Reviewerly/exHarmony huggingface dataset:
Estimating quantum coherence by noncommutativity of any observable and its incoherent part

We establish a lower bound on the quantum coherence of an arbitrary quantum state in arbitrary dimension, using a noncommutativity estimator of an arbitrary observable of sub-unit norm, where the estimator is the commutator of the observable and its incoherent or classical part. The relation provides a direct method of obtaining an estimate of the quantum coherence of an arbitrary quantum state, without resorting to quantum state tomography or the existing witness operators.
FPGA Based Hardware Design of PCA for Face Recognition

This work mainly focuses on the hardware implementation of the modules required for Principal Component analysis (PCA) algorithm, which is the most popular liner-unsupervised dimensionality reduction algorithm. The PCA algorithm has been used to reduce the dimensionality of the database, where several face images have been stored for face recognition purpose. Also, it has been used to find the total number of eigen-faces that should be used to retain a certain amount of variance in the dataset. These eigen-faces have been used later to determine, whether a new input face image can be recognized or not. The high-level synthesis has been done in Vivado HLS using the device xcvu11p-flga2577-1-e. The purpose behind this work is to make the PCA technique available as a portable device for face recognition in real time applications such as identification of criminals.
AI and ML in the multi-domain operations era: vision and pitfalls

Warfighters and commanders face tremendous challenges as they work to understand and act within a competitive environment characterized by accelerating complexity. Artificial Intelligence (AI) and Machine Learning (ML) represent tremendous potential for decision makers operating across five domains within the Multi-Domain Operations (MDO) future operating concept, especially with falling costs and increasing access to disruptive technologies enabling our adversaries. Legitimate concerns surrounding military application of AI/ML to automation also demand careful consideration. How do we get to a place where warfighters have the right tools to overcome complexity and rapidly adapt within tight decision cycles against a technologically savvy adversary? Meaningful progress requires imagining where exactly we wish to go and which traps and pitfalls might lead us astray. In this paper we introduce a framework to facilitate discussion and understanding about what a desirable machine enhanced future army looks like, emphasizing the critical importance of vision in achieving it. Given an array of MDO challenges, we highlight how AI and ML capabilities can improve human control to minimize mistakes and tragedy. Lastly, in pursuit of avoiding potentially tragic missteps, we highlight critical pitfalls of applying AI/ML in military operations given inherent uncertainty of operational environments and complex systems.
A joint cross-modal super-resolution approach for vehicle detection in aerial imagery

Vehicle detection in aerial imagery is still an open research challenge although it has received some breakthroughs in the computer vision research community. Most of the existing state-of-the-art vehicle detection algorithms have ignored to consider some major factors which may have a great influence on the detection task. The low-resolution characteristic of aerial images is considered one of the major factors. Although the super-resolution technique can resolve this problem which learns a mapping between the low-resolution (LR) images and their corresponding high-resolution (HR) counterparts, however, the problem still remains when detection needs to take place at night or in a dark environment. Therefore, RGB-based detection can be another vital problem specifically for the detection task in a dark environment. For such environment infrared (IR) imaging becomes necessary which again may not be available during training an IR detector. To address these challenges, we propose a joint cross-modal and super-resolution framework based on the Generative Adversarial Network (GAN) for vehicle detection in aerial images. Our proposed joint network consists of two deep sub-networks. The first sub-network utilizes the GAN architecture to generate super-resolved (SR) images across two different domains (cross-domain translation). The second sub-network performs detection on these cross-domain translated and super-resolved images using one of the state-of-the-art object detectors i.e., You Only Look Once version 3 (YOLOv3). To evaluate the efficacy of our proposed model, we conduct several experiments on a publicly available Vehicle Detection in Aerial Imagery (VEDAI) dataset. We further compare our proposed network with state-of-the-art image generation methods to show the adequacy of our model.
Effective Methods to Monitor IT Infrastructure Security for Small Business

