Reviewerly/exHarmony huggingface dataset:
Lossy Compression of Remote Sensing Images with Controllable Distortions

In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered.It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters.The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible.Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image.It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data.The proposed methods are verified for test and real life images.
meChat: In-Device Personal Assistant for Conversational Photo Sharing

It is still challenging to search in-device photos without the sacrifice of privacy or poor latency, while photos have been an essential medium of the communication in messaging applications. In this paper, we propose a novel in-device personal assistant for conversational photo sharing, called meChat. By understanding the user's conversations and in-device photos, meChat intelligently searches in-device photos that are semantically relevant to the conversation context. Notably, meChat works in a stand-alone, privacy-protecting manner without sending out any in-device personal content. The scenario-based user studies show that meChat efficiently searches highly relevant in-device photos with low perceived latency and energy consumption.
Discovering Latent Topics With Saliency-Weighted LDA for Image Scene Understanding

In the latent Dirichlet allocation (LDA) model, each image is represented by word distributions with their latent topics. Since the previous LDA-based models are not capable of dealing with the spatial information of visual words in images, this paper focuses on discovering the latent topics of images with visual saliency. To accomplish this, a saliency-weighted LDA (swLDA) model is proposed that incorporates visual saliency into the topic distribution of visual words, in a manner similar to human perception. The topic distributions of the visual words were learned with saliency weights reflecting whether the visual words were in the salient or nonsalient regions. The experimental results demonstrate that the swLDA model effectively incorporates visual saliency as a focus of attention, mimicking human perception behavior, remarkably outperforming previous LDA models in terms of image categorization.
Bayesian approach to multiple extended targets tracking with random hypersurface models

A Bayesian approach to multiple moving extended targets tracking is proposed for estimating the shape approximation of the extended targets in addition to their kinematics. Within this approach, the extended target extensions are modelled with random hypersurface models, and a new variant of probabilistic multi-hypothesis tracking is used for modelling assignments of measurements to extended targets. Moreover, an approximate measurement update that arises directly from the analytical techniques of the variational Bayesian framework is derived to simultaneously estimate the posterior states iteratively including the shape and kinematics of each extended target. The performance of the proposed algorithm is demonstrated with simulated data.
Use of Online Public Access Catalogue (OPAC) By Undergraduates in Selected Nigerian Universities

This article examines the use of the online public access catalogue (OPAC) among undergraduates in selected universities in Nigeria. A sample was drawn from among undergraduate students in five selected Universities. Survey design was adopted in the conduct of the study while data was collected through a self-designed/administered questionnaire. A total of 2,240 undergraduates were initially sampled and administered the questionnaire, but only two thousand copies were properly filled in and good for data analysis representing an 89.3% return rate. This 2000 represent the sample for the study. The results revealed that the majority of undergraduate students used the OPAC on a weekly basis while the majority of respondents (57.5%) spent fewer hours (between 0-3) using the OPAC. Places of accessing the OPAC identified include the school library, respondents' homes, cyber cafés and lecture rooms. The major uses of the OPAC by the undergraduate students include: to locate books and other materials; and to find non-print materials.
Super-resolution of single multi-color image with guided filter

Super-resolution reconstruction is a method that can transcend the limitations of optical imaging systems through the use of image processing algorithms. Recent techniques of super-resolution for single monochrome images develop rapidly, but for single multi-color images, to efficiently apply the monochrome super-resolution algorithms to all channels is still under exploration. In most of the recent research, the chromatic channels are simply upscaled by interpolation, which leads to the quality of the chromatic channels downgraded. This application may not be noticed by the visual systems of humans, but can affect other algorithms when super-resolution plays roles at image pre-processing. In this paper, we present a novel approach for multi-color super-resolution reconstruction. Using the super-resolution reconstructed luminance channel as the guide image, we adopt guided filters to manage the interpolated chromatic channels. Guided filters retain the sharp edges and fine details from the guided image and carry them to the output images. Meanwhile the whole process is quite computationally economic. Extensive experiments on natural images show that our method achieves better results than the method that is used in most of the algorithm in the literature in both statistic and visual aspects.
Using Expectation-Maximization for exposing image forgeries by revealing inconsistencies in shadow geometry

