Reviewerly/exHarmony huggingface dataset:
Segmented forest realities: The ontological politics of biodiversity mapping

Since the late 1990 s, biodiversity mapping has been a key instrument for preventing loss of species and habitats in Norwegian productive forests. Having major implications for the abilities of actors to pursue their interests in practical forest management, the methodologies of biodiversity mapping have been highly controversial and contested. We identify two different forest ontologies, or realities, that were enacted by the two main competing methodologies for such mapping. The methodologies, SiS and EiF, were supported by what we term the environmental and the forestry segments, respectively. Whereas a mapping approach associated with the environmental segment enacted a varied and complex forest ontology, a mapping approach related to the forestry segment enacted a comprehensible and more standardized forest ontology. In analyzing the two ontologies, we explore the links between the configuration of the mapping methodologies and 1) the forest realities they enact, 2) the scientific ideals they advocate, and 3) the relationship between mapping and management decisions. In particular, we argue that the ontologies have different political implications, generally favoring the actors that support them. On a more general level, we show that ontological politics is performed in the enactment of different ontologies related to different political segments, associated with different sectors.
Automatic event detection in football using tracking data

Abstract One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a deterministic decision tree-based algorithm to automatically extract football events using tracking data, which consists of two steps: (1) a possession step that evaluates which player was in possession of the ball at each frame in the tracking data, as well as the distinct player configurations during the time intervals where the ball is not in play to inform set piece detection; (2) an event detection step that combines the changes in ball possession computed in the first step with the laws of football to determine in-game events and set pieces. The automatically generated events are benchmarked against manually annotated events and we show that in most event categories the proposed methodology achieves $$+90\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>90</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> detection rate across different tournaments and tracking data providers. Finally, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.
Emotions are associated with the genesis of visually induced motion sickness in virtual reality

Visually induced motion sickness (VIMS) is a well-known side effect of virtual reality (VR) immersion, with symptoms including nausea, disorientation, and oculomotor discomfort. Previous studies have shown that pleasant music, odor, and taste can mitigate VIMS symptomatology, but the mechanism by which this occurs remains unclear. We predicted that positive emotions influence the VIMS-reducing effects. To investigate this, we conducted an experimental study with 68 subjects divided into two groups. The groups were exposed to either positive or neutral emotions before and during the VIMS-provoking stimulus. Otherwise, they performed exactly the same task of estimating the time-to-contact while confronted with a VIMS-provoking moving starfield stimulation. Emotions were induced by means of pre-tested videos and with International Affective Picture System (IAPS) images embedded in the starfield simulation. We monitored emotion induction before, during, and after the simulation, using the Self-Assessment Manikin (SAM) valence and arousal scales. VIMS was assessed before and after exposure using the Simulator Sickness Questionnaire (SSQ) and during simulation using the Fast Motion Sickness Scale (FMS) and FMS-D for dizziness symptoms. VIMS symptomatology did not differ between groups, but valence and arousal were correlated with perceived VIMS symptoms. For instance, reported positive valence prior to VR exposure was found to be related to milder VIMS symptoms and, conversely, experienced symptoms during simulation were negatively related to subjects' valence. This study sheds light on the complex and potentially bidirectional relationship of VIMS and emotions and provides starting points for further research on the use of positive emotions to prevent VIMS.
Multiple spatial residual network for object detection

Abstract Many residual network-based methods have been proposed to perform object detection. However, most of them may lead to overfitting or cannot perform well in small object detection and alleviate the problem of overfitting. We propose a multiple spatial residual network (MSRNet) for object detection. Particularly, our method is based on central point detection algorithm. Our proposed MSRNet employs a residual network as the backbone. The resulting features are processed by our proposed residual channel pooling module. We then construct a multi-scale feature transposed residual fusion structure consists of three overlapping stacked residual convolution modules and a transpose convolution function. Finally, we use the Center structure to process the high-resolution feature image for obtaining the final prediction detection result. Experimental results on PASCAL VOC dataset and COCO dataset confirm that the MSRNet has competitive accuracy compared with several other classical object detection algorithms, while providing a unified framework for training and reasoning. The MSRNet runs on GeForce RTX 2080Ti.
Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution (SR) methods rely on the assumption that the degradation process is predefined (e.g., bicubic downsampling). Thus, their performance would suffer from deterioration if the real degradation is not consistent with the assumption. To deal with real-world scenarios, existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme. However, degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors. In this paper, we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples, respectively. Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space. Furthermore, instead of estimating the degradation, we extract global statistical prior information to capture the character of the distortion. Considering the coupling between the degradation and the low-resolution image, we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions. We term our distortion-specific network with contrastive regularization as CRDNet. The extensive experiments on synthetic and real-world scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.
A Generating-Anchor Network for Small Ship Detection in SAR Images

