Reviewerly/exHarmony huggingface dataset:
A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures

Usually, defect detection in X-ray images of composite materials is mainly performed by humans, which is time consuming and inefficient. Some deep learning methods have been proposed, but they are only used to identify a certain type of defect or need a large number of training samples. Besides, the size of void and inclusion defects in X-ray images of spacecraft composite structures (SCS) is very small, which is difficult for common object detection methods, especially when the number of training samples is small. Thus, we propose a transfer learning object detection model for detection of both of these defects. Based on domain adaptive Faster R-CNN (DA Faster), the feature pyramid network (FPN) is added into the feature extraction sub-module for multi-scale features adaptation, and small anchor strategies and ROI Align are utilized, which are helpful for localization of small-size defects. Besides, big NMS threshold and conditional domain adaptation (CDAN) is adopted in the box classification branch for more accurate features alignment. The experiment results demonstrate that strong ability of the proposed model in detecting small-size void and inclusion defects of SCS.
Diffusion nets

Non-linear manifold learning enables high-dimensional data analysis, but requires out-of-sample-extension methods to process new data points. In this paper, we propose a manifold learning algorithm based on deep learning to create an encoder, which maps a high-dimensional dataset to its low-dimensional embedding, and a decoder, which takes the embedded data back to the high-dimensional space. Stacking the encoder and decoder together constructs an autoencoder, which we term a diffusion net, that performs out-of-sample-extension as well as outlier detection. We introduce new neural net constraints for the encoder, which preserve the local geometry of the points, and we prove rates of convergence for the encoder. Also, our approach is efficient in both computational complexity and memory requirements, as opposed to previous methods that require storage of all training points in both the high-dimensional and the low-dimensional spaces to calculate the out-of-sample-extension and the pre-image of new points.
Two density-based sampling approaches for imbalanced and overlapping data

An imbalanced dataset consists of a majority class and a minority class, where the former’s sample size is substantially larger than other classes. This difference disrupts the data learning process and drives the learning algorithms into modeling the majority class. Data overlap can exacerbate the complicated problem of imbalanced datasets, a problem for which oversampling and undersampling approaches are adopted. This paper proposes two novel density-based algorithms in order to eliminate the overlap between two classes and the noise, as well as creating balance and normalizing the class distribution. The first algorithm employs an undersampling technique, whereas the second one uses undersampling and oversampling techniques simultaneously. These two algorithms delete high-density samples from the majority class and eliminate the noises in both classes. The two proposed algorithms and other popular algorithms were run on 16 imbalanced datasets that included a variety of scenarios. The datasets balanced by these algorithms were then modeled through Random Forest, and SVM classifiers. The models obtained from the two proposed algorithms outperformed the other algorithms in all criteria. These models also achieved a balance by maximum maintenance of the class structure and form, which protects the quality of learning from any detriment.
Extending Neural P-frame Codecs for B-frame Coding

While most neural video codecs address P-frame coding (predicting each frame from past ones), in this paper we address B-frame compression (predicting frames using both past and future reference frames). Our B-frame solution is based on the existing P-frame methods. As a result, B-frame coding capability can easily be added to an existing neural codec. The basic idea of our B-frame coding method is to interpolate the two reference frames to generate a single reference frame and then use it together with an existing P-frame codec to encode the input B-frame. Our studies show that the interpolated frame is a much better reference for the P-frame codec compared to using the previous frame as is usually done. Our results show that using the proposed method with an existing P-frame codec can lead to 28.5% saving in bit-rate on the UVG dataset compared to the P-frame codec while generating the same video quality.
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels.Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://bupt-ai-cz.github.io/BCI.
Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

Abstract Background The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.
At the intersection of optics and deep learning: statistical inference, computing, and inverse design

Deep learning has been revolutionizing information processing in many fields of science and engineering owing to the massively growing amounts of data and the advances in deep neural network architectures. As these neural networks are expanding their capabilities toward achieving state-of-the-art solutions for demanding statistical inference tasks in various applications, there appears to be a global need for low-power, scalable, and fast computing hardware beyond what existing electronic systems can offer. Optical computing might potentially address some of these needs with its inherent parallelism, power efficiency, and high speed. Recent advances in optical materials, fabrication, and optimization techniques have significantly enriched the design capabilities in optics and photonics, leading to various successful demonstrations of guided-wave and free-space computing hardware for accelerating machine learning tasks using light. In addition to statistical inference and computing, deep learning has also fundamentally affected the field of inverse optical/photonic design. The approximation power of deep neural networks has been utilized to develop optics/photonics systems with unique capabilities, all the way from nanoantenna design to end-to-end optimization of computational imaging and sensing systems. In this review, we attempt to provide a broad overview of the current state of this emerging symbiotic relationship between deep learning and optics/photonics.
Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses

Chatbots have become common in marketing-related applications, providing 24/7 service, engaging customers in humanlike conversation, and reducing employee workload in handling customer calls. However, the academic literature on the use of chatbots in marketing remains sparse and scattered across disciplines. The present study combines morphological analysis and co-occurrence analysis to bring structure to this area and to identify relevant research gaps. Morphological analysis divides a problem into pertinent and clearly distinguishable components, namely dimensions (at an abstract level) and variants (at a concrete level). A Zwicky box (a cross-variant matrix of dimensions) is then constructed to identify future research opportunities. Here, the authors obtain 11 dimensions and 264 variants. To eliminate inconsistent configurations (i.e., combinations of variants across dimensions), they perform a cross-consistency assessment and identify potential research gaps. To increase objectivity in the selection of relevant gaps, the authors use VOSviewer software to conduct a co-occurrence analysis of the variants.
A shuttling-based two-qubit logic gate for linking distant silicon quantum processors

Control of entanglement between qubits at distant quantum processors using a two-qubit gate is an essential function of a scalable, modular implementation of quantum computation. Among the many qubit platforms, spin qubits in silicon quantum dots are promising for large-scale integration along with their nanofabrication capability. However, linking distant silicon quantum processors is challenging as two-qubit gates in spin qubits typically utilize short-range exchange coupling, which is only effective between nearest-neighbor quantum dots. Here we demonstrate a two-qubit gate between spin qubits via coherent spin shuttling, a key technology for linking distant silicon quantum processors. Coherent shuttling of a spin qubit enables efficient switching of the exchange coupling with an on/off ratio exceeding 1000, while preserving the spin coherence by 99.6% for the single shuttling between neighboring dots. With this shuttling-mode exchange control, we demonstrate a two-qubit controlled-phase gate with a fidelity of 93%, assessed via randomized benchmarking. Combination of our technique and a phase coherent shuttling of a qubit across a large quantum dot array will provide feasible path toward a quantum link between distant silicon quantum processors, a key requirement for large-scale quantum computation.
A novel hybrid Chaotic Aquila Optimization algorithm with Simulated Annealing for Unmanned Aerial Vehicles path planning

Unmanned Aerial Vehicles path planning is one of the critical issues in terms of guaranteeing good performance in real-world applications. Basically, it is responsible for determining and ensuring a short, smooth, and collision-free path between two positions from a source to a destination point. This paper presents a hybrid optimization scheme based on the hybridization of Chaotic Aquila Optimization with Simulated Annealing for solving the Unmanned Aerial Vehicles path planning problem. The main purpose of using Simulated Annealing is to provide a more suitable balance between exploitation/exploration. The performance of the proposed algorithm is assessed using three benchmark maps with various numbers of threats. Compared to nine well-known meta-heuristics, the results of simulated experiments demonstrate that the proposed algorithm performs better in the majority of the scenarios by obtaining a significant enhancement in minimizing the fitness value and the path cost up to 36% and 19%, respectively.
Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.
Performance-Oriented Deployment of Streaming Applications on Cloud

Performance of streaming applications are significantly impacted by the deployment decisions made at infrastructure level, i.e., number and configuration of resources allocated for each functional unit of the application. The current deployment practices are mostly platform-oriented, meaning that the deployment configuration is tuned to a static resource-set environment and thus is inflexible to use in cloud with an on-demand resource pool. In this paper, we propose P-Deployer, a deployment framework that enables streaming applications to run on IaaS clouds with satisfactory performance and minimal resource consumption. It achieves performance-oriented, cost-efficient and automated deployment by holistically optimizing the decisions of operator parallelization, resource provisioning, and task mapping. Using a Monitor-Analyze-Plan-Execute (MAPE) architecture, P-Deployer iteratively builds the connection between performance outcome and resource consumption through task profiling and models the deployment problem as a bin-packing variant. Extensive experiments using both synthetic and real-world streaming applications have shown the correctness and scalability of our approach, and demonstrated its superiority compared to platform-oriented methods in terms of resource cost.
Business process outsourcing to the cloud: Balancing costs with security risks

The cloud computing introduced security risks which make companies reluctant to adopt it. Some means exist for reducing these risks while deploying a business process (BP) in the cloud. Among them, a technique largely used is to split the BP model in a choreography of BP fragments so that a cloud has only a partial view of the model. Unfortunately, reducing security risks with this approach implies the increase of BP fragments and of hosting clouds, and corollary the increase of costs. To overcome this issue, we present two heuristics managing separation/co-location constraints for risk versus cost optimization: the first optimizes the risk while respecting a cost threshold, the second optimizes the cost while respecting a risk threshold. These heuristics are experimented with a risk model, whose characteristics is to use only client-side data, and a more traditional cost model. Finally, the heuristics are validated against an exact optimization solution.
Dual-directional shearography based on a modified common-path configuration using spatial phase shift

