Reviewerly/exHarmony huggingface dataset:
Telepresence and the Role of the Senses

The telepresence experience can be evoked in a number of ways. A well-known example is a player of videogames who reports about a telepresence experience, a subjective experience of being in one place or environment, even when physically situated in another place. In this paper we set the phenomenon of telepresence into a theoretical framework. As people react subjectively to stimuli from telepresence, empirical studies can give more evidence about the phenomenon. Thus, our contribution is to bridge the theoretical with the empirical. We discuss theories of perception with an emphasis on Heidegger, Merleau-Ponty and Gibson, the role of the senses and the Spinozian belief procedure. The aim is to contribute to our understanding of this phenomenon. A telepresence-study that included the affordance concept is used to empirically study how players report sense-reactions to virtual sightseeing in two cities. We investigate and explore the interplay of the philosophical and the empirical. The findings indicate that it is not only the visual sense that plays a role in this experience, but all senses.
Computational Interpretations of Classical Reasoning: From the Epsilon Calculus to Stateful Programs

The problem of giving a computational meaning to classical reasoning lies at the heart of logic. This article surveys three famous solutions to this problem – the epsilon calculus, modified realizability and the Dialectica interpretation – and re-examines them from a modern perspective, with a particular emphasis on connections with algorithms and programming.
Data Replication Impact on DDBS System Performance

In distributed database systems (DDBS), the utmost purpose of data distribution and replication aims at shrinking transmission costs (TC), including communication costs, and response time. In this chapter, therefore, an enhanced heuristic clustering-based technique for data fragmentation and replicated based allocation is efficaciously presented. This work is mainly sought to further enhance an existing technique so TC is to be significantly minimized. In fact, the approached enhancement is applied by suggesting different replication scenarios. Off these scenarios, one scenario is to be selected based on competitive performance evaluation process. DDBS performance is measured via its being exposed on objective function (TC). Despite the fact that this work is mildly improved, yet evaluation results show that it has been promising, particularly as TC being the foremost design objective of DDBS System. Experimental results have been analyzed under all presented scenarios as an internal evaluation and are vividly provided to demonstrate the undeniable impact of data replication on DDBS performance.
An Empiric-Stochastic Approach, Based on Normalization Parameters, to Simulate Solar Irradiance

This paper presents a methodology to estimate solar irradiance using an empiric-stochastic approach, which is based on the computation of normalization parameters from the solar irradiance data. For this study, the solar irradiance data were collected in a weather station during a year. Posttreatment included a trimmed moving average to smooth the data, the performance of a fitting procedure using a simple model to recover normalization parameters, and the estimation of a probability density, which evolves along the daytime, by means of a kernel density estimation method. The normalization parameters correspond to characteristic physical variables that allow us to decouple the short- and long-term behaviors of solar irradiance and to describe their average trends with simple equations. The normalization parameters and the probability densities allowed us to build an empiric-stochastic methodology that generates an estimate of the solar irradiance. Finally, in order to validate our method, we had run simulations of solar irradiance and afterward computed the theoretical generation of solar power, which in turn had been compared with the experimental data retrieved from a commercial photovoltaic system. Since the simulation results show a good agreement with the experimental data, this simple methodology can generate the synthetic data of solar power production and may help to design and test a photovoltaic system before installation.
Real-time state estimation and feedback control of an oscillating qubit via <i>self-fulfilling prophecy</i> (SFP)

Copyright: IOP Publishing. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. The definitive version of the work is published at https://iopscience.iop.org/article/10.1088/1681-7575/aaf7ad
Attacking Data Transforming Learners at Training Time

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, little is known about how to adapt attacks when the defender transforms data prior to model estimation. We consider the setting where the defender Bob first transforms the data then learns a model from the result; Alice, the attacker, perturbs Bob’s input data prior to him transforming it. We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression. This allows learning algorithms and data transformations to be paired and composed arbitrarily: attacks can be adapted through the use of the chain rule—analogous to backpropagation on neural network parameters—to compositional learning maps. Bestresponse attacks can be computed through matrix multiplications from a library of attack matrices for transformations and learners. Our treatment of linear regression extends state-ofthe-art attacks at training time, by permitting the attacker to affect both features and targets optimally and simultaneously. We explore several transformations broadly used across machine learning with a driving motivation for our work being autogressive modeling. There, Bob transforms a univariate time series into a matrix of observations and vector of target values which can then be fed into standard learners. Under this learning reduction, a perturbation from Alice to a single value of the time series affects features of several data points along with target values.
Prospects for strongly coupled atom-photon quantum nodes

