Reviewerly/exHarmony huggingface dataset:
Quantum coherence measurement of qudit systems using three-dimensional photonic circuits

Knowing that quantum coherence plays a central role in quantum phenomena, we propose, in this work, three-dimensional photonic circuits with compact designs that allow measurement of the quantum coherence of $N$-dimensional quantum systems from the probabilities of detecting the photons that emerge from their outputs. Here, we present two proposals: one based on the positive operator-valued measure (POVM) proposed by Carmeli et al. [New J. Phys. 20, 063038 (2018)] and the other based on the sum of the input-state partial visibilities, obtained by interfering pairs of base states. Based on the form of the POVM that we define for this task, the proposed interferometers are given by a sequence of different unitary operations. These operations act independently in each vertical sector, or layer, of the circuit, which simplifies its determination. We have thus obtained circuits whose number of beam splitters obeys a quadratic polynomial function and has a linear optical depth, which confirms its compactness and its reduced complexity.
A study on input noise second-order filtering and smoothing of linear stochastic discrete systems with packet dropouts

Abstract We investigate non-Gaussian noise second-order filtering and fixed-order smoothing problems for non-Gaussian stochastic discrete systems with packet dropouts. We present a novel Kalman-like nonlinear non-Gaussian noise estimation approach based on the packet dropout probability distribution and polynomial filtering technique. By means of properties of Kronecker product we first introduce a second-order polynomial extended system and then analyze the means and variances of the Kronecker powers of the extended system noises. To generate noise estimators in forms of filtering and smoothing, we use the innovation approach. We give an example to illustrate that the presented algorithm has better robustness against packet dropouts than conventional linear minimum variance estimation.
The Pairwise-Markov Bernoulli Filter

The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a sensor whose observations are corrupted by missed detections and a general, known clutter process.Like virtually all target-tracking algorithms it presumes restrictive independence assumptions, namely a hidden Markov model (HMM) structure on the sensor and target.That is, the current state of the target depends only on its previous state, and the measurement collected from it depends only on its current state.Pieczynski's pairwise Markov model (PMM) relaxes these restrictions.In it, the current target state can additionally depend on the previous measurement; and the current measurement can additionally depend on the previous measurement and previous target state.In this paper we show how to correctly generalize the PMM to the multitarget (MPMM) case; and use the MPMM to derive a ''PMM Bernoulli filter'' that obeys PMM rather than restrictive HMM sensor/target statistics.
Losses and gains in experiential education in a university pharmacy in Brazil: Lessons from a pandemic

&#x0D; &#x0D; &#x0D; &#x0D; The abrupt interruption of planned activities at the University Pharmacy (UP) in the Federal University of Santa Catarina forced a rapid change in routine services with the aim of ensuring continued access to medicines. Since March 2020, the UP has not been able to provide dispensing services or consultations inside its facility because of safety concerns around the spread of COVID-19. Internships are now dedicated to management tasks such as preparing documents to advise patients about particular medicines’ utilisation. Virtual communication with the patients was established. This allowed students to be put in charge of keeping in touch with special groups of patients by telephone to track health outcomes, COVID-19 symptoms, and other particular needs. The Global Competency Framework and the National Pharmaceutical Education Guidelines were used as guides to develop the study. Three domains were affected by gains and losses: organisation and management competencies, professional/personal competencies, and pharmaceutical care.&#x0D; &#x0D; &#x0D; &#x0D;
Semi-supervised machine learning with word embedding for classification in price statistics

Abstract The Office for National Statistics (ONS) is currently undertaking a substantial research program into using price information scraped from online retailers in the Consumer Prices Index including occupiers’ housing costs (CPIH). In order to make full use of these data, we must classify it into the product types that make up the basket of goods and services used in the current collection. It is a common problem that the amount of labeled training data is limited and it is either impossible or impractical to manually increase the size of the training data, as is the case with web-scraped price data. We make use of a semi-supervised machine learning (ML) method, Label Propagation, to develop a pipeline to increase the number of labels available for classification. In this work, we use several techniques in succession and in parallel to enable higher confidence in the final increased labeled dataset to be used in training a traditional ML classifier. We find promising results using this method on a test sample of data achieving good precision and recall values for both the propagated labels and the classifiers trained from these labels. We have shown that through combining several techniques together and averaging the results, we are able to increase the usability of a dataset with limited labeled training data, a common problem in using ML in real world situations. In future work, we will investigate how this method can be scaled up for use in future CPIH calculations and the challenges this brings.
Digital Making and Entrepreneurship. Imagine the Future

