Reviewerly/exHarmony huggingface dataset:
Dual-Path Image Pair Joint Discrimination for Visible-Infrared Person Re-Identification

Because the imaging spectra of infrared images and visible light images are different, there is a huge modal difference between them. Existing methods use image conversion to solve the problem of modal difference between two images, but these methods usually fail to focus on the complete information of images, which lead to the results of cross modal person re-identification are unstable. To solve this problem, we propose a dual-path image pair joint discriminant model (DPJD), which optimizes the distance within and between classes at the same time, and supervises the network learning to identify feature representations. We generate images with different modalities, and separately compose the same modality image pair and different modality image pair so as to overcome the inconsistent alignment issues. In addition, we also propose a discriminant module based on dual-path (DMDP) to improve the generation quality and discrimination accuracy of image pairs. Experiments demonstrate its effectiveness.
Plant Bot: Intelligent Plant Application based on ADDIE Model of Instructional Design

Plant Bot is a mobile application is used as an aid for beginners and professionals to undertake farming activities. Users can use this app as a reminder of the time watering plants, and can add information to know the characteristics and handling of various plants. The purpose of this study is to improve the quality of public health by encouraging people to carry out physical activities in the midst of a pandemic and consume nutritious foods such as vegetables without preservatives from planting. There are 3 target users of the application, including students for educational materials, the general public to fulfill their food needs by farming and Advanced/Professional users such as farmers who want to make farming easier. The application development refers to the stages of the development life cycle in the ADDI model. The application that has been built has been running well, as evidenced by the results of testing based on functional requirements that can function as a whole.
Synthetic Vertebral Column Fracture Image Generation by Deep Convolution Generative Adversarial Networks

In the field of medical imaging, the challenging objective is to generate synthetic, realistic images which resembles the original images. The generated synthetic images would enhance the accuracy of the computer-assisted classification, Decision Support System, which aid the doctor in diagnosis of diseases. The Generative Adversarial Networks (GANs), is a method of data augmentation which can be used to generate synthetic realistic looking images, however low quality images are generated. For AI models, it is challenging tasks to do classification using this low quality images. In this work, generation of high quality synthetic medical image using Deep Convolutional Generative Adversarial Networks (DCGANs) is presented. Data augmentation method by DCGANs is illustrated on the limited dataset of CT (Computed Tomography) images of vertebral column fracture. A total of 340 CT scan images were taken for the study, which comprises of complete burst fracture scans of vertebral column. The evaluation of the generated images was done with Visual Turing Test.
Face Mask Detection Using Optimized CNN

COVID-19 has had a rapid impact on people's lives, affecting global trade and transportation. Protecting against COVID-19 by wearing a face mask has become the new normal. Many public service providers will need clients to wear masks to access their services in the near future. As a result, in today's culture, face mask detection is essential. This study proposes attaining the aim by utilizing some basic platforms such as Machine Learning packages such as TensorFlow, Keras, and OpenCV libraries. The goal of this project is to reliably detect the face in an image and then determine whether or not the individual is wearing a mask. In addition, the model can detect the existence of a mask in real time. The mask detection dataset was compiled using Internet resources, and a Google form was constructed to collect photographs with and without masks. We examine optimum parameter values for the Sequential Convolutional Neural Network model in order to correctly detect the presence of masks without causing over-fitting. On camera or in real time, we want to see if a person wearing a face mask is actually wearing one.
Commentary on “Visualization in Operations Management Research”: Incorporating Statistical Thinking into Visualization Practices for Decision Making in Operational Management

Supplemental Material: The software that supports this study and helped produce the figures are available within the paper at https://doi.org/10.1287/ijds.2021.0008 or are supplied at https://emitanaka.org/supp-visOM/ , or the same version at Code Ocean available here http://doi.org/10.24433/CO.2743770.v1 . Data Ethics Statement: The data used for the plots are synthetic data supplied by Basole et al. (2021) .
Augmented, Virtual and Mixed Reality Passenger Experiences