We discuss the trapping of cold atoms within microscopic voids drilled perpendicularly through the axis of an optical waveguide. The dimensions of the voids considered are between 1 and 40 optical wavelengths. By simulating light transmission across the voids, we find that appropriate shaping of the voids can substantially reduce the associated loss of optical power. Our results demonstrate that the formation of an optical cavity around such a void could produce strong coupling between the atoms and the guided light. By bringing multiple atoms into a single void and exploiting collective enhancement, cooperativities ~400 or more should be achieveable. The simulations are carried out using a finite difference time domain method. Methods for the production of such a void and the trapping of cold atoms within it are also discussed.
Outlier Discrimination and Correction in Intelligent Transportation Systems

In order to choose the quickest route, drivers are used to checking the traffic conditions via navigation applications before going out. Malicious drivers can hack into intelligent transportation systems and implant misleading traffic information to build an imitatively congested path, so that they can monopolize the unobstructed path, and others would give up on such a “congested” path. This selfish behavior results in chaotic traffic conditions and reduces the efficiency of transportation systems. In addition traffic information is sometimes missing due to hardware heterogeneity and failure. So it is important to correct the outliers and reconstruct missing values in traffic information to guarantee an efficient transportation system. Nevertheless, it is challenging to detect outliers due to the fact that they are short-term, small in scale, and random. To tackle this challenge, we propose a novel method called outlier discrimination and correction (ODC), which takes advantage of the flow relationship between adjacent roads and compressive sensing theory to distinguish the misleading traffic information (i.e., outliers) and reconstruct the abnormal data. Extensive simulations have been conducted using real urban traffic datasets. Performance results demonstrate that ODC outperforms existing methods in terms of accuracy and time consumption.
Remote audit for large data-based algebraic signature DCT cloud storage

For large data cloud storage process, in order to improve data storage efficiency, a remote audit method for large data cloud storage based on algebraic signature divide and conquer table (DCT) is proposed. Firstly, an efficient remote audit scheme for cloud computing data storage based on algebraic signature is developed, it can mark the random block by signature, and it has the same data result as the corresponding block signature in the result generation. Secondly, a new divide-and-conquer table (DCT) data structure is designed, which can effectively delete and add dynamic data. It is very important to simplify the computational difficulty of the algorithm and achieve faster calculation. Finally, through the remote data audit operation of real files, and compared with the current more advanced data audit methods, the performance advantages of the algorithm in computation complexity are verified.
Cargo Cults in Information Systems Development: A Definition and an Analytical Framework

Organizations today adopt agile information systems development methods (ISDM), but many do not succeed with the adoption process and in achieving desired results. Systems developers sometimes fail in efficient use of ISDM, often due to a lack of understanding the fundamental intentions of the chosen method. In many cases organizations simply imitate the behavior of others without really understanding why. This conceptual paper defines this phenomenon as an ISDM cargo cult behavior and proposes an analytical framework to identify such situations. The concept of cargo cults originally comes from the field of social anthropology and has been used to explain irrational, ritualistic imitation of certain behavior. By defining and introducing the concept in the field of information systems development we provide a potential diagnostic tool to improve the understanding of one of the reasons why ISDM adoption sometimes fail.
Multidimensional possibility/probability domination for extending maxitive kernel based signal processing

Signal and image processing make intensive use of positive, bounded and centered functions that are called kernels. Kernels are used for defining the interplay between discrete and continuous domains, filtering, modeling a system through a point spread function, etc. The possible analogy between kernels and fuzzy sets has led to a wide use of fuzzy set theory for signal and image processing [1]. The possibilistic interpretation of fuzzy sets has recently been exploited to extend signal processing with the aim of accounting for poor knowledge of the appropriate kernel to be used. These imprecise kernels are called maxitive kernels. A maxitive kernel can be seen as a convex set of conventional kernels. Within this framework, the triangular kernel with mode 0 and spread Δ has a specific role since it can be used to represent a convex set of all bounded centered bell-shaped kernels of spread δ≤Δ, i.e. the way kernels are usually imprecisely known (shape unknown, spread imprecise). However, this principle has yet to be extended to more than one dimension despite the fact that it is needed for image processing. An extension to higher dimensions is proposed in this paper.
A deep generative directed network for scene depth ordering

