Reviewerly/exHarmony huggingface dataset:
Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis

Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem.We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence.We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: #Centralpark (n=20,937), #Brooklyn Bridge (n=14,875), and #Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations.We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17% across each site (P<.001). We found a positive correlation between hashtags (#Centralpark and #Brooklynbridge: r=0.40; #BrooklynBridge and #Timesquare: r=0.41; and #Timesquare and #Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16).Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach.
Performance Evaluation of Deep Learning Models on Embedded Platform for Edge AI-Based Real time Traffic Tracking and Detecting Applications

Edge Artificial Intelligence based traffic tracking and detecting sensors are very essential for smart cities, especially for smart transportation applications. These sensors are not only used to collect large amount of traffic data, but also reduce the bandwidth of the communication network to transfer and reduce the workload to process huge data on the cloud or server side. It is very necessary to process, store, and extract useful data at the edge of the Internet before transferring the data to central server which can be called Artificial Intelligence on The Edge. This research aims at studying, implementing and evaluating machine learning models which are suitable for running on limited computing embedded computers. Computer vision and real-time object detection techniques are applied on Nvidia Jetson Nano Embedded Computer to build an Edge-AI based traffic tracking and detecting sensor, two popular models (MobileNet-SSD and YOLOv4) have been studied and implemented to compare the performance on vehicle counting and license plate detection applications. There has been a new propose method to apply TensorRT engine for these two models to increase the processing speed. The data source used in this project is manually collected at actual traffic routes and parking lots in Vietnam with more than 11700 images of Vietnamese vehicles and license plates then trained on Google Colab. The performance evaluation results show that both models have high accuracy in real-time license plate detection and vehicle counting application when implemented on the Edge computer Jetson Nano with the mAPs of both model are higher than 90 percent during training session. The MobileNet-SSD model has good speed (40 FPS) which is very much faster than some previous works (25 FPS), this model is suitable for real-time applications. The YOLOv4 model, after being optimized by TensorRT engine, has a better speed (7.2 FPS) than the original version (1.7 FPS), although the YOLOv4 model has low speed than MobileNet-SSD model but can detect smaller size, this model is suitable for some applications that need to detect complicated objects with small sizes.
A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

In this paper, the Kalman filter (KF) and the unbiased finite impulse response (UFIR) filter are fused in the discrete-time state-space to improve robustness against uncertainties. To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics, we attempt to find a way to fuse without using noise statistics. The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response (IFIR) filter. The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process, showing that a critical feature of the UFIR filter is inherited. One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method. It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions. Moreover, the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.
Practical path planning techniques in overtaking for autonomous shuttles

Abstract This paper proposes a reliable optimized sigmoid‐based path planning algorithm that ensures smooth, fast and safe overtaking maneuver, while maintaining the necessary safety distance. In the proposed method, the desired smoothness of trajectories, the changes in steering angle and the lateral acceleration are controlled in a robust way. This paper describes the simulations, and the confirming real‐world experiments, conducted using the autonomous shuttle iseAuto. Our results suggest that the sigmoid A‐star algorithm leads to a smoother and more reliable motion when compared to other two standard methods. Specifically, the abruptness of necessary steering angle changes is reduced by factor of 4, and approaching the level of an experienced driver‐like maneuver.
Adaptation and Attention for Neural Video Coding

Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention. Our codec is organized as an intra-frame codec paired with an inter-frame codec. As one architectural novelty, we propose to train the inter-frame codec model to adapt the motion estimation process based on the resolution of the input video. A second architectural novelty is a new neural block that combines concepts from split-attention based neural networks and from DenseNets. Finally, we propose to overfit a set of decoder-side multiplicative parameters at inference time. Through ablation studies and comparisons to prior art, we show the benefits of our proposed techniques in terms of coding gains. We compare our codec to VVC/H.266 and RLVC, which represent the state-of-the-art traditional and end-to-end learned codecs, respectively, and to the top performing end-to-end learned approach in 2021 CLIC competition, E2E_T_OL. Our codec clearly outperforms E2E_T_OL, and compare favorably to VVC and RLVC in some settings.
Digital Signature Scheme over Lattices

With the rapid advancements in information technology, data security has become an indispensable component. Cryptography performs a significant role in establishing information security. Computational problems have been utilized extensively by cryptographers to construct digital signature schemes. Digital signature schemes offer security services such as confidentiality, authenticity, integrity, and non-repudiation of a message. This paper proposes a modification of the Dilithium signature scheme that is secure against unforgeability attack based on the hardness of lattice problems such as Learning With Errors and Short Integer Solution over lattices. Using the rejection sampling technique, data is sampled from a uniform distribution to generate keys that are expanded into a matrix. The keys are hashed and signed by the sender to generate a message, which is then accepted by the receiver upon verification. Finally, the security analysis for the proposed signature scheme is provided with a strong emphasis on the security of the secret key. We prove that the attacker cannot forge a signature on a message, and recommended parameters are proposed.
Cross-Layer Encryption of CFB-AES-TURBO for Advanced Satellite Data Transmission Security

