Reviewerly/exHarmony huggingface dataset:
Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

With the development of smart cities, lots of mobile cooperative sensing based nodes have emerged. However, due to the open nature of wireless transmission, attackers in the networks can use some intelligent radio devices to deteriorate the secure transmission, which imposes a severe issue of information leakage. In this paper, we consider the transmitter has some computational tasks to be computed, under the environments of intelligent attacker. Due to the limited computational capability, the sender needs to offload some tasks to the receiver. To address this problem, we propose a power allocation algorithm based on combining the technology of reinforcement learning and game theory, in order to achieve an optimal secure data rate and meanwhile reduce the whole task latency of the transmission and computation with Q learning and Nash equilibrium. Then, the Nash equilibrium and its existence conditions are derived and proven mathematically. Finally, we perform some simulations under Matlab platform, and the results show that the proposed algorithm can effectively improve the secrecy data rate and reduce the whole system latency.
Parts-of-Speech Tagging in NLP: Utility, Types, and Some Popular POS Taggers

Majority of the basic models in the area of Natural Language Processing (NLP) are based on Bag of Words. Primary limitation of Bag of Words based models is their inability to capture the syntactic relations between words. Part-of-Speech (POS) are uselful in improving on this Bag of Words technique. Part-of-Speech (PoS) tagging is involved with the process of assigning one of the parts of speech (include nouns, verb, adverbs, adjectives, pronouns, conjunction and their sub-categories) to the given word. POS tagging is the process of marking up a word in a corpus to a corresponding part of a speech tag, based on its context and definition. POS Tags are useful for building parse trees, which are used in building Named Entity Recognitions (NERs) and extracting relations between words. POS Tagging also has major application in building lemmatizers which are used to reduce a word to its root form. To understand the meaning of any sentence or to extract relationships and build a knowledge graph, POS Tagging is a very important step. This chapter gives a vivid introduction to the POS tagging problem, its various applications and types with special emphasis on markov model based POS tagging and finally some python based implementtaion of some popular POS taggers.
A Brief Overview of Natural Language Processing and Artificial Intelligence

In the modern era of technological peregrination, artificial intelligence (AI) is one of the most interesting fields of research and language is the most compelling manifestation of intelligence. In this context, natural language processing (NLP) stands as a popular field of research in current days. Basically, NLP is employed to allow the machines of how humans being speaks and basically aimed at analyzing the text. Moreover, NLP is normally employed for text mining (TM), machine translation, automated question answering, etc. Such human-computer interaction enables real-world applications such as sentiment analysis, automatic text summarization, topic extraction, stemming, machine translation, etc. This chapter further discusses the two basic aspects like natural language understanding (NLU) and natural language generation (NLG) that deal with NLU and generation respectively. Further, the current chapter throws light on various aspects of text processing like: morphological analysis, syntax analysis, semantic analysis, lexical analysis, etc.
Does MAML really want feature reuse only

Meta-learning, the effort to solve new tasks with only a few samples, has attracted great attention in recent years. Model Agnostic Meta-Learning (MAML) is one of the most representative gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples with inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. Recently, it has been hypothesized that feature reuse, which makes little change in efficient representations, is the dominant factor in the performance of meta-initialized model through MAML rather than rapid learning, which makes a big change in representations. In this work, we propose a novel meta-learning algorithm, coined as BOIL (Body Only update in Inner Loop), that updates only the body (extractor) of the model and freezes the head (classifier) of the model during inner loop updates. The BOIL algorithm thus heavily relies on rapid learning. Note that BOIL is the opposite direction to the hypothesis that feature reuse is more efficient than rapid learning. We validate the BOIL algorithm on various data sets and show significant performance improvement over MAML. The results imply that rapid learning in gradient-based meta-learning approaches is necessary.
Detecting Associations between Archaeological Site Distributions and Landscape Features: A Monte Carlo Simulation Approach for the R Environment

