Reviewerly/exHarmony huggingface dataset:
Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

Artificial intelligence has permeated all aspects of our lives today.However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing.Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot.As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility.In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts.The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete.Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study.In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.
Investigating the User Experience of Smartphone Authentication Schemes - The Role of the Mobile Context

Today's smartphones feature several authentication methods not only to protect the overall device but also to control access to mobile banking and commerce apps, for example.However, to date there is no clear understanding on how users perceive different authentication methods in light of different usage contexts.To close this gap, we report on a study (N=22) in which we compared four recent authentication schemes on Android devices (Face Unlock, fingerprint scanning, NFC ring and PIN) in four different mobile settings (private vs. public, moving vs. stationary).We found that Fingerprint scanning turned out to be a well-suited and accepted authentication scheme over all four investigated contexts.While the NFC-based ring authentication is seen as less suitable for private settings, Face Unlock is disliked for public settings.
A new approach for Steganography: Bit shifting operation of encrypted data in LSB (SED-LSB)

In this article, a new method is presented by adding bit shift of encrypted data in LSB method (SED-LSB). The message to be embedded in the cover image is encrypted by the key received from the user. Then, secret data are obtained by bit shifting operation using this encrypted text. Finally, the secret data is hidden inside the cover image using the LSB method. SED-LSB is performed by preparing GUI in MATLAB. The quality of the cover image is measured by Peak Signal-to-Noise Ratio (PSNR), besides the similarity between the cover and stego image is determined by the Structural Similarity Index (SSIM). The 512 x 512 size of colored images as Lena, Peppers, Fruits and Baboon are used to hide 87373 byte data. PSNR values are between 51.615 – 51.656 and SSIM values are between 0.99937 – 0.99982. In the proposed method, when data are hidden at a higher capacity, stage image is found not only secure but also high quality.
Power analysis attack against encryption devices: a comprehensive analysis of AES, DES, and BC3

Cryptography is a science of creating a secret message and it is constantly developed. The development consists of attacking and defending the cryptography itself. Power analysis is one of many Side-Channel Analysis (SCA) attack techniques. Power analysis is an attacking technique that uses the information of a cryptographic hardware’s power consumption. Power analysis is carried on by utilizing side-channel information to a vulnerability in a cryptographic algorithm. Power analysis also uses a mathematical model to recover the secret key of the cryptographic device. This research uses design research methodology as a research framework started from research clarification to descriptive study. In this research, power analysis attack is implemented to three symmetrical cryptographic algorithms: DES (Data Encryption Standard), AES (Advanced Encryption Standard), and BC3 (Block Cipher 3). The attack has successfully recovered 100% of AES secret key by using 500 traces and 75% DES secret key by using 320 traces. The research concludes that the power analysis attack using Pearson Correlation Coefficient (PCC) method produces more optimal result compared to a difference of means method.
Trust Analysis for Information Concerning Food-Related Risks

In last years many business activities, scientific researches and applications exploit social networks as important sources for gathering data with different aims. Knowing the habits and preferences of user can be useful for different purposes, firstly to build marketing and advertising campaigns, but also to analyse other social phenomena for statistics, demography or security reasons. Thanks to their wide adoption among people, social networks are becoming the first media adopted to publish and share real-time news about happening events and, consequently, also the main media to retrieve information on what happens around you. Taking into account this consideration, in this paper we investigate a methodology for semantic analysis of textual information obtained from social media streams, in order to perform an early identification of food contaminations. As a case study, we consider a set of reviews gathered from the social network Yelp [26], on which we perform the textual analysis foreseen in the proposed methodology.
Stratigraphy and Structure of Dhamtaur Area, District Abbottabad, Eastern Hazara, Pakistan