The contemporary dynamics of cultural, social, and economic change address us toward a global recall to a new educational design, which should be able to enhance and create new competencies for the worker and the citizen of the future. This paper proposes a reflexion on the innovations that characterize the ongoing transformation and on the challenges that educative institutions have to face. In particular, we will focus on some twists among possible educative scenarios, based on the digital making and focused on the entrepreneurship competence framework, always with a sight to the connections between technological innovation and education. The paper recalls the laboratories of digital fabrication as expression of a dynamic that put together the cultural technological characteristics and the technical development that take shape on the new social and instances. Within this frame, the paper presents some results of the research carried out by the National Institute for Documentation, Innovation and Educational Research (INDIRE) within the 2014–2020 National Operational Programme. Focusing on the development of the competencies to participate in the twenty-first century, the research analyzed ten case studies in the upper secondary school. This practice of teaching and learning is based on the increasing affordances of educational technologies combined with traditional science labs, and maximises the value and potential of digital culture by encouraging participation and creativity.
Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem

The classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields. We term this as Imbalanced HDLSS (IHDLSS). Recent theoretical results reveal that the classification criterion and tolerance similarity are crucial to HDLSS, which emphasizes the maximization of within-class variance on the premise of class separability. Based on this idea, a novel linear binary classifier, termed Population Structure-learned Classifier (PSC), is proposed. The proposed PSC can obtain better generalization performance on IHDLSS by maximizing the sum of inter-class scatter matrix and intra-class scatter matrix on the premise of class separability and assigning different intercept values to majority and minority classes. The salient features of the proposed approach are: (1) It works well on IHDLSS; (2) The inverse of high dimensional matrix can be solved in low dimensional space; (3) It is self-adaptive in determining the intercept term for each class; (4) Its computational complexity is analyzed. A series of evaluations are conducted on one simulated data set and ten real-world benchmark data sets on IHDLSS on gene analysis. Experimental results demonstrate that the PSC is superior to the state-of-art methods in IHDLSS.
Cryptographic Algorithm Identification Using Deep Learning Techniques

Pamidiparthi, Sandeep Velampalli, SirishaDeep learning techniques have recently gained momentum in cryptography and cryptanalysis. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have diverse applications in diverse fields. Encryption algorithm identification by analysis of cipher text enables focused cryptanalysis methods to be applied, thereby increasing the chances of a successful full/partial plain text recovery. In this work, we identified the encryption algorithm used, by analyzing various encrypted text files. We applied modern deep learning classification methods and compared them with existing machine learning methods. We generated cipher text corpus for multilingual dataset that contains 700 files with an average of 4000 characters per file and applied our techniques on this generated ciphertext. We used ciphers namely AES and Blowfish.
The impact of the AC922 Architecture on Performance of Deep Neural Network Training

Practical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report performance results depending on batch sizes and GPU selection and compare them with the results from another contemporary workstation based on the same set of GPUs - NVIDIA® DGX Station <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> . The results show that the AC922 performs better in all tested configurations, achieving improvements up to 10.3%. Profiling indicates that the improvement is due to the efficient I/O pipeline. The performance differences depend on the specific model, rather than on the model class (RNN/CNN). Both systems offer good scalability up to 4 GPUs. In certain cases there is a significant difference in performance depending on exactly which GPUs are used for computations.
Unpaired salient object translation via spatial attention prior