Synthetic aperture radar (SAR) ship detection especially for small ships has issues such as dense distribution of ships, interference from land and small islands. To address these issues, many deep learning (DL) methods, including anchor-based and anchor-free methods, have been successfully migrated from optical scenes to SAR images. However, when the preset scale of anchors does not match well with the ships, it will seriously reduce the detection precision. Due to the lack of anchor-based refinement process, anchor-free methods may generate missing or false alarms in complex scenarios. In this paper, a two-stage ship detection network which can generate anchors is proposed. Firstly, our method generates high-quality anchors by network, which is more beneficial for the network to capture small ships. In addition, the generated anchors are centrally set in the region of ships, which reduces the number of anchors unrelated to ships. Secondly, the receptive field enhancement module is inserted into the feature pyramid network (FPN). It sets different dilation ratios of atrous convolution according to the scale of the feature map, which further enriches the semantic information of the elements in the feature map. Therefore, the network can use the information of a wider region effectively to detect ships. Finally, to verify the effectiveness of our method, extensive experiments are carried out on SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID). The results show that our method has more strong ability of detecting small ships, and achieves better detection performance than some state-of-the-art methods.
Impact of Boundary Control Methods on Bound-Constrained Optimization Benchmarking

Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations toward good practices for benchmarking of metaheuristic algorithms, have been identified. This article is aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCMs). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different BCMs. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance.
Instant Automatic Emptying of Panoramic Indoor Scenes

Nowadays 360° cameras, capable to capture full environments in a single shot, are increasingly being used in a variety of Extended Reality (XR) applications that require specific Diminished Reality (DR) techniques to conceal selected classes of objects. In this work, we present a new data-driven approach that, from an input 360° image of a furnished indoor space automatically returns, with very low latency, an omnidirectional photorealistic view and architecturally plausible depth of the same scene emptied of all clutter. Contrary to recent data-driven inpainting methods that remove single user-defined objects based on their semantics, our approach is holistically applied to the entire scene, and is capable to separate the clutter from the architectural structure in a single step. By exploiting peculiar geometric features of the indoor environment, we shift the major computational load on the training phase and having an extremely lightweight network at prediction time. Our end-to-end approach starts by calculating an attention mask of the clutter in the image based on the geometric difference between full and empty scene. This mask is then propagated through gated convolutions that drive the generation of the output image and its depth. Returning the depth of the resulting structure allows us to exploit, during supervised training, geometric losses of different orders, including robust pixel-wise geometric losses and high-order 3D constraints typical of indoor structures. The experimental results demonstrate that our method provides interactive performance and outperforms current state-of-the-art solutions in prediction accuracy on available commonly used indoor panoramic benchmarks. In addition, our method presents consistent quality results even for scenes captured in the wild and for data for which there is no ground truth to support supervised training.
Effect of Quantum computing on Blockchain-based Electronic Health Record Systems

Modern healthcare systems are known for being extremely complicated and expensive. On the other hand, improved health record management can help lessen this. The Electronic Health Records (EHR) sharing system is cutting-edge healthcare delivery. The system’s tools can anticipate results throughout a patient’s life, measure treatment efficacy, follow therapy, and detect human errors. Centralized and cloud-based systems are susceptible to security attacks and illegal modifications that can cause privacy issues and sometimes life-threatening situations. Integrity and interoperability across the treatment continuum are top priorities for major industry players. Given the massive amount of EHR data, security concerns, and variety of healthcare information systems, achieving this aim remains a challenge. Blockchain is an immutable ledger used in EHR systems due to its inherent immutability and privacy attributes. Blockchain-based systems are reliable and efficient. Another advantage of the Blockchain application in healthcare record management is the increased interoperability of records that can further help in better availability of the patient’s records resulting in better healthcare opportunities and collaboration. Quantum computing developments threaten such systems as quantum computers can easily break the algorithms assumed to be impossible to crack in years. This paper reviews the application of Blockchain in EHR, possible challenges, and quantum computers’ effect on the reliability and security of Blockchain-based EHR systems.
Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language

In this research, a method of developing a machine model for sentiment processing in the Serbian language is presented. The Serbian language, unlike English and other popular languages, belongs to the group of languages with limited resources. Three different data sets were used as a data source: a balanced set of music album reviews, a balanced set of movie reviews, and a balanced set of music album reviews in English—MARD—which was translated into Serbian. The evaluation included applying developed models with three standard algorithms for classification problems (naive Bayes, logistic regression, and support vector machine) and applying a hybrid model, which produced the best results. The models were trained on each of the three data sets, while a set of music reviews originally written in Serbian was used for testing the model. By comparing the results of the developed model, the possibility of expanding the data set for the development of the machine model was also evaluated.
OntoSuSD: Software engineering approaches integration ontology for sustainable software development

