Reviewerly/exHarmony huggingface dataset:
Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

Automated pathology image classification through modern machine learning (ML) techniques in quantitative microscopy is an emerging AI application area aiming to alleviate the increased workload of pathologists and improve diagnostic accuracy and consistency. However, there are very few efforts focusing on fluorescence histology image data, which is a challenging task, not least due to the variable imaging acquisition parameters in pooled data, which can diminish the performance of ML-based decision support tools. To this end, this study introduces a harmonization preprocessing protocol for image classification within a heterogeneous fluorescence dataset in terms of image acquisition parameters and presents two state-of-the-art feature-based approaches for differentiating three classes of nuclei labelled by an expert based on (a) pathomics analysis scoring an accuracy (ACC) up to 0.957 ± 0.105, and, (b) transfer learning model exhibiting ACC up-to 0.951 ± 0.05. The proposed analysis pipelines offer good differentiation performance in the examined fluorescence histology image dataset despite the heterogeneity due to the lack of a standardized image acquisition protocol.
A Linear Algorithm for Multi-Target Tracking in the Context of Possibility Theory

We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-tracking practitioner must deal with when working with real data. We also introduce and study variants of the notions of point process and intensity function, which lead to the derivation of an analogue of the probability hypothesis density (PHD) filter. The gains provided by the considered modelling framework in terms of flexibility lead to the loss of some of the abilities that the PHD filter possesses; in particular the estimation of the number of targets by integration of the intensity function. Yet, the proposed recursion displays a number of advantages such as facilitating the introduction of observation-driven birth schemes and the modelling the absence of information on the initial number of targets in the scene. The performance of the proposed approach is demonstrated on simulated data.
Speaking from Experience

Voice-Activated Artificial Intelligence (VAI) is increasingly ubiquitous, whether appearing as context-specific conversational assistants or more personalised and generalised personal assistants such as Alexa or Siri. CSCW and other fields have regularly studied the (positive and negative) social consequences of VAI design and deployment. One particular focus has been questions of gender, and the implications that the (often-feminine) gendering of VAIs has for societal norms and user experiences. Studies into this have largely elided transgender (trans) existences; the few exceptions to this operate largely from an external and predetermined idea of trans and/or non-binary user needs, centered on representation. In this study, we undertook a series of qualitative interviews with trans and/or non-binary users of VAIs to explore their experiences and needs. Our results show that these needs are far more than simply improvements to representation, and that users raise substantial concerns around the underlying framing of gender by even well-intentioned developers, the privacy and safety implications of ubiquitous VAI, and the motivations of the vast for-profit companies that deploy much of this technology. We provide both immediate recommendations for designers and researchers seeking to create trans-inclusive VAIs, and wider, critical proposals for how we as researchers go about assessing technological systems and appropriate points of intervention.
Application of Blockchain for Internet of Things: A Bibliometric Analysis

As a disruptive emerging technology, the Internet of things (IoT) has rapidly developed, but its privacy risks and security vulnerabilities are still key challenges. The decentralized and distributed architecture of blockchain has the potential to satisfy IoT privacy and security requirements. This gives birth to the new domain of blockchain for IoT (BIoT). BIoT will cause significant transformations across several industries, paving the way for new business models. Based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) databases in Web of Science (WoS) Core Collection, this study aims to explore the research trends and cooperation in the field of BIoT using the bibliometric method. The results indicate that the publications in this field have increased significantly from 2016 to 2020, with China and the USA being the most productive and influential countries. Keyword co-occurrence analysis shows that the most important research topics are as follows: security issues, core technologies, application dimensions, and transaction processes. Text mining analysis indicates that future research directions for BloT will focus more on both computing paradigms and key applications. This study will provide researchers with a greater understanding on the state of the art of BIoT and will serve as a reference for researchers engaging in this field to identify their own future research directions.
Batch Normalization Processor Design for Convolution Neural Network Training and Inference

In the training process of convolutional neural networks (CNN), a batch normalization (BN) layer is often inserted after a convolution layer to accelerate the convergence of CNN training. In this work, we propose a BN processor that supports both the training and inference processes. To speed up the training of CNN, the proposed work develops an efficient dataflow integrating a novel BN processor design and the processing elements for convolution acceleration. We exploited the similarities in the calculations required for the BN forward and backward passes by sharing hardware elements between both passes, therefore reducing the area overhead. In addition to functional verification of the BN processor, we also completed Automatic Placement & Routing (APR) and conducted post-APR simulation on neural network training. Finally, the proposed solution not only significantly speeds up the CNN training process, but also achieves hardware saving.
Steganogram removal using multidirectional diffusion in fourier domain while preserving perceptual image quality