Detecting association between archaeological sites and physical landscape elements like geological deposits, vegetation, drainage networks, or areas of modern disturbance like mines or quarries is a key goal of archaeological projects. This goal is complicated by the incomplete nature of the archaeological record, the high degree of uncertainty of typical point distribution patterns, and, in the case of deeply buried archaeological sites, the absence of reliable information about the ancient landscape itself. Standard statistical approaches may not be applicable (e.g., X2 test) or are difficult to apply correctly (regression analysis). Monte Carlo simulation, devised in the late 1940s by mathematical physicists, offers a way to approach this problem. In this paper, we apply a Monte Carlo approach to test for association between Lower and Middle Palaeolithic sites in Hampshire and Sussex, UK, and quarries recorded on historical maps. We code our approach in the popular ‘R’ software environment, describing our methods step-by-step and providing complete scripts so others can apply our method to their own cases. Association between sites and quarries is clearly shown. We suggest ways to develop the approach further, e.g., for detecting associations between sites or artefacts and remotely-sensed deposits or features, e.g., from aerial photographs or geophysical survey.
Space-homogeneous quantum walks on $\mathbb{Z}$ from the viewpoint of complex analysis

The subject of this paper is quantum walks, which are expected to simulate several kinds of quantum dynamical systems. In this paper, we define analyticity for quantum walks on $\mathbb{Z}$. Almost all the quantum walks on $\mathbb{Z}$ which have been already studied are analytic. In the framework of analytic quantum walks, we can enlarge the theory of quantum walks. We obtain not only several generalizations of known results, but also new types of theorems. It is proved that every analytic space-homogeneous quantum walk on $\mathbb{Z}$ is essentially a composite of shift operators and continuous-time analytic space-homogeneous quantum walks. We also prove existence of the weak limit distribution for analytic space-homogeneous quantum walks on $\mathbb{Z}$.
ESPN VR Batting Cage

The ESPN VR Batting Cage uses an innovative combination of tracking data with dynamic graphics to create a unique, immersive experience to engage both sports fans and technologists. Although the game is primarily experienced individually through a virtual reality (VR) headset, features like Tournament Play invite users to come together and engage with sports content in a new, virtual space. Users can engage with pro players via tracked pitch data in a way that they are unlikely to in the physical world, and that is more tangible than watching a baseball game.
Effective Interaction With The Computer: Observations And Models From An Eight Year Experience

Examples 4. Conclusion 5. References 1.
On the Relation Between Structured d-DNNFs and SDDs

Abstract Structured d -DNNFs and SDDs are restricted negation normal form circuits used in knowledge compilation as target languages into which propositional theories are compiled. Structuredness is imposed by so-called vtrees. By definition SDDs are restricted structured d -DNNFs. Beame and Liew (2015) as well as Bova and Szeider (2017) mentioned the question whether structured d -DNNFs are really more general than SDDs w.r.t. polynomial-size representations (w.r.t. the number of Boolean variables the represented functions are defined on.) The main result in the paper is the proof that a function can be represented by SDDs of polynomial size if the function and its complement have polynomial-size structured d -DNNFs that respect the same vtree.
QR Code Based Smart Document Implementation Using Blockchain and Digital Signature

Every day an individual handles a large number of documents related to him like his personal, financial, education, domicile, identity, or medical-related documents. These are very important in an individual’s career as he may require to share these documents to utilize services, prove identity, gain admission, buy property, etc. Thus, many important decisions in his life are based on these documents. However, in recent times, forgery of documents has become a major concern, and tools and methods to detect the same have become obsolete. Every time these documents are required to be authenticated, the issuing authority is requested for, which then verifies the given information from the data stored in their central repository. The data, when stored in the centralized environment has various issues like a single point of failure, data availability, and denial of service (DoS), etc. So, there is a requirement of an improved record-keeping of these documents which will help in easy storage, access, and retrieval of documents and at the same time, provide a transparent and auditable environment for verification of integrity and authenticity of a given document. This paper proposes a framework for the creation of a Smart Document with the use of a digital signature that is embedded in a Quick Response (QR) code for verification of the document. Also, the hash of the document along with transactional details is stored in a blockchain that provides a shared, immutable, and transparent history of document without depending upon any third party. The proposed solution will facilitate the verification of documents both in its electronic and paper form and provides an assurance of integrity, authenticity, and availability of information in an immutable and transparent environment.
Solutions for environmentally robust interferometric optical testing

