Reviewerly/exHarmony huggingface dataset:
End‐to‐end feature fusion Siamese network for adaptive visual tracking

According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, an end-to-end feature fusion framework was proposed based on the Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilised in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular Temple-Colour, OTB50 and UAV123 benchmarks.
A Survey on Web Application Security

Web application security has become real concern due to increase in attacks and data breaches. As Application becomes critical, complex and connected, the difficulty of achieving application security increases exponentially. Also there are tools and techniques to detect such attacks, threat and vulnerabilities that exist in application which developer prevent and mitigate the risk associated to it. This paper evaluates various web application attack detection mechanisms and how resistant they are against various attacking techniques. Such an evaluation is important for not only measuring the available attack defense against web application attacks but also identifying gaps to build effective solutions for different defense techniques on web application and use it for study. Based on the research, the limitations of these application attack detection techniques are identified and remedies proposed for improving the current state attack detection on web applications.
Investment Priority Analysis of ICS Information Security Resources in Smart Mobile IoT Network Environment Using the Analytic Hierarchy Process

The industrial control system (ICS) inherits the attributes of the traditional information system, but because it has its own characteristics that availability of triad (CIA) of information security should be a top priority, it needs to be set differently from the traditional information security requirements. In response to the issue, TTAK.KO-12.0307 (Standard for Industrial Control System Information Security Requirements) proposed by the National Security Research Institute (NSRI) and established by the Telecommunications Technology Association (TTA) is being used. However, it is difficult to apply security requirements of TTAK.KO-12.0307 uniformly because of the reason that the characteristics of the ICS in each layer are different. There is also a limit to invest the security resources with equivalent priority for all requirements and ICS layers. It is still unresolved in the previous research studies which are related to information security resources, for example, Choi (2013), Ko et al. (2013), and Nah et al.’s (2016) studies. Therefore, this study tried to focus on what a top priority of information security requirements by the ICS in each layer is, using the analytic hierarchy process. As a result, we derived that the top priority requirement in the operation layer is “Identification Authentication Access Control,” in the control layer is “Event Response,” and in the field device layer is “Physical Interface Protection” with the highest importance. The results of this study can be utilized as a guideline for the security strategy and policy design by determining security requirements that should be prioritized in each layer of the ICS.
Improving Scholarly Knowledge Representation: Evaluating BERT-Based Models for Scientific Relation Classification

With the rapid growth of research publications, there is a vast amount of scholarly knowledge that needs to be organized in digital libraries. To deal with this challenge, techniques relying on knowledge-graph structures are being advocated. Within such graph-based pipelines, inferring relation types between related scientific concepts is a crucial step. Recently, advanced techniques relying on language models pre-trained on large corpora have been popularly explored for automatic relation classification. Despite the remarkable contributions that have been made, many of these methods were evaluated under different scenarios, which limits their comparability. To address this shortcoming, we present a thorough empirical evaluation of eight Bert-based classification models by focusing on two key factors: 1) Bert model variants, and 2) classification strategies. Experiments on three corpora show that domain-specific pre-training corpus benefits the Bert-based classification model to identify the type of scientific relations. Although the strategy of predicting a single relation each time achieves a higher classification accuracy than the strategy of identifying multiple relation types simultaneously in general, the latter strategy demonstrates a more consistent performance in the corpus with either a large or small number of annotations. Our study aims to offer recommendations to the stakeholders of digital libraries for selecting the appropriate technique to build knowledge-graph-based systems for enhanced scholarly information organization.
CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection

Recently, a wide range of research on object detection has shown breakthrough performance. However, in a challenging environment, such as occlusion and small object cases, object detectors still produce inaccurate or erroneous predictions. To effectively cope with such conditions, most of the existing methods have suggested loss functions to guide the object detectors by modulating the magnitude of their loss. However, when modulating the loss function, they are highly dependent on the classification score of the object detector. It is a known fact that deep neural networks tend to be overconfident in their predictions. In this article, to alleviate the problem of the object detectors which heavily rely on the prediction in the training phase, we devise a novel loss function called class uncertainty-aware (CUA) loss. CUA loss considers the predictive ambiguity as well as the predictions on classification score when modulating loss function. In addition to the classification score, CUA loss further modulates the loss gradient in an increasing way when the object detectors output an uncertain prediction. Therefore, object detectors with CUA loss effectively cope with challenging environments where prediction results are uncertain. With comprehensive experiments on three public datasets (i.e. PASCAL VOC, MS COCO, and Berkeley DeepDrive), we verified that our CUA loss enhanced the accuracy of the object detectors and outperformed previous state-of-the-art loss functions.
Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction

Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS and WA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio and structural similarity index in ECS, NWP, and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SR performance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy.
Haptic Space: The Effect of a Rigid Hand Representation on Presence when Interacting with Passive Haptics Controls in VR

