Reviewerly/exHarmony huggingface dataset:
ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos

Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as ReCal module, which can calibrate the feature maps by employing region intra-and-inter-dependencies and channel-region cross-dependencies. This calibration strategy can effectively enhance semantic representation by correlating different representations of the same semantic label, considering a multi-angle local view centering around each pixel. Thus the proposed module can deal with distant visual characteristics of unique objects as well as cross-similarities in the visual characteristics of different objects. Moreover, we propose a novel network architecture based on the proposed module termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net compared to rival state-of-the-art approaches for all relevant objects in cataract surgery. Moreover, ablation studies reveal the effectiveness of the ReCal module in boosting semantic segmentation accuracy.
Blockchain as a Tool for Increasing Information Transparency of the Business Ecosystem

The rapid development of blockchain allows us to refresh and update our technological approaches to the formation of business information. According to experts future blockchain ecosystems will form a different philosophy of organizing financial and management accounting. The aim of the study is to identify trends in the development of blockchain technology, forecast the consequences of its application in the accounting and information area of economic entities. Its methodology is based on the use of the following methods: analysis and synthesis, comparison, systemic and logical approach, the method of analogies and grouping. The paper provides an overview of studies of authoritative global platforms about the potential of using blockchain technology and distributed ledgers in the financial, economic, accounting and control spheres. The authors analyzed global initiatives for the development of blockchain technology which actively had being discussed in the world community. The essence and approaches to the definition of blockchain are shown, as well as the classification of blockchain systems types. The trends in the development of blockchain in the field of management activities and the potential possibilities of its application at certain stages of the accounting process are revealed. It is concluded that the use of a blockchain system is an effective platform for the information exchange between economic entities, ensuring its reliability, safety and transparency, forming an instrumental basis for the transactional accounting development. The study may be of interest to national regulators, investors and financial market participants, as well as international business and professional communities.
Towards high-fidelity singing voice conversion with acoustic reference and contrastive predictive coding

Recently, phonetic posteriorgrams (PPGs) based methods have been quite popular in non-parallel singing voice conversion systems. However, due to the lack of acoustic information in PPGs, style and naturalness of the converted singing voices are still limited. To solve these problems, in this paper, we utilize an acoustic reference encoder to implicitly model singing characteristics. We experiment with different auxiliary features, including mel spectrograms, HuBERT, and the middle hidden feature (PPG-Mid) of pretrained automatic speech recognition (ASR) model, as the input of the reference encoder, and finally find the HuBERT feature is the best choice. In addition, we use contrastive predictive coding (CPC) module to further smooth the voices by predicting future observations in latent space. Experiments show that, compared with the baseline models, our proposed model can significantly improve the naturalness of converted singing voices and the similarity with the target singer. Moreover, our proposed model can also make the speakers with just speech data sing.
Navigation Context Adaptive Fault Detection and Exclusion Strategy based On Deep Learning &amp; Information Theory: Application to a GNSS/IMU Integration

The growing demand for a reliable localization function in safety-relevant autonomous applications has prompted the navigation community to take a close interest in Fault Detection algorithms. Particularly powerful, these methods have as common limitation, the need to define a priori probabilities of fault(s) presence or absence in order to set a detection threshold. In the case of systems insensitive to external phenomena, these probabilities can be obtained although with difficulty. However, in a multi-sensor localization system using, among other, Global Navigation Satellites System (GNSS), this task can prove to be delicate. Indeed, these global navigation systems suffer from perturbations induced by the local environment (buildings in urban canyons, foliage in forests, intentional or nonintentional interferences...) causing local feared events such as multipath, Non-Line-Of-Sight (NLOS) or GNSS outage. The a priori probabilities to face this type of event, and therefore to observe one or more inconsistent measures, are very unpredictable. This makes the task of setting a threshold difficult. In this study, we investigate the combined use of 1) GNSS expertise: allowing to identify the different parameters, making it possible to monitor the state of health of the measurements, and therefore of the global system, and 2) the implementation of a deep learning scheme, as a decision-making support, considering these inputs, and providing the adequate a priori probability of fault presence.
HypoSVI: Hypocenter inversion with Stein Variational Inference and Physics Informed Neural Networks

