Reviewerly/exHarmony huggingface dataset:
GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates

Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but poses significant challenges. A large body of work uses 2D affine transformations or homography for the global motion. However, in this work, we introduce a more general representation scheme, which adapts any existing optical flow network to ignore the moving objects and obtain a spatially smooth approximation of the global motion between video frames. We achieve this by a knowledge distillation approach, where we first introduce a low pass filter module into the optical flow network to constrain the predicted optical flow to be spatially smooth. This becomes our student network, named as GlobalFlowNet. Then, using the original optical flow network as the teacher network, we train the student network using a robust loss function. Given a trained GlobalFlowNet, we stabilize videos using a two stage process. In the first stage, we correct the instability in affine parameters using a quadratic programming approach constrained by a user-specified cropping limit to control loss of field of view. In the second stage, we stabilize the video further by smoothing global motion parameters, expressed using a small number of discrete cosine transform coefficients. In extensive experiments on a variety of different videos, our technique outperforms state of the art techniques in terms of subjective quality and different quantitative measures of video stability. Additionally, we present a new measure for evaluation of video stabilization based on the flow generated by GlobalFlowNet and argue that it is based on a more general motion model in contrast to the affine motion model on which most existing measures are based. The source code is publicly available at https://github.com/GlobalFlowNet/GlobalFlowNet
Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation

We present a self-supervised learning (SSL) method suitable for semi-global tasks such as object detection and semantic segmentation. We enforce local consistency between self-learned features that represent corresponding image locations of transformed versions of the same image, by minimizing a pixel-level local contrastive (LC) loss during training. LC-loss can be added to existing self-supervised learning methods with minimal overhead. We evaluate our SSL approach on two downstream tasks – object detection and semantic segmentation, using COCO, PASCAL VOC, and CityScapes datasets. Our method outperforms the existing state-of-the-art SSL approaches by 1.9% on COCO object detection, 1.4% on PASCAL VOC detection, and 0.6% on CityScapes segmentation.
Quantum Computing: An Overview

This paper intends to be a starting point for the reader interested in learning the fundamentals of quantum computing. For that purpose, we give high-level explanations of the key quantum properties leveraged by a quantum computer, the quantum mathematical formalism, information about current hardware and software available to run quantum algorithms, and references for further reading.
Malaria Detection with Flask Using Deep Learning Model

Malaria disease is rapidly spreading and very dangerous disease. It is caused due to bite of mosquito. Numbers of peoples all over the world are infected by this disease. Traditionally, the microscopic method could be considered as a best method to detect the malaria from cell images, but it requires more time and it need skilled and expert person in that field so, this research is focused on developing CAD system to diagnose the malaria patient via deep learning model which gives automated accurate and faster results than traditional system. Objective of this paper is also too aware the people about symptoms, prevention techniques and treatment usually used to cure this disease. Early prevention and treatment may save number of deaths and infections. This research uses deep learning-based transfer learning technique to make CAD system with the help of flask app. Malaria detection is Web app that can be used anywhere any time and this method gives accuracy of 99.6% with fine-tuning approach of ResNet50 model.
Application of QR Code Watermarking and Encryption in the Protection of Data Privacy of Intelligent Mouth-Opening Trainer

Quick response (QR) codes are widely used in offline to online channels to transfer information from promotional materials to mobile devices. Self-service medical equipment can record the data of each test, so the use of QR codes can realize the data exchange between patients and doctors, medical institutions, and self-service medical equipment, and create a medical information platform for health files. However, since anyone can easily read the information in the QR code, it is not conducive to the protection of patient privacy. Therefore, we propose a QR code encryption and decryption model based on robust digital watermarking. We implement digital watermarking through the generative adversarial networks and increase the robustness of the watermark by adding noise to the model. At the same time, we encrypt and decrypt the QR code information through advanced encryption standards. Experimental results show that the proposed method can well protect the privacy of patients without affecting the data acquisition by patients and doctors.
Multilayer Perceptron Based Early On-Site Estimation of PGA During an Earthquake

