Reviewerly/exHarmony huggingface dataset:
Discriminative Analysis Dictionary and Classifier Learning for Pattern Classification

Sparse representation (SR) and dictionary learning (DL) have been widely used to encode the feature data and facilitate pattern classification. Existing methods generally use l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> /l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm or class-specific dictionary to enforce the class discriminative ability of the SR. The resulted class discriminative ability is limited. In this work, we propose to use the training set as the synthesis dictionary for SR of the training samples because it provides the most natural class-specific dictionary. The class information of the training set can be used to enhance an ideal discriminative property of the SR: exact block diagonal structure, meaning that each data can be represented only by data-in-class. To make the test stage easy, an analysis dictionary and a linear classifier are learnt under the supervision of the discriminative SR of the training set. Once the analysis dictionary and the classifier are learnt, the test stage is very simple and computation efficient. We call our method Discriminative Analysis Dictionary and Classifier Learning (DADCL). Extensive experiments show that our method outperforms some existing state-of-the-art methods.
Effect of virtual reality and whole-body heating on motion sickness severity: A combined and individual stressors approach

Virtual reality (VR) use is limited by the potential side effects of prolonged exposure to vection, leading to motion sickness. Air temperature (Ta) may exacerbate the severity of such side effects through a synergistic interaction. This study assessed the individual and combined impact of a hot Ta and VR on motion sickness severity. Thirteen healthy volunteers were exposed to a 20 min visual stimulus, across four experimental conditions: N_CS: 22 °C Ta with computer screen; N_VR: 22 °C Ta with VR; H_CS: 35 °C Ta with computer screen; H_VR: 35 °C Ta with VR. Motion sickness was assessed via fast motion sickness scale (FMS) and simulator sickness questionnaire (SSQ). Physiological indices of motion sickness including, sweat rate, rectal temperature, cutaneous vascular conductance (CVC), skin temperature, blood pressure and heart rate were also examined. FMS and SSQ ratings indicate a significant main effect for VR, increasing sickness severity (p < 0.001). A significant main effect of Ta was observed for SSQ, but not FMS ratings (FMS, p = 0.07; SSQ, p < 0.04). Despite trends towards synergism, no interaction (Ta × VR) was observed for FMS (p = 0.2) or SSQ scores (p = 0.07), indicating an additive response. Synergistic trends were also observed for sweat rate and CVC. Synergism between VR and heat on motion sickness remains unclear, possibly as a result of considerable inter-individual variation in the reported subjective responses. Understanding of the questions raised by this study inform safe working guidelines for the use of VR in commercial and occupational settings.
CNN-Based Deghosting in High Dynamic Range Imaging

This paper addresses the challenging problem of avoiding ghost artifacts in compositing a high dynamic range (HDR) image by multi-exposed images fusion. We propose a convolutional neural network (CNN)-based deghosting algorithm, which treats the estimation of motion maps as a labelling problem. The network input consists of multiple low dynamic range (LDR) images presenting motion-related scene and exposure differences, and the output is a set of binary motion maps where black pixels indicate inconsistency between reference and non-reference images. The network is trained using simulated data, and we propose a novel approach for generating realistic synthetic multiple LDR images and their corresponding "ground truth" motion maps. The trained CNN model is tested on a variety of dynamic indoor and outdoor scenes. It outperforms the state-of-the-art without requiring camera meta data and is more flexible to handle higher-resolution images under limited GPU memory compared to end-to-end networks for HDR imaging. Our source code, pretrained models, and data will be made publicly available to facilitate future research.
Face Recognition with Disentangled Facial Representation Learning and Data Augmentation

We address two issues for tackling face recognition across pose, one is disentangled representation learning and the other is training data augmentation. To have better training properties, we propose the Representation-Learning Wasserstein-GAN (RL-WGAN) with three component networks for learning the disentangled facial representation. As the learning based on imbalanced data often leads to biased estimation, we proposed a data augmentation scheme that exploits the 3D Morphable Model (3DMM) for generating faces of desired poses. The RL-WGAN and the data augmentation are verified in the experiments with benchmark databases, and compared with contemporary approaches for performance evaluation.
On the minimal Hamming weight of a multi-base representation