&amp;lt;p&amp;gt;High resolution earthquake hypocentral locations are of critical importance for understanding the regional context driving seismicity. We introduce a scheme to reliably approximate a hypocenter posterior in a continuous domain that relies on recent advances in deep learning.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our method relies on a differentiable forward model in the form of a deep neural network, which is trained to solve the Eikonal equation (EikoNet). EikoNet can rapidly determine the travel-time between any source-receiver pair for a non-gridded solution. We demonstrate the robustness of these travel-time solutions are for a series of complex velocity models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For the inverse problem, we utilize Stein Variational Inference, which is a recent approximate inference procedure that iteratively updates a configuration of particles to approximate a target posterior by minimizing the so-called Stein discrepancy. The gradients of this objective function can be rapidly calculated due to the differentiability of the EikoNet. The particle locations are updated until convergence, after which we utilize clustering techniques and kernel density methods to determine the optimal hypocenter and its uncertainty.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The inversion procedure outlined in this work is validated using a series of synthetic tests to determine the parameter optimisation and the validity for large observational datasets, which can locate earthquakes in 439s per event for 2039 observations. In addition, we apply this technique to a case study of seismicity in the Southern California region for earthquakes from 2019.&amp;lt;/p&amp;gt;
Generalization Guarantees for Imitation Learning

Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies. In this paper, we present rigorous generalization guarantees for imitation learning by leveraging the Probably Approximately Correct (PAC)-Bayes framework to provide upper bounds on the expected cost of policies in novel environments. We propose a two-stage training method where a latent policy distribution is first embedded with multi-modal expert behavior using a conditional variational autoencoder, and then fine-tuned in new training environments to explicitly optimize the generalization bound. We demonstrate strong generalization bounds and their tightness relative to empirical performance in simulation for (i) grasping diverse mugs, (ii) planar pushing with visual feedback, and (iii) vision-based indoor navigation, as well as through hardware experiments for the two manipulation tasks.
Feature Distillation in Deep Attention Network Against Adversarial Examples

Deep neural networks (DNNs) are easily fooled by adversarial examples. Most existing defense strategies defend against adversarial examples based on full information of whole images. In reality, one possible reason as to why humans are not sensitive to adversarial perturbations is that the human visual mechanism often concentrates on most important regions of images. A deep attention mechanism has been applied in many computer fields and has achieved great success. Attention modules are composed of an attention branch and a trunk branch. The encoder/decoder architecture in the attention branch has potential of compressing adversarial perturbations. In this article, we theoretically prove that attention modules can compress adversarial perturbations by destroying potential linear characteristics of DNNs. Considering the distribution characteristics of adversarial perturbations in different frequency bands, we design and compare three types of attention modules based on frequency decomposition and reorganization to defend against adversarial examples. Moreover, we find that our designed attention modules can obtain high classification accuracies on clean images by locating attention regions more accurately. Experimental results on the CIFAR and ImageNet dataset demonstrate that frequency reorganization in attention modules can not only achieve good robustness to adversarial perturbations, but also obtain comparable, even higher classification, accuracies on clean images. Moreover, our proposed attention modules can be integrated with existing defense strategies as components to further improve adversarial robustness.
Feature fusion and clustering for key frame extraction

Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarization. Firstly, the video content is analyzed using the image color, texture and information complexity, and video feature dataset is constructed. Then a Golden Section method is proposed to determine the threshold function optimal solution. The initial cluster center and the cluster number k are automatically obtained by employing the improved clustering algorithm. k-clusters video frames are produced with the help of K-MEANS algorithm. The representative frame of each cluster is extracted using the Maximum Weight method and an accurate video summarization is obtained. The proposed approach is tested on 16 multi-type videos, and the obtained key-frame quality evaluation index, and the average of Fidelity and Ratio are 96.11925 and 97.128, respectively. Fortunately, the key-frames extracted by the proposed approach are consistent with artificial visual judgement. The performance of the proposed approach is compared with several state-of-the-art cluster-based algorithms, and the Fidelity are increased by 12.49721, 10.86455, 10.62984 and 10.4984375, respectively. In addition, the Ratio is increased by 1.958 on average with small fluctuations. The obtained experimental results demonstrate the advantage of the proposed solution over several related baselines on sixteen diverse datasets and validated that proposed approach can accurately extract video summarization from multi-type videos.
Design of Optimal Machine Learning based Cybersecurity Intrusion Detection Systems

