Reviewerly/exHarmony huggingface dataset:
Use of portable X-ray fluorescence spectrometry for classifying soils from different land use land cover systems in India

In this study, elemental data from portable X-ray fluorescence (PXRF) spectrometry was used to test the efficiency of four machine learning techniques (random forest; linear and nonlinear support vector machine; classification and regression tree) for distinguishing three land use types in India based upon scans of mineral surface (0–20 cm) soil. Results showed similar performance among the four tested algorithms, with classification accuracy of a randomly selected validation set ranging from 83% to 91%. The classification and regression tree was favored based upon simple "IF AND THEN" rules which make classification of the data simple. In sum, PXRF data was shown highly effective at differentiating land use types in India. Future work should focus on a larger number of land use classification types and possible combination of PXRF data with complimentary proximal sensing datasets (e.g., visible near infrared spectroscopy).
Not Using the Car to See the Sidewalk — Quantifying and Controlling the Effects of Context in Classification and Segmentation

Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models are dependent on the context to make their predictions is unclear. A model overly relying on context will fail when encountering objects in different contexts than in training data and hence it is important to identify these dependencies before we can deploy the models in the real-world. We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models. We apply this methodology on two tasks, image classification and semantic segmentation, and discover undesirable dependency between objects and context, for example that ``sidewalk'' segmentation is very sensitive to the presence of ``cars'' in the image. We propose an object removal based data augmentation solution to mitigate this dependency and increase the robustness of classification and segmentation models to contextual variations. Our experiments show that the proposed data augmentation helps these models improve the performance in out-of-context scenarios, while preserving the performance on regular data.
EVS-DK: Event video skimming using deep keyframe

In this automation era, video surveillance becomes an essential component and omnipresent at ATMs, public places, airports, railways, roadways, etc. There are many challenges to store and access such massive data generated by video surveillance. Therefore, a novel technique is required to manage the comprehensive view of the content. In this work, we propose an event summarization technique using Deep learning framework for monocular videos. A spatiotemporal similarity function is developed to construct a similarity matrix based on the visual features. Video frames are represented by the sparse matrix as graph vertices based on an objective function, where Highly Connected Subgraphs (HCS) are constructed as clusters. Finally, events are obtained from such clusters assuming that the centroid of the cluster is a key-frame of the event. Consequently, this approach does not require assumption to determine the number of clusters. Due to this advantage, users can select the number of keyframes without incurring an extra computational cost. Experimental results on two benchmark datasets show that the proposed model outperforms the state-of-the-art models on Precision and F-measure and also cover the major contents of the original video.
Optimal feature selection using binary teaching learning based optimization algorithm

Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.
When the earth doesn’t stop shaking: How experiences over time influenced information needs, communication, and interpretation of aftershock information during the Canterbury Earthquake Sequence, New Zealand

The Canterbury Earthquake Sequence (CES) began with the Darfield earthquake on 4 September 2010. Continual large and small aftershocks since that time have meant communities have cycled through repeated periods of impact, response and recovery. Scientific communication about aftershocks during such a prolonged sequence has faced distinct challenges. We conducted research to better understand aftershock information needs for agencies and the public, and how people interpreted and responded to such information. We found that a wide range of information was needed from basic facts about aftershocks through to more technical information, and in different formats (e.g. maps, tables, graphs, text, analogies). Information needs also evolved throughout the sequence, and differed depending on people's roles and experiences, and the phase of impact, response and recovery communities were in. Interpretation of aftershock information was influenced by a variety of factors including how understandable and relevant the information was, whether people had prior knowledge or experience of aftershocks, whether the information was personalised or contextualised, emotions and feelings, credibility and trust, and external influences. Given that such a diversity of evolving information is required, it is imperative that geoscientists strategize how to provide such information before a significant earthquake occurs.
A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection

With multitudes of image processing applications, image quality assessment (IQA) has become a prerequisite for obtaining maximally distinctive statistics from images. Despite the widespread research in this domain over several years, existing IQA algorithms have a number of key limitations concerning different image distortion types and algorithms' computational efficiency. Images that are synthesized using depth image-based rendering have applications in various disciplines, such as free viewpoint videos, which enable synthesis of novel realistic images in the referenceless environment. In the literature, very few no-reference (NR) quality assessment metrics of three-dimensional (3-D) synthesized images are proposed, and most of them are computationally expensive, which makes it difficult for them to be deployed in real-time applications. In this paper, we attribute the geometrically distorted pixels as outliers in 3-D synthesized images. This assumption is validated using the three $sigma$ rule-based robust outlyingness ratio. We propose a novel fast and accurate blind IQA metric of 3-D synthesized images using nonlinear median filtering since the median filtering has the capability of identifying and removing outliers. The advantages of the proposed algorithm are twofold. First, it uses a simple technique, i.e., median filtering, to capture the level of geometric and structural distortions (up to some extend). Second, the proposed algorithm has higher computational efficiency. Experiments show the superiority of the proposed NR IQA algorithm over existing state-of-the-art full-, reduced-, and NR IQA methods, in terms of both predicting accuracy and computational complexity.
A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.
Embodied Engagement with Narrative: A Design Framework for Presenting Cultural Heritage Artifacts