In this paper, we present a Deep Generative Directed-Network (DGDN), which estimates the occlusion relationship of boundaries. Specially, we use a low-level segmentater to partition the image into regions, then estimate their occlusion relationship by two perceptual depth cues. We decompose our DGDN model into three sub-modules to extract regional appearance cue, edgel orientation cue and to further infer global occlusion relationship with these cues, respectively. Firstly, we predict regional scene depth by a upsampling deep dense network (DenseNet). Secondly, we simultaneously estimate edgel occlusion with logistic regression. However, the occlusion relationship always suffers from unexpected conflicts due to noisy regional and edgel cues. Therefore, we finally infer occlusion relationship in a Hidden Markov Field (HMF), which tackles conflicts with bi-direction inference and the HMF parameters are exploited by iterative EM-like procedure. Ablation experiments on NYUv2 and Make3D database prove that our DGDN model outperforms state-of-the-art methods.
Joint Color Space GMMs for CFA Demosaicking

We propose a patch-based algorithm for demosaicking a mosaicked color image produced by color filter arrays commonly used in acquiring color images. The proposed algorithm exploits a joint color space Gaussian mixture model (JCS-GMM) prior for jointly characterizing the patches from red, green, and blue channels of a color image. The inter channel correlations captured by the covariance matrices of Gaussian models are exploited to estimate the pixel values missing in the mosaicked image. The proposed JCS-GMM demosaicking algorithm can be seen as the GMM analogue of the Color-KSVD algorithm, which has produced impressive results in color image denoising and demosaicking. We demonstrate that our proposed algorithm achieves superior performance in the case of Kodak and Laurent Condat's databases, and competitive performance in the case of IMAX database, when compared with state-of-the-art demosaicking algorithms.
Direct generation of continuous-wave optical squeezing at 532 nm via degenerated parametric down-conversion

Optical squeezing is a powerful tool in quantum metrology for increasing the sensitivity of interferometries. We report in this paper the direct generation of optical squeezing at 532 nm, which with a shorter wavelength than the infrared counterpart could in principle further increase the sensitivity. The fundamental beam at 532 nm is first frequency doubled to 266 nm through a walk-off compensated Beta Barium Borate (BBO) crystal under critical phase matching. Then the second harmonic is downconverted back to the fundamental through a degenerated optical parametric amplifier (DOPA) containing a single bulk BBO crystal As a result, 0.4 ± 0.1 dB squeezing is directly generated at 3 MHz sideband on the 532 nm laser beam. The squeezing is now mainly limited by the poor spatial overlapping between the pump and seed beams in DOPA, and can hopefully be greatly promoted by using proper designed walk-off compensating crystal.
Weakly supervised segment annotation via expectation kernel density estimation

Since the labelling for the positive images/videos is ambiguous in weakly supervised segment annotation, negative mining‐based methods that only use the intra‐class information emerge. In these methods, negative instances are utilised to penalise unknown instances for ranking their likelihood of being an object, which can be considered as voting in terms of similarity. However, these methods (i) ignore the information contained in positive bags; (ii) only rank the likelihood but cannot generate an explicit decision function. In this study, the authors propose a voting scheme involving not only the definite negative instances but also the ambiguous positive instances to make use of the extra useful information in the weakly labelled positive bags. In the scheme, each instance votes for its label with a magnitude arising from the similarity, and the ambiguous positive instances are assigned soft labels that are iteratively updated during the voting. It overcomes the limitations of voting using only the negative bags. They also propose an expectation kernel density estimation algorithm to gain further insight into the voting mechanism. Experimental results demonstrate the superiority of the authors’ scheme beyond the baselines.
On elementary estimates of arithmetic sums for polynomial rings over finite fields

In this paper, a simple and elementary method is given for deriving estimates of sums of arithmetic functions in Fq[t]. The method is the function field analogue of a result first proved by Stefan A. Burr in 1973 in the number field case. A novelty of this paper is that we are able to extend Burr's result, in the function field context, and obtain secondary main terms for the appropriate sums involving the divisor functions dr(f) with an error term that improves the one given by Burr.
Possible Approaches for Character Recognition With Existing Methodologies and State-of-the-Art Techniques

The world started to talk about optical character recognition (OCR) around 1870. Then over another 25 years OCR systems were designed for industrial applications. And now the OCR software is easily available online for free, through products like Acrobat reader, WebOCR, etc. But still the research is on. Do we need to switch direction or introduce new hypothesis are some of the key questions? The purpose of this chapter is to answer the above questions and propose new methods for character recognition.
The Fundamentals of Medical Image Restoration