As the advanced satellite network can provide extremely diverse data to various types of users, it is of vital importance to achieve data security using encryption over satellite channels. However, since the avalanche effect makes encryption techniques vulnerable to bit errors in the wireless channel, a more powerful encryption technique is required for data confidentiality. In addition, an efficient method for exchanging encryption keys between receivers and transmitters should be capable of managing a large scale of satellites in the network. In this article, we propose a novel superencryption method of joint encryption and channel coding, cipher feedback-advanced encryption standard (CFB-AES)-TURBO, which combines AES in the upper layer and turbo coding as bulk encryption in the physical layer. Because we introduce turbo coding in the CFB mode with AES for each block, our scheme can accomplish dual goals of data protection and bit error correction. We select an encryption block size to maximize throughput based on channel conditions and the security level required for a user, and present a key exchanging solution. We derive processing time gains and analyze computational complexity of the CFB-AES-TURBO. The bit error rate performance improvement with about 12-dB coding gain is achieved. We also enhance the security performance by $10^{15 000}$ times and provide indistinguishability. Finally, we analyze the total expected latency, suggest the minimum key refresh time of geostationary Earth orbit satellites.
A Multi-Key Based Lightweight Additive Homomorphic Encryption Scheme

Usually, IoT devices have low power. Merging IoT and cloud computing provides the IoT environment with data management flexibility, such as data scalability, enhanced devices capacity, economies of scale, etc. But solutions based on cloud computing can put privacy at risk when operating an unreliable provider. The introduction of the homomorph allows a third party to perform operations on the data without having access to it. In this paper, we propose a partially homomorphic lightweight asymmetric encryption technique, which allows to encrypt decimal digits of the message separately, by multiplying each by a different key. There are many encryption techniques in the literature, most of them suffer from size and slowness in terms of execution time. In our proposed technique, the size of the ciphertext is close to the size of the original one. Moreover, we obtained a time complexity equal to O(n). Thus, the proposed scheme is suitable for devices and networks with limited resources, while offering high security.
Adaptive Group-Wise Consistency Network for Co-Saliency Detection

Co-saliency detection focuses on detecting common and salient objects among a group of images. With the application of deep learning in co-saliency detection, more accurate and more effective models are proposed in an end-to-end manner. However, two major drawbacks in these models hinder the further performance improvement of co-saliency detection: 1) the static manner-based inference, and 2) the constant quantity of input images. To address these limitations, we present a novel Adaptive Group-wise Consistency Network (AGCNet) with the ability of content-adaptive adjustment for a given image group with random quantity of images. In AGCNet, we first introduce intra-saliency priors generated from any off-the-shelf salient object detection model. Then, an Adaptive Group-wise Consistency (AGC) module is proposed to capture group consistency for each individual image, and is applied on three-scale features to capture the group consistency from different perspectives. This module is composed of two key components, where the content-adaptive group consistency block breaks the above limitations to adaptively capture the global group consistency with the assistance of intra-saliency priors and the ranking-based fusion block combines the consistency with individual attributes of each image feature to generate discriminative group consistency feature for each image. Following AGC modules, a specially designed Aggregated Decoder aggregates the three-scale group consistency features to adapt to co-salient objects with diverse scales for preliminary detection. Finally, we incorporate two normal decoders to progressively refine the preliminary detection and generate the final co-saliency maps. Extensive experiments on four benchmark datasets demonstrate that our AGCNet achieves competitive performance as compared with 19 state-of-the-art models, and the proposed modules experimentally show substantial practical merits.
YOLOv4 RGBT Human Detection on Unmanned Aerial Vehicle Perspective

Analysis of visual drone images in various domains is increasing due to its ability to see the object from different perspectives and certain distant. Nowadays, small object detection from drones images is expected to overcome various environmental challenges such as illumination and motion change. Thermal infrared in addition to RGB images (RGBT) appears to undertake these challenges. Research on how this combination can give the best performance in object detection is still become attractive problem to be solved. In this study, The state of the art in object detection You Only Look Once (YOLO) v4 performance has been demonstrated by conducted three scenarios training methods in both RGB and thermal infrared (TIR) images dataset with unmanned aerial vehicle (UAV) perspective view. The dataset has been manually annotated to be compatible in YOLO format and the annotation will be release to the community. The pre-trained YOLO weight with minimal fine tuning also has been utilized to determine the transfer learning influence on the new aerial perspective dataset. The experimental result shows that with pre-trained model transfer learning from MS COCO dataset can improved the YOLOv4 human detection with Average Precision (AP) up to 91.18 % and 78.24 % in RGB and TIR dataset, respectively.
Survey on Twitter Sentiment Analysis: Architecture, Classifications, and Challenges