Many virtual reality (VR) applications rely on passive haptics where virtual objects have a real counterpart that provides tactile feedback. In addition to that, many VR applications do not provide accurate hand representations, especially when there is a high chance of occlusion as this makes vision-based tracking problematic. Hence, we investigated how a simple hand representation affects user experience and presence when interacting with passive haptic controls in a virtual environment. We report on a between-subject user study where N = 45 participants experienced one of three conditions (no hands at all, hands represented as a rigid 3D model, and hands represented as a rigid 3D model with a snapping mechanism). Our results indicate that a simple hand representation using a 3D model of hands paired with a snapping mechanism significantly increases presence and user experience. That indicates that this simple and low-cost technique is effective to improve the VE as a whole. This, in return, provides a chance for improvement for many VR applications with passive haptics.
Depositional patterns constrained by slope topography changes on seamounts

Abstract Slope topography is known to control the spatial distribution of deposits on intraplate seamounts; however, relatively little is known about how slope topography changes constrain those depositional patterns. In this study, we analyse data on four lithotypes found on seamount slopes, including colloidal chemical deposits comprising mainly cobalt-rich crusts, and examine the relationships between the spatial distribution of these lithotypes and current slope topography. We use these relationships to discuss depositional patterns constrained by slope topography changes. Some depositional units in drill core samples are interpreted to have resulted from past topographic changes that created the current slope topography. Two or more types of deposits that accumulated at the same location implies that the slope topography changed over time and that the depositional patterns on seamount slopes are constrained by changes in slope topography.
Leveraging special-purpose hardware for local search heuristics

As we approach the physical limits predicted by Moore’s law, a variety of specialized hardware is emerging to tackle specialized tasks in different domains. Within combinatorial optimization, adiabatic quantum computers, complementary metal-oxide semiconductor annealers, and optical parametric oscillators are a few emerging specialized hardware technologies to solve optimization problems. The Ising optimization model unifies all of these emerging special-purpose hardware for optimization in terms of mathematical framework. In other words, they are all designed to solve optimization problems expressed in the Ising model or equivalently as a quadratic unconstrained binary optimization model. Due to various constraints specific to each type of hardware, they usually suffer from a major challenge: the number of variables that the hardware can manage to solve is very limited. The local search meta-heuristic is one of the approaches to tackle large-scale problems. However, a general optimization step within local search is not traditionally formulated in the Ising form. In this work, we introduce a new modeling framework for modeling local search heuristics for special-purpose hardware. In particular, we propose models that take the limitations of the Ising model and current hardware into account. As such, we demonstrate the advantage of our approach compared to previous methods by carrying out experiments to show that our local search models produce higher-quality solutions.
Augmented Reality (AR)-Based Intervention to Enhance Awareness of Fine Dust in Sustainable Environments

Education on fine dust is a challenging field, as it must explain abstract phenomena and accompany the demands for behavioral changes. This research studied whether the adoption of augmented reality (AR) technology can effect change in student perspectives with a view to improving the attitudes of passive students regarding fine dust-related matters. With regard to student awareness, 94.0% of students responded that the issue of fine dust in Korea is severe; the higher the degree of interest they had in fine dust, the more severe they perceived the issue to be. On the perceived severity of fine dust in schools, 47.8% viewed it as severe; again, the level differed by the degree of interest. Students (80%) who received a traditional education on fine dust expressed dissatisfaction. The effect of using AR in experiential learning in fine dust environmental education has shown enhanced results compared to before receiving the education, notably in the group with a low degree of interest in fine dust and the group without prior experience in fine dust environmental education. Likewise, in the satisfaction rate for AR-based education on fine dust, 72.5% of participants responded that they had enjoyed the class, 75.9% showed active participation, and 80.0% felt that the content of the education was useful. Considering that the objective of fine dust environmental education is not based on understanding a theory but on maintaining individual health through practice in real life, the use of AR in fine dust environmental education as applied in this study may be interpreted as having a certain level of effect. Additionally, our results demonstrate that the use of AR in classes is effective in arousing students’ interest and inducing participation for better engagement in learning activities, which is, consequently, effective in enhancing the understanding of learning materials by students.
Blockchain in Pharmaceutical Sector

The pharmaceutical research and development is a composite process that grabs lots of years in drug discovery, drug development and regulatory approval in the supply chain in the pharmaceutical sector. But due to the drugs counterfeiting, such as hidden, uncontrolled and outdated information of drug regulatory authority and manufacturers over drugs supply leads to producing, marketing and consuming counterfeit drugs. For this, blockchain is the fittest in these scenarios where data security and privacy protection is the priority. It confirms the security of drugs on the market with high trust and quality of health care for the population and recipes by using modern authentic digital devices. In this chapter, it has been discussed that how blockchain technology concerning the supply chain in the pharmaceutical sector helps to add visibility, traceability and privacy to the medicines and drugs system. Here, it has been discussed that how blockchain technology in the supply chain in the pharmaceutical sector helps to add visibility, traceability and privacy to the medicines and drugs system. Also, it has been explained how the drug usability, its effect on patient and data recorded to the blockchain database and the authorized blockchain used for these statistics to store the transactions in the pharmaceutical sector. Also, the drug usability, its effect on the patient, data recorded to the blockchain database and authorized blockchain used for these statistics to store the transactions for the future has been explained. Further, its future applications, challenges and solutions to guard people's life are discussed.
Simulation Tool for the Drone Trajectory Planning Based on Genetic Algorithm Approach