Earthquake warning systems are dependent on reliable and timely detection of p-phase arrival. However, the quality of early warning is achieved when the alarm is associated accurately with the probable damaging effect of the impending earthquake at a warning location. Hence, early prediction of peak ground acceleration (PGA) is necessary for real-time hazard assessment, automated preventive shutdown of safety–critical systems, shake-map generation for post-earthquake mitigation action, etc. In this article, a multilayer perceptron (MLP) neural network is trained for estimating on-site PGA using initial three-component p-wave features. Logarithmic transformation of PGA yielded an improved coefficient of determination (R2) from 62.09 to 70.15% when the MLP model is trained using features extracted from a 1-s-long accelerogram sampled from 0 to 7 s. At the same time, MAE and MSE reduced from 6.85 and 286.97 to 0.41 and 0.27, respectively. Effect of delay in feature window sampling from p-wave arrival further showed best R2 of 77.74% in case of 5–6 s delayed feature windows among 0–7 s.
A Four-Point Camera Calibration Method for Sport Videos

The main objective of camera calibration is to estimate the homography between a sports field template and the corresponding field in a video frame. State-of-the-art approaches exploit deep-learning technologies to achieve this aim. However, the lack of large training datasets leads to unreliable results. Search-based approaches deliver better accuracy but at the expense of a very high computational cost. In this paper, a four-point camera calibration method is proposed to address these challenges. It consists of four stages: first, a conditional generative adversarial network (cGAN) is used to generate meaningfully segmented video frames. This cGAN is a general image-to-image translation technique which has robust performance even for small training datasets. The aim is to eliminate foreground objects that greatly impede accurate estimation of four suitable points for the subsequent stage. Then, a regression network which can estimate four points from a single segmented video frame is introduced. In this stage we aim at keeping the low computational cost. In the next processing stage, the estimated four points and the original selected four points are used to calculate the homography. In this third stage a Direct Linear Transformation (DLT) algorithm is applied. In the last processing stage, the accuracy of the estimated homography is refined by running an Enhanced Correlation Coefficient (ECC) module. The proposed method is extensively evaluated using a standard 2014 World Cup dataset. The obtained results are compared to the state-of-the-art approaches and its variations based on U-net, VGG-16, ResNet-50. The obtained results show that the proposed method is superior in terms of accuracy and computational efficiency. To demonstrate the effectiveness and robustness of the proposed method, it is also validated on National Basketball, 2018 World Cup and cross dataset. In these diverse environments, the proposed method still maintains the competitive performance.
GIFT: Toward Accurate and Efficient Federated Learning With Gradient-Instructed Frequency Tuning

Federated learning (FL) enables distributed clients to collectively train a global model without revealing their private data, and for efficiency clients synchronize their gradients periodically. However, this can lead to the inaccuracy in model convergence due to inconsistent data distributions among clients. In this work, we find that there is a strong correlation between FL accuracy loss and the synchronization frequency, and seek to fine tune the synchronization frequency at training runtime to make FL accurate and also efficient. Specifically, aware that under the FL privacy requirement only gradients can be utilized for making frequency tuning decisions, we propose a novel metric called gradient consistency, which can effectively reflect the training status despite the instability of realistic FL scenarios. We further devise a feedback-driven algorithm called Gradient-Instructed Frequency Tuning (GIFT), which adaptively increases or decreases the synchronization frequency based on the gradient consistency metric. We have implemented GIFT in PyTorch, and large-scale evaluations show that it can improve FL accuracy by up to 10.7% with a time reduction of 58.1%.
A Survey on Anonymous Communication Systems With a Focus on Dining Cryptographers Networks

Traffic analysis attacks can counteract end-to-end encryption and use leaked communication metadata to reveal information about communicating parties. With an ever-increasing amount of traffic by an ever-increasing number of networked devices, communication privacy is undermined. Therefore, Anonymous Communication Systems (ACSs) are proposed to hide the relationship between transmitted messages and their senders and receivers, providing privacy properties known as anonymity, unlinkability, and unobservability. This article aims to review research in the ACSs field, focusing on Dining Cryptographers Networks (DCNs). The DCN-based methods are information-theoretically secure and thus provide unconditional unobservability guarantees. Their adoption for anonymous communications was initially hindered because their computational and communication overhead was deemed significant at that time, and scalability problems occurred. However, more recent contributions, such as the possibility to transmit messages of arbitrary length, efficient disruption handling and overhead improvements, have made the integration of modern DCN-based methods more realistic. In addition, the literature does not follow a common definition for privacy properties, making it hard to compare the approaches' gains. Therefore, this survey contributes to introducing a harmonized terminology for ACS privacy properties, then presents an overview of the underlying principles of ACSs, in particular, DCN-based methods, and finally, investigates their alignment with the new harmonized privacy terminologies. Previous surveys did not cover the most recent research advances in the ACS area or focus on DCN-based methods. Our comprehensive investigation closes this gap by providing visual maps to highlight privacy properties and discussing the most promising ideas for making DCNs applicable in resource-constrained environments.
Anomaly Detection in Social-Aware IoT Networks

Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal” areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.
E-UPQ: Energy-Aware Unified Pruning-Quantization Framework for CIM Architecture

The wide adoption of convolutional neural networks (CNNs) in many applications has given rise to unrelenting computational demand and memory requirements. Computing-in-Memory (CIM) architecture has demonstrated great potential to break the memory wall and effectively execute CNN workloads. Ongoing research focuses on pruning or quantizing CNNs to achieve higher efficiency on CIM. However, prior works preclude the possibility of integrating both techniques in the same framework. On the other hand, directly incorporating energy estimation during the model compression process has not been well explored in the literature. In this paper, we present an Energy-aware Unified Pruning-Q uantization (E-UPQ) mechanism, a novel framework for automated compression (pruning + quantization) of CNNs while considering the energy-accuracy trade-off. Specifically, E-UPQ interweaves pruning and quantization seamlessly by viewing pruning as a special case of “0-bit” quantization during the mixed-precision search. In addition, E-UPQ introduces a set of trainable parameters to incorporate energy information during the compression process, closing the gap between compression policy and energy optimization. Experimental results evaluated on DNN+NeuroSim show that E-UPQ reduces energy consumption by up to 79.3% and 66.6% for VGG-16 and ResNet-18, respectively, compared with the state-of-the-art work, while achieving similar accuracy on CIFAR-100. Layer-wise analysis and ablation studies are provided to validate the effectiveness of the E-UPQ. We also present the corresponding CIM architecture to support the proposed E-UPQ framework.
PeyeMMV: Python implementation of EyeMMV’s fixation detection algorithm

<h2>Abstract</h2> This article presents a new Python module called PeyeMMV. The module implements the two-step spatial dispersion fixation detection algorithm imported in both EyeMMV and LandRate MATLAB toolboxes. This algorithm is based on spatiotemporal criteria, belongs to the family of I-DT based algorithms and can be used as a spatial noise filtering approach during fixation identification process. PeyeMMV module could be utilized in order to extract fixation clusters among raw gaze data as well as to generate a basic plot that visualizes both gaze data and fixation centers positions. The module could be easily imported in every Python script or module.
Turning Grain Maps into Diagrams

.The present paper studies mathematical models for representing, imaging, and analyzing polycrystalline materials. We introduce various techniques for converting grain maps into diagram or tessellation representations that rely on constrained clustering. In particular, we show how to significantly accelerate the computation of generalized balanced power diagrams and how to extend it to allow for optimization over all relevant parameters. A comparison of the accuracy of the proposed approaches is given based on a three-dimensional real-world data set of \(339\times 339 \times 599\) voxels.Keywordsimagingdiagramgrain mapcoresetdata reductionpolycrystalMSC codes94A0868U1090
Orbital-angular-momentum-based optical clustering via nonlinear optics

Machine learning offers a convenient and intelligent tool for a variety of applications in the fields ranging from fundamental research to financial analysis. With the explosive growth of data streams, i.e., “big data,” optical machine learning with the inherent capacity for massive parallel processing is gradually attracting attention. Despite significant experimental and theoretical progress in this area, limited by the coherent manipulation of multibeams, high dimensional optical vector or matrix operation is still challenging. Here, by using the second harmonic generation of high dimensional orbital angular momentum superposition states, we present a compact and robust optical clustering machine, which is the crucial component in machine learning. In experiment, we conduct supervised clustering for classification of three- and eight-dimensional vectors and unsupervised clustering for text mining of 14-dimensional texts both with high accuracies. The presented optical clustering scheme could offer a pathway for constructing high speed and low energy consumption machine learning architectures.
An improved multi-copy cloud data auditing scheme and its application

