Reviewerly/exHarmony huggingface dataset:
Classification of non-Hodgkin lymphomas based on sample entropy signatures

Computational systems to provide studies and diagnoses of non-Hodgkin's lymphomas have been increasingly developed to assist specialists in their decision-making. On the other hand, the approaches have not yet fully explored the sample entropy to assess the disease's characteristics, with indications of the best features and techniques to distinguish the main groups of lymphomas. Herein, we present a method that considers sample entropy signatures to study chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma, involving images after applying multiple segmentation techniques and color normalization models. Texture signatures were defined as feature curves obtained from multiple observations, by associating different parameters. Each signature was calculated for windows of size m and ranged from 1 to 4 (0.06–0.40) tolerance levels (r). The behavior of each signature was determined from the area under the curve, skewness, maximum entropy value and area ratio, representing the signature metrics. This approach aimed to improve the quantitative ability of this descriptor, with new interpretations and associations. Thus, all features were provided for different functions, such as lazy learning, trees, genetic evolution, particle swarm and animal behavior-based approaches. The best association was tested after adding noise levels of 20%, 40% and 80%. The performances achieved with the proposal were accuracy rates between 98.72% and 99.60%. We were able to obtain an accuracy of 98.84% for multi-class classification using only ten features, which is an amount considerably lower than most of the state-of-the-art approaches. The proposed approach presents relevant advances for the investigation of computer-aided diagnosis systems since it provides accurate results and does not require preprocessing steps. Moreover, signature metrics in association with evolutionary approaches, which provided the best results, are among the main contributions of this paper, since the details of the most relevant features with a full understanding of the solutions are useful for the study and pattern recognition of non-Hodgkin's lymphomas.
Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.
Rectangular Mapping-based Foveated Rendering

With the speedy increase of display resolution and the demand for interactive frame rate, rendering acceleration is becoming more critical for a wide range of virtual reality applications. Foveated rendering addresses this challenge by rendering with a non-uniform resolution for the display. Motivated by the non-linear optical lens equation, we present rectangular mapping-based foveated rendering (RMFR), a simple yet effective implementation of foveated rendering framework. RMFR supports varying level of foveation according to the eccentricity and the scene complexity. Compared with traditional foveated rendering methods, rectangular mapping-based foveated rendering provides a superior level of perceived visual quality while consuming minimal rendering cost.
Feasibility study on ground-state cooling and single-phonon readout of trapped electrons using hybrid quantum systems

Qubits of long coherence time and fast quantum operations are long-sought objectives toward the realization of high-fidelity quantum operations and their applications to quantum technologies. An electron levitated in a vacuum by a Paul trap is expected to be a good candidate, for its light mass and hence the high secular frequency which allows for faster gate operations than those in trapped ions. Controlling the motional state of the trapped electron is a crucial issue, for it mediates an interaction between electron spins, intrinsic qubits embedded in electrons, and its decoherence results in degraded fidelity of two-qubit gates. In addition, an efficient readout of the motional state is important, regarding the possibility of detecting spin state by using it. Despite such importance, how to achieve the motional ground state and how to efficiently detect it are not reported so far. Here we propose methods addressing these issues by utilizing hybrid quantum systems involving an electron-superconducting circuit and electron-ion coupled systems and analyze the feasibility of our schemes. In both systems, we show that ground-state cooling and single-phonon readout of the motional state of the trapped electron are possible. Our work sheds light on a way to precisely control the motional states of the trapped electrons, that provides an interesting playground for the development of quantum technologies.
Review on the Application of Lexicon-Based Political Sentiment Analysis in Social Media

This chapter presented an analysis of the application of lexicon-based political sentiment analysis in social media. The aim is to identify the most frequently used lexicons in political sentiment analysis, their results, similarities, and differences. For this, the authors conducted a systematic literature review based on PRISMA methodology. Afinn, NRC, and SenticNet lexicons are tested and combined for data analysis from the 2020 U.S. presidential campaign. Findings show that political sentiment analysis is a new field studied for only 10 years. Political sentiment analysis could generate benefits in understanding problems such as political polarization, discourse analysis, politician influence, candidate profiling, and improving government-citizen interaction, among other problems in the public sphere, enhanced by the combination of lexicons and multimodal analysis. The authors conclude that polarity was one of the critical dimensions identified for finding variations in the behavior and polarity of sentiments. Limitations and future work also are presented.
RedirectedDoors: Redirection While Opening Doors in Virtual Reality