Pre-Cambrian to Paleocene age sedimentary rocks predominantly characterize the fold and thrust belt of eastern Hazara division.The Hazara Slate Formation is the oldest rock unit which represents the Precambrian sequence.The Permian and Triassic sequences are missing.The Jurassic sequence comprises Samana Suk Formation whereas the Cretaceous exposed is Chichali and Lumshiwal formations.The Eocene sequence consists of Nammal and Sakessar formations.The structural fabric of the area is mainly attributed to a series of northwest trending parallel to en echelon anticlines and synclines.Most of these folds are found to be asymmetric and are Northwest facing.Several thrust faults verging both to the north have been mapped that generally dissect the forelimbs of the anticlinal structures.But the following study going to be focus on surface structural features as well as subsurface projections of folds and faults.Study of such structural features has get prime importance in economic geology such as petroleum geology, mining geology and engineering geology.In our study area subsurface projection of folds and faults along the structural transects of the area suggests that these structures have formed as a result of shortening associated with ramping from a regional basal decollement.All the structures clearly demonstrate that the eastern Hazara area has been subjected to compressional deformation/stresses oriented northeast southwest.The repetition of rock units indicates, folding in the area and thrusting of Pre-Cambrian Hazara Formation over younger Paleocene Lockhart Formation, evidence of thrust fault.There are unconformable contacts between Hazara and Samana Suk, Chichali and Lumshiwal, Kawagarh and Hangu and Lockhart formations indicate fluctuation in the environment of deposition.The Bagnotar Fault, Dhamtaur syncline and Thai anticline are the major structural features identified and reported in the study area.
𝛍m-resolution thickness distribution measurement of transparent glass films by using a multi-wavelength phase-shift extraction method in the large lateral shearing interferometer

In this paper, a precise phase-shift extraction method was introduced for the first time to measure the thickness distribution of transparent glass films. A spatial light modulator modulated the phases of the incident laser in a large lateral shearing interferometer. The phase shifting caused by the thickness of the films can be extracted ranging from 0 to 2π, in a recursive way suitable for real-time implementation. Incident lasers with different wavelengths were utilized to measure the spatial distribution of the thickness of the films, and they can be larger than one wavelength of the incident light. Both artificial and experimental results have verified that the resolution of the thickness measurement reaches up to 1 훍m.
Virtual cleaning and unwrapping of non-invasively digitized soiled bamboo scrolls

In ancient China, symbols and drawings captured on bamboo and wooden slips were used as main communication media. Those documents are very precious for cultural heritage and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and contaminated by soil. Manual cleaning of excavated slips is a demanding and time-consuming task in which writings can be accidentally deleted. To counter this, we propose a novel approach based on conventional 3-D X-ray computed tomography to digitize such historical documents without before manual cleaning. By applying a virtual cleaning and unwrapping algorithm, the entire scroll surface is remapped into 2-D such that the hidden content becomes readable. We show that the technique also works for heavily soiled scrolls, enabling an investigation of the content by the naked eye without the need for manual labor. This digitization also allows for recovery of potentially erased writings and reconstruction of the original spatial information.
Fast local SAR image despeckling by edge-avoiding wavelets

In this paper, a local despeckling algorithm based on á trous version of edge-avoiding wavelets for synthetic aperture radar images is proposed. Edge-avoiding wavelets provide the decomposition of the input image into several detail layers and a residual image which corresponds to the despeckled result by means of weighted á trous wavelet transform. The weight function is similar to the range kernel of the well-known bilateral filter and is determined by enhancing evaluation indexes. Visual and quantitative comparisons validate the performance of the proposed method in terms of both speckle reduction and edge preservation ability.
Planning and visualization for a smart meeting room assistant1

In this paper, we report on the planning and visualization capabilities of Mr.Jones – a proactive orchestrator and decision-support agent for a collaborative decision making setting embodied by a smart room. The duties of such an agent may range across interactive problem solving with other agents in the environment, developing automated summaries of meetings, visualization of the internal decision-making process, proactive data and resource management, and so on. Specifically, we focus on how the visualization of the planning and plan recognition processes forms a key component of the smart assistant, and establishes transparency in the decision-making process. We also highlight how these processes contribute to the proactive nature of the agent. We demonstrate some of these functionalities in a successful deployment of the system in the CEL – the Cognitive Environments Laboratory at IBM’s T.J. Watson Research Center (Yorktown, USA), and report on emerging deployments of the system that have turned into success stories.
Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity

