Reviewerly/exHarmony huggingface dataset:
Quantum Computing and Quantum Communication

This chapter presents state-of-the-art and future perspectives of quantum computing and communication. Timeline of relevant findings in quantum informatics, such as quantum algorithms, quantum cryptography protocols, and quantum computing models, is summarized. Mathematics of information representation with quantum states is presented. The quantum circuit and adiabatic models of quantum computation are outlined. The functionality, limitations, and security of the quantum key distribution (QKD) protocol is presented. Current implementations of quantum computers and principles of quantum programming are shortly described.
Hidden Smile Correlation Discovery Across Subjects Using Random Walk with Restart

Fine-grained smile analysis is a complicated and challenging process. Understanding other party's smiles is one of the key tasks associated with realizing the implicit messages transmitted by the human. Considering this kind of message transmission is a major feature of human communication, understanding smiling has great potential value to promote the development of humanoid robots and animated software agents. Therefore, a fine-grained smile analysis system is proposed to uncover the hidden smile correlation across subjects. The system incorporates head pose as prior knowledge and employs conditional random forest to detect fiducial points on face. After that, a steady-state probability defined by a succession of Markov random steps is used to indicate the relevance score between smiles across subjects. We demonstrate performance of the proposed system on both constrained and unconstrained face datasets. The experimental results show that the proposed system is able to classify 4 smile levels and uncover hidden smile correlations across subjects successfully.
Interactive Hierarchical Object Proposals

Object proposal algorithms have been demonstrated to be very successful in accelerating object detection process. High object localization quality and detection recall can be obtained using thousands of proposals. However, the performance with a small number of proposals is still unsatisfactory. This paper demonstrates that the performance of a few proposals can be significantly improved with the minimal human interaction-a single touch point. To this end, we first generate hierarchical superpixels using an efficient tree-organized structure as our initial object proposals, and then select only a few proposals from them by learning an effective Convolutional neural network for objectness ranking. We explore and design an architecture to integrate human interaction with the global information of the whole image for objectness scoring, which is able to significantly improve the performance with a minimum number of object proposals. Extensive experiments show the proposed method outperforms all the state-of-the-art methods for locating the meaningful object with the touch point constraint. Furthermore, the proposed method is extended for video. By combining with the novel interactive motion segmentation cue for generating hierarchical superpixels, the performance on a single proposal is satisfactory and can be used in the interactive vision systems, such as selecting the input of a real-time tracking system.
Introduction to Blockchain

Blockchain technology underpins Web 3.0. This chapter introduces blockchain technology and details its operation. This chapter explains the primary types of blockchains including public and private and provides their respective benefits and challenges. A detailed description of each of the five blockchain components—blocks, nodes, miners, cryptography, and consensus—is made. This chapter is helpful for readers new to blockchain and Web 3.0 to understand what network configuration might benefit their business needs.
Mobile Applications for Automatic Object Recognition

In recent years, the technological improvements of mobile devices in terms of computational capacity, embedded sensors, natural interaction, and high-speed connection are enabling an ever-increasing number of designers to develop advanced mobile applications to be used in everyday life. Among these, the vision-based applications for the automatic object recognition (AOR) play a key role since enabling users to interact with the world around them in innovative way makes more productive and profitable their entertainment, learning, and working activities. The chapter is divided into four sections. The first one, “Background,” explores the most recent works in AOR mobile applications highlighting the feature extraction processes and the implemented classifiers. The second one, “MV Development Technologies,” provides an overview of the current frameworks used to support the mobile AOR applications. The third one, “Future Research Trends,” discusses the aims of the next generation of AOR applications. Finally, “Conclusion” concludes the chapter.
Z-Numbers: How They Describe Student Confidence and How They Can Explain (and Improve) Laplacian and Schroedinger Eigenmap Dimension Reduction in Data Analysis

Experts have different degrees of confidence in their statements. To describe these different degrees of confidence, Lotfi A. Zadeh proposed the notion of a Z-number: a fuzzy set (or other type of uncertainty) supplemented by a degree of confidence in the statement corresponding to fuzzy sets. In this chapter, we show that Z-numbers provide a natural formalization of the competence-vs-confidence dichotomy, which is especially important for educating low-income students. We also show that Z-numbers provide a natural theoretical explanation for several empirically heuristic techniques of dimension reduction in data analysis, such as Laplacian and Schroedinger eigenmaps, and, moreover, show how these methods can be further improved.
SPFC: An Effective Optimization for Vertex-Centric Graph Processing Systems