Small business leaders face many challenges. Cybersecurity has become a constant and growing threat to small businesses. All businesses, no matter how big or small, are vulnerable to cyber threats. While small businesses have less money and resources to combat cyber threats, small business leaders should monitor their business IT infrastructure security and implement a cybersecurity plan. Our research paper assesses the importance of cybersecurity in small business organizations and provides recommendations about critical components of an effective cybersecurity plan. This research provides answers to several questions: What are the main cyber threats facing small businesses? What strategies are effective in protecting small businesses from cyber threats in a world where technology is a necessity for organizational efficiency and competition? How can business leaders implement a comprehensive cybersecurity defensive system?
Multi-target video-based face recognition and gesture recognition based on enhanced detection and multi-trajectory incremental learning

Video-based face recognition (VFR) is one of the frontier topics in the domain of computer vision, which aims to automatically track and recognize facial regions of interests (ROIs) in video sequences.In videos with multiple faces, the trajectories of individuals are incredibly complex. This is less studied than videos with a single face per frame.In this paper, we present a multi-trajectory incremental learning (MTIL) algorithm, which categorizes trajectories using a Euclidean distance-based greedy algorithm and estimates the most likely labels for each trajectory by incremental learning to correct their classification and improve the accuracy of recognition. Furthermore, this study proposes an enhanced detection method that combines face detection with a robust tracking-learning-detection (TLD) algorithm to improve the performance of face detection in video. The method can also be extended for medical video recognition applications such as gesture recognition control based medical system.Experiments on Honda/UCSD and BMP (seq_mb) database demonstrate that our method can improve the face detection and face recognition (single or multiple) performance. The method also performs well on the gesture recognition system.The proposed MTIL algorithm can significantly improve the performance of the VFR system and the gesture recognition system.
Statistical Machine Learning for Human Behaviour Analysis

Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...].
<i>When</i> Is Happening Now

Journal AWWAVolume 112, Issue 5 p. 8-8 DC Beat When Is Happening Now Kevin M. Morley, Kevin M. MorleySearch for more papers by this author Kevin M. Morley, Kevin M. MorleySearch for more papers by this author First published: 05 May 2020 https://doi.org/10.1002/awwa.1495Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume112, Issue5May 2020Pages 8-8 RelatedInformation
Computer-assisted Diagnosis of Breast Cancer by Cell Network Matrix Extraction and Multilayer Perceptron Analysis.

Objective Diagnosis of breast cancer is based on identification of various morphologic features by histopathologic examination of the specimen. Ancillary immunohistochemical and molecular analyses provide additional information that is prognostic and guides therapy. Because of subjectivity in this approach, we sought to develop a computer model which could assist in differentiating normal or benign tissue from malignant breast carcinoma. Methods Cases of benign sclerosing adenosis (20) and high-grade invasive ductal carcinomas (20) of breast were retrieved and re-examined. Five images of the diagnostic areas from each case were captured (400x). Each image was divided into quadrants and saved as 1-megapixel each. These 800 images were then binarized and segmented using the watershed method. The cell graphs were extracted to identify the matrix of adjacent cells and the network properties were determined for each image. The local network features were fed into a PAM model and global network features were fed into a multilayer perceptrons (MLP) to distinguish between benign and malignant samples. These characteristics were evaluated by training the models on 40% (320) of the randomly assigned images followed by real-time testing of the remaining 60% (480) images. In addition, normal breast tissue from five cases was retrieved and forty (40) images were captured to further test the model. Results Both local and global network feature models had high area under the curve (AUC) (0.63 and 0.99 respectively), with their adjusted Rand indices (ARI) being 0.61 and 0.87, respectively. Pooling the pseudoprobabilities of the two neural networks greatly increased the accuracy of the model with predictions of the combined model at image level being 100% accurate with AUC of 1. Conclusion This study shows that using a combination of cell-graph extraction and a deep learning algorithm computers can accurately distinguish between benign and malignant breast lesions.
Jason-RS, A Collaboration Between Agents and an IoT Platform