Given a finite set of bases b1, b2, …, br (integers greater than 1), a multi-base representation of an integer n is a sum with summands db1α1b2α2⋯brαr, where the αj are nonnegative integers and the digits d are taken from a fixed finite set. We consider multi-base representations with at least two bases that are multiplicatively independent. Our main result states that the order of magnitude of the minimal Hamming weight of an integer n, i.e., the minimal number of nonzero summands in a representation of n, is log⁡n/(log⁡log⁡n). This is independent of the number of bases, the bases themselves, and the digit set. For the proof, the existing upper bound for prime bases is generalized to multiplicatively independent bases; for the required analysis of the natural greedy algorithm, an auxiliary result in Diophantine approximation is derived. The lower bound follows by a counting argument and alternatively by using communication complexity; thereby improving the existing bounds and closing the gap in the order of magnitude. This implies also that the greedy algorithm terminates after O(log⁡n/log⁡log⁡n) steps, and that this bound is sharp.
Delivering Data Mining Services in Cloud Computing

Cloud computing is rapidly becoming a widespread alternative to costly on-premise infrastructures for delivering computing services in general and specifically for Data Mining services. Bearing this in mind, it is fairly convenient, to propose an architecture for the deployment of Data Mining services that would allow the underlying computing platform to be abstracted, leaving out of consideration of the cloud provider, technology or the supporting architecture, and focusing on service and his flexible description, composition and deployment. For this purpose, a platform for the deployment of Data Mining services known as OC2DM: Open Cloud Computing Data Mining has been designed.
How has SPI changed in times of agile development? Results from a multi‐method study

Abstract The emergence of agile methods and practices has not only changed the development processes but might also have affected how companies conduct software process improvement (SPI). Through a set of complementary studies, we aim to understand how SPI has changed in times of agile software development. Specifically, we aim (a) to identify and characterize the set of publications that connect elements of agility to SPI, (b) to explore to which extent agile methods/practices have been used in the context of SPI, and (c) to understand whether the topics addressed in the literature are relevant and useful for industry professionals. To study these questions, we conducted an in‐depth analysis of the literature identified in a previous mapping study, an interview study, and an analysis of the responses given by industry professionals to SPI‐related questions stemming from an independently conducted survey study. Regarding the first question, we identified 55 publications that focus on both SPI and agility of which 48 present and discuss how agile methods/practices are used to steer SPI initiatives. Regarding the second question, we found that the two most frequently mentioned agile methods in the context of SPI are Scrum and Extreme Programming (XP), while the most frequently mentioned agile practices are integrate often , test‐first , daily meeting , pair programming , retrospective , on‐site customer , and product backlog . Regarding the third question, we found that a majority of the interviewed and surveyed industry professionals see SPI as a continuous activity. They agree with the agile SPI literature that agile methods/practices play an important role in SPI activities but that the importance given to specific agile methods/practices does not always coincide with the frequency with which these methods/practices are mentioned in the literature.
Implicit Background Estimation For Semantic Segmentation

Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways. It is therefore paramount that techniques for principled design of robust models be developed. In this paper, we provide analytic and empirical evidence that correcting potentially errant non-distinct mappings that result from the softmax function can result in improving robustness characteristics on a stateof-the-art semantic segmentation model with minimal impact to performance and minimal changes to the code base.
EU Trade Secrets Law and Algorithmic Transparency

To date, more and more decisions are supported or even directly assumed by algorithms. As a result, we need to understand how to prevent and govern those algorithmic choices that may prove to be wrong, unfair, or discriminatory. Algorithmic transparency, that is, the possibility to investigate the sequence of commands of which algorithms are made, could represent a solution, although partial, to this problem. However, algorithms can be protected as trade secrets within the meaning of the new European Directive 2016/943. Therefore, the paper discusses if and how much the rules foreseen in such Directive offer room to hypotheses of algorithmic transparency.
A Novel Attribute Selection Mechanism for Video Captioning