The real-world demands of mining big data and smart data of graph structure have led to an active research of distributed graph processing. Many distributed graph processing systems [19], [22], [23] adopt a vertex-centric programming paradigm. In these systems, messages are passed between vertices to propagate the latest states. The communication efficiency and the high overhead of synchronization are two key considerations of these systems [8], [12]. In this paper, we propose a Slow Passing Fast Consuming (SPFC) approach which can effectively improve the overall performance of vertex-centric graph processing systems. In our approach, the message passing is slow but the consuming is fast. More specifically, at the message sender side, priority is given to those smart messages which contribute more to the algorithm convergence, and at the message receiver side, messages are consumed right after arriving without any delay and intermediate buffer. Besides, by using a two-phase termination check protocol, the global synchronous barrier can be completely eliminated. In addition, based on the slow message passing strategy, further performance improvement can be achieved with some accuracy loss by eliminating those messages which are less useful for algorithm convergence. We implement our approach based on Apache Giraph [1] and evaluate it on a 12-machine cluster. The experimental results show that our method can effectively reduce the amount of message traffic and achieve up to an order of magnitude performance improvement compared with Giraph and GraphLab [3].
From Incident to Insight: Incident Responders and Software Innovation

Over The Last decade, new software processes have appeared that emphasize collaboration among people involved in creating successful software. For example, agile methods stress collaboration between development teams and business clients, 1 and DevOps emphasizes better collaboration between development teams and deployment teams.
Assessment and Classification of Mass Lesions Based on Expert Knowledge Using Mammographic Analysis

Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert.In this research, classification of breast masses using mammography in the two main views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry aspect. Additionally, a method was developed and proposed using the classification of breast tissue density based on the decision tree.This study therefore, aims to provide a method based on the human decision-making model that will help in designing the perfect tool for radiologists, regardless of the complexity of computing, costly procedures and also reducing the diagnosis error.Results show that the proposed system for entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant rate, respectively with cross-validation procedure.
Broken link repairing system for constructing contextual information portals

The web is an extremely powerful resource that has the potential to improve education and health. It enables access to new markets. There are, however, fundamental problems with web access in emerging regions. The primary issue is that internet connectivity is not keeping up with web complexity and size. Recently an innovative technology is developed in the form of contextual information portals (CIP) to mitigate the effect of low connectivity. CIP provides offline searchable and browse-able information portal. The information in CIP is composed of vertical slices of the internet about specific topics. CIP is an ideal tool for developing regions which have limited access to internet. It can be used in schools and colleges to enhance lesson plans and educational material. Although, as a standalone portal CIP provides an interactive searching and browsing interface enabling a web-like experience, however, a fundamental problem that users face is broken links. This is because crawling the web for constructing a collection for CIP only makes available a portion of webpages but not all possible documents. This creates several broken links. To address this problem we develop a broken link repairing system (brLinkRepair) for repairing broken links. brLinkRepair is useful when a user tries to navigate between pages through links and pointed pages of links are missing from the CIP. We provide an information retrieval system for repairing broken links. For each broken link our system recommends related pages that are similar to pointed pages. To further improve the effectiveness of system we combine all information sources using learning to rank approach. Our results indicate learning to rank (by combining information sources) improves effectiveness.
Improved online algorithms for jumbled matching

We consider the problem of jumbled matching where the objective is to find all permuted occurrences of a pattern in a text. Besides exact matching we study approximate matching where each occurrence is allowed to contain at most k wrong or superfluous characters. We present online algorithms applying bit-parallelism to both types of jumbled matching. Two of our algorithms are filtration methods applying SIMD (Single Instruction Multiple Data) computation. Most of the other new algorithms are variations of earlier methods. We show by practical experiments that our algorithms are competitive with previous solutions.
Building a Generic Feedback System for Rule-Based Problems

We present a generic framework that provides hints on how to achieve a goal to users of software supporting rule-based problem solving from different domains. Our approach consists of two parts. First, we present a DSL that relates and unifies different rule-based problems. Second, we use generic search algorithms to solve various kinds of problems. This solution can then be used to calculate a hint for the user. We present three rule-based problem frameworks to illustrate our approach: the Ideas framework, PuzzleScript and iTasks. By taking real world examples from these three example frameworks and instantiating feedback systems for them, we validate our approach.
An Assessment Conceptual Framework for the Modernization of Legacy Systems

