Reviewerly/exHarmony huggingface dataset:
DAWIS: a detection algorithm with wavelets for intracluster light studies

Context. Large numbers of deep optical images will be available in the near future, allowing statistically significant studies of low surface brightness structures such as intracluster light (ICL) in galaxy clusters. The detection of these structures requires efficient algorithms dedicated to this task, which traditional methods find difficult to solve. Aims. We present our new detection algorithm with wavelets for intracluster light studies ( DAWIS ), which we developed and optimized for the detection of low surface brightness sources in images, in particular (but not limited to) ICL. Methods. DAWIS follows a multiresolution vision based on wavelet representation to detect sources. It is embedded in an iterative procedure called synthesis-by-analysis approach to restore the unmasked light distribution of these sources with very good quality. The algorithm is built so that sources can be classified based on criteria depending on the analysis goal. We present the case of ICL detection and the measurement of ICL fractions. We test the efficiency of DAWIS on 270 mock images of galaxy clusters with various ICL profiles and compare its efficiency to more traditional ICL detection methods such as the surface brightness threshold method. We also run DAWIS on a real galaxy cluster image, and compare the output to results obtained with previous multiscale analysis algorithms. Results. We find in simulations that DAWIS is on average able to separate galaxy light from ICL more efficiently, and to detect a greater quantity of ICL flux because of the way sky background noise is treated. We also show that the ICL fraction, a metric used on a regular basis to characterize ICL, is subject to several measurement biases on galaxies and ICL fluxes. In the real galaxy cluster image, DAWIS detects a faint and extended source with an absolute magnitude two orders brighter than previous multiscale methods.
A Cloud-based Mobile Healthcare Monitoring Framework with Location Privacy Preservation

Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.
Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict

Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo delivery drones will require on-demand scheduling of large numbers of aircraft. We demonstrate an algorithm known as FastMDP-GPU that performs first-come-first-served pre-departure flight plan scheduling where conflict free flight plans are generated on demand. We demonstrate a parallelized implementation of the algorithm on a Graphics Processor Unit (GPU) and show the level of performance and scaling that can be achieved. Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher. Our results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both centralized or distributed flight planning systems.
Context-Aware Computing for IoT: History, Applications and Research Challenges

The new Internet of Things (IoT) technology has the potential to generate breakthrough opportunities in real-life applications. The merger of IoT with other technologies like context-aware computing, big data, artificial intelligence, machine learning, etc. will give rise to new services that will improve the quality of life. The article discusses the emergence of context-aware computing and some use cases of context-aware IoT applications. The study presented in this article focuses on some crucial and open problems in the practical implementation of context-aware IoT applications.
A secure index resisting keyword privacy leakage from access and search patterns in searchable encryption

The keyword-based searchable encryption is a very promising technology in the cloud storage, which can supply data users with accurate search services over encrypted data based on queried keywords. An important security objective of this kind of schemes is to resist keyword privacy leakage because it may cause content leakage of to data itself and search preferences of data users. To protect keyword privacy, the existing methods mainly are implemented by keyword ciphertext indistinguishability and trapdoor indistinguishability. And there is few works to resist keyword privacy leakage from access pattern and search pattern simultaneously, which will inevitably affects the further application of this promising technology. In order to avoid keyword privacy leakage from above two patterns, we first construct a novel secure keyword index called the global index pair which is used to construct a k-nearest neighbour search, i.e., a data user can always receive the top-k encrypted files which are the most relevant to the search query. To effectively save computing costs and storage costs for DUs, a new order-preserving transformation is designed in our scheme to generate trapdoor without DUs computing the inverse of matrix. Secondly, our construction is independent of a specific searchable encryption scheme. Any kind of a keyword-based searchable encryption scheme can apply our method to resist the keyword privacy leakage both from access pattern and search pattern. Finally, the detailed security analyses are given to demonstrate that the advantage of any Level-3 attacker launching efficient inside attack from search pattern and access pattern is negligible, i.e., our construction can protect the keyword privacy against an inside attacker.
An efficient video summarization for surveillance system using normalized k-means and quick sort method