We propose RedirectedDoors, a novel space-efficient technique for redirection in VR focused on door-opening behavior. This technique manipulates the user&#x2019;s walking direction by rotating the entire virtual environment (VE) at a certain angular ratio of the door being opened. This ratio is called door rotation gain. At the same time, the virtual door&#x2019;s position is kept unmanipulated so that a realistic door-opening user experience can be ensured. We designed and implemented the rotational manipulation algorithm and two types of door-opening interfaces; with and without a doorknob-type passive haptic prop. We then conducted a user study (N = 12) to investigate redirection performance and user feedback as we examined three independent variables: door rotation gain, door-opening interface, and door-opening direction (push/pull). From the results, the estimated detection thresholds generally showed a higher space efficiency of redirection with our technique. Our results also showed that providing the haptic feedback led to a higher noticeability of redirection, but at the same time supported a higher subjective sense of realism and less discomfort. Following our results, we discuss which combinations of gain and door-opening direction can jointly provide lower noticeability and higher acceptability.
Online on-Road Motion Planning Based on Hybrid Potential Field Model for Car-Like Robot

The application of Middle-sized Car-like Robots (MCRs) in indoor and outdoor road scenarios is becoming broader and broader. To achieve the goal of stable and efficient movement of the MCRs on the road, a motion planning algorithm based on the Hybrid Potential Field Model (HPFM) is proposed in this paper. Firstly, the artificial potential field model improved with the eye model is used to generate a safe and smooth initial path that meets the road constraints. Then, the path constraints such as curvatures and obstacle avoidance are converted into an unconstrained weighted objective function. The efficient least-squares & quasi-Newton fusion algorithm is used to optimize the initial path to obtain a smooth path curve suitable for the MCR. Finally, the speed constraints are converted into a weighted objective function based on the path curve to get the best speed profile. Numerical simulation and practical prototype experiments are carried out on different road scenes to verify the performance of the proposed algorithm. The results show that re-planned trajectories can satisfy the path constraints and speed constraints. The real-time re-planning period is 184 ms, which demonstrates the proposed approach's effectiveness and feasibility.
A Comparative Study on Part-of-Speech Taggers’ Performance on Examination Questions Classification According to Bloom’s Taxonomy

Abstract Examination questions classification according to Bloom’s Taxonomy uses Natural Language Processing (NLP) approach, a series of text processing approach that generally can divided into the keywords identification stage and then the identified keywords classification to Bloom’s Taxonomy levels stage. Since this NLP approach is a pipeline processes, the keywords identification stage’s performance in term of accuracy is affecting the subsequent stage - the identified keywords classification and subsequently limits the final accuracy performance of the questions classification. The keywords identification stage’s performance is mainly depending on the effectiveness of Part-Of-Speech (POS) tagging. Thus, this paper aims to identify the best performing POS tagger in keywords identification stage and enhance the tagger’s performance with rule-based approach to achieve high accuracy performance and benefit the subsequent keyword classification and then the questions classification accuracy. The Perceptron tagger and the Stanford POS tagger are selected to be evaluated their performance in identifying the keywords of the randomly selected 200 examination questions from STEM subjects. This paper has observed the Stanford POS tagger is the best performing tagger in POS tagging with accuracy of 80.5%. Some rules are applied to the POS tagging to improve the accuracy further to 91.5%.
HeadBox: A Facial Blendshape Animation Toolkit for the Microsoft Rocketbox Library

HeadBox is a series of opensource tools to do facial animation on the Microsoft Rocketbox avatar library. It includes a tool to create blendshapes out of the facial bones inside Maya and transfer the new blendshapes to the other avatars in the library. We have created a total of 15 visemes, 48 FACS, 30 for the Vive facial tracker. These blendshapes have been released with the original library. An additional Unity demo shows the use these tools with Openface and Oculus Lipsync.
SkiSim: A comprehensive Study on Full Body Motion Capture and Real-Time Feedback in VR Ski Training