In this study, a new approach and mathematical framework are proposed for exposing image forgeries by detecting inconsistencies in the geometry of cast shadows. The main difficulty in detecting shadow inconsistencies is the precise establishment of correspondences between object points and their corresponding shadow points. To counter the problem, a mathematical framework is proposed to formulate the geometric transformation between the object points and their corresponding shadow points. We assume a rough correspondence between the object and shadow points and use Expectation-Maximization (EM) algorithm to simultaneously calculate the transformation parameters and categorize rough correspondences as inliers or outliers. To enhance the efficiency of the proposed algorithm, we extend the proposed algorithm to handle the ambiguity in initial correspondence by using the one-to-many correspondence strategy. Experimental results on the provided database comprising forged and authentic images showed the accuracy of 84% and 98% for one-to-one and one-to-many correspondence strategies, respectively.
New second‐order threshold implementation of AES

In this work, the authors propose some alternative hardware efficient masking schemes dedicated to protect the Advanced Encryption Standard (AES) against higher order differential power analysis (DPA). In general, the existing masking schemes all have in common an intrinsic trade-off between the two main parameters of interest, namely the generation of fresh random masking values and the cost of hardware implementation. The design of efficient masking schemes which are non-expensive in both aspects appears to be a difficult task. In this study, the authors propose a second-order threshold implementation of AES, which is characterised by a beneficial trade-off between the two parameters. More precisely, compared to the masking scheme of De Cnudde et al. at CHES 2016, which currently attains the best practical trade-off, the proposed masking scheme requires 28.4% less random masking bits, whereas the implementation cost is slightly increased for about 13.7% (thus the chip area is 1.4 kGE larger). This masking scheme has been used to implement AES on an field-programmable gate array (FPGA) platform and its resistance against the second-order DPA in a simulated attack environment has been confirmed.
A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.
New adaption based mutation operator on differential evolution algorithm

Among meta-heuristics algorithms, differential evolution (DE) is powerful nature-inspired algorithm used to solve the nonlinear problems. However, at higher generations there is an increase in the computational cost. In this paper, a new approach has been proposed from a new adaption based mutation operator in which the variations of a particular element is kept constant. In the same way, to keep an element constant called as “diversity” in DE, new adaption based mutation operator has been incorporated. The proposed variants are proliferated new adaption based operator with one more vector, named as new adaption based mutation operator in the existing mutation vector to provide more diversity for selecting effective mutant solutions. The proposed approach provides more promising solutions to explorer the evolution and helps DE evade the circumstance of stagnation. Comparisons with other DE variants such as CPI-DE, TSDE, ToPDE, MPEDE and JADEcr establishes that the proposed new adaption based mutation operator is able to improve the performance of DE.
Theoretical Foundations for Design of a Quantum Wigner Interferometer

In this paper, we discuss and analyze a new quantum-based approach for gravimetry. The key feature of this design is that it measures effects of gravitation on information encoded in qubits in a way that can provide resolution beyond the de Broglie limit of atom-interferometric gravimeters. We show that it also offers an advantage over current state-of-the-art gravimeters in its ability to detect quadrupole field anomalies. This can potentially facilitate applications relating to search and recovery, e.g., locating a submerged aircraft on the ocean floor, based on the difference between the specific quadrupole signature of the object of interest and that of other objects in the environment.
QPSO with avoidance behaviour to solve electromagnetic optimization problems

The paper proposes a new set of improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by using the avoidance from the worst behaviour. The avoidance behaviour, which consist in introducing personal/local or global worst terms, are applied to the attractor’s formula. The enhanced QP SO algorithms are targeted towards benchmark electromagnetic optimization problems, and their efficiency is tested and tuned on Loney’s solenoid.
PeQASO: Perceptual Quality Assessment of Streamed Videos Using Optical Flow Features

