Reviewerly/exHarmony huggingface dataset:
An argumentation-based approach for identifying and dealing with incompatibilities among procedural goals

During the first step of practical reasoning, i.e. deliberation, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibilities. The suggested approach considers the three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal incompatibility, the instrumental or resources incompatibility and the superfluity. We characterize computationally these forms of incompatibility by means of arguments that represent the plans that allow an agent to achieve his goals. Thus, the incompatibility among goals is defined based on the conflicts among their plans, which are represented by means of attacks in an argumentation framework. We also work on the problem of goals selection; we propose to use abstract argumentation theory to deal with this problem, i.e. by applying argumentation semantics. We use a modified version of the “cleaner world” scenario in order to illustrate the performance of our proposal.
Co-Recognition of Multiple Fingertips for Tabletop Human–Projector Interaction

We present a depth-based fingertip recognition method for interactive projectors. We use a depth camera attached to a projector, so it is possible to change the relative pose between the projector and the projection surface without manual recalibration. For detection and classification of fingertips, we propose using cascaded random forests boosted by our 3-D pose-normalized pixel-difference features. The ensemble probabilities from the cascaded random forests are used to define a score function of a subset of detected fingertips. By finding the subset maximizing the score function, the fingertips in the subset are correctly classified, and the remaining incorrectly detected fingertips are rejected. Experiments show that the proposed method outperforms conventional random forest and convolutional neural network classifiers. In addition, our developed applications show the advantage of the proposed method in assigning different roles to different fingers.
Efficient scheme for passive decoy-state reference-frame-independent quantum key distribution

Reference-frame-independent quantum key distribution (RFI-QKD) has been demonstrated to be reliable and useful both in theories and experiments, which is intrinsically robust against slowly varying reference frames. In this paper, we propose an efficient scheme of passive decoy-state RFI-QKD based on the parametric down-conversion source, where a beam splitter splits the idler pulses into four local detection events to improve the performance of RFI-QKD systems. In addition, we demonstrate the worst relative rotation of reference frames in our scheme. Simulation results show that our scheme can achieve good performance even at the worst-case scenario.
Scene Parsing Via Dense Recurrent Neural Networks With Attentional Selection

Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units. In this paper, we propose dense RNNs for scene labeling by exploring various long-range semantic dependencies among image units. Different from existing RNN based approaches, our dense RNNs are able to capture richer contextual dependencies for each image unit by enabling immediate connections between each pair of image units, which significantly enhances their discriminative power. Besides, to select relevant dependencies and meanwhile to restrain irrelevant ones for each unit from dense connections, we introduce an attention model into dense RNNs. The attention model allows automatically assigning more importance to helpful dependencies while less weight to unconcerned dependencies. Integrating with convolutional neural networks (CNNs), we develop an end-to-end scene labeling system. Extensive experiments on three large-scale benchmarks demonstrate that the proposed approach can improve the baselines by large margins and outperform other state-of-the-art algorithms.
Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts

Evolutionary algorithms, i.e., Genetic Programming (GP), have been successfully used for the task of classification, mainly because they are less likely to get stuck in the local optimum, can operate on chunks of data and allow to compute more solutions in parallel. Ensemble techniques are usually more accurate than component learners constituting the ensemble and can be built in an incremental way, improving flexibility, adapting to changes and maintaining part of the history present in the data. This paper proposes a framework based on a distributed GP ensemble algorithm for coping with different types of concept drift for the task of classification of large data streams. The framework is able to detect changes in a very efficient way using only a detection function based on the fractal dimension, which can also works on new incoming unclassified data. Thus, a distributed GP algorithm is performed only when a change is detected in order to improve classification accuracy and this, together with the exploitation of an adaptive procedure, permits to answer in short time to these changes. Experiments are conducted on a real and on some artificial datasets in order to assess the capacity of the framework to detect the drift and quickly respond to it.
Three Families of Monomial Functions With Three-Valued Walsh Spectrum