Cybersecurity is the process of protecting critical systems and confidential data from digital attacks. With the advent of machine learning, cybersecurity systems can examine the patterns and learns them from preventing similar attacks and responds to fluctuating behavior. Cybersecurity intrusion detection system helps to detect the existence of intrusions in the network and achieves security in confidential data storage and transmission. In this view, this study designs an efficient cockroach optimization (CSO) with kernel extreme learning machine (KELM) model for cybersecurity intrusion detection. The proposed CSO-KELM model can accomplish cybersecurity by the detection and classification of intrusions. The proposed CSO-KELM technique encompasses a three-level process, namely preprocessing, classification, and parameter tuning. The design of the CSO algorithm for the appropriate selection of KELM parameters results in improved classification performance. For examining the betterment of the CSO-KELM technique, a series of experiments were performed on benchmark datasets. The experimental results pointed out the superiority of the CSO-KELM technique concerning several measures.
Enhancement of quantum correlations in a cavity–magnon system with feedback control

We propose a scheme to generate and enhance the quantum correlations between two magnons separated in two microwave cavities with feedback control of the cavity field. In each cavity, the magnon is coupled to the cavity mode by magnetic dipole interaction, and the interaction between two microwave cavities is realized via linear beam splitter interaction without any nonlinear interaction in the system. We show that the quantum entanglement, as a special quantum correlation, between the two magnons is improved in the system with double-feedback control, and the entanglement is highly robust to temperature. It is always possible to optimize the entanglement by modulating the gain of the other feedback loop if the parameters of one of the feedback loops are given. Also, there is one-way steering between two magnons of the system in the presence of a single feedback loop.
A feasible route for the design and manufacture of customised respiratory protection through digital facial capture

The World Health Organisation has called for a 40% increase in personal protective equipment manufacturing worldwide, recognising that frontline workers need effective protection during the COVID-19 pandemic. Current devices suffer from high fit-failure rates leaving significant proportions of users exposed to risk of viral infection. Driven by non-contact, portable, and widely available 3D scanning technologies, a workflow is presented whereby a user's face is rapidly categorised using relevant facial parameters. Device design is then directed down either a semi-customised or fully-customised route. Semi-customised designs use the extracted eye-to-chin distance to categorise users in to pre-determined size brackets established via a cohort of 200 participants encompassing 87.5% of the cohort. The user's nasal profile is approximated to a Gaussian curve to further refine the selection in to one of three subsets. Flexible silicone provides the facial interface accommodating minor mismatches between true nasal profile and the approximation, maintaining a good seal in this challenging region. Critically, users with outlying facial parameters are flagged for the fully-customised route whereby the silicone interface is mapped to 3D scan data. These two approaches allow for large scale manufacture of a limited number of design variations, currently nine through the semi-customised approach, whilst ensuring effective device fit. Furthermore, labour-intensive fully-customised designs are targeted as those users who will most greatly benefit. By encompassing both approaches, the presented workflow balances manufacturing scale-up feasibility with the diverse range of users to provide well-fitting devices as widely as possible. Novel flow visualisation on a model face is presented alongside qualitative fit-testing of prototype devices to support the workflow methodology.
Improvement with Chi Square Selection Feature using Supervised Machine Learning Approach on Covid-19 Data