Ski Training has many restrictions, such as environmental requirements and the constraint for indirect cyclic feedback given by coaches. Though, several researcher introduced different systems in order to overcome these limitations, there is no comprehensive study on which body parts trainees concentrate on the most and which feedback is suited for an intuitive training of the skier. We therefore conducted two user studies to determine which body parts the user concentrates on while using a ski simulator and whether or not a more informative non-visual feedback on said body parts can compete to the state of the art visual feedback.
Research challenges of Blockchain in 6G Network

Nowadays, video calling is very much on demand, and it is working well in 4G and 5G networks. It is a time for 3D calling, virtual reality live streaming and holographic communication; Accessing this type of data requires ultrahigh data rates and traffic volumes, so there is a need to speed up the network and increase bandwidth. On the other hand, a system has to maintain reliability, security, availability and flexibility in a wireless network. The network should be extended to the needs of human life such as entertainment, healthcare, smart cities, and transportation expected to enhance service quality with a high-end user experience. To establish this network, the telecommunications infrastructure must upgrade its unmatched service level requirements. The two major disruptive emerging components in this modern Internet-enabled era of technology are both the blockchain and the Internet of Things (IoT). In research and industry, IoT has experienced exponential growth, although it suffers from several limitations, such as poor interoperability, decentralization, privacy, and security vulnerabilities. 6G applications can be based on telecommunications networks and IoT, therefore, the network must ensure strong connectivity between nodes and also ensure the privacy and security of user data. Blockchain is the appropriate solution for these issues. Blockchain is a distributed ledger technology and it enables many industries to exchange data over the Internet between trusted parties without using a central server. The blockchain facilitates verifiable, transparent and secure digital asset transactions with proof of right and ownership. There is a need to identify the role of blockchain in 6G networks. This paper explores the challenges and opportunities related to blockchain for 6G networks, future application possibilities and upcoming research directions.
Real-Time Big Data Architecture for Processing Cryptocurrency and Social Media Data: A Clustering Approach Based on k-Means

Cryptocurrencies have recently emerged as financial assets that allow their users to execute transactions in a decentralized manner. Their popularity has led to the generation of huge amounts of data, specifically on social media networks such as Twitter. In this study, we propose an iterative kappa architecture that collects, processes, and temporarily stores data regarding transactions and tweets of two of the major cryptocurrencies according to their market capitalization: Bitcoin (BTC) and Ethereum (ETH). We applied a k-means clustering approach to group data according to their principal characteristics. Data are categorized into three groups: BTC typical data, ETH typical data, BTC and ETH atypical data. Findings show that activity on Twitter correlates to activity regarding the transactions of cryptocurrencies. It was also found that around 14% of data relate to extraordinary behaviors regarding cryptocurrencies. These data contain higher transaction volumes of both cryptocurrencies, and about 9.5% more social media publications in comparison with the rest of the data. The main advantages of the proposed architecture are its flexibility and its ability to relate data from various datasets.
A hybrid deep learning approach for driver anomalous lane changing identification

Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly detection in driving behavior means recognizing anomalous driving states as a direct result of either environmental or psychological factors. This paper provides an efficient anomaly recognition approach to identify anomalous lane-changing events in a personalized manner. The proposed framework includes three unsupervised algorithms. First, a Recurrent-Convolutional Autoencoder extracts the spatio-temporal characteristics from a high-dimensional naturalistic driving dataset. Second, in order to recognize anomalous lane-changing events of individual drivers, the extracted latent feature space is analyzed using Pauta criterion-based reconstruction loss analysis, as well as one-class Support Vector Machine. Last, t-Distributed Stochastic Neighbor Embedding is employed to visualize the latent space for better understanding and interpretability. Temporal anomalies of lane-changing events were analyzed by a personalized grey relational coefficient analysis, to represent robust similarities for individual drivers. Validation and calibration were performed with a natural driving study dataset collected from 50 drivers with 59,372 lane change events. The results showed heterogeneity in the pattern of abnormal lane changing behavior across the sample. At the same time, each driver exhibited heterogeneous anomalous behaviors in both temporal and spatial sequences. Without prior labels, the proposed model effectively captures personalized driving patterns and abnormal lane-changing events from high-dimensional time-series data. This unsupervised hybrid approach is a novel attempt to complete personalized anomalous lane-changing behaviors identification based on naturalistic driving data involving various traffic environments. Our approach enables the extraction of natural individual lane-changing behavior patterns and provides insights for the improvement of personalized driving behavior monitoring systems.
DAM-GAN : Image Inpainting Using Dynamic Attention Map Based on Fake Texture Detection