Digitalization of the imaging in radiology is a reality in several healthcare institutions worldwide. The challenges of filing, confidentiality, and manipulation have been brilliantly solved in radiology. However, digitalization of hematoxylin- and eosin-stained routine histological slides has shown slow movement. Although the application for external quality assurance is a reality for a pathologist with most of the continuing medical education programs utilizing virtual microscopy, the abandonment of traditional glass slides for routine diagnostics is far from the perspectives of many departments of laboratory medicine and pathology. Digital pathology images are captured as images by scanning and whole slide imaging/virtual microscopy can be obtained by microscopy (robotic) on an entire histological (microscopic) glass slide. Since 1986, services using telepathology for the transfer of images of anatomic pathology between detached locations have benefited countless patients globally, including the University of Alberta. The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists is a milestone in virtual reality. Challenges, such as high bandwidth requirement, electronic platforms, the stability of the operating systems, have been targeted and are improving enormously. The encryption of digital images may be a requirement for the accreditation of laboratory services—quantum computing results in quantum-mechanical phenomena, such as superposition and entanglement. Different from binary digital electronic computers based on transistors where data are encoded into binary digits (bits) with two different states (0 and 1), quantum computing uses quantum bits (qubits), which can be in superpositions of states. The use of quantum computing protocols on encrypted data is crucial for the permanent implementation of virtual pathology in hospitals and universities. Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine.
IARNN-Based Semantic-Containing Double-Level Embedding Bi-LSTM for Question-and-Answer Matching

We propose a novel end-to-end approach, namely, the semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-LSTM), to solve the three key problems of Q&A matching in the Chinese medical field. In the similarity calculation of the Q&A core module, we propose a text similarity calculation method that contains semantic information, to solve the problem that previous Q&A methods do not incorporate the deep information of a sentence into the similarity calculations. For the sentence vector representation module, we present a double-level embedding sentence representation method to reduce the error caused by Chinese medical word segmentation. In addition, due to the problem of the attention mechanism tending to cause backward deviation of the features, we propose an improved algorithm based on Bi-LSTM in the feature extraction stage. The Q&A framework proposed in this paper not only retains important timing features but also loses low-frequency features and noise. Additionally, it is applicable to different domains. To verify the framework, extensive Chinese medical Q&A corpora are created. We run several state-of-the-art Q&A methods as contrastive experiments on the medical corpora and the current popular insuranceQA dataset under different performance measures. The experimental results on the medical corpora show that our framework significantly outperforms several strong baselines and achieves an improvement of top-1 accuracy of up to 14%, reaching 79.15%.
Special issue on knowledge graphs and semantics in text analysis and retrieval

Knowledge graphs are an efective way to store semantics in a structured format that is easily used by computer systems. In the past few decades, work across diferent research communities led to scalable knowledge acquisition techniques for building large-scale knowledge graphs. The result is the emergence of large publicly available knowledge graphs (KGs) such as DBpedia (Lehmann et al. 2014), Freebase (Bollacker et al. 2008), and others. While knowledge graphs are designed to support a wide set of diferent applications, this special issue focuses on the use case of text retrieval and analysis. Utilizing knowledge graphs for text analysis requires efective alignment techniques that associate segments of unstructured text with entries in the knowledge graph, for example using entity extraction and linking algorithms (Carmel et al. 2014; Mendes et al. 2011; Blanco et al. 2015). A wide range of approaches that combine query-document representations and  machine learning repeatedly demonstrate signifcant improvements for such tasks across diverse domains (Dalton et al. 2014; Liu and Fang 2015; Hasibi et al. 2015; Xiong and Callan 2015; Raviv et al. 2016; Ensan and Bagheri 2017; Xiong et al. 2017). The goal of this special issue is to summarize recent progress in research and practice in constructing, grounding, and utilizing knowledge graphs and similar semantic resources for text retrieval and analysis applications. The scope includes acquisition, alignment, and utilization of knowledge graphs and other semantic resources for the purpose of optimizing end-to-end performance of information retrieval systems.
Plug-and-play approach to class-adapted blind image deblurring

Most of the existing single-image blind deblurring methods are tailored for natural images. However, in many important applications (e.g., document analysis, forensics), the image being recovered belongs to a specific class (e.g., text, faces, fingerprints) or contains two or more classes. To deal with these images, we propose a class-adapted blind deblurring framework, based on the plug-and-play scheme, which allows forward models of imaging systems to be combined with state-of-the-art denoisers. We consider three patch-based denoisers, two suitable for images that belong to a specific class and a general purpose one. Additionally, for images with two or more classes, we propose two approaches: a direct one, and one that uses a patch classification step before denoising. The proposed deblurring framework includes two priors on the blurring filter: a sparsity-inducing prior, suitable for motion blur and a weak prior, for a variety of filters. The results show the state-of-the-art performance of the proposed framework when applied to images that belong to a specific class (text, face, fingerprints), or contain two classes (text and face). For images with two classes, we show that the deblurring performance is improved by using the classification step. For these images, we choose to test one instance of the proposed framework suitable for text and faces, which is a natural test ground for the proposed framework. With the proper (dictionary and/or classifier) learning procedure, the framework can be adapted to other problems. For text images, we show that, in most cases, the proposed deblurring framework improves OCR accuracy.
One-Shot Randomized and Nonrandomized Partial Decoupling