Legacy systems modernization is one of the most common issues for enterprise organizations. As technology is changing rapidly, enterprises need to upgrade legacy systems and ensure suitable modernization. Evolving legacy systems to new ones can give new life to the existing applications; however, if the modernization process is not done correctly, it can lead to failure. Making valid decisions on the choice of modernization strategy and achieving successful evolutionary system require addressing the full modernization project life cycle. Accordingly, we propose in this paper an assessment and decisional framework for legacy systems modernization (ADF-LSM). ADF-LSM includes the main steps of migration strategy to guide the modernization process. Also it introduces quality metrics modeling at business, information systems and infrastructure levels, to assess the system and integrate the new needs of evolution. We validate the applicability of the quality metrics of the application level thorough a case study involving a control system for practically arbitrary automated vehicles. Our case study results show that the quality metrics identify well the current issues list and benefits needed to modernize a system.
Exact Upper and Lower Bounds on the Misclassification Probability

Exact upper and lower bounds on the best possible misclassification probability for a finite number of classes are obtained in terms of the total variation norms of the differences between the sub-distributions over the classes. These bounds are compared with the exact upper and lower bounds in terms of the conditional entropy obtained by Feder and Merhav.
Using B-spline frames to represent solutions of acoustics scattering problems

Although frames, which are a generalization of bases, are important tools used in signal processing, their potential in other fields of engineering and applied mathematics (e.g. acoustics) has not been fully explored yet. Gabor frames, that are specific type of frames, are very well adapted to oscillating functions, and therefore have a great potential to efficiently represent functions in connection with the Helmholtz operator. In this paper representations of the solution of a scattering problem in 2D using Gabor frames based on B-splines as building blocks are investigated. Practical issues concerning the implementation of frames like the restriction of the frame elements to a finite interval and methods to determine the unknown coefficients for the representation, i.e. by using dual frames or by solving a least squares problem, are discussed. Numerical experiments are made comparing the different ways to determine the unknown expansion coefficients and the representations are compared in terms of their efficiency, i.e. the number of used frame coefficients versus the accuracy of the approximation. In all cases the coefficients calculated with a slightly modified orthogonal matching pursuit algorithm provide the best accuracy versus sparsity ratio.
Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets

Sentiment analysis has become one of the most important tools in natural language processing, since it opens many possibilities to understand people's opinions on different topics. Aspect-based sentiment analysis aims to take this a step further and find out what exactly someone is talking about, and if he likes or dislikes it. Real world examples of perfect areas for this topic are the millions of available customer reviews in online shops. There have been multiple approaches to tackle this problem, using machine learning, deep learning and neural networks. However, currently the number of labeled reviews for training classifiers is very small. Therefore, we undertook multiple steps to research ways of improving ABSA performance on small datasets, by comparing recurrent and feed-forward neural networks and incorporating additional input data that was generated using different readily available NLP tools.
Quantum cryptography with structured photons

Structured photons are particularly useful for quantum information tasks, including quantum communications and simulations. This is attributed to the fact that structured states provide access to a theoretically unbounded Hilbert space for encoding information. Communication schemes based on high-dimensional encryption (qudits) have the advantage of increased noise tolerance and information capacity due to the increased difficulty of cloning and eavesdropping in high dimensions. Here, we discuss the applications of structured photons in quantum communication schemes with a focus on high-dimensional demonstrations through different channels (optical fiber, free-space, and underwater). In particular, we describe the challenges each quantum channel possesses with respect to practical implementations beyond the laboratory, for example, atmospheric and underwater turbulence.
The Outcome of the ArtFrame Project: A Domain-Specific BIBFRAME Exploration

The ArtFrame Project, a part of the Linked Data for Production (LD4P) collaboration, was a domain-specific, linked-open-data (LOD) initiative that explored the metadata practices of art libraries and museums. The project, headed by Columbia University Libraries and including major art institutions and the Cataloging Advisory Committee (CAC) of the Art Libraries Society of North America (ARLIS/NA), focused on developing an extension to the Bibliographic Framework Initiative (BIBFRAME) tailored to the needs of art catalogers. This article describes the history of the project and its collaboration with the LD4P Rare Materials Extension Group to produce a shared ontology, the Art & Rare Materials BIBFRAME Ontology Extension (ARM).[This article is an expansion of a presentation at the ARLIS/NA conference held in New York, New York, in February 2018.]
A monadic semantics for quantum computing in an object oriented language