Optical interferometry is widely used for surface metrology, and phase-shifting interferometry (PSI) remains the “gold standard” for measuring surface form and texture. In the past, the environmental sensitivity of PSI relegated its use to dedicated metrology labs with well-controlled environments. As metrology requirements have expanded beyond the lab, advances in metrology techniques now enable high-precision optical testing in places previously considered inappropriate for interferometric techniques. A class of environmentally-robust methods preserve the gold-standard accuracy of PSI by measuring and accounting for rigid-body motions from vibration and large-scale airflow during the PSI acquisition. These methods, including model-based PSI, use a physical model of the interference to measure cavity rigid body motion and accommodate a wide variety of measurement geometries, surface shapes, surface departure, reflectivities, and environmental conditions without changing the user’s measurement process. For more extreme environments, spatial carrier methods analogous to off-axis holography extract the surface phase fast enough to “freeze” vibration and air turbulence in a single camera frame. The price paid for single-frame acquisition is a loss of lateral resolution as well as a modest increase in sensitivity to optical aberrations, requiring calibration for compensation. This paper will describe current techniques and methods, as well as provide examples and results for practical measurement scenarios in the manufacture of high-precision optical components and systems.
CYBER CRIME IN BANKING SECTOR

With the headways in innovation, the Indian Managing an account Division has been at standard with the developing patterns and critical changes required in its tasks. The call for development has given this unit monstrous chances and therefore, banks are currently among the greatest recipients of the IT Insurgency. The multiplication in online exchanges mounting on advancements like NEFT (National Electronic Store Exchange), RTGS (Constant Gross Settlement) , ECS ( Electronic Clearing Administration) and portable exchanges is a look at the profound established innovation in saving money and budgetary issues. Be that as it may, similar to opposite sides to a coin, openings accompany dangers and achievement accompanies its equal difficulties. Consequently, with the quick development of PCs and web innovation, new types of overall violations known as 'Digital Wrongdoings' has advanced in the scene. Over some undefined time frame, the nature and example of Digital Wrongdoing occurrences have turned out to be progressively modern and complex. Banks and Money related Foundations remain the unabated focuses of digital culprits in the most recent decade. Prominently monetary profit is as yet the real inspiration driving most cybercriminal exercises and there is minimal shot of this changing soon. This paper centers around the specialized parts of different kinds of cybercrimes concerning the saving money units and their related effects. Furthermore, it recognizes the danger vectors supporting these wrongdoings and creates measures to help in battling the subsequent digital assaults with the goal that such assaults can be better avoided later on for improved security.
A Vision of the University of the Future

In this paper a vision of the university of the future is introduced. The vision summarizes ideas and concepts that come as a result of long-term research and experience of the Centre of Innovative Educational Technologies at the University of Ruse. The vision outlines the evolution of the higher education as well as the roles and the future development of the physical against the virtual university and the traditional against the distance education. The new emerging skills of the academic staff are also discussed.
Dynamic inquisitive semantics: anaphora and questions

This paper develops a dynamic inquisitive semantics and illustrates its potential to capture interactions between anaphora and questions.
Estimation Method of Blurred Parameters in Moving Blurred Image

Abstract In order to improve the quality of image restoration and remove the blur caused by motion in the image, a blurred parameter recognition algorithm with point spread function is proposed. This algorithm combines the Fourier spectrum of the image with the edge detection based on phase consistency and identifies the blurred angle by determining the direction of the edge of the center bright stripe. Then bilinear interpolation is used to generate the sub-pixel image of the spectrum, calculate the distance between the dark bands, and estimate the blurred length. Experimental results show that, compared with the traditional algorithm, the proposed method can not only obtain higher precision of blurred angle and blurred length, but also have better stability, and can correctly detect blurred parameters of blurred images with different scales and contents.
Nested covariance determinants and restricted trek separation in Gaussian graphical models