Abstract Different software development approaches (SDAs) are developed with broad portfolios of development processes. Each of the approaches has certain exclusive principles, practices, thinking, and values, which are informally represented, implemented, and improperly institutionalized. Ontologies are developed for the representation, assessment, and adaptation of SDAs separately without having a shared terminology which may lead to terminological conflict and confusion affecting the simultaneous representation and implementation in software development industry and academia. The software engineering approaches does not consider and support sustainability as priority concern. However, the approaches have capabilities of supporting sustainable software development in different sustainability aspects. This research article aims for the designing and development of an integrated ontology of software engineering approaches (i.e., agile, lean, and green) named OntoSuSD (ontology for sustainable software development) to support sustainable software development knowledge, awareness, and implementation. The goal of OntoSuSD is to propose, design and develop a formal, generic, consistent, and shared knowledge base containing semantic terminology and description of concepts and relationships generated around the representation and implementation of lean, agile, and green approaches in software development processes, which will facilitate their simultaneous implementation and assessment for sustainable software development. The OntoSuSD is developed using practical ontology engineering methodology by reusing relevant ontologies and explicit concepts and properties are defined to fulfill knowledge requirements and representations of the domain. The OntoSuSD is evaluated, and results infer OntoSuSD has high ontological design, good domain coverage, potential applications and achieves purpose of the ontology development.
AFINet: Attentive Feature Integration Networks for image classification

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Residual-like networks, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. However, the skip connection mechanism limits the utilization of intermediate features due to simple iterative updates. To mitigate the redundancy of residual-like networks, we design Attentive Feature Integration (AFI) modules, which are widely applicable to most residual-like network architectures, leading to new architectures named AFI-Nets. AFI-Nets explicitly model the correlations among different levels of features and selectively transfer features with a little overhead. AFI-ResNet-152 obtains a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs by about 10% and the number of parameters by about 9.2% compared to ResNet-152.
Efficient Semi-Supervised Multimodal Hashing With Importance Differentiation Regression

Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous multi-modal features at both the model training and online retrieval stages to support large-scale multimedia retrieval. Previous multi-modal hashing methods mainly focus on supervised and unsupervised hashing. The performance of supervised hashing largely relies on the number of labeled data, which is practically expensive to obtain. Unsupervised hashing methods cannot effectively capture the semantic correlations of multi-modal data without any labels for supervision. In this paper, we propose an Efficient Semi-supervised Multi-modal Hashing with Importance Differentiation Regression (ESMH-IDR) model, which can alleviate the existing problems by learning from both labeled and unlabeled data. Specifically, in this paper, we develop an efficient semi-supervised multi-modal hash code learning module. It learns the hash codes for labeled data in an efficient asymmetric way, and simultaneously performs nonlinear regression using the same projection matrix as the labeled samples to preserve the intrinsic data structure of unlabeled data. Besides, different from existing methods, we propose an importance differentiation regression strategy to learn hash functions by specially considering the different importance of hash codes learned from the labeled and unlabeled samples. Finally, we develop an efficient discrete optimization method guaranteed with convergence to iteratively solve the hash optimization problem. Experiments on several public multimedia retrieval datasets demonstrate the superiority of our proposed method on both retrieval effectiveness and efficiency. Our source codes and testing datasets can be obtained at https://github.com/ChaoqunZheng/ESMH.
Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets

Human activity recognition (HAR) plays an important role in a wide range of applications, such as health monitoring and gaming. Inertial sensors attached to body segments constitute a critical sensing system for HAR. Diverse inertial sensor datasets for HAR have been released with the intention of attracting collective efforts and saving the data collection burden. However, these datasets are heterogeneous in terms of subjects and sensor positions. The coupling of these two factors makes it hard to generalize the model to a new application scenario, where there are unseen subjects and new sensor position combinations. In this paper, we design a framework to combine heterogeneous data to learn a general representation for HAR, so that it can work for new applications. We propose an Augmented Adversarial Learning framework for HAR (AALH) to learn generalizable representations to deal with diverse combinations of sensor positions and subject discrepancies. We train an adversarial neural network to map various sensor sets' data into a common latent representation space which is domain-invariant and class-discriminative. We enrich the latent representation space by a hybrid missing strategy and complement each subject domain with a multi-domain mixup method, and they significantly improve model generalization. Experiment results on two HAR datasets demonstrate that the proposed method significantly outperforms previous methods on unseen subjects and new sensor position combinations.
SwipePass

Pattern lock-based authentication has been widely adopted in modern smartphones. However, this scheme relies essentially on passwords, making it vulnerable to various side-channel attacks such as the smudge attack and the shoulder-surfing attack. In this paper, we propose a second-factor authentication system named SwipePass, which authenticates a smartphone user by examining the distinct physiological and behavioral characteristics embedded in the user's pattern lock process. By emitting and receiving modulated audio using the built-in modules of the smartphone, SwipePass can sense the entire unlocking process and extract discriminative features to authenticate the user from the signal variations associated with hand dynamics. Moreover, to alleviate the burden of data collection in the user enrollment phase, we conduct an in-depth analysis of users' behaviors under different conditions and propose two augmentation techniques to significantly improve identification accuracy even when only a few training samples are available. Finally, we design a robust authentication model based on CNN-LSTM and One-Class SVM for user identification and spoofer detection. We implement SwipePass on three off-the-shelf smartphones and conduct extensive evaluations in different real-world scenarios. Experiments involving 36 participants show that SwipePass achieves an average identification accuracy of 96.8% while maintaining a false accept rate below 0.45% against various attacks.