Nowadays, several languages and libraries have been proposed to program and reason about quantum programs in the imperative and functional paradigms. Having in mind that the object-oriented paradigm is one of the most used for developing general purpose software systems, in this work we propose to investigate quantum programming using the object-oriented paradigm. Hence, we present a monadic semantics for FJQuantum1, an object-oriented language based on Featherweight Java, created to reason and to develop programs handling quantum data and quantum operations. We also provide an interpreter for this semantics and a set of examples of quantum programs to run with it.
Stochastic Methods for Global Optimization and Problem Solving

Stochastic algorithms provide an effective framework to solve complex optimization problems, when derivative information is not available. Simulated Annealing and Genetic Algorithms are stochastic optimization methods that can identify putative optimal solutions without using derivative information. This article presents the mathematical foundations and the algorithmic framework of these two methods.
Learning scene-specific object detectors based on a generative-discriminative model with minimal supervision

One object class may show large variations due to diverse illuminations, backgrounds, and camera viewpoints in the multi-scene object detection task. Traditional object detection methods generally perform poorly under unconstrained video environments. To address this problem, many modern approaches provide deep hierarchical appearance representations for object detection. Most of these methods require time-consuming training procedures on large manually annotated sample sets. In this paper, we propose a self-learning object detection framework to resolve the multi-scene detection problem in a bottom-up manner. A scene-specific objector is obtained from an autonomous learning process triggered by marking several bounding boxes around an object in the first video frame via a mouse. Here, artificially labeled training data or generic detectors are not needed. This learning process is conveniently replicated many times in different surveillance scenarios and produces scene-specific detectors from various camera viewpoints. Obviously, the initial scene-specific detector, initialized by several bounding boxes, exhibits poor detection performance and is difficult to be improved by traditional online learning algorithms. Consequently, we propose the Generative-Discriminative model (GDM) based detection method to partition detection response space and assign each partition an individual descriptor that progressively achieves high classification accuracy. Online gradual optimization process is proposed to optimize the Generative-Discriminative model and focus on those hard samples lying near the decision boundary. Experimental results on nine video datasets show that our approach achieves comparable performance to that of robust supervised methods, and outperforms state-of-the-art scene-specific object detection methods under varying imaging conditions.
Noisy Quantum State Redistribution With Promise and the Alpha-Bit

We consider a variation of the well-studied quantum state redistribution task, in which the starting state is known only to the receiver Bob and not to the sender Alice. We refer to this as quantum state redistribution with a one-sided promise. In addition, we consider communication from Alice to Bob over a noisy channel $\mathcal{N}$, instead of the noiseless channel, as is usually considered in state redistribution. We take a natural approach towards the solution of this problem where we "embed" the promise as part of the state and then invoke known protocols for quantum state redistribution composed with known protocols for transfer of quantum information over noisy channels. Using our approach, we are able to reproduce the Alpha-bit capacities with or without entanglement assistance in Ref. [ArXiv:1706.09434], using known protocols for quantum state redistribution and quantum communication over noisy channels. Furthermore, we generalize the entanglement assisted classical Alpha-bit capacity, showing that any quantum state redistribution protocol can be used as a black box to simulate classical communication.
Secure sustainable storage auditing protocol (SSSAP) with efficient key updates for cloud computing

The urgent requirement of the sustainable computing determines that many security assurance protocols are designed with low computational cost. The design of the sustainable cloud storage auditing protocol is an attractive research filed in the security community. As we all know, secret keys of the data storage auditing in the cloud system are very sensitive. To enhance the security of the cloud data storage auditing, some studies focused on the research of key updates. In this paper, we propose a secure sustainable storage auditing protocol that can support key updates for clients in cloud computing. In order to alleviate the high overhead of key updates at the local side, the partial key update tasks are outsourced to the TPA. Moreover, clients can verify the validity of the new updated keys by using the technology of the BLS signature. The security analysis shows that the proposed protocol can provide the security properties of the correctness, the verifiability and the accountability. Compared to previous studies, the proposed protocol is more efficient which is very suitable for the real-world sustainable cloud storage auditing.