In this article we start from the observation that REST services are the most used as tools of interoperability and orchestration in the Internet of Things (IoT). But REST does not make it possible to inject artificial intelligence into connected objects, i.e. it cannot allow autonomy and decision-making by the objects themselves. To define an intelligence to a connected object, one can use a Believe Desire Intention agent (BDI an intelligent agent that adopts human behavior) such as Jason Agentspeak. But Jason AgentSpeak does not guarantee orchestration or choreography between connected objects. There are platforms for service orchestration and choreography in IoT, still the interconnection with artificial intelligence needs to be built. In this article, we propose a new approach called Jason-RS. It is a result of pairing Jason BDI agent with the web service technologies to exploit the agent capacity as a service, Jason-RS turn in Java SE and it does not need any middleware. The architecture that we propose allows to create the link between Artificial Intelligence and Services choreography to reduce human intervention in the service choreography. In order to validate the proposed approach, we have interconnected the Iot BeC3 platform and the REST agent (Jason-RS). The decision-making faculty offered by Jason-RS is derived from the information sent by the objects according to the different methods of REST (GET, POST, PUT, and DELETE) that Jason-RS offers. As a result, the objects feed the inter-agent collaborations and decision-making inside the agent. Finally, we show that Jason-RS allows the Web of Objects to power complex systems such as an artificial intelligence responsible for processing data. This performance is promising.
Recognition of Rare Low-Moral Actions Using Depth Data

Detecting and recognizing low-moral actions in public spaces is important. But low-moral actions are rare, so in order to learn to recognize a new low-moral action in general we need to rely on a limited number of samples. In order to study the recognition of actions from a comparatively small dataset, in this work we introduced a new dataset of human actions consisting in large part of low-moral behaviors. In addition, we used this dataset to test the performance of a number of classifiers, which used either depth data or extracted skeletons. The results show that both depth data and skeleton based classifiers were able to achieve similar classification accuracy on this dataset (Top-1: around 55%, Top-5: around 90%). Also, using transfer learning in both cases improved the performance.
QUANTUM FIELD THEORY FROM FIRST PRINCIPLES

The mathematical description of quantum systems univocally identies their nature. In other words we treat a system as quantum if we describe its behaviour adopting Hilbert spaces and structures thereof, as prescribed by the postulates of quantum theory. The choice of using quantum systems as the elementary systems of physics can be justied in terms of informational principles, thanks to results of the last decade. Such results come as the conclusion of a research program that lasted almost one century, with the aim of reformulating quantum theory in terms of operational principles. This achievement now poses a new challenge, that we face here. If the systems of quantum theory are thought of as elementary information carriers in the rst place, rather than elementary constituents of matter, and their connections are logical connections within a given algorithm, rather than space-time relations, then we need to nd the origin of mechanical conceptsâ€”that characterise quantum mechanics as a theory of physical systems. To this end,we will illustrate howphysical laws can be viewed as algorithms for the update of memory registers that make a physical system. Imposing the characteristic properties of physical laws to such an algorithm, i.e. homogeneity, reversibility and isotropy, we will show that the physical laws thus selected are particular algorithms known as cellular automata. Further assumptions regarding maximal simplicity of the algorithm lead to two cellular automata only, that in a suitable regime can be described by Weylâ€™s dierential equations, lying at the basis of the dynamics of relativistic quantum elds. We will nally discuss how the same cellular automaton can give rise to both Fermionic eld dynamics and to Maxwellâ€™s equations, that rule the dynamics of the electromagnetic eld. We will conclude reviewing the discussion of the relativity principle, that must be suitably adapted to the scenario where space-time is not an elementary notion, through the denition of a change of inertial reference frame, and whose formulation leads to the recovery of the symmetry of Minkowski space-time, identied with PoincarÂ´eâ€™s group. Space-time thus emerges as one of the manifestations of physical laws, rather than the background where they occur, and its features are determined by the dynamics of systems, necessarily equipped with dierential equations that express it. In brief, there is no space-time unless an evolution rule requires it.
A New Method for Assessment of Engineering Drawing Answer Scripts Using Fuzzy Logic