In this paper, we introduce PeQASO, a perceptual quality assessment framework for streamed videos using optical flow features. This approach is a reduced-reference pixel-based and relies only on the deviation of the optical flow of the corrupted frames. This technique compares an optical flow descriptor from the received frame against the descriptor obtained from the anchor frame. This approach is suitable for videos with complex motion patterns. Our technique does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. In this paper, we consider both sources of artifacts and distortions in streaming, including compression artifacts. We validate our proposed metric by testing it on a variety of distorted sequences from three proposed and commonly utilized video quality assessment databases. Our results show that our metric estimates the perceptual quality at the sequence level accurately. We report the correlation coefficients with the differential mean opinion scores reported in the database. For compression artifacts, the results show Spearman's and Pearson's correlations of 0.96 and 0.94 for all the tested sequences, respectively. For channel-induced distortion, the results show Spearman's and Pearson's correlations of 0.88 and 0.89, respectively. For all other distortions, the average Spearman's and Pearson's correlations are 0.82 and 0.79, respectively.
Context-aware long-term correlation tracking with hierarchical convolutional features

Visual tracking is of great significance in computer vision. Utilization of efficient correlation filters and discriminative convolutional features has gained much attention. In this paper, we propose a long–term tracking method (LHCAT) to deal with appearance variation caused by occlusion, deformation, out–of–view, etc. Firstly, we integrate context features into correlation filters by sampling surrounding patches around the target. Secondly, we reformulate the HCFT tracker with modified correlation filters. Then a HOG based scale correlation filter is applied to search target pyramids cropped around target position for optimal scale. In case of tracking failure and heavy occlusion, we train a SVM based module to re–detect the target. Additionally, for computational efficiency, proper strategy is proposed to determine when to activate the re–detector and update correlation filters. Experimental results on OTB–50 and OTB–100 show that our algorithm is of effectiveness and robustness in case of heavy appearance changes.
Model-Based Machine Learning for Transportation

This chapter describes the Model-Based approach to Machine Learning (MBML). When faced with a modeling problem, the first step in MBML is to formulate the uncertainty structure of the problem as a probabilistic model. Then, given observed evidence, the next step is to update the distribution of unknown parameters in the model accordingly. We rigorously define these steps, and demonstrate the advantages of MBML through several case studies in the transport domain
Monotonicity of skew information and its applications in quantum resource theory

An alternative proof about the monotonicity of skew information via operator algebra approach is formulated. Furthermore, the strong monotonicity of skew information under particular quantum Trace-Preserving and Completely Positive maps is confirmed. A family of new quantum resource measures is introduced if the resource can be characterized by a resource destroying map and the free operation should be also modified. The proposed measure is easy-calculating and applicable to the quantum coherence resource theory as well as the quantum asymmetry theory. The operational interpretation needs to be further investigated.
Qubit positive-operator-valued measurements by destructive weak measurements

Many quantum measurements, such as photodetection, can be destructive. In photodetection, when the detector ``clicks'' a photon has been absorbed and destroyed. Yet the lack of a click also gives information about the presence or absence of a photon. In monitoring the emission of photons from a source, one decomposes the strong measurement into a series of weak measurements, which describe the evolution of the state during the measurement process. Motivated by this example of destructive photon detection, a simple model of destructive weak measurements using qubits was studied in earlier work by the authors. It has shown that the model can achieve any positive-operator-valued measurement (POVM) with commuting POVM elements including projective measurements. In this paper, we use a different approach for decomposing any POVM into a series of weak measurements. The process involves three steps: randomly choose, with certain probabilities, a set of linearly independent POVM elements; perform that POVM by a series of destructive weak measurements; and output the result of the series of weak measurements with certain probabilities. The probabilities of the outcomes from this process agree with those from the original POVM, and hence this model of destructive weak measurements can perform any qubit POVM.
A robust non-local total-variation based image registration method under illumination changes in medical applications