The security of the data in the cloud server suffers great challenges, and how to ensure the integrity of the outsourced data in the cloud servers is a problem that should be solved. Cloud data auditing technique is such a solution, which allows to check data integrity without retrieving them from the cloud. In case single-copy data may be violated by malicious attackers, Zhang et al. proposed a multi-copy cloud data auditing scheme MCAM in 2020. However, in their scheme, an attacker can forge the evidence for integrity verification only by exploiting public information and the cloud server can compromise the integrity verification even it does not store any data. In this paper, we identify these security flaws in MCAM and design a new evidence verification algorithm to fix them. Then, we propose a multi-copy cloud data auditing scheme based on our evidence verification algorithm and MCAM. In security analysis, we prove that our multi-copy cloud data auditing scheme can fix the security flaws of MCAM. Thus, our scheme is more secure than MCAM. In performance analysis, we show that our scheme is more communication efficient than MCAM. Finally, we point out one potential application of our scheme.
Text-Based Emotion Detection using CNN-BiLSTM

Social media is not only used by the public to convey information but also to express emotions in writing. Text-based emotion detection is needed to find out the emotions contained in the text. This study combines CNN and BiLSTM for text-based emotion detection and compares the use of word embedding such as Word2Vec and GloVe. The data types used in this study are commuter line, transjakarta and commuter line + transjakarta. This study uses two scenarios, namely scenario I which classifies the dataset into emotion and no-emotion classes and scenario II which classifies emotions into five types of emotions, namely happiness, anger, sadness, fear, surprise. This study has two experimental types to emotion detection based on text, namely Word2Vec-CNN-BiLSTM, and GloVe-CNN-BiLSTM. In scenario I, Word2Vec-CNN-BiLSTM outperforms GloVe-CNN-BiLSTM in terms of accuracy. The accuracy values generated by Word2Vec-CNN-BiLSTM on the commuter line, transjakarta and commuter line + transjakarta data are 84.34%,83.%73%, and 83.88%, respectively. Word2Vec-CNN-BiLSTM provides the best overall Precision, Recall and F1-Measure compared with GloVe-CNN-BiLSTM on all data. The same result is also shown in scenario II where accuracy, precision, recall and F1-measure in Word2Vec-CNN-BiLSTM are better than other methods. The results of this study also improve the performance of emotion detection in text compared to the results of previous studies using Word2Vec-BiLSTM.
Smartphone-Based Virtual Reality Systems (SBVRs) as a Promotion Tools for Foodservice Industry

Virtual Reality (VR) has been increasingly popular in the consumer market in recent years, allowing the customers to fully immerse in a virtual environment. This technology has become one of important communication tools that expectedly transform the way the businesses present their products and services to the potential customers. This study aims to investigate how Smartphone-Based Virtual Reality Systems (SBVRs) perceived usefulness and enjoyment influence customers' future intention to make a restaurant reservation. Past study in hospitality found that the adoption of SBVR positively improves potential consumers' booking intentions. Therefore, the current research aims to examine whether SBVR utilization has similar effect in foodservice industry, particularly how it would impact potential customers' purchase intention. This study used convenient sample that consists of 128 respondents. The current study made use of the Statistical Package for the Social Sciences in order to examine the data (SPSS). A correlation analysis was done to investigate the relationship between the research variables. The finding of the study indicate that the perceived usefulness and enjoyment of a restaurant virtual reality application has a positive influence on the user's intention to make restaurant reservation in the future.
E-Passport COVID-19 Adopting RFID Implants based on Microservices

COVID-19 has spread throughout the global and has restrained humans in all aspects of life including society, economy, education, etc. The first Corona virus appeared in China on 2019 and had mutated into several variants such as B.1.1.7 (Alpha), B.1.351 (Beta), E484Q (Delta), and BA.2 (Omicron). In order to block the virus to mutate and spread in the communities, we proposed a design of E-Passport by adopting RFID Implants with integrated Microservices technology. Our concepts is we design an android application (E-passport) that will be used by every institution on public places to stop the positive patients getting inside the public space to spread the virus. The checker in every public places need to scan the RFID implants on every humans hand by using NFC technology embedded on android phone with installed E-Passport to determine whether can enter or forbidden to enter. The RFID Implant stored a unique code that can be read as the reference of the person's data information stored in cloud database based on Microservices infrastructure. Our proposed design and architecture are dedicated to stop the COVID-19 virus to spread among public communities.