Reviewerly/exHarmony huggingface dataset:
Hybrid Methodology for Analysis of Structured and Unstructured Data to Support Decision-Making in Public Security

This work proposes a hybrid methodology that enables the integration of structured and unstructured data to support the decision-making process in public security contexts. The proposed methodology facilitates classification and prediction of crime in a given region, making it possible to identify actions to improve public security based on the results. The integration of the data takes place in two main steps: (1) loading and analyzing structured data made available by government agencies; and (2) absorbing, classifying, and analyzing unstructured data from digital platforms such as Twitter, Where I Was Robbed, and CityCop. In this way, it becomes possible to transform these unstructured data into structured data to be incorporated into a historical database on which algorithms can act to classify, measure, and predict crime. To illustrate the applicability of this methodology, we conducted a study in the city of Recife, Brazil. Structured and unstructured data were gathered in order to conduct a neighborhood classification analysis of crime hot spots. Based on that analysis, we conducted a series of actions intended to bring improvements to the region by the local police. We obtained an increase in the algorithmsâ€™ accuracy rate of 80%, indicating that public security organizations can base their actions on the results of the proposed methodology.
Efficient Multimedia Frame-Skipping Architecture Using Deep Learning in Vehicular Networks

With the development of 5G networks, vehicle-to-vehicle communication is helping to make travel safe. However, vehicle type detection in the multimedia feed remains a problem. This helps reduce processing time and enable more dynamic connectivity between different vehicles. The development of object classes requires more robust computer vision models and algorithms. However, the main difficulty still lies in image quality, which depends on the lighting conditions, viewing angle, and physical structure of the vehicles. This research mainly focuses on the development and deployment of a deep learning-based system for traffic congestion analysis. The model uses multiple video feeds and vehicle information to detect, classify, and count vehicles in the live traffic feed. The model is trained with a deep learning approach to align the video image and detect the object in top–down multimedia. The dynamic skipping method helps to process a long video feed and accurately compares the video image with the viewer. The standard query for the vehicle can help in recognizing and creating the models in real-time traffic situations. The proposed model is suitable for many applications that require a specific area for monitoring real-time data analysis and multimedia routine tasks.
MARS: Assisting Human with Information Processing Tasks Using Machine Learning

This article studies the problem of automated information processing from large volumes of unstructured, heterogeneous, and sometimes untrustworthy data sources. The main contribution is a novel framework called Machine Assisted Record Selection (MARS). Instead of today’s standard practice of relying on human experts to manually decide the order of records for processing, MARS learns the optimal record selection via an online learning algorithm. It further integrates algorithm-based record selection and processing with human-based error resolution to achieve a balanced task allocation between machine and human. Both fixed and adaptive MARS algorithms are proposed, leveraging different statistical knowledge about the existence, quality, and cost associated with the records. Experiments using semi-synthetic data that are generated from real-world patients record processing in the UK national cancer registry are carried out, which demonstrate significant (3 to 4 fold) performance gain over the fixed-order processing. MARS represents one of the few examples demonstrating that machine learning can assist humans with complex jobs by automating complex triaging tasks.
Guided Filter in Least Squares to Remove Non-uniform Strong Noise of Underwater Target Image

Underwater target detection will be interfered by underwater non-uniform strong noise (organic matter, suspended particles, etc.), in order to solve this problem, a new denoising method (DF-LS) is proposed based on the method of embedding bilateral filter (BLF-LS) in least square method to keep image edge smooth. Firstly, the guiding filter and the fast edge-preserving two-dimensional smoothing filter based on indicator function are introduced into the model to retain the edge of the underwater target and remove some small noise. At the same time, the guiding filter is embedded into the least square model to make the algorithm more efficient. Secondly, according to the non-uniform characteristics of noise, the multi-scale iteration of characteristic pyramid is adopted to decompose the noise of different sizes iteratively, and in the process of multi-scale decomposition, the noise of different scales is decomposed and removed step by step. Experimental results show that, compared with other classical denoising methods, the proposed algorithm can better remove the underwater non-uniform strong noise and retain the target information.
A Comparative Survey on Face Recognition Techniques