An increasing number of museum exhibits incorporate multi-modal technologies and interactions; yet these media divert visitors’ attention away from the cultural heritage artifacts on display. This paper proposes an overarching conceptual structure for designing tangible and embodied narrative interaction with cultural heritage artifacts within a museum exhibit so that visitors can interact with them to comprehend their cultural context. The Tangible and Embodied Narrative Framework (TENF) consists of three spectra (diegetic vs. non-diegetic, internal vs. external, and ontological vs. exploratory) and, considering how different interactions map along these three spectra, can guide designers in the way they integrate digital media, narrative, and embodiment. In this paper, we examine interactive narrative scholarship, existing frameworks for tangible and embodied interactions, and tangible and embodied narrative projects. We then describe the design of the TENF and its application to the pilot project, Mapping Place, and to the case study project, Multi-Sensory Prayer Nuts. The findings indicate that embodied engagement with artifacts through a narrative role can help visitors (1) contextualize the meaning of artifacts and (2) make personalized connections to the artifacts. Based on this work, we suggest design recommendations for tailoring the use of the TENF in the cultural heritage domain: simulate cultural practices, associate visitors with cultural perspectives, and provide simultaneous digital feedback. We conclude by describing future directions for the research, which include generating other possible projects using the TENF; collaborating with other designers and museum professionals; and exploring applications of the TENF in museum spaces.
Facial expression recognition using distance and texture signature relevant features

Distance and texture characteristics among the landmark points reflected in human faces are important features in so far as the recognition of human faces is concerned. In this article we consider (i) normalized distance signature obtained from Active Appearance Model (AAM) based grid, (ii) normalized texture signature derived from salient landmarks within the grid, (iii) stability indices arising out of these signatures and (iv) relevant statistical measures as the set of features for training of artificial neural models such as Multilayer Perceptron (MLP), Radial Basis Function Network (RBF), Nonlinear AutoRegressive with eXogenous input (NARX) and Convolutional Neural Network (CNN) to achieve the task of recognition of facial expressions. The Cohn-Kanade (CK+), Japanese Female Facial Expression (JAFFE), MMI and MUG benchmark databases are used to conduct the experiments and the results obtained justify the effectiveness of the proposed procedure. The combined distance-texture (D-T) signature is found to perform convincingly better than the distance signature and texture signature individually. The effectiveness of the proposed technique based on combined D-T signature is substantiated by its extremely encouraging performance compared to other existing arts.
Economic feasibility of solar power plants based on PV module with levelized cost analysis

In this study, the cost analysis of solar power system, where is located in Elazığ, Turkey is calculated according to levelized cost analysis method. In the economic feasibility studies carried out by the firms, many parameters such as interest rates, cost of money, detailed sunshine duration, monthly net profit-loss status in one year, cost of investment according to changing interest rates are not taken into consideration. All these parameters, which were not considered by the firms, have been calculated in this study. The payback period of investing in the solar power plant is calculated as 13 years, the payback period of it is calculated as an average of 6.6 years by the firms. The annual profit of a 1 MW solar energy plant is 89,467 US $. Present worth and annual capital cost of the solar power plant are calculated as 1,156,763 US $ and 1,181,875 US $, respectively. The capital cost flow of the investing in solar power plant is determined as 5.628 US $/h. When the results obtained from this study are evaluated in a general framework, the high interest rates in developing countries will have a negative effect on the investments in the solar power plants.
The uncanny valley: No need for any further judgments when an avatar looks eerie

Considering that a 3D scanning technology allows to create avatars that embody more authentic and rich information about its users, it could be expected that enhancing avatar realism will increase the accuracy of thin-slice judgments of the person represented by the avatar. However, previous studies suggest that the objective realism of avatars may not always lead to positive outcomes, and the activation of negative affect and the aversive motivational system through the uncanny valley effects may rather harm the accuracy of thin-slice judgments by making people distance themselves from available information in avatars. To validate this speculation, a 2 (Realism: Cartoonish vs. Hyper-realistic) x 2 (Animacy: Still vs. Animate) between-subjects experiment (N = 134) was conducted. In support of our prediction, results from an online experiment confirmed that hyper-realistic and animate avatars can induce a greater feeling of eeriness as compared to cartoonish and still avatars. This feeling of eeriness, evoked by the uncanny valley effects, suppressed the amount of information processing oriented for thin-slice judgments, and subsequently decreased the accuracy of extraversion and agreeableness judgments. Further theoretical implications of the findings are discussed.
Deep Learning-Based Channel Estimation and Equalization Scheme for FBMC/OQAM Systems

