Reviewerly/exHarmony huggingface dataset:
Spectral-Spatial-Temporal Attention Network for Hyperspectral Tracking

Thanks to the abundant spectral bands, hyperspectral videos (HSVs) are able to describe objects at material level, i.e., the physical property, providing more benefits for object tracking than color videos. Considering limited HSV dataset for training, a band attention aware ensemble network was recently proposed for hyperspectral tracking, which leverages band attention to select several three-channel images for deep hyperspectral tracking. However, it fails to fully consider the joint spectral-spatial-temporal information in HSVs, compromising its tracking performance in challenging scenarios. To this end, we introduce a spectral-spatial-temporal attention neural network (SST-Net) for hyperspectral tracking in this paper. Specifically, the spatial attention with convolution and deconvolution structure focuses on the salient spatial features. Moreover, the temporal attention with an RNN structure is adopted to depict the temporal relationship among adjacent frames. By combining the spatial, spectral, and temporal attention, the band relationship can be better depicted thus valuable hyperspectral bands can be better selected for deep ensemble tracking. Experimental results show the improved effectiveness of SST-Net in tracking over serval alternative trackers.
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition

Visual Place Recognition (VPR) approaches have typically attempted to match places by identifying visual cues, image regions or landmarks that have high “utility” in identifying a specific place. But this concept of utility is not singular - rather it can take a range of forms. In this letter, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues `specific' to an environment, and to a particular place. We employ contrastive learning principles to estimate both the environment- and place-specific utility of Vector of Locally Aggregated Descriptors (VLAD) clusters in an unsupervised manner, which is then used to guide local feature matching through keypoint selection. By combining these two utility measures, our approach achieves state-of-the-art performance on three challenging benchmark datasets, while simultaneously reducing the required storage and compute time. We provide further analysis demonstrating that unsupervised cluster selection results in semantically meaningful results, that finer grained categorization often has higher utility for VPR than high level semantic categorization (e.g. building, road), and characterise how these two utility measures vary across different places and environments. Source code is made publicly available at https://github.com/Nik-V9/HEAPUtil.
Cryptocurrency Portfolio Selection—A Multicriteria Approach

This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.
Resource Allocation in Data Centers Using Fast Reinforcement Learning Algorithms

Dynamic resource allocation to satisfy varying, concurrent and unpredictable demands from multiple applications is a key need in cloud systems. A fundamental challenge is the need to find the right balance between over-allocation, which satisfies each application’s varying needs without requiring frequent allocation changes, and system efficiency which requires that the allocation exactly matches the application needs. However, allocating resources close to current needs will result in frequent allocation changes. This can be detrimental to applications since there may be fixed costs (state replication, policy reconfiguration, etc.) that need to be incurred by applications for each allocation change. In this paper, we develop an MDP-based dynamic allocation scheme that uses reinforcement learning to satisfy unpredictable application demands. It minimizes the overall resource allocation needed to satisfy varying application demands while meeting application constraints on the rate of allocation changes. We prove convergence bounds and use real-world traces to study the performance.
Investigating the Demand for Blockchain Talents in the Recruitment Market: Evidence from Topic Modeling Analysis on Job Postings

The demand for blockchain talents has surged in recent years, and there is a pressing need to understand what core skills and essential qualifications the blockchain talents demand and how employers value them in recruitment. This paper aims to reveal the crucial facts and patterns regarding the status quo of the recruitment market for blockchain-related jobs in China and clarify the heterogeneous nature of professionals demanded by blockchain-related jobs. We propose a data-driven approach by combining descriptive statistics, topic modeling, and regression analysis based on large-scale job posting data collected from a nationwide Chinese recruitment website. By this approach, we provide an overall picture of the landscape of the recruitment market for blockchain-related jobs and identify 11 skill sets that are most in-demand for blockchain-related jobs. On this basis, the skill sets of Blockchain Development and Cryptocurrency have emerged to be the most valued ones that affect salary levels: the talents mastering these two skill sets can obtain 21.4%, 10.4% higher average salaries, respectively, than those without these skills. The analyses also provide evidence that employers could attach importance to job candidates with T-shaped characteristics. Drawing on the findings, we also provide implications for blockchain practitioners.
Authenticated Key Exchange and Signatures with Tight Security in the Standard Model

We construct the first authenticated key exchange protocols that achieve tight security in the standard model. Previous works either relied on techniques that seem to inherently require a random oracle, or achieved only "Multi-Bit-Guess" security, which is not known to compose tightly, for instance, to build a secure channel. Our constructions are generic, based on digital signatures and key encapsulation mechanisms (KEMs). The main technical challenges we resolve is to determine suitable KEM security notions which on the one hand are strong enough to yield tight security, but at the same time weak enough to be efficiently instantiable in the standard model, based on standard techniques such as universal hash proof systems. Digital signature schemes with tight multi-user security in presence of adaptive corruptions are a central building block, which is used in all known constructions of tightly-secure AKE with full forward security. We identify a subtle gap in the security proof of the only previously known efficient standard model scheme by Bader et al. (TCC 2015). We develop a new variant, which yields the currently most efficient signature scheme that achieves this strong security notion without random oracles and based on standard hardness assumptions.
Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining

The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villages started. Mental, financial and emotional agony inflicted due to job-loss, lack of job and livelihood opportunities led to this. A massive macroeconomic crisis for the country with serious ramifications has consequently exploded. The present study explores and captures the diffusion and discovery of information about the various facets of reverse migration in India using Twitter mining. Tweets provide extensive opportunities to extract social perceptions and insights relevant to migration of workers. The massive Twitter data were analysed by applying text mining technique and sentiment analysis. The results of the analysis highlight five major themes. The sentiment analysis confirms the confidence and trust in the minds of masses about tiding through this crisis with government support. The study brings out the major macroeconomic ramifications of this reverse migration. The study's findings indicate that a concentrated joint intervention by the State and Central Governments is critical for successfully tiding through this crisis and restoring normalcy. The subsequent policy measures announced by the government are being critically gauged. In addition, the authors have proposed measures to ameliorate this damage on the formal and informal sectors.
Deep Learning for predicting neutralities in Offensive Language Identification Dataset

Deep learning is advancing rapidly; it has aided in solving problems that were thought impossible. Natural language understanding is one such task that has evolved with the advancement of deep learning systems. There have been several sentiment analysis attempts, but they aim to classify it as a single emotion. Human emotion in natural language is generally a complex combination of emotions, which may be indeterminate or neutral at times. Neutrosophy is a branch of philosophy that identifies neutralities and uses membership functions (positive, negative, neutral) to quantify a sample into Single Valued Neutrosophic Set (SVNS) values. Our work aims to combine the power of deep learning with SVNS to represent a sample’s sentiment into membership functions of SVNS. We have worked on the Offensive Language Identification Dataset (OLID). Combining the power of state-of-the-art neural network techniques with neutrosophy allowed us to quantify the sentiments and identify the transition phase between positive and negative ones. We used the transition phase to capture neutral samples, which is beneficial if we want to obtain purely positive/negative samples. We performed experiments using Bi-directional Long Short Term Memory (BiLSTM) with attention, Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), A Robustly Optimised BERT Approach (RoBERTa), and MPNet. Our SVNS model performed equivalent to state-of-the-art neural network models on the OLID dataset. Here, we propose a novel framework that can integrate with any neural network model and quantify sentiments using SVNS.
Multi-view spectral clustering via common structure maximization of local and global representations

The essential problem of multi-view spectral clustering is to learn a good common representation by effectively utilizing multi-view information. A popular strategy for improving the quality of the common representation is utilizing global and local information jointly. Most existing methods capture local manifold information by graph regularization. However, once local graphs are constructed, they do not change during the whole optimization process. This may lead to a degenerated common representation in the case of existing unreliable graphs. To address this problem, rather than directly using fixed local representations, we propose a dynamic strategy to construct a common local representation. Then, we impose a fusion term to maximize the common structure of the local and global representations so that they can boost each other in a mutually reinforcing manner. With this fusion term, we integrate local and global representation learning in a unified framework and design an alternative iteration based optimization procedure to solve it. Extensive experiments conducted on a number of benchmark datasets support the superiority of our algorithm over several state-of-the-art methods.
Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals.
SpoC: Spoofing Camera Fingerprints

Thanks to the fast progress in synthetic media generation, creating realistic false images has become very easy. Such images can be used to wrap rich fake news with enhanced credibility, spawning a new wave of high-impact, high-risk misinformation campaigns. Therefore, there is a fast-growing interest in reliable detectors of manipulated media. The most powerful detectors, to date, rely on the subtle traces left by any device on all images acquired by it. In particular, due to proprietary in-camera processes, like demosaicing or compression, each camera model leaves trademark traces that can be exploited for forensic analyses. The absence or distortion of such traces in the target image is a strong hint of manipulation. In this paper, we challenge such detectors to gain better insight into their vulnerabilities. This is an important study in order to build better forgery detectors able to face malicious attacks. Our proposal consists of a GAN-based approach that injects camera traces into synthetic images. Given a GAN-generated image, we insert the traces of a specific camera model into it and deceive state-of-the-art detectors into believing the image was acquired by that model. Likewise, we deceive independent detectors of synthetic GAN images into believing the image is real. Experiments prove the effectiveness of the proposed method in a wide array of conditions. Moreover, no prior information on the attacked detectors is needed, but only sample images from the target camera.
Two-Stage Evolutionary Neural Architecture Search for Transfer Learning