There has been a vast augmentation in quantity of Video Content (VC) generated amid the last some years. The Video Summarization (VS) approach is introduced for managing the VC. Prevailing VS techniques have endeavored to render the VS but the systems have Execution Time (ET) as well as condensing the video's content in domain specific manner. To triumph over such disadvantages, this paper proposed efficient VS for surveillance system using normalized k-means along with quick sort method. The proposed technique comprises eight stages, like split video into frames, pre-sampling, provide ID number, feature extraction, Feature Selection (FS), clustering, extract frames, video summary. Initially, the video frames are pre-sampled utilizing the proposed Three Step Cross Searching Algorithm (TSCS) technique. Then, give the ID number for every frame. Next, the features are extracted as of the frames. Then, the necessary features are selected using Entropy based Spider Monkey Algorithms (ESMA). In next stage, the features are grouped using Normalized K-Means (N-Kmeans) algorithm for identifying best candidate frames. Then select the minimum distance value based cluster set is the Key Frame (KF) selection. At last, the video is orderly summarized using quick sort method. Finally, in experimental evaluation the proposed work is compared with the prevailing methods. The proposed VS gave better outcome than the existing approaches.
Towards accurate RGB-D saliency detection with complementary attention and adaptive integration

Saliency detection based on the complementary information from RGB images and depth maps has recently gained great popularity. In this paper, we propose Complementary Attention and Adaptive Integration Network (CAAI-Net), a novel RGB-D saliency detection model that integrates complementary attention based feature concentration and adaptive cross-modal feature fusion into a unified framework for accurate saliency detection. Specifically, we propose a context-aware complementary attention (CCA) module, which consists of a feature interaction component, a complementary attention component, and a global-context component. The CCA module first utilizes the feature interaction component to extract rich local context features. The resulting features are then fed into the complementary attention component, which employs the complementary attention generated from adjacent levels to guide the attention at the current layer so that the mutual background disturbances are suppressed and the network focuses more on the areas with salient objects. Finally, we utilize a specially-designed adaptive feature integration (AFI) module, which sufficiently considers the low-quality issue of depth maps, to aggregate the RGB and depth features in an adaptive manner. Extensive experiments on six challenging benchmark datasets demonstrate that CAAI-Net is an effective saliency detection model and outperforms nine state-of-the-art models in terms of four widely-used metrics. In addition, extensive ablation studies confirm the effectiveness of the proposed CCA and AFI modules.
DeepFake Detection on Publicly Available Datasets using Modified AlexNet

Deep learning has been applied successfully in many areas, including computer vision, natural language processing, cyber physical systems and big data analytics. Recently, a synthesis of deep leaning techniques has been deployed to create fake images and videos that are not easily distinguishable from the real ones; this technology is known as DeepFakes. In this paper, we looked at various DeepFakes related datasets and created a model in order to identify whether a frame of a video is fake or real. This is important as videos can be easily manipulated in a way that can spread misinformation, and that can cause major problems in the world today, especially if the videos have political implications. In order to create a model, we utilize a modified AlexNet constructed of an arrangement of 6 layers: convolution2d, max pooling, dense, flatten, activation and dropout layers. UADFV, FaceForensics++, and Celeb-DF are the 3 datasets used in this research. There are many publicly available datasets, however, we found the UADFV, FaceForensics++ and Celeb-DF to be the most convenient in how the data was formatted. All data for each dataset was organized into videos of varying classes. While the UADFV dataset is straightforward and only has 2 classes: real and fake, the FaceForensics++ dataset looks at the various kinds of video manipulations and has 5 different classes. Our model was able to achieve a 9S.73% accuracy when identifying whether a video is real or fake on the UADFV dataset, a slightly adjusted version was able to accomplish an 87.49% accuracy with the FaceForenisics++dataset, and reached 98.85% accuracy on the Celeb-df dataset.
Bi-directional skip connection feature pyramid network and sub-pixel convolution for high-quality object detection