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Bbb F_{p}$ </tex-math></inline-formula> be a finite field with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> elements, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is a prime. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N \ge 2$ </tex-math></inline-formula> be an integer and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> be the least positive integer satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{d} \equiv -1 \pmod N$ </tex-math></inline-formula> . Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q = p^{2sd}$ </tex-math></inline-formula> for some integers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> . In some special cases, we obtain the explicit evaluation of the following exponential sums: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S(a,b)=\sum _{x\in \Bbb F_{q}^{*}}\zeta _{p}^{ \mathrm {Tr}_{q/p}(ax^{(({q-1})/{N})}+bx)}$ </tex-math></inline-formula> . As applications, Walsh spectrums of the monomial functions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {Tr}_{q/p}(x^{(({q-1})/{N})})$ </tex-math></inline-formula> in three cases are investigated. Our results show that Walsh spectrums of the monomial functions have at most four, five, or seven distinct values. Furthermore, three families of the monomial functions with three-valued Walsh spectrums are presented, see <xref ref-type="corollary" rid="corollary12" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Corollaries 12</xref> , <xref ref-type="corollary" rid="corollary21" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</xref> , <xref ref-type="corollary" rid="corollary31" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">31</xref> , and <xref ref-type="corollary" rid="corollary32" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</xref> . Consequently, certain previously known results by Li and Yue and Moisio are extended.
Regularization-based multi-frame super-resolution: A systematic review

High-resolution is generally required and preferred for producing more detailed information inside the digital images; therefore, this leads to improve the pictorial information for human analysis and interpretation and to enhance the automatic machine perception. However, the real imaging systems may introduce some degradation or artifacts in the digital images. These distortions in the images are caused by a variety of factors such as blurring, aliasing, and noise, which may affect the resolution of imaging systems and produce low-resolution images. Numerous strategies like frequency and spatial domain approaches have been proposed in the literature. Spatial domain approaches are classified as one of the most popular approaches and split into interpolation-based approaches and regularization-based approaches. Nevertheless, these techniques still suffer from artifacts. Regularization-based approaches are a challenging in image super-resolution in the last decade. This paper attempts to investigate the current regularization-based super-resolution approaches which are commonly used for reconstructing the high-resolution image in the last decade. Furthermore, the focus is given on highlighting the strengths and limitations of these approaches aiming at determining their effectiveness and quality in reconstructing high-resolution images.
Augmented unbiased minimum-variance input and state estimation for tracking a maneuvering satellite

The technique of tracking a maneuvering satellite is significantly important for Space Situation Awareness (SSA). Traditional Kalman filters (KF) cannot robustly track unknown maneuvers. Motivated by the problem of tracking a non-cooperative maneuvering satellite, an augmented unbiased minimum-variance input and state estimation (AUMVISE) method is developed for estimating the state and the maneuver acceleration in this study. The maneuver acceleration estimate of the proposed method is proven to be more accurate than the original unbiased minimum-variance input and state estimation (UMVISE) method. Approaches based on the measurement residuals and the acceleration estimates are developed for maneuver start and end detection. The filter is switched between the AUMVISE method and the classical extended Kalman filter (EKF) to obtain an accurate tracking result during both the maneuver period and the non-maneuver period. Simulation results show that the proposed method has a suitable maneuver detection delay and outperforms the UMVISE method in estimating the state and maneuver acceleration.
The constructions of non-continuous additive generators of t-conorms based on discrete additive generators of discrete t-conorms

The relation between discrete additive generators of discrete t-conorms (t-norms) and one class of non-continuous additive generators of t-conorms (t-norms) is analysed. The constructions of non-continuous additive generators of t-conorms (t-norms) based on discrete additive generators of discrete t-conorms (t-norms) are introduced.
3D sign language recognition using spatio temporal graph kernels

3D Sign language recognition is challenging from capturing to recognition. 3D signs are a set of spatio temporal variations of hands and fingers with respect to face, head and torso. 3D motion capture technology has enabled us to capture these complex 3D human motions preserving 95% of the visual information required for recognition. A twin motion algorithm is proposed to recognize 3D signs with variable motion joints. Variable motions in joints arise duo to non-uniform distances between the joints. For example, finger motions are different from hand motions. A common measure to extract motion features from 3D skeletal data is relative range of joint relative distance (RRJRD). However, relative range of joint relative distance cannot quantify all the relative joint motions for characterizing a sign because of the different in motion ranges between different body parts used in defining a sign. Hence, we propose a wide RRJRD and narrow RRJRD based characterization to project the motion features on to graph. Each sign is characterized by a set of spatio temporal projections on to a constructed sign graph. The experiment results show that the proposed method is signer invariant, motion invariant and faster compared to state-of-the-art graph kernel methods.
Modified inferential POD/ML for data-driven inverse procedure of steam reformer for 5-kW HT-PEMFC