Filter bank multicarrier (FBMC) modulation is a promising candidate modulation method for future communication systems. However, FBMC systems cannot directly use channel estimation methods proposed for orthogonal frequency-division multiplexing systems due to its inherent imaginary interference. In this letter, we propose a channel estimation and equalization scheme based on deep learning (DL-CE) for FBMC systems. In the DL-CE scheme, the channel state information and the constellation demapping method are learned by a deep neural networks model, and then the distorted frequency-domain sequences are equalized implicitly to obtain binary bits directly. Simulation results show that the proposed DL-CE scheme achieves state-of-the-art performance on channel estimation and equalization.
Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments

In this paper, a novel incremental learning algorithm is presented for reinforcement learning (RL) in dynamic environments, where the rewards of state-action pairs may change over time. The proposed incremental RL (IRL) algorithm learns from the dynamic environments without making any assumptions or having any prior knowledge about the ever-changing environment. First, IRL generates a detector-agent to detect the changed part of the environment (drift environment) by executing a virtual RL process. Then, the agent gives priority to the drift environment and its neighbor environment for iteratively updating their state-action value functions using new rewards by dynamic programming. After the prioritized sweeping process, IRL restarts a canonical learning process to obtain a new optimal policy adapting to the new environment. The novelty is that IRL fuses the new information into the existing knowledge system incrementally as well as weakening the conflict between them. The IRL algorithm is compared to two direct approaches and various state-of-the-art transfer learning methods for classical maze navigation problems and an intelligent warehouse with multiple robots. The experimental results verify that IRL can effectively improve the adaptability and efficiency of RL algorithms in dynamic environments.
Safety for mobile robotic systems: A systematic mapping study from a software engineering perspective

Robotic research is making huge progress. However, existing solutions are facing a number of challenges preventing them from being used in our everyday tasks: (i) robots operate in unknown environments, (ii) robots collaborate with each other and even with humans, and (iii) robots shall never injure people or create damages. Researchers are targeting those challenges from various perspectives, producing a fragmented research landscape. We aim at providing a comprehensive and replicable picture of the state of the art from a software engineering perspective on existing solutions aiming at managing safety for mobile robotic systems. We apply the systematic mapping methodology on an initial set of 1274 potentially relevant research papers, we selected 58 primary studies and analyzed them according to a systematically-defined classification framework. This work contributes with (i) a classification framework for methods or techniques for managing safety when dealing with the software of mobile robotic systems (MSRs), (ii) a map of current software methods or techniques for software safety for MRSs, (iii) an elaboration on emerging challenges and implications for future research, and (iv) a replication package for independent replication and verification of this study. Our results confirm that generally existing solutions are not yet ready to be used in everyday life. There is the need of turn-key solutions ready to deal with all the challenges mentioned above.
Artificial Intelligence Hits the Barrier of Meaning

Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.
Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets

Offline writer independent (WI) signature verification is conceivably a challenging task in the domain of handwritten biometrics. This work addresses it by introducing a feature extraction scheme that relies on the detection of first order transitions between asymmetrical lattice arrangements of simple pixel structures. The experiments are conducted with a decision stumps committee, accompanied with boosting feature selection, by employing only unrelated or blind training and testing datasets, all derived from four widely used signature databases. In addition, a fifth signature dataset which contains disguised signatures, originating from a signature verification contest, is also used for testing purposes. The impact of the preprocessing stage per dataset is exploited by allowing various pruning levels of the raw binary signature. In addition to standard training protocols, we introduce the use of the applicability domain (AD) in WI signature verification and examine its effectiveness in providing reliable classifier predictions by quantifying subregions of the input distance space that have been sufficiently covered by training examples. The derived experimental results expressed by means of equal error rate, along with best average error rate, are considered to be very competitive to those provided from state of the art WI methods.
Streaming feature selection algorithms for big data: A survey

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.
Do bitcoins follow a random walk model?

Bitcoins have become a fad among investors despite of the ambiguity surrounding on its nature and characteristics. This study aims to contribute to the existing literature of examining bitcoin returns under a financial asset purview. Through multiple robust tests, the market efficiency of daily bitcoin returns is analyzed for the time frame of July 2010 till March 2018. Strong evidence of market inefficiency characterized by absence of random walk model is found. The market inefficiency was found attributable to the presence of asymmetric volatility clustering. More studies are needed to examine the temporal dynamics of bitcoin returns.
Experimental time-reversed adaptive Bell measurement towards all-photonic quantum repeaters

An all-optical network is identified as a promising infrastructure for fast and energy-efficient communication. Recently, it has been shown that its quantum version based on 'all-photonic quantum repeaters'-inheriting, at least, the same advantages-expands its possibility to the quantum realm, that is, a global quantum internet with applications far beyond the conventional Internet. Here we report a proof-of-principle experiment for a key component for the all-photonic repeaters-called all-photonic time-reversed adaptive (TRA) Bell measurement, with a proposal for the implementation. In particular, our TRA measurement-based only on optical devices without any quantum memories and any quantum error correction-passively but selectively performs the Bell measurement only on single photons that have successfully survived their lossy travel over optical channels. In fact, our experiment shows that only the survived single-photon state is faithfully teleported without the disturbance from the other lost photons, as the theory predicts.