Attributes are more and more popular for enhancing the performance of video captioning which requires semantic understanding of videos and the ability of generating natural language descriptions. However, existing methods have flaws in detecting visual attributes. As a result, the captioning model may be misled by those erroneous attributes. Besides, each semantic attribute plays a different role in the next-word generation. How to utilize them effectively in video captioning task is still a challenge. To tackle these problems, in this paper, we propose a novel framework which imposes an attention mechanism guided by the visual attention on the detected video attributes to make a soft-selection over them. Simultaneously, the reinforcement learning algorithm is employed with the motivation to better select the useful attributes. Experimental results on benchmark datasets demonstrate that the proposed attribute selection mechanism can focus on appropriate attributes and boost the caption models.
A Hierarchical Network for Diverse Trajectory Proposals

Autonomous explorative robots frequently encounter scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either manoeuvring around the obstacles or moving towards various frontiers. In this work, we propose a 2 stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment which can be occupied in the future. The second stage is a Trajectory Sampling network which provides a finegrained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings. For the outdoor case, we use the KITTI dataset and our own outdoor driving dataset. In the indoor setting, we use an autonomous drone to navigate various scenarios and also a ground robot which can explore the environment using the trajectories proposed by our framework. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons portray the performance gain of the proposed architecture over a diverse set of methods against which it is compared.
Capital flight and extent of corruption control in the least corrupt African countries

Purpose The purpose of this paper is to investigate the effect of corruption control on capital flight in the least corrupt African countries. Design/methodology/approach Using panel data covering the period of 1996-2010. Findings The results show that the extent of corruption, the total natural resources rent are statistically significant and affect positively the capital across the pooled, random and fixed effects. Inflation and economic growth are also found to have a negative impact on capital flight. Moreover, the exchange rate has a negative and significant effect on capital flight. Practical implications The findings of this study suggest that the extent of corruption control by responsible institutions can be considered as one of the most effective weapons in the fight against capital flight in the least corrupt African countries. Social implications The paper recommends to the government of the least corrupt countries in Africa to create an enabling political and economic environment for investor’s attractiveness. This, in turn, will reduce the occurrence of capital flight and lead to the sustainable development. Originality/value The findings of this study suggest that the extent of corruption control by responsible institutions can be considered as one of the most effective weapons in the fight against capital flight in the least corrupt African countries. The paper recommends to the government of the least corrupt countries in Africa to create an enabling political and economic environment for investor’s attractiveness. This, in turn, will reduce the occurrence of capital flight and lead to the sustainable development.
Getting Under Alexa’s Umbrella: Infiltration Attacks Against Internet Top Domain Lists

Top domain rankings such as Alexa are frequently used in security research. Typical uses include selecting popular websites for measurement studies, and obtaining a sample of presumably "benign" domains for model training or whitelisting purposes in security systems. Consequently, an inappropriate use of these rankings can result in unwanted biases or vulnerabilities. This paper demonstrates that it is feasible to infiltrate two domain rankings with very little effort. For a domain with no real visitors, an attacker can maintain a rank in Alexa's top 100 k domains, for instance, with seven fake users and a total of 217 fake visits per day. To remove malicious domains, multiple research studies retained only domains that had been ranked for at least one year. We find that even those domains contain entries labelled as malicious. Our results suggest that researchers should refrain from using these domain rankings to model benign behaviour.
Implementation of a real-time fall detection system for elderly Korean farmers using an insole-integrated sensing device

Korea’s rural communities suffer from serious health issues from a rise in fall accidents due to an aging population. This study proposes a real-time fall detection system using an insole-integrated sensor device that detects fall accidents during farming activities and enables rapid responses to these emergencies. This study developed an insole-integrated fall detection device comprising a three-axial accelerometer and force sensitive resistor pressure sensors. Threshold values were set using data captured by the sensors to distinguish fall and emergency situations from other normal activities. In addition, a real-time monitoring system was implemented so that accident-related information may be automatically communicated to a third party or the monitoring center when a fall or emergency is recognized. A reliability test of the system showed 94.8% accuracy, 98.9% sensitivity, and 91.3% specificity. Furthermore, the detection rate for emergency conditions showed a high accuracy of 100%. Based on the results of this study, the fall detection system based on the insole-integrated sensor device may be implemented as a useful tool to address the issue of fall accidents involving elderly farmers in agricultural activities.
Script Identification from Handwritten document Images Using LBP Technique at Block level