In this work, we applied and evaluated modified inferential proper orthogonal decomposition (POD)/machine learning (ML) to a steam reformer for 5-kW high-temperature proton-exchange membrane fuel cells (HT-PEMFC) involving heterogeneous chemical reactions, combustion, and fluid flow. The number of snapshots is limited by the inverse problem of a steam reformer yielding an intractable computational burden, and a limited number of snapshots and modes can yield unfavorable POD subspace projection results. In order to solve this problem, characteristic vectors are derived from the residual after POD projection and employed to the feature. We analyzed the details and distribution of the characteristic vector and investigated the extent of its influence on the inferential POD. Consequently, inferential POD/ML is improved by adding the characteristic vector of observation to the feature for ML.
T-DCORAL: A Threshold-Based Dynamic Controller Resource Allocation for Elastic Control Plane in Software-Defined Data Center Networks

The existing elastic control planes (ECPs) suffer from the immediacy and the computing overhead issues in software-defined data center networks (SD-DCNs). In this letter, we propose T-DCORAL which is a new ECP to mitigate both issues in SD-DCNs. T-DCORAL accelerates/decelerates the control plane by allocating a virtual CPU to controllers in runtime, whereas the existing ECPs resize the controller pool. As a result, T-DCORAL maximally reduces the latency to adjust the control plane from 46 s to 38 ms, the average CPU load by 22%, and the average rule installation time by 64.28%.
Sequence Pattern Mining with Variables

Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that the relevant differences between items are captured through creating distinct items. In some domains, this leads to an exponential increase in the number of items. This paper presents a new SPM, Sequence Mining of Temporal Clusters (SMTC), that allows item differentiation through attribute variables for domains with large numbers of items. It also provides a new technique for addressing interleaving, a phenomena that occurs when two sequences occur simultaneously resulting in their items alternating. By first clustering items temporally and only focusing on sequences after the temporal clusters are established, it sidesteps the traditional interleaving issues. SMTC is evaluated on a digital forensics dataset, a domain with a large number of items and frequent interleaving. Its results are compared with Discontinuous Varied Order Sequence Mining (DVSM) with variables added (DVSM-V). By adding variables, both algorithms reduce the data by 96 percent, and identify 100 percent of the events while keeping the false positive rate below 0.03 percent. SMTC mines the data in 20 percent of the time it takes DVSM-V and provides a lower false positive rate even at higher similarity thresholds.
An Adaptive Triangular Partition Algorithm for Digital Images

The partition algorithm as a digital image processing technique is significant to many applications, such as data encryption, image denoising, and 3-D reconstruction. In order to achieve well partition that can availably reduce the distortion phenomenon, a novel approach named image adaptive triangular partition (IATP) is proposed, which considers the grayscale distribution of the image and removes the shared edges between the adjacent triangles in the partition mesh. The least-squares method is used to fit the sampled position-associated gray value of the image to determine whether further partition should be performed, that is, if the sum of squared residuals is bigger than the preselected control value, the current area will be divided into four separated sub-triangles by using the self-similar method, and then preparing the next fitting on each of them in recursion; otherwise, the terminal operation is reached. When the recursive partition of the image is done, the triangular partition mesh with the quaternary notations is obtained. The experimental results demonstrate that the performance of the IATP algorithm proposed in this paper is better than the existing state-of-the-art nonuniform partitions, and it solves the redundant coding problem and reduces the image quality losses. In addition, two applications-image steganography and information encryption-are selected to verify that the proposed algorithm has good feasibility and robustness.
Nonexistence results on generalized bent functions <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msubsup><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msubsup><mml:mo stretchy="false">→</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math> with odd m…

