Reviewerly/exHarmony huggingface dataset:
Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

Computational techniques are becoming more common in persona development.However, users of personas may question the information in persona profiles because they are unsure of how it was created.This problem is especially vexing for data-driven personas because their creation is an opaque algorithmic process.In this research, we analyze the effect of increased transparencyi.e., explanations of how the information in data-driven personas was producedon user perceptions.We find that higher transparency through these explanations increases the perceived completeness and clarity of the personas.Contrary to our hypothesis, the perceived credibility of the personas decreases with the increased transparency, possibly due to the technical complexity of the persona profiles disrupting the facade of the personas being real people.This finding suggests that explaining the algorithmic process of data-driven persona creation involves a "transparency trade-off".We also find that the gender of the persona affects the perceptions, with transparency increasing perceived completeness and empathy of the female persona, but not for the male persona.Therefore, transparency may specifically assist in the acceptance of female personas.We provide practical implication for persona creators regarding transparency in persona profiles.
Human Attention in Image Captioning: Dataset and Analysis

In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences in human attention during free-viewing and image captioning tasks. We look into the relationship between human atten- tion and language constructs during perception and sen- tence articulation. We also analyse attention deployment mechanisms in the top-down soft attention approach that is argued to mimic human attention in captioning tasks, and investigate whether visual saliency can help image caption- ing. Our study reveals that (1) human attention behaviour differs in free-viewing and image description tasks. Hu- mans tend to fixate on a greater variety of regions under the latter task, (2) there is a strong relationship between de- scribed objects and attended objects (97% of the described objects are being attended), (3) a convolutional neural net- work as feature encoder accounts for human-attended re- gions during image captioning to a great extent (around 78%), (4) soft-attention mechanism differs from human at- tention, both spatially and temporally, and there is low correlation between caption scores and attention consis- tency scores. These indicate a large gap between humans and machines in regards to top-down attention, and (5) by integrating the soft attention model with image saliency, we can significantly improve the model's performance on Flickr30k and MSCOCO benchmarks. The dataset can be found at: https://github.com/SenHe/ Human-Attention-in-Image-Captioning.
Deep learning improves automated rodent behavior recognition within a specific experimental setup

Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-the-art conventional rat behavior recognition (RBR) system to an advanced deep learning method and evaluate its performance within and across experimental setups. We show that using a multi-fiber network (MF-Net) in conjunction with data augmentation strategies within-setup dataset performance improves over the conventional RBR system. Two new methods for video augmentation were used: video cutout and dynamic illumination change. However, we also show that improvements do not transfer to videos in different experimental setups, for which we discuss possible causes and cures.
A Hybrid Active Contour Model based on New Edge-Stop Functions for Image Segmentation

Edge-based active contour methods are popular algorithms for image segmentation, with the purpose to extract the area of interest. However, they may face to boundary leakage and improper segmentation when handle images under weak edges or complex shapes. The extensive edge-stop functions adopt edge information, which cannot apply to guide the evolving curve approaching to target boundaries. To resolve this issue, a novel level set algorithm based on non-local means (NLM) filtering is constructed in this study. Firstly, the images are subjected to non-local means filtering to generate edge map. Secondly, a new edge-stop function constructed from this edge map as well as the fuzzy k-NN classification algorithm is incorporated into the variational model. Our experiments demonstrate that non-local means filtering is able to sharp edges both on medical and natural images. Thus, this analysis seems to be useful for clinical medical diagnosis.
Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs

In this article, we propose an online and unsupervised anomaly detection algorithm for streaming data using an array of sliding windows and the probability density-based descriptors (PDDs) (based on these windows). This algorithm mainly consists of three steps: 1) we use a main sliding window over streaming data and segment this window into an array of nonoverlapping subwindows; 2) we propose the PDDs with dimension reduction, based on the kernel density estimation, to estimate the probability density of data in each subwindow; and 3) we design the distance-based anomaly detection rule to determine whether the current observation is anomalous. The experimental results and performances are presented based on the Numenta anomaly benchmark. Compared with the anomaly detection algorithm using the hierarchical temporal memory proposed by Numenta (which outperforms a wide range of other anomaly detection algorithms), our algorithm can perform better in many cases, that is, with higher detection rates and earlier detection for contextual anomalies and concept drifts.
Adversarial Learning With Margin-Based Triplet Embedding Regularization

