Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization

Classification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used ’heart and iris’ datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown.
CADNet: Top-Down Contextual Saliency Detection Network for High Spatial Resolution Remote Sensing Image Shadow Detection

In order to improve the feature richness of remote sensing images and meet the needs of remote sensing image interpretation, shadow detection has become a hotspot in high-resolution remote sensing (HSR) research. Traditional threshold-based and machine learning-based methods do show their effectiveness, but they may not take the inherent details of the shadow into consideration, and it is difficult to cope with the saliency and intricate distribution pattern of the shadow in HSR images, which results in the lack of robustness in extracting sufficient global and local shadow contexts. To solve those problems, a top-down contextual saliency detection network (CADNet) is proposed. Compared with the traditional shadow detection network, more contextual information can be retained by the double-branch strategy of the encoder and residual dilation upsampling of the decoder in CADNet. The low-level and high-level semantic information can be combined to accurately predict the salient regions through the proposed short connection. The proposed CADNet is evaluated on a public shadow detection dataset, and the experimental results demonstrate the effectiveness of the proposed CADNet.
Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively. The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.90S and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.
Learning-based Fast Path Planning in Complex Environments

In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly find a feasible path in complex environments or even fail. However, our proposed framework can overcome this difficulty by using a learning-based prediction module and a sampling-based path planning module. The prediction module utilizes an auto-encoder-decoder-like convolutional neural network (CNN) to output a promising region where the feasible path probably lies in. In this process, the environment is treated as RGB image to feed in our designed CNN module, and the output is also RGB image. No extra computation is required so that we can maintain a high processing speed of 60 frame-per-second (FPS). Incorporated with a sampling-based path planner, we can extract a feasible path from the output image so that the robot can track it from start to goal. To demonstrate the advantage of the proposed algorithm, we compare it with conventional path planning algorithms in a series of simulation experiments. The results reveal that the proposed algorithm can achieve much better performance in terms of planning time, success rate, and path length.
Immersive Technologies

Few digital technologies have captured the world's imagination as much as the cluster of immersive experiences usually labelled virtual reality, augmented reality, and extended reality. A certain mythology has grown up around these technologies, their purpose, application, benefits, and risks. This chapter addresses these elements, offering insights into real-world applications and some thoughts about how the technologies could evolve. A limiting factor when writing about immersive technologies is the lack of comprehensive research into real-life applications and their long-term effects. In addition, there is a tendency for potential users and commentators to become overly optimistic about the latest developments in this niche. This research adopts an interpretivist qualitative approach, based on a review of existing literature and web sources, and the author's personal experiences as an industry professional. What is clear is that these technologies are not a passing fad and are likely to shape the human experience in social, economic, and technological terms in future years.
Visual Analytics Meets Process Mining: Challenges and Opportunities

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Visual Analytics integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. In other words, Visual Analytics is the science of analytical reasoning facilitated by interactive interfaces and captures the information discovery process keeping the human in the loop. Process Mining aims to extract information and knowledge from event logs to discover, monitor, and improve processes in a variety of application domains. Such event data or traces of activities often possess data quality issues as well as exhibit unexpected behavior and complex relations. Consequently, before and during the implementation of interactive (automated or semi-automated) analysis methods, such as Process Mining algorithms, the analyst needs to explore, investigate, and understand the data at hand in order to decide which analysis methods might be appropriate. The combination of interactive visual data analyses and exploration with Process Mining algorithms makes complex information structures more comprehensible and facilitates new insights. In this talk, I will illustrate the concepts of Visual Analytics, how Visual Analytics combined with Process Mining techniques supports to extract more insights from complex event data, and elaborate about the challenges and opportunities for analyzing process data with Visual Analytics methods. Various examples will illustrate what has been achieved so far and show possible future directions and challenges.
Improved Identification of Negative Tweets related to Covid-19 Vaccination by Mitigating Class Imbalance

With an exponential rise in the number of cases of Covid-19, researchers have been painstakingly focused towards developing an effective vaccine. Consequently, there has been ongoing discussion about the vaccine on the social media platform filled with positive and negative sentiments. In this paper, we narrow down our research space by focusing on only identifying tweets imparting negative sentiment towards vaccines on social media. This identification model holds vital importance for government and medical agencies as it can help them analyse the possible reasons or causes behind the negative sentiment via tweets. Empirical results of the experiments conducted in this paper indicated that Support Vector Machine is best suited to identify negative tweets on a balanced dataset with the highest F1-Score of 87.179, and K-Nearest Neighbour shows the highest improvement after mitigating class imbalance using Edited Nearest Neighbour, which indicates the class dependency of distance based methods.
Smart structural stability and NN based intelligent control for nonlinear systems