The face is an intrinsic part of the human body that distinguishes individuals from large groups of people. Thus, it has become the most commonly used and accepted biometric approach because of its universality and uniqueness. Face recognition has piqued the interest of many scientists and has thereby become a standard prerequisite for successful recognition. The goal of this work is to show the broad variety of techniques used for facial detection and recognition and try to address their benefits and drawbacks to overcome occlusion problems which would help to strengthen security measures for tacking and redacting people. The final segment presents the prospects and possible consequences for the area to be more progressed.
Peran Preservasi Kuratif dalam Menjamin Keselamatan dan Pelestarian Arsip Statis

This paper aims to determine the role of curative preservation in ensuring the safety and preservation of static archives. The method used in this research is descriptive qualitative with data collection techniques using library studies. The results of the study found that curative preservation plays a very important role in ensuring the safety and preservation of archives because the actions and instructions contained in it can repair and maintain archives that have started or have been damaged. So that these actions and instructions can be used as guidelines by any archival institution to repair and maintain the static archives they have.
The Development of the Massive Open Online Course on Distance Forms of Research and Training

The article is devoted to the description of the problem of organizing effective research work and educational process in a distance format. As the solution to support transition from face-to-face to distance work, the authors of the article propose using the massive open online course (MOOC) they have developed. The content of the course covers the main stages of such transition process - the preparation and configuration of the appropriate network infrastructure (addressing and routing in networks, mesh Wi-Fi) and software supporting distance formats of work (cloud data access and office applications suites services).
Virtual machine consolidation using SLA-aware genetic algorithm placement for data centers with non-stationary workloads

Cloud computing is a computing model based on computer networks such as the Internet that provides a new model for the provision, use and delivery of computing services (including infrastructure, software, and platform) using network facilities. In fact, Cloud service providers provide services to cloud users based on user demand, and users pay as much as they need to use cloud resources. Until recently, efficiency was one of the most important concern in the problem of data center resources provisioning, but now, due to high energy costs, the issue of reducing energy consumption has also become very important. Dynamic virtual machine (VM) consolidation is one of the technologies that has been considered for green computing in cloud data centers. In this paper, we present a service level agreement aware system based on genetic algorithms for consolidation of VMs in an Infrastructure as a Service (IaaS) Cloud environment. The proposed approach considers workloads as non-stationary stochastic processes, and automatically models workloads as stationary processes using a dynamic sliding window method. Simulation results in the CloudSim confirms that the proposed VM consolidation algorithms in this paper provides significant cost savings.
Deep Representation for the Classification of Ultrasound Breast Tumors

An automated classification of ultrasound breast tumor is a vital step for the early prevention of abnormal breast cells. In general, radiologists manually handle this procedure, but manual analysis performed by individual poses a problem of consistency depending on the experts. One of the standardized alternatives was to apply automated deep learning method in this field. In fact, majority ideas in literature are dominantly based on the supervised learning framework, but even such methods have still failed to present promising discrimination performance. In this work, we assume that unsupervised learning still can be a potential option and beneficial attribute that enables to accelerate discrimination is inherent in it. Hence, we present a deep representation for the ultrasound breast data utilizing two types of independent supervised and unsupervised network to reconstruct the principal features, while the volume of supervised features is set to be minimum and the volume of unsupervised is the maximum. Specifically, we adopted pretrained Resnet34 as a supervised network, and a convolutional autoencoder (CAE) was designed for the unsupervised network. Each representation vector is combined into a single vector, and the generated vector is given to the support vector machine as an input for the final discrimination. The results are verified that the proposed method shows far better performance compared to several conventional deep learning methods and the single use of each method. The value of accuracy, sensitivity and specificity are obtained by 88.18%, 85.25% and 100.00% respectively.
FLC-ROS: A generic and configurable ROS package for developing fuzzy logic controllers1

Fuzzy logic controllers can handle complex systems by incorporating expert’s knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system’s components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV).
Kernel-Based Lifelong Multitask Multiview Learning

Lifelong learning capitalizes on the shared skill structure present in a stream of tasks that arrive over time to improve upon the performance of single-task learners. In contemporary lifelong learning applications, it is often the case that there are multiple sensing modalities or views associated with each task. A crucial aspect in lifelong multitask multiview learning is to capture not only the shared structure among the tasks but also across views effectively. In this work, a nonparametric kernel-based learning framework is adopted to model even nonlinear shared structures in the tasks and views in a flexible and robust way. An efficient lifelong learning formulation is derived by judicious approximation of the per-task learning objectives, based on which the shared skill libraries can be updated online in function space. Numerical tests verify the efficacy of the proposed approach.
Designing an open-source application to record library gate counts in response to COVID-19