This paper describes a dual-directional shearography system to address the issue of two-dimensional characterization of the surface strain. A common-path configuration coupled with an additional light path is used to provide the shearing in two directions. One of the three interfering beams is shared by both directional shearograms to improve the light efficiency and enhance the robustness of the system. The two directional shearograms are carried by different spatial carriers to distinguish one from the other. The spatial carrier is introduced by the single-aperture-lens Wollaston prism configuration. Rather than the conventional method in which the aperture is fixed at the front focal point of the imaging lens, a general case is considered by introducing a variable distance between the aperture and the imaging lens. The influence of the aperture-lens distance on the spatial carrier is then analyzed, which enables the separate control of the shearing amount and the spatial carrier. Two types of dual-directional shearography are presented to demonstrate the feasibility and the flexibility of the system. Type I is the simultaneous dual lateral shearography in orthogonal directions, and Type II is the simultaneous lateral and radial shearography. The spatial carrier introduced by the single-aperture-lens Wollaston prism configuration is discussed, and a configuration in which the Wollaston prism and the aperture are located at different sides of the lens is recommended for further shearography applications.
Endorsement in Hyperledger Fabric via service discovery

Hyperledger Fabric (HLF) is a modular and extensible permissioned blockchain platform. The platform's design exhibits principles required by enterprise-grade business applications, such as supply chains, financial transactions, asset management, etc. For that end, HLF introduces several innovations, two of which are smart contracts in general-purpose languages (chaincode in HLF), and flexible endorsement policies, which govern whether a transaction is considered valid. Typical blockchain applications comprise two tiers: The “platform” tier defines the data schema and embedding of business rules by means of chaincode and endorsement policies; the “client-side” tier uses the HLF software development kit (SDK) to implement client application logic. The client side should be aware of the deployment address of chaincode and endorsement policies within the platform. In past releases, this was statically configured into the client side. As of HLF v1.2, a new feature called service discovery, presented in this paper, provides APIs that allow dynamic discovery of the configuration required for the client SDK to interact with the platform. This enables the client to rapidly adapt to changes in the platform, thus improving the reliability of the application layer and making the HLF platform more consumable.
Statistical Parametric Speech Synthesis Using Deep Gaussian Processes

This paper proposes a framework of speech synthesis based on deep Gaussian processes (DGPs), which is a deep architecture model composed of stacked Bayesian kernel regressions. In this method, we train a statistical model of transformation from contextual features to speech parameters in a similar manner to deep neural network (DNN)-based speech synthesis. To apply DGPs to a statistical parametric speech synthesis framework, our framework uses an approximation method, doubly stochastic variational inference, which is suitable for an arbitrary amount of data. Since the training of DGPs is based on the marginal likelihood that takes into account not only data fitting, but also model complexity, DGPs are less vulnerable to overfitting compared with DNNs. In experimental evaluations, we investigated a performance comparison of the proposed DGP-based framework with a feedforward DNN-based one. Subjective and objective evaluation results showed that our DGP framework yielded a higher mean opinion score and lower acoustic feature distortions than the conventional framework.
BrainRun: A Behavioral Biometrics Dataset towards Continuous Implicit Authentication

The widespread use of smartphones has dictated a new paradigm, where mobile applications are the primary channel for dealing with day-to-day tasks. This paradigm is full of sensitive information, making security of utmost importance. To that end, and given the traditional authentication techniques (passwords and/or unlock patterns) which have become ineffective, several research efforts are targeted towards biometrics security, while more advanced techniques are considering continuous implicit authentication on the basis of behavioral biometrics. However, most studies in this direction are performed “in vitro” resulting in small-scale experimentation. In this context, and in an effort to create a solid information basis upon which continuous authentication models can be built, we employ the real-world application “BrainRun”, a brain-training game aiming at boosting cognitive skills of individuals. BrainRun embeds a gestures capturing tool, so that the different types of gestures that describe the swiping behavior of users are recorded and thus can be modeled. Upon releasing the application at both the “Google Play Store” and “Apple App Store”, we construct a dataset containing gestures and sensors data for more than 2000 different users and devices. The dataset is distributed under the CC0 license and can be found at the EU Zenodo repository.
Comparing machine learning to knowledge engineering for student behavior modeling: a case study in gaming the system

Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of a case study in building models able to detect when students “game the system”, a behavior in which learners abuse the environment’s support functionalities in order to succeed by guessing or copying answers. We compare the predictive performance, interpretability and generalizability of models created using each approach, doing so across multiple intelligent tutoring systems. In our case study, we show that the machine-learned model required less resources to develop, but was less interpretable and general. In contrast, the knowledge engineering approach resulted in the most interpretable and general model. Combining both approaches in a hybrid model allowed us to create a model that performed best across the three dimensions, but requiring increased resources to develop.