Independence of neighboring pixels and image stationarity are major concepts in conventional similarity metrics, used in image registration tasks. The accuracy of image registration decreases due to the presence of spatially varying intensity distortion in images. In this study, we hypothesized that changes in image illumination have limited total variation (TV). Accordingly, we introduced a similarity metric by reducing the weighted TV in the residual image. The primal dual method was then chosen to solve the proposed registration problem. The efficiency of the proposed method was compared to conventional methods, including the residual complexity (RC) method, the robust Huber similarity measure (RHSM), and the local linear reconstruction method (LLRM) which have been very successful in this field. The efficacy of the proposed method was confirmed by experimental findings on real-world and synthetic images.
Representational dynamics in the domain of iterated mental paper folding

Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them.
Geological mapping of the Central Cairo-Suez District of Egypt, using space-borne optical and radar dataset

The Central Cairo-Suez District (CCSD) has a special urban extension, particularly the fast growth of the new Cairo and New Administration Capital of Egypt (NACE). Accurate discrimination of the lithology and surface and near-surface structures will be a valuable contribution to the geotechnical characterization of the foundation rocks and geohazards. The present work utilizes remote sensing datasets; including Phased Array L-band Synthetic Aperture Radar (PALSAR-2) scenes, Landsat-8 OLI/TIRS data and high-resolution images of Google Earth accompanied with field work to update the geological map and highlighting the information on the spatial lithological distribution and the faulting history of the CCSD. Radar data is utilized to make use of its penetration capabilities to image the possible near-surface structures and rock contacts. The PALSAR-2 data improved the surface and shallow structural imaging around Gebel Oweibed and in other selected subsets. The NW-striking shear faults in Quaternary sediments were recognized for the first time. Their recent activation has been proved by the recent seismic events. The False Color Composites (FCC), Band ratios (BR), Decorrelation Stretch (DS) image processing techniques have been successfully applied to differentiate between sandstones, shales, marls, carbonates, basalts, and gravels which represent eight lithological units of Middle Eocene to Quaternary time. The results were enhanced and mapped by supervised classification with accuracy assessment exceeding 88%. Integration of remote sensing data, previous mapping, and field check resulted in a more accurate mapping of the study area with new important modifications.
Weakly Supervised Deep Depth Prediction Leveraging Ground Control Points for Guidance

Despite the tremendous progress made in learning-based depth prediction, most methods rely heavily on large amounts of dense ground-truth depth data for training. To solve the tradeoff between the labeling cost and precision, we propose a novel weakly supervised approach, namely, the Guided-Net, by incorporating robust ground control points for guidance. By exploiting the guidance from ground control points, disparity edge gradients, and image appearance constraints, our improved network with deformable convolutional layers is empowered to learn in a more efficient way. The experiments on the KITTI, Cityscapes, and Make3D datasets demonstrate that the proposed method yields a performance superior to that of the existing weakly supervised approaches and achieves results comparable to those of the semisupervised and supervised frameworks.
An Evaluation of Methods for Assessing the Competence of Laboratories Based on Performance in the Geo<i>PT</i> Proficiency Testing Scheme

The original certification protocol, published by the International Association of Geoanalysts in 2003, specified that the competence of laboratories selected as competent to contribute certification measurements should be evaluated from their performance in the Geo PT proficiency testing programme. Round 39 of the IAG Geo PT proficiency testing programme provided an opportunity to examine four methods of evaluating laboratory competence based largely on the use of proficiency testing z ‐scores as performance indicators. This opportunity arose because two test materials were co‐analysed by participating laboratories in this round: a syenite, SyMP‐1, supplied by the USGS, and an established CRM, the nepheline syenite, CGL 006. The performance of laboratories was assessed in four ways; in each case, consensus values and their uncertainties as derived from selective data sets of competent laboratories were compared with results derived from the routine Geo PT data assessment, involving all submitted measurements. An overall comparison of results showed no significant statistical differences in either consensus values or uncertainties between these data sets. This conclusion was unexpected and calls into question the widely held assumption that ‘better’ consensus data would be obtained from a subset of laboratories judged to be competent on the basis of proficiency testing performance indicators.