In response to the COVID-19 pandemic, the Oklahoma State University Library developed an open-source application to record real-time building capacity. Library student security tracks building entrances and exits via the program, with current visitor numbers appearing on the library’s website via an API. This effort helps administrators have a clear picture of building capacity during opening hours for data-driven decision-making and provides users information regarding the library’s level of activity.
VMS - Visitation Management System

Under the growing uncertainties prevailing around the world, one of the most debated topics that have brought worldwide medical facilities to the test would be the ongoing Covid-19 pandemic devastating people from all walks of life. The constant intake of chronically ill or possible Covid-19 infected patients into the hospital along with the high influx of visitors is harming the safety and wellbeing of both the front liners and patients especially when hospitals are one of the most important facilities to combat the spread of Covid-19 into the community. Concerning this, the researcher proposes a Visitation Management System (VMS) that solves certain key problems arising due to the high influx of hospital's visitors that causes a significant risk of Covid-19 infection, additional hospital staff to cope with the high influx of visitors at the entrance of the hospital, the difficulty of identifying high-risk Covid-19 infection locations in the hospital, as well as risk of shutting down hospitals if there are Covid-19 clusters found while losing profits or income. Apart from that, this research uses Rapid Application Development as the system development methodology for the development of VMS. Both requirement gathering methods via interview and questionnaire have been applied among various users. This research has included the findings of the collected analytical data to highlight the user requirements of VMS and a system providing proof of concept to underline the implementation of VMS to help curb the spread of Covid-19 in hospitals.
Similarity between Base and Novel Classes: a Predictor of the Performance in Few-Shot Classification of Brain Activation Maps?

The classification of neuroimaging data, also known as brain decoding, is often limited by the lack of training data. Recently, few-shot learning methods have been developed to take advantage of deep neural networks in a context where few examples are available. Some of these methods have shown to be promising in few-shot classification of brain activation maps. They involve a pretraining phase on a generic dataset containing base classes and an adaptation phase to the few-shot problem containing novel classes. In this article, we propose several experiments to study the influence of base classes on the performance of few-shot problems.
Secure Outsourcing of Boneh-Franklin Identity-Based Encryption Scheme

In recent years pairing-based cryptographic protocols have attracted so much attention. Meanwhile identity-based cryptography based on bilinear pairings has received particular attention. The IEEE, IETF, and ISO organizations have been working on standardization of pairing-based cryptographic schemes. The Boneh-Franklin identity-based encryption is the most well-known identity-based scheme that has been standardized. So far, various schemes have been proposed to reduce the computational cost of pairing operations. All these schemes are trying to outsource pairing operations in a secure manner. But in addition to pairing operations, there are other basic and costly operations in pairing-based cryptography and identity-based schemes, including scalar multiplication on elliptic curves. In this research, we outsource the Boneh-Franklin encryption in a more secure and efficient (in terms of computational and communication complexity) way than existing schemes. The proposed scheme is secure in the OMTUP model. Also, unlike previous schemes, we considered communication channels insecure. Moreover, compared with the trivial solution which outsources every single operation (such as pairing, scalar multiplication and modular exponentiation) as a separate subroutine, our scheme offers less complexity by seamlessly outsourcing the whole encryption scheme for the first time.
Using Sparse Spectral Shifts in Graph CNNs

Graph convolutional neural networks (graph CNNs) have shown great promise in graph classification problems with graph data. Current graph CNNs use vertex domain convolutional layers, defined using a polynomial of the adjacency matrix, A. However, spectral domain convolutional layers, defined using a polynomial of the spectral shift, M, have shown comparable or sometimes better performance than their vertex domain counterparts. The adjacency matrix A is usually sparse, while the corresponding M is usually dense. In this paper, we explore methods to produce a sparser M, comparing several different ways to remove edges of M and their impact on graph CNN accuracy. This paper shows that graph classification accuracy using graph CNNs can be improved by dropping out edges in M.
Material culture, design studies, and human ecology: A biocentered perspective