Let p be an odd prime, let a be a positive integer, let m be an odd positive integer, and suppose that a generalized bent function from Z 2 p a m to Z 2 p a exists. We show that this implies m ≠ 1 , p ≤ 2 2 m + 2 m + 1 , and ord p ( 2 ) ≤ 2 m − 1 . We obtain further necessary conditions and prove that p = 7 if m = 3 and p ∈ { 7 , 23 , 31 , 73 , 89 } if m = 5 . Our results are based on new tools for the investigation of cyclotomic integers of prescribed complex modulus, including “minimal aliases” invariant under automorphisms, and bounds on the ℓ 2 -norms of their coefficient vectors. These methods have further applications, for instance, to relative difference sets, circulant Butson matrices, and other kinds of bent functions.
Lifelog retrieval for memory stimulation of people with memory impairment

Recent advances in lifelogging, mainly due to fast development of wearable cameras, made it possible to continuously capture moments from our life from a first-person point of view. Extracting and re-experimenting moments illustrated by autobiographic images is of special interest in order to stimulate episodic memory of patients with neurodegenerative diseases (Alzheimer, mild cognitive impairment, etc.). Using a wearable camera, it is possible to generate a huge amount of images captured on a daily basis (around 2,000 images per day on a 30 s time-lapse mode). Since not all images obtained are valuable and semantically rich, there is a need for efficient and scalable techniques to separate the wheat from the chaff, that is. to extract egocentric images that are semantically rich enough and not redundant in order to use them for memory stimulation. By using state-of-the-art retrieval systems based on convolutional neural network features obtained from these rich, filtered egocentric images, we show how to cope with those requirements and apply the filtered images within a memory stimulation program specially developed to improve memory of patients with Mild Cognitive Impairment.
Contrast enhancement by multi-level histogram shape segmentation with adaptive detail enhancement for noise suppression

The usual problems associated with image enhancement include over- and under-enhancement, halo effects at edges and the degradation of the signal-to-noise ratio as the enhancement of details increases. Some of those problems manifest in the background and some in the details of the enhanced image. Our proposed method is to apply different techniques to enhance the background and details separately. For enhancement of the image background, a novel multi-level histogram shape segmentation method which will detect abrupt changes in the histogram is proposed so that regions of intensity values with a similar frequency of occurrence are segmented for individual equalization to avoid over-enhancement. For detail enhancement, a novel adaptive median based enhancement method is applied to the details to avoid over- and under-enhancement while suppressing noise by limiting the degree of enhancement in homogeneous regions. Halo effects due to the over-enhancement of edges are avoided in our proposed method by using an edge preserving filter for the separation of the background and details so that edges are excluded from detail enhancement. It has been shown that our proposed method is able to avoid the usual adverse problems of image enhancement while producing adequate overall enhancement.
Detection of alteration zones using hyperspectral remote sensing data from Dapingliang skarn copper deposit and its surrounding area, Shanshan County, Xinjiang Uygur autonomous region, China

In application of hyperspectral remote sensing, alteration zones are primarily detected by identifying alteration mineral assemblages, but the interpretation of alteration mineral maps is often complicated by surface materials and by minerals not directly associated with alteration. This study was conducted in the Dapingliang skarn copper deposit and its surrounding area, the Shanshan County of the Xinjiang Uygur autonomous region, China. In order to successfully detect alteration zones associated with skarns, this study identified skarns rather than alteration minerals using field spectra of skarn outcrops. In this study, skarn in pixels was identified from spectral overall shape and spectral shapes of absorption-bands; SAM (spectral angle mapper) was applied in the identification. When SAM scores of spectral overall shape were less than 0.022 rad, the identified skarns were mainly distributed in the contact zones of intrusive rocks and carbonates; in particular, the three identified skarns areas (R1, R2 and R3) were consistent with the skarn areas in the geological map of the study area. The field inspection of skarns showed that the identified objects of the three marked targets (A, B and C) were almost consistent with the ground objects. These obtained results demonstrated that using the field spectra, it was possible to identify skarns in the hyperspectral image. To evaluate the identified skarn zones for use in mineral exploration, the end-member spectra extracted from the image were analysed, and alteration zones were detected using these end-member spectra. Compared with these alteration zones, the identified skarn zones were more reliable for mineral exploration of the study area.