The Deep neural networks (DNNs) have achieved great success on a variety of computer vision tasks, however, they are highly vulnerable to adversarial attacks. To address this problem, we propose to improve the local smoothness of the representation space, by integrating a margin-based triplet embedding regularization term into the classification objective, so that the obtained models learn to resist adversarial examples. The regularization term consists of two steps optimizations which find potential perturbations and punish them by a large margin in an iterative way. Experimental results on MNIST, CASIA-WebFace, VGGFace2 and MS-Celeb-1M reveal that our approach increases the robustness of the network against both feature and label adversarial attacks in simple object classification and deep face recognition.
Blended Modelling - What, Why and How

Empirical studies indicate that user experience can significantly be improved in model-driven engineering. Blended modelling aims at mitigating this by enabling users to interact with a single model through different notations. Blended modelling contributes to various modelling qualities, including comprehensibility, analysability, and acceptability. In this paper, we define the notion of blended modelling and propose a set of dimensions that characterise blended modelling. The dimensions are grouped in two classes: user-oriented dimensions and realisation-oriented dimensions. Each dimension describes a facet that is relevant to blended modelling together with its domain (i.e., the range of values for that dimension). The dimensions offer a basic vocabulary to support tool developers with making well-informed design decisions as well as users to select appropriate tools and configure them according to the needs at hand. We illustrate how the dimensions apply to different cases relying on our experience with blended modelling. We discuss the impact of blended modelling on usability and user experience and sketch metrics to measure it. Finally, we outline a number of core research directions in this increasingly important modelling area.
Detecting Fraudulent Accounts on Blockchain: A Supervised Approach

Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system’s consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.
Simulation based calculation of the inverse kinematics solution of 7-DOF robot manipulator using artificial bee colony algorithm

This paper presents an artificial bee colony algorithm for solving the inverse kinematics of 7-degree-of-freedom robotic arm which has been newly designed and not used in the literature. The kinematics analysis of this manipulator which has an excessive number of joints, is quite complex. In this study, artificial bee colony, which is one of the swarm-based heuristic algorithms, has been used for inverse kinematics solution and its results have been analyzed in terms of position error and calculation time. In order to ensure the accuracy of the algorithm, calculations have been also carried out in 100 different points selected from the workspace of the robot manipulator. The results have been compared with particle swarm optimization, which is another swarm algorithm in terms of position error and computation time. The results obtained by computer simulation clearly show that the artificial bee colony algorithm produces effective results compared with the literature.
Energy-Efficient Real-Time UAV Object Detection on Embedded Platforms

The recent technology advancement on unmanned aerial vehicle (UAV) has enabled diverse applications in vision-related outdoor tasks. Visual object detection is a crucial task among them. However, it is difficult to actually deploy detectors on embedded devices due to the challenges among energy consumption, accuracy, and speed. In this article, we address a few key challenges from the platform, application to the system, and propose an energy-efficient system for real-time UAV object detection on an embedded platform. The proposed system can achieve speed of 28.5 FPS and 2.7-FPS/W energy efficiency on the data set from 2018 low-power object detection challenges (LPODCs).
Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes

In hospitals, caregivers are trained to chronicle the subtle changes in the clinical conditions of a patient at regular intervals, for enabling decision-making. Caregivers' text-based clinical notes are a significant source of rich patient-specific data, that can facilitate effective clinical decision support, despite which, this treasure-trove of data remains largely unexplored for supporting the prediction of clinical outcomes. The application of sophisticated data modeling and prediction algorithms with greater computational capacity have made disease prediction from raw clinical notes a relevant problem. In this paper, we propose an approach based on vector space and topic modeling, to structure the raw clinical data by capturing the semantic information in the nursing notes. Fuzzy similarity based data cleansing approach was used to merge anomalous and redundant patient data. Furthermore, we utilize eight supervised multi-label classification models to facilitate disease (ICD-9 code group) prediction. We present an exhaustive comparative study to evaluate the performance of the proposed approaches using standard evaluation metrics. Experimental validation on MIMIC-III, an open database, underscored the superior performance of the proposed Term weighting of unstructured notes AGgregated using fuzzy Similarity (TAGS) model, which consistently outperformed the state-of-the-art structured data based approach by 7.79% in AUPRC and 1.24% in AUROC.
Complementation-Reinforced Attention Network for Person Re-Identification

Fine-grained information has been proved helpful for person re-identification, and multi-head attention mechanism offers a feasible solution for this. However, we observe severe redundancy among the multiple branches, which might make the learned representation over-emphasize certain discriminative regions and correspondingly ignore other potentially informative regions. Therefore, we tackle this issue by two aspects yielding the so-called Complementation-Reinforced Attention Network (CRAN). One is the redundancy among branches, and we propose to impose complementing constraints among multiple attention heads. The constraints are two-fold: on the one hand, it encourages each branch to attend to complementary attention regions; on the other hand, it enforces orthogonality among the learned features of different regions in the embedding space. The other is the redundancy among query positions for each attention head. So we simplify the attention block by sparsifying the query positions. Besides, in order to achieve efficient retrieval, we propose an adaptive feature fusion method for dimensional reduction. Compared with the commonly used feature ensemble, our method effectively reduces the dimensionality while keeping the discriminative ability. We demonstrate the effectiveness of our method on MSMT17, Market-1501, DukeMTMC-reID, and CUHK03 datasets.
Adversarial attacks on Faster R-CNN object detector

Adversarial attacks have stimulated research interests in the field of deep learning security. However, most of existing adversarial attack methods are developed on classification. In this paper, we use Projected Gradient Descent (PGD), the strongest first-order attack method on classification, to produce adversarial examples on the total loss of Faster R-CNN object detector. Compared with the state-of-the-art Dense Adversary Generation (DAG) method, our attack is more efficient and more powerful in both white-box and black-box attack settings, and is applicable in a variety of neural network architectures. On Pascal VOC2007, under white-box attack, DAG has 5.92% mAP on Faster R-CNN with VGG16 backbone using 41.42 iterations on average, while our method achieves 0.90% using only 4 iterations. We also analyze the difference of attacks between classification and detection, and find that in addition to misclassification, adversarial examples on detection also lead to mis-localization. Besides, we validate the adversarial effectiveness of both Region Proposal Network (RPN) and Fast R-CNN loss, the components of the total loss. Our research will provide inspiration for further efforts in adversarial attacks on other vision tasks.
Deep cascaded cross-modal correlation learning for fine-grained sketch-based image retrieval

Fine-grained Sketch-based Image Retrieval (FG-SBIR), which utilizes hand-drawn sketches to search the target object images, has recently drawn much attention. It is a challenging task because sketches and images belong to different modalities and sketches are highly abstract and ambiguous. Existing solutions to this problem either focus on visual comparisons between sketches and images and ignore the multimodal characteristics of annotated images, or treat the retrieval as a one-time process. In this paper, we formulate FG-SBIR as a coarse-to-fine process, and propose a Deep Cascaded Cross-modal Ranking Model (DCCRM) that can exploit all the beneficial multimodal information in sketches and annotated images and improve both the retrieval efficiency and the top-K ranked effectiveness. Our goal concentrates on constructing deep representations for sketches, images, and descriptions, and learning the optimized deep correlations across such different domains. Thus for a given query sketch, its relevant images with fine-grained instance-level similarities in a specific category can be returned, and the strict requirement of the instance-level retrieval for FG-SBIR is satisfied. Very positive results have been obtained in our experiments by using a large quantity of public data.
A General Approach for Simulating Rain Effects on Sensor Data in Real and Virtual Environments

