaic (PV) technologies is increasing rapidly all over the world. Well-planned and maximum usage of sunlight elevates the efficiency of PV panels. Due to the different geographical conditions of the earth, solar radiation is not the same for all locations. Optimum tilt angle helps the solar panels tap the highest solar irradiance, consequently, provides maximum PV electricity. In this work, a method of determining optimum tilt angle is presented, which is implemented in MATLAB environment. Eight sites of Okinawa prefecture of Japan are selected to measure the optimum tilt angles, which render the highest incident global solar radiation over the year. Results show that optimum tilt angle ranges from 1 degree to 58 degrees in Okinawa. It is also observed that optimum tilt angle based PV panel orientation obtains a significant increase in solar radiation on its tilted surface.
Presenting a dataset for collaborator recommending systems in academic social network

Collaborator finding systems are a special type of expert finding models. There is a long-lasting challenge for research in the collaborator recommending research area, which is the lack of the structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset is prepared for designing a consistent, collaborator finding system. The next one, called a co-author finding model, models an academic social network as a table that contains different relations between the pair of users. Both of them provide an opportunity for introducing potential collaborators to each other. These two models have been extracted from ResearchGate (RG) data set and are available publicly. RG dataset has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users. The dataset consists of almost complete information about users. In the preprocessing phase, the well-known Elmo was used for analyzing textual data. We call this as ResearchGate dataset for Recommending Systems (RGRS). For assessing the validity of data, we analyze each layer of data separately, and the results are reported. After preparing data and evaluating the collaborator finding models, we have done some assessments on RGRS. Some of these assessments are co-author, following-follower, and question answering relations. The outcomes indicate that it is the best relation in propagating knowledge in the network. To the best of our knowledge, there is no processed and analyzed dataset of this size.
A comparative analysis of automatic deep neural networks for image retrieval

Feature descriptor and similarity measures are the two core components in content-based image retrieval and crucial issues due to “semantic gap” between human conceptual meaning and a machine low-level feature. Recently, deep learning techniques have shown a great interest in image recognition especially in extracting features information about the images. In this paper, we investigated, compared, and evaluated different deep convolutional neural networks and their applications for image classification and automatic image retrieval. The approaches are: simple convolutional neural network, AlexNet, GoogleNet, ResNet-50, Vgg-16, and Vgg-19. We compared the performance of the different approaches to prior works in this domain by using known accuracy metrics and analyzed the differences between the approaches. The performances of these approaches are investigated using public image datasets corel 1K, corel 10K, and Caltech 256. Hence, we deduced that GoogleNet approach yields the best overall results. In addition, we investigated and compared different similarity measures. Based on exhausted mentioned investigations, we developed a novel algorithm for image retrieval.
IVIST: Interactive Video Search Tool in VBS 2021

This paper presents a new version of the Interactive VIdeo Search Tool (IVIST), a video retrieval tool, for the participation of the Video Browser Showdown (VBS) 2021. In the previous IVIST (VBS 2020), there were core functions to search for videos practically, such as object detection, scene-text recognition, and dominant-color finding. Including core functions, we newly supplement other helpful functions to deal with finding videos more effectively: action recognition, place recognition, and description searching methods. These features are expected to enable a more detailed search, especially for human motion and background description which cannot be covered by the previous IVIST system. Furthermore, the user interface has been enhanced in a more user-friendly way. With these enhanced functions, a new version of IVIST can be practical and widely-used for actual users.
Impacts of Augmented Reality in Legal Studies: Students’ Reflections

The use of augmented reality (AR) in education is relatively new particularly for legal studies. Yet, its use and application may benefit students of law schools not only in helping them to be more interactive in the learning process, but also engaging them in a more experiential education whereby students' learning motivation can be improved. Scarcity of past research and literature on the application of AR in legal studies has led to this study to be carried out primarily to bridge the gaps in the existing literature. Based on the above premises, this article focuses on exploring the impacts of AR in the context of legal studies from the perspective of the students. By employing a pure qualitative methodology, this article engaged with a case study method, whereby one of the law schools of a higher learning institution in Malaysia has been chosen. Survey data from the Google Form was exported for the purpose of analysis in the computer-aided qualitative analysis software (CAQDAS) ATLAS.ti version 8.4. In order to make data ready for analysis, codes were built using a purely inductive approach. It was found that the application of AR has had a significant impact on students of law school particularly within the domains of active learning, unique learning and helpfulness for their studies. The implication for the study is better understanding of the impacts of AR in legal studies among law students. Hopefully, the paper would shed light into the body of literature of technological studies, AR and virtual reality, particularly within the context of higher education.