Mixed RealityMixed Reality (MR) headsets enable the rendering of virtual content selectively intermixed with reality. These headsets have the capacity to allow passengersPassengers to break free from the restraints of physical displays placed in constrained environments such as cars, trains and planes. Moreover, they have the potential to allow passengersPassengers to make better use of their time by making travel more productive and enjoyable, supporting both privacy and immersion. This is of particular note given the predicted adoption of autonomous vehicles. This chapter explores both the applications of MR headsets in passengerPassengers transit scenarios, and the key barriers to headset usage by passengers, ranging from impediments that would entirely prevent safe usage and function (e.g. motion sicknessMotion sickness, see Chap. 1.6) to those that might impair their adoption (e.g. social acceptabilitySocial acceptability). We discuss the key challenges that need to be overcome and the necessary research required to facilitate adoption and realise the potential advantages of using MR headsets in transit.
The Effects of a Rowing Ergometer Interval Exercise on Body Compositions and Aerobic Capacity in Obese Middle-aged Women

The purpose of this study was to investigate the effect of rowing ergometer interval exercise on body compositions and aerobic capacity in obese middle-aged women. Statistical Data was processed by SPSS(Windows Version 12.0) using descriptive statistics regarding the physical characteristics of subjects as well as each variables(body compositions, aerobic capacity). The difference of within and between in two group was analyzed repeated measures ANOVA, significant level was set a=.05. The results were as follows: Body Compositions, there was significant difference according to the measurement, Aerobic capacity the results, there was significant difference according to the measurement. From the results, We findings suggest that obese middle-aged women could improve their body compositions and aerobic capacity through rowing ergometer interval exercise program.
Privacy-Preserving Localization using Enclaves

Localization is one form of cooperative spectrum sensing that lets multiple sensors work together to estimate the location of a target transmitter. However, the requisite exchange of spectrum measurements leads to exposure of the physical location of participating sensors. Furthermore, in some cases, a compromised participant can reveal the sensitive characteristics of all participants. Accordingly, a lack of sufficient guarantees about data handling discourages such devices from working together. In this paper, we provide the missing data protections by processing spectrum measurements within attestable containers or enclaves. Enclaves provide runtime memory integrity and confidentiality using hardware extensions and have been used to secure various applications [1]–[8]. We use these enclave features as building blocks for new privacy-preserving particle filter protocols that minimize disruption of the spectrum sensing ecosystem. We then instantiate this enclave using ARM TrustZone and Intel SGX, and we show that enclave-based particle filter protocols incur minimal overhead (adding 16 milliseconds of processing to the measurement processing function when using SGX versus unprotected computation) and can be deployed on resource-constrained platforms that support TrustZone (incurring only a 1.01x increase in processing time when doubling particle count from 10,000 to 20,000), whereas cryptographically-based approaches suffer from multiple orders of magnitude higher costs. We effectively deploy enclaves in a distributed environment, dramatically improving current data handling techniques. To our best knowledge, this is the first work to demonstrate privacy-preserving localization in a multi-party environment with reasonable overhead.
The Universal Fog Proxy: A Third-party Authentication Solution for Federated Fog Systems with Multiple Protocols

Fog computing is suitable for latency constrained applications useful to end users and IoT devices in smart cities, factories, and homes. A federation among fogs is beneficial for subscribers and providers in terms of enhanced capability, capacity, coverage, and services. To realize such a federation, a third-party authentication mechanism among fog providers is required, so that a subscriber of a fog can access the services provided by the other fogs without having to create new accounts. In this article, we propose a transparent and standard-compliant universal fog proxy that provides third-party authentication among OpenID Connect (OIDC), 802.1x, and Protocol for Carrying Authentication for Network Access (PANA) without requiring a new protocol. The proxy consists of virtual counterparts of the entities involved in these protocols so that it provides transparency. For example, when a fog using OIDC receives an authentication request, the proxy relays and behaves as a virtual Identity Provider (vIdP) for the fog using OIDC and a virtual supplicant for the fog using 802.1x. We applied our solution to nine scenarios across OIDC, 802.1x, and PANA. Experimental results show that the proxy takes 4&#x2013;52 percent of the total authentication time of 0.128-3.504s for nine scenarios, with a larger percentage in scenarios involving OIDC due to multiple re-directions among virtual components. The scenarios involving 802.1x take a considerably lon-ger time, though a low percentage (4&#x2013;12 percent) by the proxy, as the spanning tree protocol in an 802.1x switch takes about one second to converge when adding a new device to the network.
How to get to the end of the world

research-article Share on How to get to the end of the world Author: Jen Liu Cornell University Cornell UniversityView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 39–43https://doi.org/10.1145/3501291Online:06 January 2022Publication History 0citation414DownloadsMetricsTotal Citations0Total Downloads414Last 12 Months414Last 6 weeks50 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Assured Encryption Strategy for Private Data Protection in Hybrid Cloud