This paper has proposed an intelligent Evolutionary Bat Algorithm (afterward, EBA) Fuzzy NN (Neural Network) controller used to ensure the asymptotic simulation stability of a mathematics nonlinear system for a smart structure. The smart evolutionary fuzzy NN model adopts an NN numerical model and the linear differential inclusion (LDI) concept. Denotation of the nonlinear dynamics is constructed by transforming the nonlinear model into a multi-rule-based sector nonlinear form of mathematics linear numerical models, and implementing a new sufficient mathematics condition whereby the asymptotic simulation stability of the intelligent structure is guaranteed by the Lyapunov mathematics function, linear matrix inequality (LMI). The high frequency is also injected as an auxiliary to stabilize these nonlinear systems. According to the relaxed method injected with dithered auxiliary, the nonlinear system can be guaranteed stable by appropriately regulating the parameters. Finally, there is a numerical resultant example with simulation results which is designated in order to precisely demonstrate the advantages of the smart intelligent controller and the proposed control scheme compared to previous schemes.
On Factors Influencing Typing Time: Insights from a Viral Online Typing Game

Author(s): Chen, Robert; Levy, Roger; Eisape, Tiwalayo | Abstract: Context effects in human spoken language are well-documented and play a central role in the theory of language production. However, the role of context in written language production is far less well understood, even though a considerable proportion of the language produced by many people today is written. Here we analyze the factors predictive of English language typing times in a large, naturalistic corpus from the popular TypeRacer.com website. We find broad consistency with the major documented effects of linguistic context on spoken language production, suggesting potential modality-independence in the cognitive mechanisms underlying language production and/or similar optimization pressures on the production systems in both modalities.
Secret sharing based watermarking for copy-protection and ownership control of medical Image

Medical data transmission and sharing, especially during this COVID-19 pandemic period, on the open channel have become more important for remote diagnosis and treatment purpose. However, the alteration and unauthorized distribution of image data has become easier, and thus the big issue of copy-protection and ownership conflicts has attracted more attention for healthcare research community. Further, large amount of confidential and personal medical records is often stored on cloud environments. However, outsourcing medical data possibly brings the great security and privacy issue, since the confidential records are shared to the third party. In this paper, a robust X-Ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD). For watermark embedding, the maximum entropy component of X-Ray carrier image is firstly decomposed using NSCT. Then, low and high frequency details of carrier and mark image is obtained using MSVD. Further, conceal the watermark detail through modifying the detail of carrier image via the suitable factor. Finally, Shamir's (k, n) secret sharing algorithm is employed to obtain secure marked carrier image. Objective evaluations on 200 X-Ray images of COVID-19 patients demonstrate that the proposed algorithm has not only an excellent invisibility but a strong robustness against the various attacks. The results also show that our algorithm outperforms the related image watermarking algorithms, since it is also suitable for applications in the multi-cloud.
Sim-to-Real Transfer for Vision-and-Language Navigation

We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions. Recent work on the task of Vision-and-Language Navigation (VLN) has achieved significant progress in simulation. To assess the implications of this work for robotics, we transfer a VLN agent trained in simulation to a physical robot. To bridge the gap between the high-level discrete action space learned by the VLN agent, and the robot's low-level continuous action space, we propose a subgoal model to identify nearby waypoints, and use domain randomization to mitigate visual domain differences. For accurate sim and real comparisons in parallel environments, we annotate a 325m2 office space with 1.3km of navigation instructions, and create a digitized replica in simulation. We find that sim-to-real transfer to an environment not seen in training is successful if an occupancy map and navigation graph can be collected and annotated in advance (success rate of 46.8% vs. 55.9% in sim), but much more challenging in the hardest setting with no prior mapping at all (success rate of 22.5%).
Predictability effects and parafoveal processing in older readers.

Normative aging is accompanied by visual and cognitive changes that impact the systems that are critical for fluent reading. The patterns of eye movements during reading displayed by older adults have been characterized as demonstrating a trade-off between longer forward saccades and more word skipping versus higher rates of regressions back to previously read text. This pattern is assumed to reflect older readers' reliance on top-down contextual information to compensate for reduced uptake of parafoveal information from yet-to-be fixated words. However, the empirical evidence for these assumptions is equivocal. This study investigated the depth of older readers' parafoveal processing as indexed by sensitivity to the contextual plausibility of parafoveal words in both neutral and highly constraining sentence contexts. The eye movements of 65 cognitively intact older adults (61-87 years) were compared with data previously collected from young adults in two sentence reading experiments in which critical target words were replaced by valid, plausible, related, or implausible previews until the reader fixated on the target word location. Older and younger adults showed equivalent plausibility preview benefits on first-pass reading measures of both predictable and unpredictable words. However, older readers did not show the benefit of preview orthographic relatedness that was observed in young adults and showed significantly attenuated preview validity effects. Taken together, the data suggest that older readers are specifically impaired in the integration of parafoveal and foveal information but do not show deficits in the depth of parafoveal processing. The implications for understanding the effects of aging on reading are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Deep Learning Based Audio Assistive System for Visually Impaired People

Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the current research work presents an Efficient Object Detection Model with Audio Assistive System (EODM-AAS) using DL-based YOLO v3 model for visually-impaired people. The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people. The presented model involves a DL-based YOLO v3 model for multi-label object detection. Besides, the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people. In order to validate the detection performance of the presented method, a detailed simulation analysis was conducted on four datasets. The simulation results established that the presented model produces effectual outcome over existing methods.
Abacus Datagraphy: A Speculative Enactment

Anticipating an increasingly data-driven life, this speculative design project critically examines a future service called Abacus, for documenting weddings in the mode of the quantified self.“At Abacus, we offer a full suite of wedding datagraphy services, personally tailored to you. Our services can help you plan for, collect and craft your personal data, into beautiful products and memories.”Our design-led research explores how people may experience, envision and remember their lives with data – living with a ‘quantified past’. We present bespoke design work that took place with two couples engaged to be married. In the context of a ‘Speculative Enactment’, the couples met a ‘wedding datagrapher’ from Abacus, as part of the service on offer. Through this design-led, but empirically focussed work, we offer design reflections about making artefacts to remember a data-driven life. We introduce our practice of Speculative Enactments as a Research through Design (RtD) approach for generating consequential speculation with participants, and argue for new and diverse ways to present and interact with speculative design work.
Implementation of Feature Selection Using Gain Ratio Towards Improved Accuracy of Support Vector Machine (SVM) on Youtube Comment Classification

The development of technology in modern times makes it easier for people to obtain information, especially online media, one of which is Y ouTube. Y ouTube has attracted many users all over the world. With several features such as like, subscribe and comment. By analyzing the comments given by the public to videos on Y ouTube, it is hoped that the results of the analysis of these comments can become reference material for Y outubers when making videos to be uploaded on Y ouTube. To find out the nature of every comment that the public gives to videos on Y ouTube, analysis is needed to assess the nature of these comments, positive or negative. To analyze these comments, the authors classify comments using the Support Vector Machine (SVM) algorithm. While the method, the gain ratio is used by the author for the feature selection process stage to help the performance of the Support Vector Machine (SVM) algorithm where the features used are from 100&#x0025; features to 5&#x0025; features so that they get different Accuracy, Precision, Recall values. From the results of research conducted by the author, the best precision results are at 50&#x0025; features, while for accuracy and recall are at 5&#x0025; features.
Service Composition Recommendation Method Based on Recurrent Neural Network and Naive Bayes

Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users.
Absolutely secure distributed superdense coding: entanglement requirement for optimality

Abstract Superdense coding uses entanglement as a resource to communicate classical information efficiently through quantum channels. A superdense coding method is optimal when its capacity reaches Holevo bound. We show that for optimality, maximal entanglement is a necessity across the bipartition of Alice and Bob, but neither absolute nor genuine multipartite entanglement is required. Unlike the previous schemes, which can transmit either even or odd bits of information, we demonstrate a generalized dense coding protocol using the genuine multipartite entangled GHZ state to send arbitrary information bits. Expressed in the eigenbasis of different Pauli operators, GHZ state is characterized by a unique parity pattern which enables us to formulate a security checking technique to ensure absolute security of the protocol. We show this method is better applicable in a scenario, where the initial information is distributed among spatially separated parties. Finally, optimizing the number of qubit(s) sent to Bob, we construct a distributed dense coding method, which completely depicts absolutely secure quantum communication between many to one party.
Lightweight end-to-end image steganalysis based on convolutional neural network

With the continuous improvement in the accuracy of steganalysis based on convolutional neural networks (CNNs), the network scale has shown explosive growth. Consequently, CNNs have a high demand for hardware resources and time-consuming training. To reduce the number of CNN parameters and improve the efficiency of steganalysis, we propose a lightweight steganalysis CNN called W-Net. The proposed W-Net first uses grouped convolution and channel shuffling units to extract noise residuals, strengthen the information exchange between groups, and improve feature extraction. In addition, the depth-wise separable convolution is applied to obtain different channels and spaces. The fusion of position information achieves the effect of conventional convolution while reducing the number of network parameters. We verified the effect of activation functions on steganalysis accuracy through experiments. In addition, the proposed W-Net can detect the steganographic data from the S-UNIWARAD spatial steganography algorithm with an embedding rate of 0.4 bpp. Compared with Xu-Net and Zhu-Net, the proposed W-Net improves the detection accuracy by 12.70% and 6.38%, respectively.