Popular method for assessment of final exam answer scripts in university and among the engineering drawing answer scripts based on absolute true or false judgment and assigning a single number or letter to answer of each problem cannot be so fair. To obtain a fair assessment method, we considered “imagination”, “accuracy”, “drawing” and “innovation” that are objectives of engineering drawing course to be separately assessed for each problem. Flexibility and linguistic properties of fuzzy logic made us use it as the basis of our method. In addition, fuzzy variables and membership functions are easily linguistic explainable, and adjustable to different conditions. “Answering time” was added as a factor with only a positive effect on the final grade. Between these five factors, imagination has special importance because it supports one of seven human intelligences which is spatial ability Finally, however we applied the proposed method to engineering drawing course, it can be applied to other courses with considering their properties.
Big Data Processing Tools Navigation Diagram

Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.
BUCC2020: Bilingual Dictionary Induction using Cross-lingual Embedding

This paper presents a deep learning system for the BUCC 2020 shared task: Bilingual dictionary induction from comparable corpora. We have submitted two runs for this shared Task, German (de) and English (en) language pair for “closed track” and Tamil (ta) and English (en) for the “open track”. Our core approach focuses on quantifying the semantics of the language pairs, so that semantics of two different language pairs can be compared or transfer learned. With the advent of word embeddings, it is possible to quantify this. In this paper, we propose a deep learning approach which makes use of the supplied training data, to generate cross-lingual embedding. This is later used for inducting bilingual dictionary from comparable corpora.
Modified Dual Path Network With Transform Domain Data for Image Super-Resolution

Recently, studies on single image super-resolution using Deep Convolutional Neural Networks (DCNN) have been demonstrated to have made outstanding progress over conventional signal-processing based methods. However, existing architectures have grown wider and deeper, resulting in a large amount of computation and memory cost, but only a small improvement in performance. To address this issue, in this paper, we present a Wavelet- and Saak-transform Dual Path Network (WSDPN), which considers not only low-resolution images but also transform-domain information. The proposed network exploits the rich information extracted from the transform domain to reconstruct more accurate high-resolution images. In addition, to reap the benefits from both residual network (ResNet) and densely convolutional network (DenseNet) topologies, we use dual-path blocks as the basic building blocks which allow feature re-use while ensuring the ability to continue extracting new features. Thanks to extensive research on the attention mechanism, we further introduce spatial and self-attention blocks to refine features based on feature correlations at different layers. The experimental results show that our proposed approach achieves better performance on extensive benchmark evaluation than other state-of-the-art methods.
Random Hyperboxes

This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks, popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.
cEnTam: Creation and Validation of a New English-Tamil Bilingual Corpus

Natural Language Processing (NLP), is the field of artificial intelligence that gives the computer the ability to interpret, perceive and extract appropriate information from human languages. Contemporary NLP is predominantly a data driven process. It employs machine learning and statistical algorithms to learn language structures from textual corpus. While application of NLP in English, certain European languages such as Spanish, German, etc. and Chinese, Arabic has been tremendous, it is not so, in many Indian languages. There are obvious advantages in creating aligned bilingual and multilingual corpora. Machine translation, cross-lingual information retrieval, content availability and linguistic comparison are a few of the most sought after applications of such parallel corpora. This paper explains and validates a parallel corpus we created for English-Tamil bilingual pair.
The Evaluation of Semantic Mapping

Abstract The increasing of the importance of links in the network of data influences the idea that links should be considered more in the mapping relational to graph model. Semantic abstraction gaps often occur during the mapping process where the link in the real world is mapped as a node in a graph model. This paper focused on evaluating the result of mapping and converting without losing the semantics. We propose the evaluation of our approach by using schema.org as the semantic standard. The experiments in three data sets show that the semantic mapping approach is pretty effective. We obtain quite good score matching without considering the gap index (the average is 0.6922) and with considering the gap index (the average is 0.5264) and the average precision score, 0.7042, is pretty good too.