The purpose of this study is to make improvements to previous studies with better accuracy than 80.90%. This study will add a Chi-Square selection feature with a supervised machine learning approach, namely the Naïve Bayes algorithm and SVM, to improve accuracy. The test before using the Chi-Square selection feature on the SVM algorithm got 85.56% results, and the Naïve Bayes algorithm got 85.19% accuracy. After adding the selection feature, the SVM algorithm gets an accuracy of 83.86%, and the Naïve Bayes algorithm gets an accuracy of 87.09%. The Chi-Square feature selection method is an algorithm for selecting features, discarding irrelevant features. There are differences in results when testing. SVM, after using the selection feature, the accuracy decreases, while in Naïve Bayes, the accuracy increases. However, when compared to previous studies, our research has improved the accuracy of both classification algorithms, namely SVM and Naïve Bayes.
Towards an Italian Healthcare Knowledge Graph

Electronic Health Records (EHRs), Big Data, Knowledge Graphs (KGs) and machine learning can potentially be a great step towards the technological shift from the one-size-fit-all medicine, where treatments are based on an equal protocol for all the patients, to the precision medicine, which takes count of all their individual information: lifestyle, preferences, health history, genomics, and so on. However, the lack of data which characterizes low-resource languages is a huge limitation for the application of the above-mentioned technologies. In this work, we will try to fill this gap by means of transformer language models and few-shot approaches and we will apply similarity-based deep learning techniques on the constructed KG for downstream applications. The proposed architecture is general and thus applicable to any low-resource language.
Benchmarking knowledge-driven zero-shot learning

External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with a few classic and state-of-the-art methods for each task, including a method with KG augmented explanation. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.
HASTE: Software Security Analysis for Timing Attacks on Clear Hardware Assumption

Information leakage via timing side-channel analysis can compromise embedded systems used in diverse applications that are otherwise secure. Most state-of-the-art timing side-channel detection techniques focus on analyzing the software code while paying little to no attention to the underlying hardware. This limits the ability of such techniques in terms of detection and repair. In this letter, we propose a timing side-channel analysis framework that takes into consideration both the software and the underlying hardware microarchitecture to detect vulnerabilities with high precision. We also propose a set of metrics to quantify the severity of the vulnerabilities. We verify our proposed framework on two different computation subroutines which are widely used in crypto and secure systems.
An adaptive wavelet collocation method for the optimal heat source problem

Purpose This paper aims to propose a new adaptive numerical method to find more accurate numerical solution for the heat source optimal control problem (OCP). Design/methodology/approach The main aim of this paper is to present an adaptive collocation approach based on the interpolating wavelets to solve an OCP for finding optimal heat source, in a two-dimensional domain. This problem arises when the domain is heated by microwaves or by electromagnetic induction. Findings This paper shows that combination of interpolating wavelet basis and finite difference method makes an accurate structure to design adaptive algorithm for such problems which usually have non-smooth solution. Originality/value The proposed numerical technique is flexible for different OCP governed by a partial differential equation with box constraint over the control or the state function.
Class-Aware Domain Adaptation for Coastal Land Cover Mapping Using Optical Remote Sensing Imagery

Coastal land cover mapping is a significant yet challenging pixel-level segmentation task. Domain shift between optical remote sensing imagery will give rise to remarkable performance degradation for deep supervised methods. Besides, the ground objects characterized with inter-class variance and class imbalance may further aggravate the adverse effect. Traditional adversaries-based domain adaptation algorithms always leverage a binary discriminator to conduct global adaptation, ignoring the detailed class information. In this paper, we develop a novel class-aware domain adaptation method to address these issues. Unlike the naive single one, we propose a joint local and global adversarial adaptation framework to separately execute class-specific and global domain alignment on feature and output spaces. For the former, the introduced class-wise discriminator possesses different strategies to extract labels for both data domains. Meanwhile, we restore to entropy minimization to produce high-confident target prediction rather than using the early generated pseudo label with high confidence. Furthermore, we additionally adopt comprehensive reweighting on the supervised segmentation loss to track the class imbalance problem. This manner mainly comprises the sample-based median frequency balancing and focal loss function for the minority and hard classes. We measure the proposed method on two typical coastal datasets and compare it with other state-of-the-art models. The experimental results confirm its excellent and competitive performance on cross-domain land cover mapping.