Driving automation systems typically use surround sensors to perceive their local environment. Accidents with automated vehicles have shown that errors in sensor data measurement and interpretation can lead to fatal injuries. It is thus necessary to test the reliability of environmental perception systems before market introduction. Since critical weather conditions are random, rare, and change quickly, relevant data sets are biased towards clear conditions. Consequently, detection algorithms based on these data suffer from limited performance. This article focuses on a two-step approach based on a) an indoor rain facility to test under reproducible, realistic conditions and b) physical-based models to enrich sensor data from clear conditions with virtual rain effects in a post-processing step. We concentrate on data from camera, lidar, and radar sensors. Experimental results show that both approaches simulate critical effects on raw sensor data and, therefore, enable replicable testing and validation at every stage of development.
Virtual reality in the HE classroom: feasibility, and the potential to embed in the curriculum

Learner engagement is a challenge within Geography education, and Higher Education more generally and immersive Virtual Reality (VR) has a wealth of possibilities, but finding simple, straightforward applications that are also pedagogically worthwhile can be a challenge. Three trial runs of full earth simulations in VR in classroom environments were conducted using high-end VR hardware. The trials were conducted with Geography and Digital Humanities students and the aim was to evaluate the use of immersive VR would enhance learner engagement. The technology acceptance model was used to some extent to get appropriate survey data and an inductive approach to thematic analysis was applied. Based on the data collected there seemed a reasonable evidence base that students found the tool relatively easy to use and it enhanced their understanding and engagement. The findings are in line with those of previous studies which show that immersive VR environments create a strong sense of perceived presence which leads to higher learner engagement and motivation. Challenges for greater adoption are also presented.
Deep Learning: A Breakthrough in Medical Imaging

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.
Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks

Abstract Efficient and robust facial landmark localisation is crucial for the deployment of real-time face analysis systems. This paper presents a new loss function, namely Rectified Wing (RWing) loss, for regression-based facial landmark localisation with Convolutional Neural Networks (CNNs). We first systemically analyse different loss functions, including L2, L1 and smooth L1. The analysis suggests that the training of a network should pay more attention to small-medium errors. Motivated by this finding, we design a piece-wise loss that amplifies the impact of the samples with small-medium errors. Besides, we rectify the loss function for very small errors to mitigate the impact of inaccuracy of manual annotation. The use of our RWing loss boosts the performance significantly for regression-based CNNs in facial landmarking, especially for lightweight network architectures. To address the problem of under-representation of samples with large pose variations, we propose a simple but effective boosting strategy, referred to as pose-based data balancing. In particular, we deal with the data imbalance problem by duplicating the minority training samples and perturbing them by injecting random image rotation, bounding box translation and other data augmentation strategies. Last, the proposed approach is extended to create a coarse-to-fine framework for robust and efficient landmark localisation. Moreover, the proposed coarse-to-fine framework is able to deal with the small sample size problem effectively. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits of our RWing loss and prove the superiority of the proposed method over the state-of-the-art approaches.
For Science and Self: Youth Interactions with Data in Community and Citizen Science

In youth-focused community and citizen (YCCS) youth in classrooms and community-based programs produce data that scientists, resource managers and community members will use. This “nested” data situates learners’ scientific activity within larger datasets, projects, and communities, with consequences for youth agency. To document opportunities for agentive learning with data in YCCS, we report on how youth interact with data across eight school and community-based project sites and how youth talk about their data and work. From analysis of 54 participant interviews, we found that youth perceived the data they produced as being used for: (1) broader scientific work, (2) their own learning, and (3) community endeavors. Nested uses of data were most meaningful when youth interacted with end users, were exposed to the larger datasets to which they contributed, or took action linked to data. Not all youth saw, believed, or valued that data would be used by others. Framing of task and purpose, nature of engagement with community users, data production protocols, and level of emphasis on youth-identified questions may contribute to youth perceptions and can foster or undermine conditions for learner agency. The results shed light on when and how conditions for expansive learning and agency get established.