Reviewerly/exHarmony huggingface dataset:
Estimating the degree of non-Markovianity using machine learning

In the last years, the application of machine learning methods has become increasingly relevant in different fields of physics. One of the most significant subjects in the theory of open quantum systems is the study of the characterization of non-Markovian memory effects that emerge dynamically throughout the time evolution of open systems as they interact with their surrounding environment. Here we consider two well-established quantifiers of the degree of memory effects, namely, the trace distance and the entanglement-based measures of non-Markovianity. We demonstrate that using machine learning techniques, in particular, support vector machine algorithms, it is possible to estimate the degree of non-Markovianity in two paradigmatic open system models with high precision. Our approach can be experimentally feasible to estimate the degree of non-Markovianity, since it requires a single or at most two rounds of state tomography.
Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements

This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.
Electronic Voting Using Blockchain And Smart Contracts: Proof Of Concept

Blockchain technology has been presented as a support for trust needs between transactions in electronic information systems. Its successful use in cryptocurrencies has allowed it to explore its capabilities in commercial, industrial, and service systems, backed by the operational alternatives offered by Ethereum Smart Contracts and the cryptographic security of public and private key. These keys are used as a way to make online transactions anonymously, with the guarantee offered by the Blockchain network that they are executed safely. With the above in mind, this concept can be extended to the electoral processes, thus allowing its application in electronic voting systems, especially when the protocols currently used lack the trust factor between the different social actors. This document presents a proof of concept in which Blockchain and other technologies are applied, to allow interaction as an electronic voting system for the election of unique candidates. This has been achieved through the specification of an architecture designed especially for electoral processes, from which it is implemented and a simulation is carried out in order to obtain data that generates value, when evaluating Blockchain technology as an alternative to current voting systems.
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation; the other one constructs another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations via the learned attention maps, leading to the final high-resolution LF image. Extensive experiments demonstrate the significant superiority of our approach over state-of-the-art ones. That is, our method not only improves the PSNR by more than 2 dB, but also preserves the LF structure much better. To the best of our knowledge, this is the first end-to-end deep learning method for reconstructing a high-resolution LF image with a hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and also be beneficial to LF data storage and transmission. The code is available at https://github.com/jingjin25/LFhybridSR-Fusion.
Blockchain Ecosystem for Verifiable Qualifications

Human resource contracting processes depend on trustworthy qualifications (diplomas or certificates) that may be counterfeit or falsified and are hard to verify manually. This paper leverages blockchain technology and smart contracts, to enforce a decentralized verification solution for higher education certificates, e.g., university diplomas. The solution allows Higher-Education Institutions (HEI) to register the certificates they issue in the blockchain, and recruiting organizations to check the authenticity and integrity of these certificates. The solution is implemented through five major components: i) the consortium smart contract, ii) the HEI smart contract, iii) the HEI client, iv) the recruiter app, and v) the consortium app. A prototype of the implementation is tested in a real blockchain (a testnet) and the challenges raised with the experiment are identified and discussed with a main focus on the decentralization mechanism performance.
Joint architecture and knowledge distillation in CNN for Chinese text recognition

The distillation technique helps transform cumbersome neural networks into compact networks so that models can be deployed on alternative hardware devices. The main advantage of distillation-based approaches include a simple training process, supported by most off-the-shelf deep learning software and no special hardware requirements. In this paper, we propose a guideline for distilling the architecture and knowledge of pretrained standard CNNs. The proposed algorithm is first verified on a large-scale task: offline handwritten Chinese text recognition (HCTR). Compared with the CNN in the state-of-the-art system, the reconstructed compact CNN can reduce the computational cost by >10×and the model size by >8×with negligible accuracy loss. Then, by conducting experiments on two additional classification task datasets: Chinese Text in the Wild (CTW) and MNIST, we demonstrate that the proposed approach can also be successfully applied on mainstream backbone networks.
Breast Cancer Prediction and Classification Using Supervised Learning Techniques

Approximately 2.1 million women every year are affected due to breast cancer which has become one of the major causes for cancer related deaths among women. World Health Organization’s (WHO) report 2018, reveals that around 15% of deaths among women are due to breast cancer. Lack of awareness is one of the major reason which has led to the detection of breast cancer at the later stage. Another major reason is access to limited health resources which make the problem worse. Early or timely detection of breast cancer is utmost important to increase the survival rate of the patients. World Health Organization’s (WHO) cancer awareness guidelines recommend that women aged between 40–49 years of age or 70–75 years of age must be subjected to mammographic screening which will provide the timely detection of the problem, if it persist. This article uses Breast Cancer dataset from UCI machine learning repository to predict and diagnose the class of breast cancer: benign or malignant by using supervised learning. Supervised machine learning algorithms: K Nearest Neighbor (K-NN), Naive Bayes, logistic regression and decision tree have been utilized for breast cancer prediction. The performance evaluation of these classification algorithms is done based on various performance measures: accuracy, sensitivity, specificity and F -measure.
DTVNet: Dynamic Time-Lapse Video Generation via Single Still Image