This chapter simply encapsulates the basics of image restoration, various noise models, and degradation model including some blur and image restoration filters. The mining of high resolution information from the low-resolution images is a very vital task in several applications of digital image processing. In recent times, a lot of research work has been carried out in this field in order to improve the resolution of real medical images especially when the given images are corrupted with some kind of noise. The displayed images are the result of the various stages that might cause imperfections in the digital images, for instance the so-called imaging and capturing process can itself degrade the original scene. The imperfections present in the image need to be studied and analyzed if the noise present in the images is not modelled properly. There are different types of degradations which are considered such as noise, geometrical degradations, imperfections (due to improper illumination and color), and blur. Blurring in the images is generally caused by the relative motion between the camera and the original object being captured or due to poor focusing of an optical system. In the production of aerial photographs for remote sensing purposes, blurs are introduced by the atmospheric turbulence, aberrations in the optical system, and relative motion between the camera and the ground. Apart from the blurring effect, noise also creates imperfections in the images that corrupt the images under analysis. The noise may be introduced by several factors (e.g., medium, recording or capturing system, or by the quantization process). Due to this noise or blur present in the images, resolution needs to be improved and the image is to be restored from the geometrically warped, blurred, and noisy images.
Implementing Argumentation-Enabled Empathic Agents

In a previous publication, we introduced the core concepts of empathic agents as agents that use a combination of utility-based and rule-based approaches to resolve conflicts when interacting with other agents in their environment. In this work, we implement proof-of-concept prototypes of empathic agents with the multi-agent systems development framework Jason and apply argumentation theory to extend the previously introduced concepts to account for inconsistencies between the beliefs of different agents. We then analyze the feasibility of different admissible set-based argumentation semantics to resolve these inconsistencies. As a result of the analysis, we identify the maximal ideal extension as the most feasible argumentation semantics for the problem in focus.
Simultaneous Calibration and Mapping

We present evaluation experiments of a hand-eye calibration and camera-camera calibration method, which is applicable to cases where classical calibration methods fail. As described in our earlier works, the calibration works by performing rotational movements with the robot and estimating the rotational axes by tracking fiducial markers or other static parts of the environment (if the camera is moved with the robot, as in this experiment; otherwise tracking the robot itself or markers on it with a static camera). We extend our earlier work by virtually increasing the field of view of the cameras by using a mapping approach. We compare our results with an extended classical approach for the challenging case of calibrating a compliant humanoid robot having cameras with non-overlapping fields of view.
A Tale of Location-Based User Authentication

The attitude towards passwords has drastically changed over the past years. Although they protected workstations from illicit access for decades, with todays increased computational power, simple passwords became easy targets for attacks, whereas complex passwords are difficult to remember for the users. It appears as if the classical password protection has become obsolete and has to give way to similarly secured schemes, which are seamless for users. Novel methodologies may be sound and secure from a technical point of view, their success will be challenged by the simple question whether a user feels secure or not. In this work, we propose a proximity based login and session locking scheme, based on bluetooth beacons. We describe the big data architecture required to implement secured location-based services in smart buildings. To round our contribution out, we describe a medium scale user study with 40 participants, conducted to answer the question: Do users feel secure?
Polygonal Approximation of Digital Planar Curve Using Novel Significant Measure

This chapter presents an iterative smoothing technique for polygonal approximation of digital image boundary. The technique starts with finest initial segmentation points of a curve. The contribution of initially segmented points toward preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation point that is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points. The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment. It not only identifies these situations but also computes its significant contribution for these situations differently. This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived. Moreover, the technique may find its application in parallel manipulators in detecting target boundary of an image with varying scale. The experimental results show that the proposed technique competes well with the state-of-the-art techniques.
A simpler lambda calculus

Closure calculus is simpler than pure lambda-calculus as it does not mention free variables or index manipulation, variable renaming, implicit substitution, or any other meta-theory. Further, all programs, even recursive ones, can be expressed as normal forms. Third, there are reduction-preserving translations to calculi built from combinations of operators, in the style of combinatory logic. These improvements are achieved without sacrificing three fundamental properties of lambda-calculus, being a confluent rewriting system, supporting the Turing computable numerical functions, and supporting simple typing.