Reviewerly/exHarmony huggingface dataset:
Image classification based on sparse coding multi-scale spatial latent semantic analysis

In the face of huge amounts of image data, how to let the computer simulate human cognition of images and automatically classify images into different semantic categories have become a key issue in image semantic analysis. Image classification is based on some attribute of the image, and it is divided into pre-set categories. For human beings, image classification is not difficult but there is a series of problems in using computers to classify images: (1) images contain a large amount of information, which is complex, diverse, and indescribable; and (2) there is a huge difference between the physical expression of images and the conceptual information known by human beings. The traditional sparse coding method loses the spatial information when classifying images. In this paper, spatial pyramid multi-partition method is used to add spatial information restriction to the feature. The proposed multi-scale spatial latent semantic analysis method based on sparse coding has higher average classification accuracy than many existing methods, which verifies its effectiveness and robustness. Experiments also show that the classification accuracy of this paper is 2.1% higher than that of sparse coding for image classification (ScSPM) and the classification performance is 3.1% higher than that of ScSPM when the number of training images is 40. Compared with other methods, the classification performance of the proposed method is improved significantly.
Query-based integration of heterogeneous knowledge bases for search and rescue tasks

Recently, advances in robotics’ technology and research focus on complex scenarios. In these scenarios, robots have to act and respond fast to situational demands. First, they require heterogeneous knowledge from various sources. Then, they need to integrate this knowledge with their reasoning methodologies. These reasoning methodologies are typically different for every domain. This paper introduces an integrated knowledge processing methodology. This methodology uses query mechanisms and model-to-model transformations. Combining these two mechanisms enables processing of heterogeneous knowledge bases. The methodology is demonstrated for an outdoor scenario with diverse systems. In this scenario knowledge and reasoning methods from various sources are integrated. This includes static knowledge from. Open Sreet Map and Digital Elevation Models. The Robot Scene Graph tracks changes in the world and provides geometric reasoning. KnowRob with its Sherpa ontology and openEASE provide further reasoning capabilities.
The Vulnerability of Multiplicative Noise Protection to Correlation-Attacks on Continuous Microdata

When multiplicative noises are used to protect values of a sensitive attribute in a microdata, it is frequently assumed that data intruders use the noise-multiplied value to estimate the corresponding unobservable original value of a target record. In this paper, we show that, data intruders could easily construct another estimate instead of using the noise-multiplied value to attack an original value. The new estimate, namely "correlation-attack" estimate, is obtained by exploiting the potentially high correlation between the noise-multiplied data and the original data. We provide a detailed comparison between the two estimates (noise-multiplied value and the correlation-attack estimate) by comparing the mean squared errors of the two underlying estimators, and we propose that data providers should always assess the disclosure risks from both estimators when generating noise-multiplied data. Correspondingly, we propose a disclosure risk measure which could be used by data providers for noise generating variable selection during data masking stage. A simulation study is provided to illustrate how the disclosure risk measure could help with noise generating variable selection for masking a set of original data.
[Invited papers] A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia

With the advances in digital media processing technologies and the tremendous growth in the amount of digital media that have been created, new artworks are becoming possible and drawing much attention from researchers, industry, and consumers. A related emerging research area is the evaluation of such multimedia artworks by machine learning techniques. We call this research area “attractiveness computing.” Attractiveness computing is made possible by the great accumulation of such multimedia artworks and of consumers' responses. In this paper, we review existing research on multimedia artworks analysis and attractiveness computing.
The damped interaction between a single-mode cavity field with Caldirola–Kanai Hamiltonian and a three-level atom

In this paper, we studied the damped interaction between a Λ-type three-level atom and a quantized single-mode cavity field, where the cavity field features a damped harmonic oscillator with a time-dependent mass that is described by the so-called Caldirola–Kanai (CK) Hamiltonian. Assuming that, initially, the atom is prepared in a superposition state and the field is prepared in coherent states, the temporal behavior of the atomic population inversion, von Neumann entropy, fidelity and Husimi function Q have been investigated by numerically solving the master equation.
Grid-connected and off-grid solar PV system design using long-term climatological data and techno-economic analysis for ecological conservation