With only set-level constraints, unpaired image translation is challenging in discovering the correct semantic-level correspondences between two domains. This limitation often results in false positives such as significantly changing color and appearance of the background during image translation. To address this limitation, we propose the Spatial Attention-Aware Generative Adversarial Network (SAAGAN), a novel approach to jointly learn salient object discovery and translation. Specifically, our generator consists of (1) spatial attention prediction branch and (2) image translation branch. For attention branch, we extract spatial attention prior from a pre-trained classification network to provide weak supervision for object discovery. The proposed attention loss can largely stabilize the training process of attention-guided generator. For translation branch, we revise classical adversarial loss for salient object translation. Such a discriminator only distinguish the distribution of the object between two domains. What is more, we propose a fake sample augmentation strategy to provide extra spatial information for discriminator. Our approach allows simultaneously locating the attention areas in each image and translating the related areas between two domains. Extensive experiments and evaluations show that our model can achieve more realistic mappings compared to state-of-the-art unpaired image translation methods.
Review of Concept Drift Detection Method for Industrial Process Modeling

With the advent of big data era in industry, data-driven modeling methods have been applied widely. The concept drift problem in industrial process modeling has also attracted widespread attention. However, the current research on concept drift focuses on classification tasks and computer fields, with less work on regression modeling of industrial processes. Aiming at the above problems, this paper summarizes the concept drift detection methods for industrial process modeling, and guide for solving this problem. First, the general definition of concept drift and its existence in industrial processes are introduced. Then, the existing drift detection technologies based on process variables, based on prediction error of difficulty-to-measure parameter and based on combine multiple factors are addressed. Thirdly, these methods are discussed, and some research difficulties are given out. Finally, the conclusion and the future research directions for the existing concept detection difficulties are presented.
Spatial Attention Based Real-Time Object Detection Network for Internet of Things Devices

Target detection algorithms for Internet of things (IoT) devices often require both high real-time performance and low computational complexity.Real-time object detection network: You Only Look Once Version 3 (YOLOv3) makes full use of multi-scale features to detect objects by using feature pyramid network structure, and achieves good performance on the premise of guaranteeing fast detection speed.The feature pyramid network of YOLOv3 includes bottom-up feature extraction, top-down sampling and lateral connection of low-level detail features and high-level semantic features.But not all features are useful for object detection.In this article, a novel object detection network Spatial Attention based YOLOv3 (SA-YOLOv3) is proposed.The proposed method adds spatial attention network to the top-down sampling process.The spatial attention network calculates the feature weight matrix based on the up-sampling feature map.SA-YOLOv3 uses the feature weight matrix to filter low-level features and retain more valuable features.Finally, the selected low-level feature map and high-level feature map are concatenated together and feature maps with both spatial information and rich semantic information are obtained.The experimental results on PASCAL VOC2012 datasets and RSOD datasets show that SA-YOLOv3 outperforms YOLOv3.
View-Decision Based Compound Match Learning for Vehicle Re-identification in UAV Surveillance

Focus on the vehicle re-identification task in the unmanned aerial vehicle surveillance, the View-Decision Based Compound Match Learning (VD-CML) method is proposed, which embeds image viewpoint information in network structure and metric learning to enhance the ability to re-identify vehicles in different viewpoints. First, the viewpoint judgment model is constructed to obtain the viewpoint information, and the viewpoint decision model generates the composite sample pair embedding viewpoint information. Then, the multi-branch separable Siamese network and the multivariate compound contrastive loss function are designed to learn the feature of composite sample pairs in three viewpoints. Finally, the similarity sequence of the target vehicle image is obtained by the metric calculation of the sample features extracted by the trained model. Through experiments on the new constructed large-scale UAV-captured dataset called VeRi-UAV, the VD-CML algorithm has better performance on the vehicle re-identification problem compared with other methods.
Road-Deterioration Detection using Road Vibration Data with Machine-Learning Approach

Recently, the maintenance and management of infrastructure, such as paved roads and bridges, at a low cost has become important. Although some measurement methods including the falling weight deflectometer test have been developed to assess the soundness of paved roads, it is difficult to measure the data in a constant manner, for instance, on a daily basis. Therefore, we present an approach as per which we install vibration sensors on paved roads and automatically detect the deterioration of the paved roads via the installed vibration sensor and a machine-learning technique. Deterioration detection techniques that exploit vibration sensors have been studied; however, those were limited to bridge monitoring. No studies for the vibration measurement of paved roads using fixed sensors have been conducted. Herein, we focus on the deterioration of paved roads, specifically, in the form of road cracks, and conduct vibration measurements that highlight the differences in the vibrations of roads with and without cracks. In this paper, we describe the vibration measurements of a paved road with and without cracks and propose a framework for detecting cracks. An anomaly detection technique is necessary for using our detection framework. In this paper, we also evaluate the detection performance using anomaly detection techniques - namely, one-class support vector machine, isolation forest, and local outlier factor - using the measured vibration data.
Leading countries in computer science: A bibliometric overview