This chapter explores the intersection of three epistemologies, namely, material culture, design studies, and human ecology, linked to fields of study that engage in innovative biocentered practice. Different ways of knowing can help identify alternative ways of seeing biomimetic practice and move design practices from anthropocentric and degenerative approaches to a more regenerative and sustainable way of knowing, thinking, and doing. Designers in fields such as industrial, graphic, fashion, and architectural design should be educated as critical thinkers and problem identifiers, generalists, and reflexive practitioners conscious of design in wicked (-complex multifaceted-)problem environments. The chapter emphasizes the importance of a posthuman-biocentered mindset with four entangled alternatives for innovating beyond the anthropocentric, including: embracing complexity; designing by considering intrarelationships and intraactions; acknowledging the agency of nonhumans; and considering the contexts of habitat, affordances, and taskscapes. One goal of this exploration is to trigger deep(er) contemplation and acknowledge the complexities involved in design practices in which the value for nature and biocentered practice can move to the forefront.
Correlation Filter RGB-T Tracker with Modality and Channel Reliability

RGB-T object tracking is developing rapidly in the past decade due to the complementarity of visible (RGB) and thermal infrared (T) images. However, many trackers using multi-modal information by simple feature concatenation, which ignores both the modality and channel reliability. In this paper, we propose a correlation filter-based RGB-T tracker to learn the reliability weights in terms of modality and inter-channel. Specifically, the channel regularization collaborates with the spatial regularization to jointly learn the filter and channel weights. Besides, we design a novel objective function to optimize the modality reliability weight frame by frame. Through the reliability evaluation, the useful information hidden in the modalities and channels is fully exploited. We perform extensive experiments on the RGB-T benchmark, i.e., GTOT, to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion strategy can improve tracking performance.
fVSS-Based Secure Database for IoT Cloud Platform

IoT cloud platforms integrate streaming data generated by heterogeneous devices and also profoundly affected in many application domains. However, public clouds still be concerned about the data security of their databases handled by database management systems In this paper, we propose two versions of an IoT cloud platform named ENG-KPS cloud. They were launched for several research and service projects in the university and collaborative communities. fVSS further enhances a traditional database to be a secure distributed database that guarantees data privacy, data availability, and data integrity during storing, computing and transferring. Our experiments show the efficiency o f f VSS a nd E NG-KPS c loud platforms. Moreover, ENG-KPS cloud platforms are practical and stable platforms guaranteed by long term services.
Fully Reversible Steganography with Authentication in Wavelet Domain for Telemedicine Applications

Last year has witnessed a growing reliance on telemedicine practices requiring the transmission of medical images along with sensitive patient records and diagnosis information to/from remote locations. However, maintaining optimal security and authentication of appended information along with complete reversibility of medical image is a challenging telemedicine task. Traditional approaches to medical image steganography attempt to hide a large amount of information and make it imperceptible at the cost of full reversibility of all regions in the medical image. In this paper, we propose an Integer Wavelet Transform (IWT) based efficient approach to strike an optimal balance between large hiding capacity and imperceptibility while attaining full reversibility of the entire underlying medical image. The redundancy present in multiple optimally selected bit-planes of the detail coefficients of IWT is exploited to hide a large amount of data along with encoded transform bit-plane original data for full reversibility. For authentication purposes, the hospital logo is also embedded in the image for complete recovery and verification at the receiver end. The experimental results demonstrated on CT, MRI, and PET datasets, show improved steganography performance in terms of capacity without appreciable loss in imperceptibility and without compromising full reversibility of the entire medical image.
EGCN: Ensemble Graph Convolutional Network for Neural Architecture Performance Prediction

Neural Architecture Search (NAS) is proposed to automatically search novel neural networks. Currently, one typical problem of NAS is that its computation requirements are too high to stand for most researchers. In fact, it consumes a lot of resources to train subnetworks for architecture search. If the performance of each subnetwork can be predicted accurately without training, the computational burden will be alleviated. Graph Convolutional Network (GCN) is proven to have powerful capabilities for topological information perception and extraction. It is suitable to use GCN for predicting neural architecture performance which is related to its topology.In this paper, we treat GCN as the performance predictor with two improvements. First, a novel neural architecture data processing method named DATAPRO2 is designed to improve GCN&#x2019;s performance. Then, we propose EGCN, a model-based performance predictor which employs ensemble technique on GCN with DATAPRO2 to alleviate the overfitting issue caused by the imbalanced dataset for neural architecture performance prediction. Experimental results on CVPR-2021-NAS-TRACK2 dataset show that EGCN contributes to obtaining better predictive performance than vanilla GCN and other popular predictors.