This study aims at designing the solar power system for ecological conservation, i.e., sea turtle conservation at the Banyuwangi Sea Turtle Foundation (BSTF), which requires electricity for the pump station, heating and lighting. The solar power system design was aided with the daily solar irradiance derived from sunshine duration data from 1980 to 2018. Mann-Kendall test and Sen’s slope estimator were applied to estimate the rate of long-term of solar irradiance. The results reveal that the solar irradiance is generally higher in December, January, and April, and shows no significant trend. The average of solar irradiance during the dry and wet season is 5.03 and 4.58 kWh/m²/day, respectively. Using the geographical data of BTSF, approximately 943 m2 area can be exploited for the solar power system. The simulation using PVsyst predicts an energy production up to 198.4 MWh/year which can be generated from the proposed system. Despite the higher levelized cost of electricity (LCOE) of the solar power system, i.e., USD 0.32/kWh, the LCOE tends to decrease in the upcoming years due to increased performance of solar PV and batteries. Furthermore, the development of the solar power system for sea turtle conservation is necessary to promote sustainable energy for a sustainable environment.
A Blind Fragile Based Medical Image Authentication Using Schur Decomposition

Image watermarking is an effective and powerful solution in multimedia security. Imperceptibility and less computational complexity are the most desirable properties for any watermarking approach. For that purpose, several fragile watermarking based methods have been proposed in the last decade. Many of those methods suffer from low ratios of imperceptibility. In order to authenticate medical images, this paper proposes a new blind fragile watermarking method using Schur decomposition. Indeed, perturbation due to watermark embedding is reduced using Schur decomposition, which can be considered in designing a watermarking approach to enhance imperceptibility and computational complexity. The main idea is to embed the watermark in the Schur decomposition coefficients of the host image using a new embedding technique. The experiments results show interesting values of imperceptibility with low computational complexity.
Sensations Associated with Motion Sickness Response during Passenger Vehicle Operations on a Test Track

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. The University of Michigan Transportation Research Institute has developed a vehicle-based platform to study motion sickness in passenger vehicles. A test-track study was conducted with 52 participants who reported susceptibility to motion sickness. The participants completed in-vehicle testing on a 20-minute scripted, continuous drive that consisted of a series of frequent 90-degree turns, braking, and lane changes at the U-M Mcity facility. In addition to quantifying their level of motion sickness on a numerical scale, participants were asked to describe in words any motion-sickness-related sensations they experienced. Prior to in-vehicle testing, participants were shown a list of sensations that are commonly experienced during motion sickness: head sensations, body temperature change, drowsiness, dizziness, mouth sensations, nausea, or other sensations, which refer to difficulty focusing, irritability, eyestrain, or difficulty concentrating. Participants were instructed not to limit themselves to the list, but rather to report in their own words how they felt throughout the drive. For each sensation, they were also asked to describe the level of the sensation they experienced as mild, moderate, or severe. Chi-square analysis demonstrated that the sensations experienced were associated with in-vehicle test conditions and participant’s motion sickness susceptibility.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This study is the first to continuously quantify the type, incidence, intensity, and timeline progression of self-reported sensations associated with motion sickness response during passenger vehicle operations on a test track. Sensations were multidimensional and highly variable across individuals indicating that motion sickness is a multi-faceted response that extends beyond nausea.&lt;/div&gt;&lt;/div&gt;
Probabilistic Topic Model for Context-Driven Visual Attention Understanding

Modern computer vision techniques have to deal with vast amounts of visual data, which implies a computational effort that has often to be accomplished in broad and challenging scenarios. The interest in efficiently solving these image and video applications has led researchers to develop methods to expertly drive the corresponding processing to conspicuous regions that either depend on the context or are based on specific requirements. In this paper, we propose a general hierarchical probabilistic framework, independent of the application scenario, and relied on the most outstanding psychological studies about attention and eye movements which support that guidance is not based directly on the information provided by early visual processes but on a contextual representation that arose from them. The approach defines the task of context-driven visual attention as a mixture of latent sub-tasks, which are, in turn, modeled as a combination of specific distributions associated to low-, mid-, and high-level spatio-temporal features. Learning from fixations gathered from human observers, we incorporate an intermediate level between feature extraction and visual attention estimation that enables to obtain comprehensively guiding representations. The experiments show how our proposal successfully learns particularly adapted hierarchical explanations of visual attention in diverse video genres, outperforming several leading models in the literature.
Impacts of Unbalanced Test Data on the Evaluation of Classification Methods