This paper presents a novel end-to-end dynamic time-lapse video generation framework, named DTVNet, to generate diversified time-lapse videos from a single landscape image, which are conditioned on normalized motion vectors. The proposed DTVNet consists of two submodules: Optical Flow Encoder (OFE) and Dynamic Video Generator (DVG). The OFE maps a sequence of optical flow maps to a normalized motion vector that encodes the motion information inside the generated video. The DVG contains motion and content streams that learn from the motion vector and the single image respectively, as well as an encoder and a decoder to learn shared content features and construct video frames with corresponding motion respectively. Specifically, the motion stream introduces multiple adaptive instance normalization (AdaIN) layers to integrate multi-level motion information that are processed by linear layers. In the testing stage, videos with the same content but various motion information can be generated by different normalized motion vectors based on only one input image. We further conduct experiments on Sky Time-lapse dataset, and the results demonstrate the superiority of our approach over the state-of-the-art methods for generating high-quality and dynamic videos, as well as the variety for generating videos with various motion information ( https://github.com/zhangzjn/DTVNet ).
Breast Cancer Detection Based on Feature Selection Using Enhanced Grey Wolf Optimizer and Support Vector Machine Algorithms

Breast cancer is the leading cause of high fatality among women population. Identification of the benign and malignant tumor at correct time plays a critical role in the diagnosis of breast cancer. In this paper, an attempt has been made to extract the valuable information by selecting the relevant features using our proposed EGWO-SVM (enhanced grey wolf optimization-support vector machine) approach. Grey wolf optimizer (GWO) has gained a lot of popularity among other swarm intelligence methods due to its various characteristics like few tuning parameters, simplicity and easy to use, scalable, and most importantly its ability to provide faster convergence by maintaining the right balance between the exploration and exploitation during the search. Therefore, an enhanced GWO has been proposed in combination with SVM to determine the optimum subset of tumor features for accurate identification of benign and malignant tumor. The proposed approach has been tested and compared with numerous existing, state-of-the-art as well as recently published breast cancer classification approaches on the standard benchmark Wisconsin Diagnostic Breast Cancer (WDBC) database. The proposed approach outperforms all the compared approaches by improving the classification accuracy to 98.24% demonstrating its effectiveness in identifying the breast cancer.
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo.In such a case, automatically choosing a proper cover frame of the video and generating an appropriate textual summary of the article can help editors save time, and readers make the decision more effectively.Hence, in this paper, we propose the task of Videobased Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.The main challenge in this task is to jointly model the temporal dependency of video with semantic meaning of article.To this end, we propose a Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator.In the dual interaction module, we propose a conditional self-attention mechanism that captures local semantic information within video and a global-attention mechanism that handles the semantic relationship between news text and video from a high level.Extensive experiments conducted on a large-scale real-world VMSMO dataset 1 show that DIMS achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.
Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin tumor, are accessed by pathologists on a daily basis and are therefore well suited for automated prescreening by neural networks for the identification of cancerous regions and swift tumor classification.In this proof-of-concept study, we implemented an accurate and intuitively interpretable artificial neural network (ANN) for the detection of BCCs in histological whole-slide images (WSIs). Furthermore, we identified and compared differences in the diagnostic histological features and recognition patterns relevant for machine learning algorithms vs. expert pathologists.An attention-ANN was trained with WSIs of BCCs to identify tumor regions (n = 820). The diagnosis-relevant regions used by the ANN were compared to regions of interest for pathologists, detected by eye-tracking techniques.This ANN accurately identified BCC tumor regions on images of histologic slides (area under the ROC curve: 0.993, 95% CI: 0.990-0.995; sensitivity: 0.965, 95% CI: 0.951-0.979; specificity: 0.910, 95% CI: 0.859-0.960). The ANN implicitly calculated a weight matrix, indicating the regions of a histological image that are important for the prediction of the network. Interestingly, compared to pathologists' eye-tracking results, machine learning algorithms rely on significantly different recognition patterns for tumor identification (p < 10-4).To conclude, we found on the example of BCC WSIs, that histopathological images can be efficiently and interpretably analyzed by state-of-the-art machine learning techniques. Neural networks and machine learning algorithms can potentially enhance diagnostic precision in digital pathology and uncover hitherto unused classification patterns.
FELIX: Flexible Text Editing Through Tagging and Insertion

We present FELIX -a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining.In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations.We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM).Both of these models are chosen to be non-autoregressive to guarantee faster inference.FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification.
Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision

Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpus annotated with Knowledge Graph to reduce the cost of acquisition. Nevertheless, the data noise problem limits the performance of the DSRE. Dependency trees can be used to filter the wrong-labeled instances in the distant supervision bag. However, existing dependency tree relation extraction strategies are all based on manually-set paths between the subject and object entities, and suffer from the problem of pruning the trees too aggressively or too insufficiently. To circumvent the shortcomings, in this paper, we propose a novel DSRE framework A2DSRE, based on the Adaptive dependency-path and Additional KG supervision. To obtain the dependency paths related to entity relations adaptively, we introduce an advanced graph neural network—GeniePath into DSRE, which assigns higher weights to those direct neighbor words that contribute more to relation prediction through breadth exploration, and conducts depth exploration to determine the correlation between relations and high-order neighbors. In this way, the irrelevant nodes are pruned while the relevant nodes are kept, our method can obtain more appropriate paths associated with relations. At the same time, to further reduce the noises in the data, we incorporate additional supervision information from the knowledge graph by retracting the margin between the representation of the bag and the pre-training knowledge graph embedding. Extensive numerical experiments validate the effectiveness of our new method.
Automatically eliminating speculative leaks from cryptographic code with blade

We introduce Blade, a new approach to automatically and efficiently eliminate speculative leaks from cryptographic code. Blade is built on the insight that to stop leaks via speculative execution, it suffices to cut the dataflow from expressions that speculatively introduce secrets ( sources ) to those that leak them through the cache ( sinks ), rather than prohibit speculation altogether. We formalize this insight in a static type system that (1) types each expression as either transient , i.e., possibly containing speculative secrets or as being stable , and (2) prohibits speculative leaks by requiring that all sink expressions are stable. Blade relies on a new abstract primitive, protect , to halt speculation at fine granularity. We formalize and implement protect using existing architectural mechanisms, and show how Blade’s type system can automatically synthesize a minimal number of protect s to provably eliminate speculative leaks. We implement Blade in the Cranelift WebAssembly compiler and evaluate our approach by repairing several verified, yet vulnerable WebAssembly implementations of cryptographic primitives. We find that Blade can fix existing programs that leak via speculation automatically , without user intervention, and efficiently even when using fences to implement protect .
Differential Evolution Based Layer-Wise Weight Pruning for Compressing Deep Neural Networks

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models

A key challenge in adversarial robustness is the lack of a precise mathematical characterization of human perception, used in the definition of adversarial attacks that are imperceptible to human eyes. Most current attacks and defenses try to get around this issue by considering restrictive adversarial threat models such as those bounded by L2 or L∞ distance, spatial perturbations, etc. However, models that are robust against any of these restrictive threat models are still fragile against other threat models, i.e. they have poor generalization to unforeseen attacks. Moreover, even if a model is robust against the union of several restrictive threat models, it is still susceptible to other imperceptible adversarial examples that are not contained in any of the constituent threat models. To resolve these issues, we propose adversarial training against the set of all imperceptible adversarial examples. Since this set is intractable to compute without a human in the loop, we approximate it using deep neural networks. We call this threat model the neural perceptual threat model (NPTM); it includes adversarial examples with a bounded neural perceptual distance (a neural network-based approximation of the true perceptual distance) to natural images. Through an extensive perceptual study, we show that the neural perceptual distance correlates well with human judgements of perceptibility of adversarial examples, validating our threat model. Under the NPTM, we develop novel perceptual adversarial attacks and defenses. Because the NPTM is very broad, we find that Perceptual Adversarial Training (PAT) against a perceptual attack gives robustness against many other types of adversarial attacks. We test PAT on CIFAR-10 and ImageNet-100 against five diverse adversarial attacks: L2, L∞, spatial, recoloring, and JPEG. We find that PAT achieves state-of-the-art robustness against the union of these five attacks—more than doubling the accuracy over the next best model—without training against any of them. That is, PAT generalizes well to unforeseen perturbation types. This is vital in sensitive applications where a particular threat model cannot be assumed, and to the best of our knowledge, PAT is the first adversarial defense with this property. Code and data are available at https://github.com/cassidylaidlaw/perceptual-advex
InP-based single-photon sources operating at telecom C-band with increased extraction efficiency

In this work, we demonstrate a triggered single-photon source operating at the telecom C-band with photon extraction efficiency exceeding any reported values in this range. The non-classical light emission with low probability of the multiphoton events is realized with single InAs quantum dots (QDs) grown by molecular beam epitaxy and embedded directly in an InP matrix. Low QD spatial density on the order of 5 × 108 cm−2 to ∼2 × 109 cm−2 and symmetric shape of these nanostructures together with spectral range of emission make them relevant for quantum communication applications. The engineering of extraction efficiency is realized by combining a bottom distributed Bragg reflector consisting of 25 pairs of InP/In0.53Ga0.37Al0.1 As layers and cylindrical photonic confinement structures. Realization of such a technologically non-demanding approach even in a non-deterministic fashion results in photon extraction efficiency of (13.3 ± 2) % into 0.4 numerical aperture detection optics at approximately 1560 nm emission wavelength, i.e., close to the center of the telecom C-band.