In existing state-of-the-art object detectors, feature pyramid networks (FPN) and multiscale feature fusion are still typically used. The traditional FPN fusion strategy is based on the top-down fusion of high-level semantic information. The top-down fusion method generally uses upsampling based on interpolation, which often results in jagged edges, mosaic distortion, and edge blurring. Moreover, in order to improve accuracy, the FPN-based fusion strategy must add multiple top-down components for fusion, which increases computational costs and leads to a poor balance between precision and speed. In this paper, we propose a novel fusion strategy based on a backbone network. We aim to design simple and efficient components for high-quality object detection. Our proposed strategy, bi-directional skip connection FPN (BiSCFPN), consists of three components: a bi-directional skip connection (BiSC), a selective dilated convolution module (SDCM), and sub-pixel convolution (SP). The BiSC aims to enhance semantic information between different feature layers in the backbone network and simultaneously uses the SDCM to improve the receptive fields of differently sized targets in the fusion stage. Finally, SP learns the relationship between the features of upsampling and downsampling images to effectively mitigate the problems caused by the traditional interpolation method. BiSCFPN achieves an average precision of 38.2% in tests with the Microsoft Common Objects in Context (MS COCO) test-dev dataset at a real-time speed of ~50 FPS (608×608) using an Nvidia GeForce RTX 2080 Ti graphics card and significantly improves the balance between precision and speed.
Sensitivity of Age Estimation Systems to Demographic Factors and Image Quality: Achievements and Challenges

Recently, impressively growing efforts have been devoted to the challenging task of facial age estimation. The improvements in performance achieved by new algorithms are measured on several benchmarking test databases with different characteristics to check on consistency. While this is a valuable methodology in itself, a significant issue in the most age estimation related studies is that the reported results lack an assessment of intrinsic system uncertainty. Hence, a more in-depth view is required to examine the robustness of age estimation systems in different scenarios. The purpose of this paper is to conduct an evaluative and comparative analysis of different age estimation systems to identify trends, as well as the points of their critical vulnerability. In particular, we investigate four age estimation systems, including the online Microsoft service, two best state-of-the-art approaches advocated in the literature, as well as a novel age estimation algorithm. We analyse the effect of different internal and external factors, including gender, ethnicity, expression, makeup, illumination conditions, quality and resolution of the face images, on the performance of these age estimation systems. The goal of this sensitivity analysis is to provide the biometrics community with the insight and understanding of the critical subject-, camera- and environmental-based factors that affect the overall performance of the age estimation system under study.
On simplified NP-complete variants of Monotone <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e23" altimg="si14.svg"><mml:mn>3</mml:mn></mml:math> -Sat

We consider simplified versions of 3 -Sat, the variant of the famous Satisfiability Problem where each clause is made up of exactly three distinct literals formed over pairwise distinct variables. More precisely, the focus of this work is laid on Monotone 3 -Sat, the restriction of 3 -Sat to formulas with monotone clauses, where a clause is monotone if it contains only unnegated variables or only negated variables. We prove several hardness results for Monotone 3 -Sat with respect to a variety of restrictions imposed on the variable appearances. In particular, we show that for any k≥2, Monotone 3 -Sat turns out to be NP-complete even if each variable appears exactly k times unnegated and exactly k times negated. Therewith, for Monotone 3 -Sat with balanced variable appearances we establish a sharp boundary between NP-complete and polynomial time solvable cases. In addition, we prove that for any k≥5, Monotone 3 -Sat is NP-complete even if each variable appears exactly k times unnegated and exactly once negated. Further, we prove that the problem remains NP-complete when restricted to instances in which each variable appears either exactly once unnegated and three times negated or the other way around. Thereby, we improve on a result by Darmann et al. (2018) showing NP-completeness for four appearances per variable. Our stronger result also implies that 3 -Sat remains NP-complete even if each variable appears exactly three times unnegated and once negated, therewith complementing a result by Berman et al. (2003).
Social Media Analytics during Pandemic for Covid19 using Topic Modeling

The entire world is facing the Covid19 pandemic. This pandemic has various consequences on the political, cultural, economical and social life of the community. Lockdown has affected the psychological impact on society. This is reflected in various social media sites. In such a phase social media analytics for twitter data can be useful for understanding public opinion. In this paper, we have applied the Latent Dirichlet Allocation Algorithm as a topic modeling algorithm. Topic modeling finds the main theme that pervades the large data set. Twitter media is considered as the most popular microblogging platform, hence data during this pandemic is extracted from twitter. Natural language processing Techniques applied as preprocessing and then topic modeling applied which has given satisfactory results in terms of perplexity as a performance measure. Topic extracted gives an idea of the impact of Covid19 on society through their opinion on twitter. This can be helpful for making future policies by policymakers.
From Blackwell Dominance in Large Samples to Rényi Divergences and Back Again