We introduce a task that we call partial decoupling, in which a bipartite quantum state is transformed by a unitary operation on one of the two subsystems and then is subject to the action of a quantum channel. We assume that the subsystem is decomposed into a direct-sum-product form, which often appears in the context of quantum information theory. The unitary is chosen at random from the set of unitaries having a simple form under the decomposition. The goal of the task is to make the final state, for typical choices of the unitary, close to the averaged final state over the unitaries. We consider a one-shot scenario, and derive upper and lower bounds on the average distance between the two states. The bounds are represented simply in terms of smooth conditional entropies of quantum states involving the initial state, the channel and the decomposition. Thereby we provide generalizations of the one-shot decoupling theorem. The obtained result would lead to further development of the decoupling approaches in quantum information theory and fundamental physics.
Coagmento v3.0

As understanding of web search behavior grows, researchers rapidly develop new study designs to capture and understand search behavior. Researchers have restricted time in which to design a study, develop a collection tool, collect data, analyze it, and report new insights. In particular, sufficient time and development skills are often required to create a tool that meets the needs of any particular web search behavior study. Coagmento is a tool that is developed for facilitating many of the needs for designing and running a lab study, from executing a session flow to collecting log data. By streamlining the programming of unique parts for a specific study, Coagmento helps researchers tailor various parts of running a user study, lowering the barrier for designing and conducting lab study experiments. One-click interactions with a graphical user interface permit researchers to operate through a web-based administrative service to generate stages, search tasks, and questionnaires for their interactive information retrieval studies. In this demonstration, Coagmento provides a solution to increase efficiency in the production of laboratory experiments for web search behavior.
Implementation and comparison of the functions of building blocks in SHA-2 family used in secured cloud applications

A Secure hash algorithm is grounded on the principles of message digest algorithms that are used in the design of the algorithms like MD4 and MD5. The main application of this algorithm is that it can be used in security applications for authentication and data validation like message authentication, digital signature and fingerprinting. It can also be used in detecting duplicate data and uniquely identifying files. In this paper we analyzed the role of some of the building blocks of SHA-2 family .We have demonstrated that the disturbance correction strategy is applicable to the SHA-512 architecture and proved that functions Σ, ∂ are vital for the security of SHA-512, without them it is possible to find collisions with complexity 264 hash operations. We also demonstrated the functioning of 256- bit which was previously designed for 32 bit architecture but in this paper it is implemented using 64- bit architecture.
Positive-partial-transpose distinguishability for lattice-type maximally entangled states

We study the distinguishability of a particular type of maximally entangled states -- the "lattice states" using a new approach of semidefinite program. With this, we successfully construct all sets of four ququad-ququad orthogonal maximally entangled states that are locally indistinguishable and find some curious sets of six states having interesting property of distinguishability. Also, some of the problems arose from \cite{CosentinoR14} about the PPT-distinguishability of "lattice" maximally entangled states can be answered.
Real Scale Augmented Reality. A Novel Paradigm for Archaeological Heritage Fruition

3D contents have great potential in improving the communication and fruition of Cultural Heritage (CH). The visiting experience on an archaeological site or historical building can be improved by digital contents that help visitors to discover and learn how they once appeared. Augmented Reality (AR) is one of the technologies nowadays used for CH exploitation and it has the great quality of superimposing digital contents on elements of the reality. This paper shows a new interactive application on the archaeological site of Forum Sempronii: thanks to SLAM technology it allows the visitors to see several virtual reconstructions superimposed on the ruins walking around the site, so that they can see how anciently the roman city was. Great attention has been given to the creation of 3D contents: after the creation of high poly models they have been decimated and optimized in terms of number of polygons and textures in order to be fluently managed on mobile devices. The fruition of real scale contents on the real context increases immersive users' experiences.