This paper proposes a novel and efficient method for destructing any secret information hidden (steganograms) inside an image by any steganographic technique while preserving the visual quality of the images as well. The method involves an iterative multi-directional diffusion process in the Fourier domain that disrupts the stego content present in the image until the visual quality of the image does not drop below the desired threshold. Most importantly, our presented method is universal and blind and does not entail any knowledge about the employed steganography methods, the hidden message or the cover image. Simulations ran on 12600 images created by eight different state-of-the-art steganography algorithms prove that our technique succeeded in erasing from 80% to 95% of stego content on average and is superior to other similar systems.
Random Forest Classifier for Detecting Credit Card Fraud based on Performance Metrics

There are many classification algorithms available, however, one classifier that can be used for a problem domain with paramount accuracy is hard to find. Classification algorithm is a technique used to map data into known classes or outputs. A problem area that has seen a lot of application of classification algorithm is the Credit Card Fraud. Credit card fraud is not a new area that needs exploration but still there is scope to narrow down the best classification algorithm to rely upon to detect frauds in real time. In this paper, the focus is on investigating and determining which classification algorithm is the best one for detecting Credit Card Fraud through benchmark datasets. It has been found that Random Forest has the best accuracy when compared to other classifiers. The study would assist researchers in choosing the best classification scheme with the guideline provided for any credit card fraud dataset. The two datasets used in this research are imbalanced datasets, therefore, for better comparison of the algorithms, a balanced set is also used. The balancing of dataset is done through Synthetic Minority Oversampling Technique (SMOT). The comparison of results is done on 6 algorithms, namely, Random Forest, Logistic Regression, Neural Networks, Support Vector Machines (SVMs), Naive Bayes and K-Nearest Neighbor (KNN). The results are compared through two software; Weka and Python. The outcome of the experiment show that the methodology is indeed of great assistance in any practical applications.
Fighting capital flight in Nigeria: have we considered global uncertainties and exchange rate volatilities? Fresh insights via quantile ARDL model

This study aims to fill the observed gap in the literature related to capital flight and its determinants. Following this observation, the main objective of the current effort is to unravel the potential influence of global economic policy uncertainties and exchange rate deviations on capital flight. Annual time series data between 1970 and 2015 were used for empirical investigations through the application of the newly introduced quantile autoregressive distributed lag model. Following this extensive investigation, the following evidence emerged. A long-run relationship exists between global economic uncertainties, exchange rate volatilities, and capital flight in Nigeria. Second, we discover that the effects of exchange rate volatility and global uncertainty are sensitive to different quantiles of capital flight distributions. Third, capital flight shows strong evidence of hysteresis in all quantiles. This makes capital flight a systemic problem as past capital flight induces more capital flight. In addition, we found evidence of long-run distributional asymmetric effects between the past values of capital flight and that of economic growth on capital flight. In addition, exchange rate and GDP, rather than global uncertainty are the major determinants of capital flight in Nigeria in terms of their significant positive long-term impacts on capital flight in all quantiles of capital flight distributions. Therefore, if the fight against capital migration must be won in Nigeria and other countries facing similar challenges, stabilization of the exchange rate, among other things is paramount. In addition, due to the presence of distributional asymmetric effects, a one-size-fits all policy may not be helpful. The overall findings have strong policy implications for the fight against capital flight.
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization

Deep neural networks generalize well despite being exceedingly overparameterized and being trained without explicit regularization. This curious phenomenon has inspired extensive research activity in establishing its statistical principles: Under what conditions is it observed? How do these depend on the data and on the training algorithm? When does regularization benefit generalization? While such questions remain wide open for deep neural nets, recent works have attempted gaining insights by studying simpler, often linear, models. Our paper contributes to this growing line of work by examining binary linear classification under a generative Gaussian mixture model in which the feature vectors take the form ${{\it x}}=\pm{{\eta}}+{{\it q}}$, where for a mean vector $\eta$ and feature noise ${{\it q}} \sim \mathcal{N}(0,{{\Sigma}})$. Motivated by recent results on the implicit bias of gradient descent, we study both max-margin support vector machine (SVM) classifiers (corresponding to logistic loss) and min-norm interpolating classifiers (corresponding to least-squares loss). First, we leverage an idea introduced in [V. Muthukumar et al., arXiv:2005.08054, 2020a] to relate the SVM solution to the min-norm interpolating solution. Second, we derive novel nonasymptotic bounds on the classification error of the latter. Combining the two, we present novel sufficient conditions on the covariance spectrum and on the signal-to-noise ratio (SNR) $SNR={||{{\eta}}||_2^4}/{{\eta}}^T{{\Sigma\eta}}$ under which interpolating estimators achieve asymptotically optimal performance as overparameterization increases. Interestingly, our results extend to a noisy model with constant probability noise flips. Contrary to previously studied discriminative data models, our results emphasize the crucial role of the SNR and its interplay with the data covariance. Finally, via a combination of analytical arguments and numerical demonstrations we identify conditions under which the interpolating estimator performs better than corresponding regularized estimates.
Out-of-Band Electromagnetic Injection Attack on a Quantum Random Number Generator

Random number generators underpin the security of current and future cryptographic systems and are therefore a likely target for attackers. Quantum random number generators have been hailed as the ultimate sources of randomness. However, as shown in this work, the susceptibility of the sensitive electronics required to implement such devices poses a serious threat to their security. We present an out-of-band electromagnetic injection attack on a photonic quantum random number generator through which an adversary can gain full control of the output. In our first experiment, the adversary forces the binary output of the generator to become an alternating string of 1s and 0s, with near 100% success. This attack may be spotted by a vigilant user performing statistical tests on their output strings. We therefore envisage a second more subtle attack in which the adversary forces the output to be a random pattern known to them, thus rendering any protection based on statistical tests ineffective.
Composite recurrent network with internal denoising for facial alignment in still and video images in the wild

Facial alignment is an essential task for many higher level facial analysis applications, such as animation, human activity recognition and human - computer interaction. Although the recent availability of big datasets and powerful deep-learning approaches have enabled major improvements on the state of the art accuracy, the performance of current approaches can severely deteriorate when dealing with images in highly unconstrained conditions, which limits the real-life applicability of such models. In this paper, we propose a composite recurrent tracker with internal denoising that jointly address both single image facial alignment and deformable facial tracking in the wild. Specifically, we incorporate multilayer LSTMs to model temporal dependencies with variable length and introduce an internal denoiser which selectively enhances the input images to improve the robustness of our overall model. We achieve this by combining 4 different sub-networks that specialize in each of the key tasks that are required, namely face detection, bounding-box tracking, facial region validation and facial alignment with internal denoising. These blocks are endowed with novel algorithms resulting in a facial tracker that is both accurate, robust to in-the-wild settings and resilient against drifting. We demonstrate this by testing our model on 300-W and Menpo datasets for single image facial alignment, and 300-VW dataset for deformable facial tracking. Comparison against 20 other state of the art methods demonstrates the excellent performance of the proposed approach.
Application of ESN prediction model based on compressed sensing in stock market

Echo State Network (ESN) is often used for time series prediction and chaotic series prediction.But in the training process of ESN model, the reserve pool will involve a large number of calculations, so the reserve pool may have node redundancy, resulting in inaccurate training model. It is very important to select the active nodes in the reserve pool because the predicted nodes are very few compared with all the nodes in the reserve pool. Based on the characteristics of the original vector, compressed sensing can reduce the dimension of the vector so as to obtain the activated nodes. In this paper, the ESN model is added with compressed sensing, which simplifies the reserve pool part of the ESN model. The simulation results show that our method can not only reduce the calculation amount of training effectively, but also improve the accuracy.
Non-interactive Half-Aggregation of EdDSA and Variants of Schnorr Signatures

Schnorr's signature scheme provides an elegant method to derive signatures with security rooted in the hardness of the discrete logarithm problem, which is a well-studied assumption and conducive to efficient cryptography. However, unlike pairing-based schemes which allow arbitrarily many signatures to be aggregated to a single constant sized signature, achieving significant non-interactive compression for Schnorr signatures and their variants has remained elusive. This work shows how to compress a set of independent EdDSA/Schnorr signatures to roughly half their naive size. Our technique does not employ generic succinct proofs; it is agnostic to both the hash function as well as the specific representation of the group used to instantiate the signature scheme. We demonstrate via an implementation that our aggregation scheme is indeed practical. Additionally, we give strong evidence that achieving better compression would imply proving statements specific to the hash function in Schnorr's scheme, which would entail significant effort for standardized schemes such as SHA2 in EdDSA. Among the others, our solution has direct applications to compressing Ed25519-based blockchain blocks because transactions are independent and normally users do not interact with each other.
Pyramidal Graph Convolutional Network for Skeleton-Based Human Action Recognition