Deep neural advancements have recently brought remarkable image synthesis performance to the field of image inpainting. The adaptation of generative adversarial networks (GAN) in particular has accelerated significant progress in high-quality image reconstruction. However, although many notable GAN-based networks have been proposed for image inpainting, still pixel artifacts or color inconsistency occur in synthesized images during the generation process, which are usually called fake textures. To reduce pixel inconsistency disorder resulted from fake textures, we introduce a GANbased model using dynamic attention map (DAM-GAN). Our proposed DAM-GAN concentrates on detecting fake texture and products dynamic attention maps to diminish pixel inconsistency from the feature maps in the generator. Evaluation results on CelebA-HQ and Places2 datasets with other image inpainting approaches show the superiority of our network.
Commutativity-guaranteed Docker Image Reconstruction towards Effective Layer Sharing

Owing to the benefit of light weight, containers have become a promising enabler for cloud native computing. Container images composed of applications and dependencies support flexible service deployment and migration. Rapid adoption and integration of containers generate millions of images to be stored. Additionally, non-local images have to be frequently downloaded from the registry, resulting in huge amounts of traffic. Content Addressable Storage (CAS) has been adopted for saving storage and networking by enabling identical layers sharing across images. However, according to our measurements, the implication of CAS is significantly limited as layers are rarely fully identical in practice. In this paper, we propose to reconstruct the docker images to raise the number of identical layers and thereby reduce storage and network consumption. We explore the layered structure of images and define the commutativity of files to assure image validity. The image reconstruction is formulated as an integer nonlinear programming problem. Inspired by the observed similarity of layers, we design a similarity-aware online image reconstruction algorithm. Extensive evaluations are conducted to verify the performance of the proposed approach.
Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification

Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a small single perturbation called a universal adversarial perturbation (UAP), is a realistic security threat to the practical application of a DNN. In particular, such attacks cause serious problems in medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only queries on inputs are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to a white-box condition in which adversaries can access the model weights and loss gradients. Nevertheless, we demonstrate that UAPs are easily generatable using a relatively small dataset under black-box conditions. In particular, we propose a method for generating UAPs using a simple hill-climbing search based only on DNN outputs and demonstrate the validity of the proposed method using representative DNN-based medical image classifications. Black-box UAPs can be used to conduct both non-targeted and targeted attacks. Overall, the black-box UAPs showed high attack success rates (40% to 90%), although some of them had relatively low success rates because the method only utilizes limited information to generate UAPs. The vulnerability of black-box UAPs was observed in several model architectures. The results indicate that adversaries can also generate UAPs through a simple procedure under the black-box condition to foil or control DNN-based medical image diagnoses, and that UAPs are a more realistic security threat.
Detection and Parameter Estimation Analysis of Binary Shift Keying Signals in High Noise Environments

In this paper, a new method for detecting and estimating the parameters of a binary phase shift keying (BPSK) signal, based on a cross-correlation function, is proposed. The proposed method consists of two stages. The first stage is used to detect or estimate a signal carrier frequency, and the second stage is used to estimate its pulse width or symbol rate. Firstly, the proposed method is investigated by use of a simulated BPSK signal in the form of Barker Codes 7, 11, and 13 in the MATLAB environment. Based on the simulation results, the functionality of this method is verified using a real-time BPSK signal generated by an E8267C generator. This is described in the second part of this paper. The experimental test results confirm that the proposed method is able to detect and estimate the parameters of all BPSK signals with SNR≥−21 dB.