Due to the overwhelming adoption of cloud computing, the dire need to secure one’s data is essential for a trustworthy cloud service provider. The key concerns of cloud security include unauthorized access, insecure interfaces, external sharing of data, malicious insiders, and denial-of-service attacks. Each business organization applies strong security mechanisms to protect sensitive data in the cloud. This includes the adoption of private cloud to prevent critical data from being accessible by outsiders.
Profiling Celebrity Profession from Twitter Data

Twitter is one of the most popular social media platforms which enables users from different walks of life to have two-way communications. User categorization in the Twitter platform categorizes a group of users based on their profiles, posts, and tweeting behaviors. The purpose of user categorization is to deliver relevant information to a specific class of users based on their interests. In this work, we perform user-level categorization of celebrities based on their professions. Different classification systems are developed to classify users into coarse-grained and fine-grained categories. The accuracy of the proposed models is evaluated on three datasets (Indian, non-Indian and combined dataset) using various machine learning and deep learning frameworks such as Naïve Bayes (NB), Support Vector Machine (SVM), Multilayer perceptron (MLP) and Convolutional Neural Network (CNN). Four different categories of features are used for this purpose - stylistic, hashtag, embeddings, and topic-based features. CNN turned out to be the best performing model for all three datasets. CNN produced accuracies of 83.66%, 87.51 %, and 84.02% on the Indian, non-Indian, and the combined datasets respectively. For the fine-grained classification task, maximum average accuracies of 80.34 % and 85.07 % are obtained using multilayer perceptron on the Indian and non-Indian datasets, respectively.
Fast and Context-Aware Framework for Space-Time Video Super-Resolution

Increasing the spatial resolution and frame rate of a video simultaneously has attracted attention in recent years. The current one-stage space-time video super-resolution (STVSR) methods are difficult to deal with large motion and complex scenes, and are time-consuming and memory intensive. We propose an efficient STVSR framework, which can correctly handle complicated scenes such as occlusion and large motion and generate results with clearer texture. In REDS dataset, our method outperforms all existing one-stage methods. Our method is lightweight and can generate 720p frames at 16fps on a NVIDIA GTX 1080 Ti GPU.
BEN: Brightness Enhancement Network for Low-Light Image Enhancement in Complex Environment

In recent years, a large number of methods have been used for Low-Light (LL) image processing. Convolutional Neural Networks (CNNs) are widely used in the field of LL image enhancement because they avoid complicated image preprocessing. Although many studies are devoted to the application of LL image processing in industrial environments, intelligent transportation and other fields, there are few studies on structures suitable for this task. Based on the idea of fusing image features of different illumination layers, the Brightness Enhancement Network (BEN) is proposed. We verified the performance of the structure on public datasets and compared it with some existing advanced methods for processing LL images. Experiments show that our method is superior to other methods in SSIM, PSNR, and NIQE, and it can enhance the illumination while retaining the original details.
Augmented Reality Approach to Introduce Batik Garutan Using Features Accelerated Segment Test

This study aims to introduce the art of Batik Garutan with a variety of existing motifs using Augmented Reality (AR) technology so that Batik Garutan can be known interestingly, the method used is Features from Accelerated Segment Test (FAST), where there are several stages of the process including the process of resizing the image, the process of changing the image to grayscale, the process of alignment of gray color (Histogram), and the threshold process for determining the point on the marker. Marker objects are used in Batik Garutan motifs in the form of fabrics, shirts, and dresses. The FAST Corner Detection algorithm on Batik Garutan can detect markers with an average detection time of 0.315. The introduction of Batik Garutan on android mobile devices is very beneficial, practical, and easy to use. It is seen from questionnaires conducted by 42 respondents with an average percentage of answers reached 85.94%, which means it falls into the category very well.
Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.
How DAOs Optimize Open-Source Code Reviews and Create Open-Source Standards