This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995–2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.
Adversarial Attacks for Object Detection

Deep neural networks have been demonstrated as the rule of thumb in various industrial applications. However, they are also verified vulnerable to adversarial examples that are designed to make models predict erroneously. In this paper, we aim to launch the systematic evaluation of adversarial attacks for object detection, mainly with Faster R-CNN and YOLO models. They are important milestones in the field of object detection and represent the state-of-the-arts (SOTA) for the one-stage and two-stage detection paradigms. First, we comprehensively analyze several popular adversarial attacks and the principles will be discussed in a common scope. Then the attack performances will be evaluated comparatively by indices like mean average precision (MAP), time cost, attack iteration number and the distortion. Following the attack methods, we further investigate the resistance of these adversaries against some common defense methods. Finally, some perspectives are elaborated on the challenge of adversarial attack and defense for object detectors.
Multi-View Model-Driven Projection to Facilitate the Control of the Evolution and Quality of the Architecture

In model driven-engineering, there is a myriad of approaches that use models and transformations to develop software systems. However, a few works in the literature have discussed the simplification of these models to make them more readable, understandable, and easy to navigate. This article proposed an approach that simplifies models in order to assist architects in controlling architecture evolution and quality, especially with large systems. This approach consists of two main concepts, multi-view modeling and on-demand model projection. In the former, formally specified models are divided into two views. The first one is dedicated to describe quality attributes. The second view represents the architectural view. The multi-viewing can enrich stakeholder reasoning about the developed architecture and simplify the mapping between quality attributes and architectural decisions at different abstraction levels. In the latter, the projection concept consists of extracting from source models only elements of interest to generate simpler and narrower models as output.
The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact Verification

Complex fact verification (FV) requires fusing scattered sequences and performing multi-hop reasoning over these composed sequences. Recently, by employing some FV models, knowledge is obtained from context to support the reasoning process based on pretrained models (e.g., BERT, XLNET), and this model outperforms previous out-of-the-art FV models. In practice, however, the limited training data cannot provide enough background knowledge for FV tasks. Once the background knowledge changed, the pretrained models’ parameters cannot be updated. Additionally, noise against common sense cannot be accurately filtered out due to the lack of necessary knowledge, which may have a negative impact on the reasoning progress. Furthermore, existing models often wrongly label the given claims as ‘not enough information’ due to the lack of necessary conceptual relationship between pieces of evidence. In the present study, a Dynamic Knowledge Auxiliary Graph Reasoning (DKAR) approach is proposed for incorporating external background knowledge in the current FV model, which explicitly identifies and fills the knowledge gaps between provided sources and the given claims, to enhance the reasoning ability of graph neural networks. Experiments show that DKAR put forward in this study can be combined with specific and discriminative knowledge to guide the FV system to successfully overcome the knowledge-gap challenges and achieve improvement in FV tasks. Furthermore, DKAR is adopted to complete the FV task on the Fake NewsNet dataset, showing outstanding advantages in a small sample and heterogeneous web text source.
A Novel Exhaust Gas Temperature Prediction Method of Hot Blast Stove

Hot blast Stoves (HBSs) is a piece of important equipment to provide hot air for the blast furnace. In order to control its operation process and save resources, it is necessary to predict its exhaust gas temperature. Considering a large amount of production data in this system, a novel prediction method based on echo state network (ESN), correlation analysis and differential evolution (DE) algorithm are presented in the paper. ESN is a special kind of recurrent neural network. Due to its simple training and fast convergence, the ESN model is widely used in time series prediction of a nonlinear system. To improve the availability of samples and the accuracy of results, we analyze the correlation between different features of samples for time hysteresis search before prediction. Besides, the differential evolution algorithm is used to search optimal solutions for crucial parameters of the echo state network. Simulation results show that the proposed method can effectively predict the exhaust gas temperature in a short time.