Directed graphical models specify noisy functional relationships among a collection of random variables.In the Gaussian case, each such model corresponds to a semi-algebraic set of positive definite covariance matrices.The set is given via a parametrization, and much work has gone into obtaining an implicit description in terms of polynomial (in)equalities.Implicit descriptions shed light on problems such as parameter identification, model equivalence and constraint-based statistical inference.For models given by directed acyclic graphs, which represent settings where all relevant variables are observed, there is a complete theory: All conditional independence relations can be found via graphical d-separation and are sufficient for an implicit description.The situation is far more complicated, however, when some of the variables are hidden (or in other words, unobserved or latent).We consider models associated to mixed graphs that capture the effects of hidden variables through correlated error terms.The notion of trek separation explains when the covariance matrix in such a model has submatrices of low rank and generalizes d-separation.However, in many cases, such as the infamous Verma graph, the polynomials defining the graphical model are not determinantal, and hence cannot be explained by d-separation or trek-separation.In this paper, we show that these constraints often correspond to the vanishing of nested determinants and can be graphically explained by the (more general) notion of restricted trek separation.
Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction

A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.
A Security Upgrade on the GGH Lattice-based Cryptosystem

Due to the Nguyen's attack, the Goldreich-Goldwasser-Halevi (GGH) encryption scheme, simply referred to as GGH cryptosystem, is considered broken.The GGH cryptosystem was initially addressed as the first practical latticebased cryptosystem.Once the cryptosystem is implemented in a lattice dimension of 300 and above, its inventors was conjectured that the cryptosystem is intractable.This conjecture was based on thorough security analyses on the cryptosystem against some powerful attacks.This conjecture became more concrete when all initial efforts for decrypting the published GGH Internet Challenges were failed.However, a novel strategy by the Nguyen's attack for simplifying the underlying Closest-Vector Problem (CVP) instance that arose from the cryptosystem, had successfully decrypted almost all the challenges and eventually made the cryptosystem being considered broken.Therefore, the Nguyen's attack is considered as a fatal attack on the GGH cryptosystem.In this paper, we proposed a countermeasure to combat the Nguyen's attack.By implementing the proposed countermeasure, we proved that the simplification of the underlying CVP instance could be prevented.We also proved that, the upgraded GGH cryptosystem remains practical where the decryption could be done without error.We are optimistic that, the upgraded GGH cryptosystem could make a remarkable return into the mainstream discussion of the lattice-based cryptography.
AWNet: Attentive Wavelet Network for Image ISP

As the revolutionary improvement being made on the performance of smartphones over the last decade, mobile photography becomes one of the most common practices among the majority of smartphone users. However, due to the limited size of camera sensors on phone, the photographed image is still visually distinct to the one taken by the digital single-lens reflex (DSLR) camera. To narrow this performance gap, one is to redesign the camera image signal processor (ISP) to improve the image quality. Owing to the rapid rise of deep learning, recent works resort to the deep convolutional neural network (CNN) to develop a sophisticated data-driven ISP that directly maps the phone-captured image to the DSLR-captured one. In this paper, we introduce a novel network that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to tackle this learnable image ISP problem. By adding the wavelet transform, our proposed method enables us to restore favorable image details from RAW information and achieve a larger receptive field while remaining high efficiency in terms of computational cost. The global context block is adopted in our method to learn the non-local color mapping for the generation of appealing RGB images. More importantly, this block alleviates the influence of image misalignment occurred on the provided dataset. Experimental results indicate the advances of our design in both qualitative and quantitative measurements. The code is available publically.
All entangled states are quantum coherent with locally distinguishable bases

We find that a bipartite quantum state is entangled if and only if it is quantum coherent with respect to complete bases of states in the corresponding system that are distinguishable under local quantum operations and classical communication. The corresponding minimal quantum coherence is the entanglement of formation. Connections to the relative entropy of entanglement and quantum coherence, and generalizations to the multiparty case are also considered.
Quantum Deep Learning For Phoniatrics Biomarker Based Disease Detection Deep Learning Model For Disease Detection By Phoniatrics Biomarkers Using Quantum Computation

It is known that humans phoniatrics changes while suffering from diseases and phoniatrics based analysis is on the rise with more data flowing in and used in artificial intelligence. Phoniatrics biomarkers are being discovered and implemented for the detection of diseases with the help of machine learning which is still a huge challenge. This is a tough challenge due to the computation power required and the implementation of neural nets. While there are some conventional neural nets are being used for phoniatrics analysis, still it takes a lot of computation time. In the era of quantum computation, it will be easy to implement the deep Boltzmann machine neural net on a quantum computer model. This helps in providing a solution for detection of disease using the various phoniatrics biomarkers to access a quantum based deep learning model to provide insights for the detection of disease.