The paper examines the existing shortcomings in the open-source code review process and how decentralized autonomous organizations can help optimize the code review process and quality. Optimized code reviews and code review processes, in turn, can help create open-source software standards. The author evaluates the issuance of decentralized code reviews that are verified through a decentralized autonomous organization, the code review DAO (CRDAO). Key distinguishing features of the CRDAO include code review price discovery, lower prices for code reviews, increased speed of code reviews, community governance, full transparency, and, at a later stage, insurance for code reviews. Over time, the CRDAO code reviews can be combined with an underwriting service, through a code review underwriting DAO, where the CRDAO revenue from code reviews guarantees the consumer in certain circumstances that CRDAO code reviews are backed by assets.
Attack Resistant Graphical Password Authentication Method Against Shoulder Surfing, Smudge and Brute Force Attacks

nowadays, the increasing rate of using smart devices has emphasized the important role of information security more than ever. Alphabetical passwords are a primary type of password, consisting of a combination of alphabetical letters and numbers. Usually people write alphanumeric strong passwords on papers or save in a computer file because they cannot memorize them. Besides, text passwords have their flaws and are vulnerable to attacks. Graphical password can be used instead of text-based passwords to resistant against shoulder surfing, brute force and smudge attacks. In this type, the users choose some images as the password, which is easier to use and memorize and more secure than the previous password types. Various methods have been proposed to create this type of password. These methods have been evaluated in different ways, and some are vulnerable to attacks. In this research a more secure graphical password system has designed to resistant against shoulder surfing, smudge, and brute force attacks. This proposed scheme is provided based on integration of two main method: recognition based and Cued recall based. Evaluation of this method has been carried out by users in different age groups and education, demonstrating that the proposed method has an acceptable level in terms of users.
Modeling for Project Portfolio Benefit Prediction Via a Ga-Bp Neural Network

Project portfolio benefit (PPB) prediction can effectively help managers monitor the acquisition of PPBs, thereby better achieving their target benefits. However, no valid model is available for PPB prediction. To narrow this research gap, we develop a model based on a backpropagation neural network improved by the genetic algorithm (GA-BPNN) to quantitatively forecast PPBs. The evaluation criteria for benefits are first determined. Subsequently, the input and output variables of the model are determined and calculated. Then, the initial weights and thresholds of the BPNN are improved by the GA. In light of the above optimization results, the GA-BPNN model is trained and tested. Finally, a numerical example is used to demonstrate the application of the proposed model. The results indicate that the established model is feasible and effective in predicting PPBs with an average prediction accuracy rate of 98.64%, which is 18.24% better than that of the base BPNN. The model proposed in this paper realizes the quantitative prediction of PPBs, enriching the research on project portfolio management (PPM) and providing managers with a tool to effectively predict PPBs.
Improved argumentative paragraphs detection in academic theses supported with unit segmentation

Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches.
Cooperative Multi-Agent UAS Task Assignment for Disaster Response Scenario

Task assignment problems have evolved as computation methods and resources have allowed, utilizing Unmanned Aerial Systems (UAS) as a resource for accomplishing a variety of complex tasks. The ability of UAS to operate in complex environments, cover large areas, and be deployed quickly opens the door for a variety of applications, with one such being disaster response. This research is inspired by a scenario where UAS are used to help dispatch aid to a region after a natural disaster such as a hurricane. A heterogeneous fleet of both fixed-wing and multirotor UAS is available to be allocated to the region of interest, with the goal of accomplishing all known tasks in the minimum amount of time. The multiple-depot multiple travelling salesman problem (MDMTSP) is used as a benchmark to show the effectiveness of the developed algorithm. Several additional constraints are then added to the MDMTSP to better represent the challenges of disaster response task assignment including search tasks, heterogeneous sensors, fuel constraints, and Dubins vehicles. Ongoing research focuses on applying the solution in a hardware integration and performing a real-world flight test demonstration.