The performance of a classifier in a supervised machine learning problem is popularly evaluated by using the accuracy, precision, recall, and F1-score.These parameters could evaluate very well classifiers in the case that the number of positive label sample and the number of negative label sample in the testing set are balanced or nearly balanced.However, these parameters may miss-evaluate the classifiers in some case where the positive and negative samples in the testing set is unbalanced.This paper proposes some update in these parameters by taking into account the unbalanced factor which represents the unbalance ratio of positive and negative samples in the testing set.The new updated parameters are then experimentally evaluated to compare to the traditional parameters.
An investigation into smartphone based weakly supervised activity recognition systems

With smart-devices becoming increasingly more commonplace, methods of capturing an individual's activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition
Syntax-Aware Representation for Aspect Term Extraction

Aspect Term Extraction (ATE) plays an important role in aspect-based sentiment analysis. Syntax-based neural models that learn rich linguistic knowledge have proven their effectiveness on ATE. However, previous approaches mainly focus on modeling syntactic structure, neglecting rich interactions along dependency arcs. Besides, these methods highly rely on results of dependency parsing and are sensitive to parsing noise. In this work, we introduce a syntax-directed attention network and a contextual gating mechanism to tackle these issues. Specifically, a graphical neural network is utilized to model interactions along dependency arcs. With the help of syntax-directed self-attention, it could directly operate on syntactic graph and obtain structural information. We further introduce a gating mechanism to synthesize syntactic information with structure-free features. This gate is utilized to reduce the effects of parsing noise. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmark datasets.
Math and Music, Models and Metaphors: Alberto Posadas' ‘Tree-Like Structures’

Alberto Posadas (b. 1967) often draws upon extramusical sources in order to nurture his compositional practices; among them, mathematical ones remain as the most persistent influence in his growing body of works. In particular, Lindenmayer systems stand as his most recurrent mathematical model, governing the compositional strategies of nine pieces to date, from Arborescencias to Anklänge an Robert Schumann. This paper analyses the way the aforementioned fractal model takes part in the Spanish composer’s creative practices. For that purpose, I first define the model and contextualise it within the field of musical composition, summoning for that purpose names such as Hanspeter Kyburz, Philippe Manoury or Enno Poppe. Next, I describe the evolution of Lindenmayer systems in Posadas’ oeuvre over time. Some techniques of genetic criticism are used in order to achieve this main goal. Moreover, I will underline some cognitive aspects around the metaphorical thinking underlying this choice.
Active and Contemplative Lives in a Changing Climate: The Emersonian Roots of Thoreau’s Political Asceticism

This article addresses an existential quandary for scholars of religion in an age of climate change. Given climate problems, it might seem like we ought to spend our lives doing something more civically productive than reading and writing books. Yet, we continue in our professions. I address this by examining Henry David Thoreau’s experiment at Walden. Thoreau drew on a variety of religious traditions and texts in his thinking about religious asceticism and coincident questions about the value of active and contemplative life. In this article, I focus especially on a portion of the Harivaṃśa Thoreau translated and a very quiet controversy that arose between Emerson and Thoreau about the value of practical effect. Thoreau’s investment in ascetic life, both active and contemplative, shows one way in which the writing life itself sometimes aims to resist the drive for growth that powers contemporary climate change.
On inverses of some permutation polynomials over finite fields of characteristic three

By using the piecewise method, Lagrange interpolation formula and Lucas' theorem, we determine explicit expressions of the inverses of a class of reversed Dickson permutation polynomials and some classes of generalized cyclotomic mapping permutation polynomials over finite fields of characteristic three.
Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with Cloud WorkBench

The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services with different performance characteristics. To support service selection, researchers and practitioners conduct cloud performance benchmarking by measuring and objectively comparing the performance of different providers and configurations (e.g., instance types in different data center regions). In this tutorial, we demonstrate how to write performance tests for IaaS clouds using the Web-based benchmarking tool Cloud WorkBench (CWB). We will motivate and introduce benchmarking of IaaS cloud in general, demonstrate the execution of a simple benchmark in a public cloud environment, summarize the CWB tool architecture, and interactively develop and deploy a more advanced benchmark together with the participants.
Quantum Game-Based Recommender Systems for Disruptive Innovations