Sentiment analysis, also called opinion mining, is an extensive research field due to the rapid growth in social media. It has widespread applications in almost all areas of today's life, from business services to political field. Sentiment analysis refers to the opinion or feelings of a person toward a particular topic. It gives results or a subjective impression, not facts, and can be expressed as a polarity to negative, positive, or neutral. Sentiments can be analyzed by utilizing statistics, natural language programming, or machine learning techniques. These techniques are implemented on data previously collected from social networking sites or blogs, the most famous of which is Twitter. Twitter is one of the essential sources of people's opinions on various topics. This source permits individuals to state their views and offer different perspectives on any field. Many organizations have become interested in analyzing people's feelings through social networks, especially in political and economic domains. The main task is to classify the level of messages or tweets to their polarity. In this research, we will look at the most important approaches used in sentiment analysis and how they process the collected data. An explanation of the feature extraction methods will be presented. We will also clarify the levels of sentiment analysis and the challenges facing sentiment analysis.
A Fault Detection Method Based on Enhanced GRU

Fault detection has been deployed in many cases. It will help improve the stability of the system. Data-driven methods can provide credible evidence for fault detection. For time series which may include a lot of noise, the performance of typical methods may be affected. This article raises an enhanced gate recurrent unit (GRU) method to analyze unmanned aerial vehicle (UAV) flight data that are affected by the vibration of motors or wind. Firstly, the raw data are denoised and normalized to improve the effect of the analysis. Secondly, a gate recurrent unit (GRU) model is built to estimate one of the sensor data based on others. Finally, to detect fault data, the method based on residuals and threshold is applied. To evaluate the effectiveness of the method, the simulation data of UAV are applied to the method, and it can be found that the proposed method is effective in fault detection.
Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements.Efficient QoS-aware service composition has been a challenging task in cloud computing.This challenge becomes more formidable in emerging resource-constrained computing paradigms, such as the Internet of Things and Fog.Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard.Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics.Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods.As a result, research on service composition shifts towards the hybridization of metaheuristics.Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings.Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics.This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020.Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study.Finally, research trends, directions and challenges are discussed to benefit future endeavours.
Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images

The quantification of biomarkers on immunohistochemistry breast cancer images is essential for defining appropriate therapy for breast cancer patients, as well as for extracting relevant information on disease prognosis. This is an arduous and time-consuming task that may introduce a bias in the results due to intra- and inter-observer variability which could be alleviated by making use of automatic quantification tools. However, this is not a simple processing task given the heterogeneity of breast tumors that results in non-uniformly distributed tumor cells exhibiting different staining colors and intensity, size, shape, and texture, of the nucleus, cytoplasm and membrane. In this research work, we demonstrate the feasibility of using a deep learning-based instance segmentation architecture for the automatic quantification of both nuclear and membrane biomarkers applied to IHC-stained slides. We have solved the cumbersome task of training set generation with the design and implementation of a web platform, which has served as a hub for communication and feedback between researchers and pathologists as well as a system for the validation of the automatic image processing models. Through this tool, we have collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and HER2 (membrane biomarker) IHC-stained images. Using the same deep learning network architecture, we have trained two models, so-called nuclei- and membrane-aware segmentation models, which, once successfully validated, have revealed to be a promising method to segment nuclei instances in IHC-stained images. The quantification method proposed in this work has been integrated into the developed web platform and is currently being used as a decision-support tool by pathologists.
‘Sanna, Aren't You Ashamed?’ Affective‐discursive practices in online misogynist discourse of Finnish prime minister Sanna Marin

Abstract This research examines online misogynist discourse related to the Finnish prime minister (PM) Sanna Marin's image published in Trendi magazine in October 2020. The affective‐discursive analysis of online commentaries resulted in the identification of four affective‐discursive practices: an immoral woman, incompetent woman, calculating woman and inferior woman. Misogynist discourse related to the PM Marin took the form of a moral act, drew from stereotypical images of women as less rational than men, appeared as accusations targeted at Marin for playing the gender card as a political tactic, and constructed an image of Marin and female politicians as objects of men's sexual desires and as inferior to men. These affective‐discursive practices mobilized dehumanizing discourse ranging from milder forms of derogation, scorn and other‐condemning practices to harsher forms of belittlement, humiliation and animalistic dehumanization. This study contributes to the current knowledge on the affective‐discursive processes underlying online misogyny against female politicians.
Modeling Perception in Autonomous Vehicles via 3D Convolutional Representations on LiDAR

This paper proposes an algorithm to model and process streams of LiDAR data under an autonomous vehicle framework.LiDAR is assumed to be an exteroceptive sensor that allows the vehicle to have dynamic 3D scene perception of its surroundings.We employ an encoder-decoder architecture based on 3D-Convolutional layers called 3D Convolution Encoder-Decoder (3D-CED), together with a transfer learning strategy to extract a set of features from point clouds, which are relevant in the context of autonomous driving.The resulting features allow to make predictions of the future point cloud data and detect multiple abstraction level anomalies in controlled scenarios by utilizing a probabilistic switching dynamic model called High Dimensional Markov Jump Particle Filter (HD-MJPF).Moreover, a comparison is provided between piecewise linear, piecewise nonlinear, and nonlinear predictive models at multiple abstraction levels of anomaly detection.Our approach is evaluated with data collected from the LiDAR sensors of the autonomous vehicle while performing certain tasks in a controlled environment.