We study repeated independent Blackwell experiments; standard examples include drawing multiple samples from a population, or performing a measurement in different locations. In the baseline setting of a binary state of nature, we compare experiments in terms of their informativeness in large samples. Addressing a question due to Blackwell (1951), we show that generically an experiment is more informative than another in large samples if and only if it has higher Rényi divergences. We apply our analysis to the problem of measuring the degree of dissimilarity between distributions by means of divergences. A useful property of Rényi divergences is their additivity with respect to product distributions. Our characterization of Blackwell dominance in large samples implies that every additive divergence that satisfies the data processing inequality is an integral of Rényi divergences.
Automated Essay Scoring Using Natural Language Processing And Text Mining Method

The use of technology really helps to maximized the effectiveness and efficiency of work expecially in the education field. Elearning is the concept of education that has begun to be widely implemented at this covid-19 pandemic to avoid the spread of transmission through social distancing. One of elearning types is essay but for large participants, it need much effort for evaluate by human rater. The inconsistency of assessment by the rater due to fatigue can also affect the quality of the assessment. Developing a system that can learn and understand on its own without having to be repeatedly programmed by humans used machine learning and computational linguistics to study the interaction between computers and human natural language used natural language processing proposed in this research. Natural language processing and text mining methods are able to provide a good assessment which is influenced by several processes, namely tokenization, stopword, stemming and support with the number of keywords, and the synonym of more complex keywords. The automated essay scoring system is proven to provide consistent and objective assessments and is able to approach human raters assessments.
Traffic Police Gesture Recognition by Pose Graph Convolutional Networks

Gestures from traffic police give the authorized information, especially in some urgent situation. Thus, understanding of traffic police instruction accurately and promptly is particularly crucial for the automated driving system. However, this task is a great challenge not only because of the dynamic and diversity characteristics of the human gesture, but also the high requirement for real-time performance in each frame. We propose an online activity recognition method based on pose estimation and Graph Convolutional Networks (GCN) to recognize the traffic police gesture in frame level. The main contribution in this work is the development of an online framework based on graph convolutional networks for traffic police recognition. Our approach obtained the state-of-the-art results on Traffic Police Gesture Recognition (TPGR) dataset.
Clothing generation by multi-modal embedding: A compatibility matrix-regularized GAN model

Clothing compatibility learning has gained increasing research attention due to the fact that a properly coordinated outfit can represent personality and improve an individual's appearance greatly. In this paper, we propose a Compatibility Matrix-Regularized Generative Adversarial Network (CMRGAN) for compatible item generation. In particular, we utilize a multi-modal embedding to transform the image and text information of an input clothing item into a latent feature code. Sequentially, compatibility learning among latent features is performed to obtain a compatibility style space. The feature of the input image is then regularized by the style space. Finally, a compatible clothing image is generated by a decoder which is fed by the regularized features. To verify the proposed model, we train an Inception-v3 classification model to evaluate the authenticity of synthesized images, a regression scoring VGG model to measure the compatibility degree of the generated image pairs and a deep attentional multimodal similarity model to evaluate the semantic similarity between generated images and ground truth text descriptions. In order to give an objective evaluation, these models are trained based on datasets consisting of fashion data only. The results demonstrate the effectiveness of the proposed method on image-to-image translation based on compatibility space.
Hierarchical Generative Adversarial Networks for Single Image Super-Resolution

Recently, deep convolutional neural network (CNN) have achieved promising performance for single image super-resolution (SISR). However, they usually extract features on a single scale and lack sufficient supervision information, leading to undesired artifacts and unpleasant noise in super-resolution (SR) images. To address this problem, we first propose a hierarchical feature extraction module (HFEM) to extract the features in multiple scales, which helps concentrate on both local textures and global semantics. Then, a hierarchical guided reconstruction module (HGRM) is introduced to reconstruct more natural structural textures in SR images via intermediate supervisions in a progressive manner. Finally, we integrate HFEM and HGRM in a simple yet efficient end-to-end framework named hierarchical generative adversarial networks (HSR-GAN) to recover consistent details, and thus obtain the semantically reasonable and visually realistic results. Extensive experiments on five common datasets demonstrate that our method shows favorable visual quality and superior quantitative performance compared to state-of-the-art methods for SISR.