The emergence of low-cost depth sensors opens up new potentials for skeleton-based human action recognition. The recent methods for this task have made significant progress by incorporating graph convolution. However, they (1) have limitations in modeling the complex and variable temporal dynamics, and (2) cannot maximize the complementarity of the spatial and temporal features. Besides, (3) the loss function of these methods has an inherent weakness in optimizing the intraclass compactness. To this end, we propose a pyramidal graph convolutional network (PY-GCN) in this paper. Specifically, (1) an effective yet efficient single-oriented pyramidal convolution is proposed. It involves multiple kernels with varying sizes and depths that are capable of capturing different levels of the temporal dynamics at multiple scales. (2) A pseudo-two-stream structure for the basic block of the network is proposed to comprehensively aggregate discriminative cross-spatiotemporal features. Moreover, (3) a pairwise Gaussian loss together with the cross-entropy loss is introduced to the model, which can focus on both intraclass compactness and interclass separability. Our PY-GCN achieves state-of-the-art performance on three challenging large-scale datasets.
Bio-inspired intent communication for automated vehicles

Various external human-machine interfaces (eHMIs) have been proposed that communicate the intent of automated vehicles (AVs) to vulnerable road users. However, there is no consensus on which eHMI concept is most suitable for intent communication. In nature, animals have evolved the ability to communicate intent via visual signals. Inspired by intent communication in nature, this paper investigated three novel and potentially intuitive eHMI designs that rely on posture, gesture, and colouration, respectively. In an online crowdsourcing study, 1141 participants viewed videos featuring a yielding or non-yielding AV with one of the three bio-inspired eHMIs, as well as a green/red lightbar eHMI, a walk/don't walk text-based eHMI, and a baseline condition (i.e., no eHMI). Participants were asked to press and hold a key when they felt safe to cross and to answer rating questions. Together, these measures were used to determine the intuitiveness of the tested eHMIs. Results showed that the lightbar eHMI and text-based eHMI were more intuitive than the three bio-inspired eHMIs, which, in turn, were more intuitive than the baseline condition. An exception was the bio-inspired colouration eHMI, which produced a performance score that was equivalent to the text-based eHMI when communicating 'non-yielding'. Further research is necessary to examine whether these observations hold in more complex traffic situations. Additionally, we recommend combining features from different eHMIs, such as the full-body communication of the bio-inspired colouration eHMI with the colours of the lightbar eHMI.
Unified framework for learning with label distribution

As a recently arisen framework, Label Distribution Learning (LDL) is one of the most appropriate machine learning paradigms to solve the label ambiguity problems. Due to the high cost, it is intractable to directly collect annotated distribution-level data. Therefore, Label Enhancement (LE) is proposed to obtain the label distribution for training LDL model by mining the information hidden in the logical labels. Accordingly, LE is usually taken as the pre-processing of LDL algorithm to learn with logical labels in previous methods. These two-stage learning methods may reduce the performance of LDL. To this end, we propose a unified framework called L2 which simultaneously conducts Label Enhancement and Label Distribution Learning on samples and logical labels to fully exploit the implicit information for learning optimal LDL model. Specifically, the recovery of label distribution benefits from not only the optimization of the conventional LE objective function but also the feedback of LDL loss. What is more, the recovered distribution labels can be directly applied to the supervision of LDL training in an end-to-end way. Extensive experiments illustrate that L2 can correctly recover the distribution-level data from the logical labels, and the trained LDL model can perform favorably against state-of-the-art LDL algorithms with the recovered distribution data.1
A Fast Partial Video Copy Detection Using KNN and Global Feature Database

Unlike in most previous partial video copy detection (PVCD) algorithms, where reference videos are scanned one by one, we treat the PVCD as a video search/retrieval problem. We propose a fast partial video copy detection framework in this paper. In this framework, all frame CNN features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results are used to generate a shortlist of candidate videos. A modified temporal network is then used to localize the copy segment in the candidate videos. Furthermore, We propose to use a transformer encoder to improve the CNN feature. We evaluate our algorithm on the VCDB dataset. Our benchmark F1 scores exceed state-of-the-art by a big margin. The speed of our algorithm is also improved significantly.