Reviewerly/exHarmony huggingface dataset:
Predicting Heart Rate Variations of Deepfake Videos using Neural ODE

Deepfake is a technique used to manipulate videos using computer code. It involves replacing the face of a person in a video with the face of another person. The automation of video manipulation means that deepfakes are becoming more prevalent and easier to implement. This can be credited to the emergence of apps like FaceApp and FakeApp, which allow users to create their own deepfake videos using their smartphones. It has hence become essential to detect fake videos, to avoid the spread of false information. A recent study shows that the heart rate of fake videos can be used to distinguish original and fake videos. In the study presented, we obtained the heart rate of original videos and trained the state-of-the-art Neural Ordinary Differential Equations (Neural-ODE) model. We then created deepfake videos using commercial software. The average loss obtained for ten original videos is 0.010927, and ten donor videos are 0.010041. The trained Neural-ODE was able to predict the heart rate of our 10 deepfake videos generated using commercial software and 320 deepfake videos of deepfakeTIMI database. To best of our knowledge, this is the first attempt to train a Neural-ODE on original videos to predict the heart rate of fake videos.
Learning to See Moving Objects in the Dark

Video surveillance systems have wide range of utilities, yet easily suffer from great quality degeneration under dim light circumstances. Industrial solutions mainly use extra near-infrared illuminations, even though it doesn't preserve color and texture information. A variety of researches enhanced low-light videos shot by visible light cameras, while they either relied on task specific preconditions or trained with synthetic datasets. We propose a novel optical system to capture bright and dark videos of the exact same scenes, generating training and groud truth pairs for authentic low-light video dataset. A fully convolutional network with 3D and 2D miscellaneous operations is utilized to learn an enhancement mapping with proper spatial-temporal transformation from raw camera sensor data to bright RGB videos. Experiments show promising results by our method, and it outperforms state-of-the-art low-light image/video enhancement algorithms.
Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

In order to enhance performance of robot systems in the manufacturing industry, it is essential to develop motion and task planning algorithms. Especially, it is important for the motion plan to be generated automatically in order to deal with various working environments. Although PRM (Probabilistic Roadmap) provides feasible paths when the starting and goal positions of a robot manipulator are given, the path might not be smooth enough, which can lead to inefficient performance of the robot system. This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous action. Besides, hindsight experience replay (HER) is employed in the TD3 to enhance sample efficiency. Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER. The proposed algorithm is applied to 2-DOF and 3-DOF manipulators and it is shown that the designed paths are smoother and shorter than those designed by PRM.
Compositional Data Analysis

Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1% or 100%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.
Medical image enhancement based on histogram algorithms

Medical images constitute important information that clinicians need to diagnose and make the suitable treatment decisions. The diagnostic process extremely involves the image visual perception. Unfortunately, the possibility of error existence in perception is not acceptable as it mainly affects the patients’ lives. Image enhancement improves the visual quality of image, helps the clinician in his decision and thus saves the patients’ lives. Histogram is a common tool for improving contrast in medical imaging. It recovers the lost contrast by redistributing the image brightness values that unfortunately may generate undesirable artifacts. Therefore, researchers developed the histogram-based algorithms to overcome this problem. This paper presents a comprehensive study of many histogram-based algorithms. We utilized the powerful MATLAB package to analyze the enhancement performance of these histogram-based algorithms. Moreover, this paper quantitatively compares the results and thus evaluates their performance by three metric parameters, which are the mean square error, standard deviation, and the peak signal to noise ratio.
Automated Requirements Identification from Construction Contract Documents Using Natural Language Processing

Contract documents are a critical legal component of a construction project that specify all wishes and expectations of the owner toward the design, construction, and handover of a project. Precise comprehension of the contract documents is critical to ensure that all important contractual requirements of the project scope are captured and managed. A contract package typically includes both requirements and other unimportant texts such as instructions and supporting statements; thus, practitioners are required to read and identify texts indicating the requirements. The conventional manual practice of scope comprehension requires much time and effort and may include human errors. Little attention has been paid toward automated identification of requirement texts. This study introduces an effective way to identify contractual requirements by developing an automated framework using natural language processing (NLP) and machine learning techniques. Four different machine learning algorithms, namely Naïve Bayes, support vector machines, logistic regression, and feedforward neural network were used to develop the classification models. The models classified the contractual text into requirement and nonrequirement text. Experiments showed that the support vector machine model outperforms the other models in terms of accuracy, precision, recall, and F1-score. In addition, unigrams yield better results than higher n-gram features. An experimental study including human participants further proves that the developed model is efficient and effective that can help reduce reading time and improve contract scope comprehension.
Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap

Photonic artificial neural networks have garnered enormous attention due to their potential to perform multiply-accumulate (MAC) operations at much higher clock rates and consuming significantly lower power and chip real-estate compared to digital electronic alternatives. Herein, we present a comprehensive power consumption analysis of photonic neurons, taking into account global design parameters and concluding to analytical expressions for the neuron's energy- and footprint efficiencies. We identify the optimal design-space and analyze the performance plateaus and their dependence on a range of physical parameters, highlighting the existence of an optimal data-rate for maximizing the energy efficiency. Following a survey of the best-in-class integrated photonic devices, including on-chip lasers, photodetectors, modulators and weighting elements, the mathematically calculated energy and footprint efficiencies are mapped into real photonic neuron deployment scenarios. We reveal that silicon photonics can compete with the best-performing currently available digital electronic neural network engines, reaching TMAC/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> footprint- and sub-pJ/MAC energy efficiencies. Simultaneously, neuromorphic plasmonics, plasmo-photonics and sub-wavelength photonics hold the credentials for 1 to 3 orders of magnitude improvements even when the laser requirements and a reasonable waveguide pitch are accounted for, promising performance at a few fJ/MAC and up to a few TMAC/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform

Recent technological evolution enables Artificial Intelligence (AI) model training by users' mobile devices, which accelerates decentralized big data analysis. In particular, Federated Learning (FL) is a key enabler to realize decentralized AI model update without user's privacy disclosure. However, since the behaviour of workers, who are assigned a training task, cannot be monitored, the state-of-the-art methods require a special hardware and/or cryptography to force the workers behave honestly, which hinders the realization. Furthermore, although blockchain-enabled FL has been proposed to give workers reward, any rigorous reward policy design has not been discussed. In this paper, to tackle these issues, we present a novel method using mechanism design, which is an economic approach to realize desired objectives under the situation that participants act rationally. The key idea is to introduce repeated competition for FL so that any rational worker follows the protocol and maximize their profits. With mechanism design, we propose a generic full-fledged protocol design for FL on a public blockchain. We also theoretically clarify incentive compatibility based on contest theory which is an auction-based game theory in economics.
Automatic Modulation Classification Based on Constellation Density Using Deep Learning

Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal's constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.
Hyperspectral Mixed Noise Removal By $\ell _1$-Norm-Based Subspace Representation

This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> data fidelity instead of using the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis

Multimodal sentiment analysis is an important research area that predicts speaker’s sentiment tendency through features extracted from textual, visual and acoustic modalities. The central challenge is the fusion method of the multimodal information. A variety of fusion methods have been proposed, but few of them adopt end-to-end translation models to mine the subtle correlation between modalities. Enlightened by recent success of Transformer in the area of machine translation, we propose a new fusion method, TransModality, to address the task of multimodal sentiment analysis. We assume that translation between modalities contributes to a better joint representation of speaker’s utterance. With Transformer, the learned features embody the information both from the source modality and the target modality. We validate our model on multiple multimodal datasets: CMU-MOSI, MELD, IEMOCAP. The experiments show that our proposed method achieves the state-of-the-art performance.
Spectector: Principled Detection of Speculative Information Flows

Since the advent of Spectre, a number of counter-measures have been proposed and deployed. Rigorously reasoning about their effectiveness, however, requires a well-defined notion of security against speculative execution attacks, which has been missing until now.In this paper (1) we put forward speculative non-interference, the first semantic notion of security against speculative execution attacks, and (2) we develop Spectector, an algorithm based on symbolic execution to automatically prove speculative non-interference, or to detect violations.We implement Spectector in a tool, which we use to detect subtle leaks and optimizations opportunities in the way major compilers place Spectre countermeasures. A scalability analysis indicates that checking speculative non-interference does not exhibit fundamental bottlenecks beyond those inherited by symbolic execution.
A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network

Network adversaries, such as malicious transit autonomous systems (ASes), have been shown to be capable of partitioning the Bitcoin's peer-to-peer network via routing-level attacks; e.g., a network adversary exploits a BGP vulnerability and performs a prefix hijacking attack (viz. Apostolaki et al. [3]). Due to the nature of BGP operation, such a hijacking is globally observable and thus enables immediate detection of the attack and the identification of the perpetrator. In this paper, we present a stealthier attack, which we call the EREBUS attack, that partitions the Bitcoin network without any routing manipulations, which makes the attack undetectable to control-plane and even to data-plane detectors. The novel aspect of EREBUS is that it makes the adversary AS a natural man-in-the-middle network of all the peer connections of one or more targeted Bitcoin nodes by patiently influencing the targeted nodes' peering decision. We show that affecting the peering decision of a Bitcoin node, which is believed to be infeasible after a series of bug patches against the earlier Eclipse attack [29], is possible for the network adversary that can use abundant network address resources (e.g., spoofing millions of IP addresses in many other ASes) reliably for an extended period of time at a negligible cost. The EREBUS attack is readily available for large ASes, such as Tier-1 and large Tier-2 ASes, against the vast majority of 10K public Bitcoin nodes with only about 520 bit/s of attack traffic rate per targeted Bitcoin node and a modest (e.g., 5-6 weeks) attack execution period. The EREBUS attack can be mounted by nation-state adversaries who would be willing to execute sophisticated attack strategies patiently to compromise cryptocurrencies (e.g., control the consensus, take down a cryptocurrency, censor transactions). As the attack exploits the topological advantage of being a network adversary but not the specific vulnerabilities of Bitcoin core, no quick patches seem to be available. We discuss that some naive solutions (e.g., whitelisting, rate-limiting) are ineffective and third-party proxy solutions may worsen the Bitcoin's centralization problem. We provide some suggested modifications to the Bitcoin core and show that they effectively make the EREBUS attack significantly harder; yet, their non-trivial changes to the Bitcoin's network operation (e.g., peering dynamics, propagation delays) should be examined thoroughly before their wide deployment.
A Survey on Blockchain Technology Concepts, Applications, and Issues

The blockchain technology first emerged with the Bitcoin whitepaper, which was the first successful proposal to implement a decentralized digital currency with ability to execute completely non-reversible transactions without a trusted and centralized third party. Blockchain concept provided an inherent part of this decentralization together with hash-based proof-of-work, public key cryptography, and peer-to-peer network. Even though blockchain technology was introduced to solve the double-spending problem of electronic money without relying on a trusted third party, this particular concept is being researched and already used to solve problems in many other areas. This paper captures concepts of blockchain, its applications, issues, and suggested improvements referring to blockchain-related subsequent publications.
Federated Learning with Quantization Constraints

Traditional deep learning models are trained on centralized servers using labeled sample data collected from edge devices. This data often includes private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. A major challenge that arises in this method is the need of each user to efficiently transmit its learned model over the throughput limited uplink channel. In this work, we tackle this challenge using tools from quantization theory. In particular, we identify the unique characteristics associated with conveying trained models over rate-constrained channels, and characterize a suitable quantization scheme for such setups. We show that combining universal vector quantization methods with FL yields a decentralized training system, which is both efficient and feasible. We also derive theoretical performance guarantees of the system. Our numerical results illustrate the substantial performance gains of our scheme over FL with previously proposed quantization approaches.
Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine

The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization. However, this method faces the issue of data redundancy and calculation complexity, and high-dimensional problems raise great challenges for anomaly detection. This is the curse of dimensionality problem, which hinders the development of a variety of techniques for anomaly detection. In this study, a hybrid model that combines unsupervised deep belief networks (DBNs) and one-class support vector machine (OCSVM) is adopted to address the high-dimensional geochemical anomalies detection problem. In this model, the relevant features first extracted through the DBN are used as the input of the OCSVM. The decision function values of the hybrid method are employed to map the geochemical patterns related to iron mineralization. The comparative results on the performance of the hybrid model and the other three anomaly detection models (deep autoencoder model, OCSVM, and hybrid model with principal component analysis and OCSVM) in terms of the area under curve(AUC) values, suggest that the hybrid method of the DBN and OCSVM can efficiently recognize the geochemical anomalies related to iron mineralization. The DBN can extract the geochemical information, reduce the redundant features, and further enhance the scalability of the OCSVM for processing high-dimensional geochemical data. The extracted geochemical anomalies, which show a close spatial relationship with the Yanshanian intrusions, can provide significant guidance for the next round of mineral exploration.
Ten simple rules for innovative dissemination of research

Author summary How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.
Masked Face Recognition Using Convolutional Neural Network

Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Multi-Task Cascaded Convolutional Neural Network (MTCNN). Then facial features extraction is performed using the Google FaceNet embedding model. And finally, the classification task has been performed by Support Vector Machine (SVM). Experiments signify that this mentioned approach gives a remarkable performance on masked face recognition. Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.