In recent years, unmanned aerial vehicles (UAVs) have been found in many applications, from farm monitoring to civil infrastructure, from military to fire safety services. Drones are being actively introduced and are already being put into practice in emergency response services. The value of their use is primarily in saving time and resources. At minimal cost, the device covers a large area of the surveyed area. One of the main problems is estimation the optimal path of the UAV to cover the target area. In the present paper, we consider the problem of the estimation of the optimal parameters of trajectory planning algorithms. The main focus of the paper is the development of software utility to conduct the simulations of the drone trajectory generation, evaluation and optimization approaches, which is based on genetic algorithm approach. In particular, we explore the influence of the parameters genetic operators on the improvement of fitness values of the trajectory. A quantitative evaluation of the algorithm was conducted based on the results of experiments.
Quantifying measurement quality and load distribution in Tor

Tor is a widely used anonymization network.Traffic is routed over different relay nodes to conceal the communication partners.However, if a single relay handles too much traffic, de-anonymization attacks are possible.The Tor Load Balancing Mechanism (TLBM) is responsible for balanced and secure load distribution.It must verify that relays cannot attract more traffic than they should by lying about their self-reported bandwidth.This work shows that the current bandwidth measurement method used for bandwidth verification is not suitable to verify the bandwidth of many relays.Most importantly, multiple measurements of high-bandwidth relays are uncorrelated to each other.Furthermore, we analyze the current load distribution in Tor.We show that the current load distribution reduces the resources necessary for several large-scale de-anonymization attacks by more than 80%.Additionally, as Tor favors fast relays during path selection, verifiable relays only handle a small fraction of Tor's traffic.More precisely, we show that only 7.21% of all paths consist of entry and exit relays verifiable by measurements.We discuss these results' security implications and argue that future TLBM research should focus at least as much on secure load distribution as on high traffic performance. CCS CONCEPTS• Security and privacy → Pseudonymity, anonymity and untraceability; • Networks → Network privacy and anonymity.
Using visual stimuli to promote healthy snack choices among children

Most interventions against obesity use information to persuade people to change their behavior, with moderate results. Because eating involves automatic routines, new approaches have emerged appealing to non-reflective cognitive processes. Through a randomized controlled trial, we evaluated the impact of visual stimuli (positive and negative) on children's snack-choices at school. Results showed that the negative stimulus had no effect, while the positive stimulus increased the probability among girls of choosing a healthy snack. We also found that children with excess weight had a larger baseline probability of choosing the healthy snack than those without. We conclude that happy emojis, used to nudge non-reflective processes, can steer children towards healthy choices.
Deconvoluting kernel density estimation and regression for locally differentially private data

Abstract Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive individual data points in a privacy-preserving manner. However, locally differential data can twist the probability density of the data because of the additive noise used to ensure privacy. In fact, the density of privacy-preserving data (no matter how many samples we gather) is always flatter in comparison with the density function of the original data points due to convolution with privacy-preserving noise density function. The effect is especially more pronounced when using slow-decaying privacy-preserving noises, such as the Laplace noise. This can result in under/over-estimation of the heavy-hitters. This is an important challenge facing social scientists due to the use of differential privacy in the 2020 Census in the United States. In this paper, we develop density estimation methods using smoothing kernels. We use the framework of deconvoluting kernel density estimators to remove the effect of privacy-preserving noise. This approach also allows us to adapt the results from non-parametric regression with errors-in-variables to develop regression models based on locally differentially private data. We demonstrate the performance of the developed methods on financial and demographic datasets.
Constellation Optimization for Phase-Shift Keying Coherent States With Displacement Receiver to Maximize Mutual Information

An important problem in quantum information theory is finding the best possible capacity of the optical communication channel employing suitable codewords, receiver design, and constellation optimization techniques. In this paper, we derive an alternative channel capacity, C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> , of phase-shift keying coherent state with a realizable displacement receiver by maximizing mutual information over symbol priors and pre-detection displacement. We find that C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> is higher than the capacity achieved by maximizing mutual information over symbol prior but with zero displacement. The overall scheme demonstrates designing an improved, yet easy-to-implement receiver for better communication performance by tuning it at different photon number regime. Further, we present a comparative analysis of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> with existing receiver designs. We extend our study to account for detector imperfections.imperfections.
Is Warping-based Cancellable Biometrics (still) Sensible for Face Recognition?

We conduct an ISO/IEC Standards 24745 and 30136 compliant assessment of block-based warping sample transformation techniques aiming for template protection. Particular focus is laid on the results' evaluation considering the evolution of face recognition technology ranging from more “historic” hand-crafted features to state-of-the-art deep-learning (DL) based schemes. It turns out that the high robustness of todays face recognition technology can handle geometrical distortions introduced by warping as another form of variability like pose, illumination, and expression variations, thereby disabling the intended protection functionality of warping. Therefore, block-based warping sample transformation must not be used as template protection technique for todays state-of-the-art face recognition schemes, while some settings could be identified providing template protection to some extent for less recent face recognition technology.