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many image classification tasks. However, training a deep CNN requires a massive amount of training data, which can be expensive or unobtainable in practical applications, such as defect inspection and medical diagnosis. Transfer learning has been developed to address this issue by transferring knowledge learned from source domains to target domains. A common approach is fine-tuning, which adapts the parameters of a trained neural network for the new target task. Nevertheless, the network architecture remains designed for the source task rather than the target task. To optimize the network architecture in transfer learning, we propose a two-stage evolutionary neural architecture search for transfer learning (EvoNAS-TL), which searches for an efficient subnetwork of the source model for the target task. EvoNAS-TL features two search stages: 1) structure search and 2) local enhancement. The former conducts a coarse-grained global search for suitable neural architectures, while the latter acts as a fine-grained local search to refine the models obtained. In this study, neural architecture search (NAS) is formulated as a multiobjective optimization problem that concurrently minimizes the prediction error and model size. The knee-guided multiobjective evolutionary algorithm, a modern multiobjective optimization approach, is employed to solve the NAS problem. In this study, several experiments are conducted to examine the effectiveness of EvoNAS-TL. The results show that applying EvoNAS-TL on VGG-16 can reduce the model size by 52%–85% and simultaneously improve the testing accuracy by 0.7%–6.9% in transferring from ImageNet to CIFAR-10 and NEU surface detection datasets. In addition, EvoNAS-TL performs comparably to or better than state-of-the-art methods on the CIFAR-10, NEU, and Office-31 datasets.
Scam Pandemic: How Attackers Exploit Public Fear through Phishing

As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends.We find that phishing attack traffic in March and April 2020 skyrocketed up to 220% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims' uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem's collective response.
Boltzmann–Dirichlet Process Mixture: A Mathematical Model for Speech Recognition

Abstract This article deliberates a mathematical model for the estimation of speech signals probability density function. Speech recognition is analyzed using an integration of Boltzmann equations with Dirichlet Process Mixture sequences. Usually, environmental noise, white noise, echo noise interferes with the speech signal. So, the speech identification rate decreases abruptly. By estimating the noise sequences in the speech signal, the speech identification rate increases. Rather than using a conventional Gaussian Mixture Model (GMM) procedure to recognize a pure speech, an integration of mathematical equations of Boltzmann and Dirichlet Process Mixture is proposed in this article. An uttered speech signal is identified using mean, variance, and standard deviation generated by Boltzmann-DPM. For an added white, particle, shaver percentage of noises, the speech signal to noise ratio is improved and proved experimentally using the Nil filter, GMM filters, and Extended Kalman filter.
Quantum computation and simulation with superconducting qubits*

Superconducting circuits based on Josephson junctions are regarded as one of the most promising technologies for the implementation of scalable quantum computers. This review presents the basic principles of superconducting qubits and shows the progress of quantum computing and quantum simulation based on superconducting qubits in recent years. The experimental realization of gate operations, readout, error correction codes, as well as some quantum algorithms are summarized, followed by an introduction of quantum simulation. And then some important applications in fields including condensed matter physics, quantum annealing, and quantum chemistry are discussed.
New Database of French Computer Science Words Handwritten Vocabulary

Handwriting recognition still considered a difficult problem statement. In this paper, we present a new database containing educational vocabulary written by adolescent’s learners. 200 Moroccan secondary school students whose age varies between 14 and 16 years participated in the development of the first version of the Computer Science Vocabulary Database (CSVD). Our database contains a set of 15000 images. Word images are scanned and converted to image format to prepare them for subsequent processing steps. Preprocessing techniques were applied to the images to decrease the variability of colors, contrast, and brightness often presented in handwritten word images. This database is designed for the training and testing of handwritten French word recognition systems. Furthermore, the CSVD database can constitute an opportunity for other researchers to evaluate their recognition methods and systems.
Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics

Recently, semi-supervised domain adaptation (SSDA) approaches have shown impressive performance for the domain adaptation task. They effectively utilize few labeled target samples along with the unlabeled data to account for the distribution shift across the source and target domains. In this work, we make three-fold contributions, concentrating on the role of target samples and semantics for the SSDA task. First, we observe that choosing a few, but an equal number of labeled samples from each class in the target domain requires a significant amount of manual effort. To address this, we propose an active learning-based framework by modeling both the sample diversity and the classifier uncertainty. By utilizing k-means initialized cluster centers for picking a small pool of diverse unlabeled target samples, we compute a novel classifier adaptation uncertainty term to select the most effective samples from this pool, which are queried to obtain their true labels from an oracle. Second, we propose to weigh the hard target samples more, without explicitly using their predicted, possibly incorrect labels, which guides the adaptation process. Third, we note that irrespective of the domain shift, the semantics of the classes remain unchanged, so they can be effectively utilized for this task. We show that initializing the class-representations or prototypes with the class-semantics helps in bridging the domain gap significantly. These along with adversarially learnt entropy objective results in a novel framework, termed STar (Select TARgets), which sets a new state-of-the-art for the SSDA task.