A recommender system based on quantum game for disruptive innovations is proposed. Permutation operators are used to determine the dominance of players on the game, while several possibilities of measurement scheme of quantum game are utilized for harnessing disruptive innovations. It is shown in this manuscript that mathematical properties in quantum mechanics can be implemented for the extensions of recommender system capabilities.
Addiction to Social Robots: A Research Proposal

This research proposal aims to attract interest towards a future problem in the design of social robots. Persuasive technologies have demonstrated their effectiveness to negatively impact a user's behavior and generate addictions towards current social technologies. It could be the case that in the future similar persuasive technologies will be implemented in the design of social robots, leading to a recurrence of the negative impacts mentioned above. This research aims to investigate this phenomenon to prevent it and encourage the ethical design of social robots.
Korean Time Information Analysis of Hierarchical Annotation Rules from Natural Language Text

Documents contain temporal information that can be used for various applications such as recommendation systems. In order to find the information, analysis of time expressions is important from the documents written by natural language texts. As inherent linguistic characteristics of the Korean language, it is hard to capture every time expression from a given text without considering various patterns of temporal information. In this paper, we propose a hierarchical patterns to annotate time information that can reflect the characteristics in Korean language texts. We define 12 patterns of Korean time expression and 5 types of pattern groups which are hierarchically referred for the given text including digits, number expressions, particular expressions (e.g., season, day of week, etc.), event patterns, and Lunar holidays. Also, we study on their effects by analyzing the frequencies and coverages in a human-annotated corpus.
A Behavioral Facilitation Tweet Detection Method

Many tweets encourage people to behave in a certain way. For instance, a tweet might say “You can lose weight if you eat only apples.” to encourage readers to eat only apples if readers want to lose weight. For this study, we designate such tweets, which compel users to adopt a behavior, as behavioral facilitation tweets. These tweets do not always provide reliable advice; users should be alerted to unreliable tweets. To accomplish this goal, we first extract tweets that are behavioral facilitation tweets. Our studied behavioral facilitation tweets explicitly include behavior-related advice or encouragement. Furthermore, we target not only behavioral facilitation by the author but also behavioral facilitation by others. As described herein, we propose methods of three types to extract behavioral facilitation tweets automatically from numerous tweets: rule-based, support vector machine (SVM), and long short-term memory (LSTM). We use topics of three types to measure the benefits of our proposed methods, comparing rule-based, SVM, and LSTM-based methods to extract behavioral facilitation tweets. Results demonstrated that the LSTM-based method works best.
Illumination Invariant Skin Texture Generation Using CGAN from a Single Image for Haptic Augmented Palpation

The problem of illumination normalization in the field of computer vision is a subject that has been studied steadily so far. However, the treatment of strong illumination has not been done to date. In this paper, we present a method of illumination normalization using a deep learning method, the conditional generative adversarial network (CGAN), to reconstruct accurate 3D skin textures from a single image toward efficient haptic palpation. After normalizing the illumination through the conditional generative adversarial network in which the input image is conditioned to obtain the desired output image, bilateral filtering that removes noise while preserving the boundaries enhances fine wrinkles. As the refinement process of enhancing skin textures, intrinsic image decomposition is performed to obtain a shading layer image with histogram equalization that is merged into the normalized image obtained by the illumination normalization process to enhance skin tactile properties (wrinkle and roughness). Through these processes, we can obtain a depth image with normalized illumination and enhanced skin wrinkle texture. Using this, the depth of the skin surface texture is restored in three dimensions precisely. The superiority of the illumination normalization method proposed in this paper over three other illumination normalization methods (CIDRE, LDCT, and TT) is verified through comparison. We also confirm the illumination normalization performance of our method through restoration of the three-dimensional skin surface.
The application of netnography to the online dating service experiences of female users

The primary purpose of online dating sites, a specialised form of social media, is to aid their users in finding love and making friends. However, while such sites are very popular, only limited research has been conducted to investigate the experiences of female dating site users. Moreover, the factors underlying the popularity of online dating sites have also yet to be fully clarified. Therefore, the current study utilised netnography and online interviews to explore the experiences of female dating site users, while also observing the physical travel paths of such users within the field. More specifically, the particular situational context of online dating was investigated through an approach that included the following three stages: the observation and collection of online data, participation in an active manner, and emergent design. The study’s key theoretical contribution is its establishment of a netnography model along with eight associated propositions. Overall, the study’s findings substantially illuminate the online dating site experiences of female site users.