The documents are in multilingual form in India, it is required to automatically identify type of the script and feed script document to the appropriate Optical Character Recognition system for information retrieval. This paper presents an efficient handwritten script recognition method using Local Binary Pattern operator. The features are extracted from a block of handwritten document image. Recognition of the script type is done using Nearest Neighbor and Support Vector Machine classifiers. Experiments are performed on images of handwritten documents written in English, Hindi, Kannada, Malayalam, Telugu, and Urdu scripts. KNN and SVM classifiers yielded recognition accuracy of 98.46% and 99.5%, respectively.
WaSP Encoder with Breakpoint Adaptive DWT Coding of Disparity Maps

An encoder architecture that employs view warping and sparse prediction (WaSP) has recently been proposed by the JPEG Pleno standards activity for the compression of light field imagery. The proposed WaSP encoder utilises disparity information to exploit the correlation that exists between multiple views and therefore requires disparity data to be communicated. The WaSP framework currently employs JPEG2000 for the coding of disparity maps. In this work we explore the impact of introducing breakpoint adaptive DWT (BPA-DWT) coding of disparity maps. While prior work has shown promising results for the coding of depth maps with breakpoints, its impact on multi-view compression where the depth or disparity data forms part of the communicated side information has not been previously explored. Our investigations show important gains in RD performance coupled with improvements in the visual quality of decoded views by the introduction of BPA-DWT coding of disparity maps.
Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day-1). It is developed using recurrent neural networks of reservoir computing type, for estimating SGR based on the known data of three key biological factors relating to growth. These factors are: (1) weight (g) for specifying the age of the growth stage; (2) digestive efficiency through the pyloric caecal activity ratio of trypsin to chymotrypsin (T/C ratio) for specifying genetic differences in food utilization and growth potential, basically resulting from food consumption under variations in food quality and environmental conditions; and (3) protein growth efficiency through the condition factor (CF, 100 × g cm-3), as higher dietary protein level affecting higher skeletal growth (length) and resulting in lower CF. The computational model was trained using four datasets of different salmonids with size variations. It was evaluated with 15% of each dataset, resulting in an acceptable range of SGR outputs. Additional tests with different species indicated similarity between the estimated SGR outputs and the real SGR values, and the same ranking of wild population growth. The developed model GrowthEstimate is exceptionally useful for the precise and comparable growth estimation of living resources at individual levels, especially in natural ecosystems where the studied individuals, environmental conditions, food availability and consumption rates cannot be controlled. It is a revelation and will help to minimize uncertainty in wild stock assessment process. This will improve our knowledge in nutritional ecology, through the biochemical effects of climate change and environmental impact on the growth performance quality of aquatic living resources in the wild, as well as in aquaculture. The original GrowthEstimate software is available at GitHub repository (https://github.com/RungruangsakTorrissenManoonpong/GrowthEstimate). All other relevant data are within the paper. It will be improved for generality for future use, and required co-operations of the biodata collections of different species from different climate zones. Therefore, a co-operation will be available.
Dual Reverse Attention Networks for Person Re-Identification

In this paper, we enhance feature representation ability of person re-identification (Re-ID) by learning invariances to hard examples. Unlike previous works of hard examples mining and generating in image level, we propose a dual reverse attention networks (DRANet) to generate hard examples in the convolutional feature space. Specifically, we use a classification branch of attention mechanism to model that `what' in channel and `where' in spatial dimensions are informative in the feature maps. Meanwhile, we introduce two branches of reverse attention modules in parallel way, which convert informative feature maps into hard examples of uninformative ones. In the proposed framework, both classification and dual reverse attention branches are learned in a joint way. Experimental results on three mainstream datasets demonstrate the efficacy of the proposed method.
Multi-Seam Coal Deposit Modeling via Principal Component Analysis &amp; GIS

Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis.