Reviewerly/exHarmony huggingface dataset:
Annotation vs. Virtual Tutor: Comparative Analysis on the Effectiveness of Visual Instructions in Immersive Virtual Reality

In this paper we present a comparative study of visual instructions in Immersive Virtual Reality (IVR), i.e., annotation (ANN) that employs 3D texts and objects for instructions and virtual tutor (TUT) that demonstrates a task with a 3D character. The comparison is based on three tasks, maze escape (ME), stretching exercise (SE), and crane manipulation (CM), defined by the types of a unit instruction. We conducted an automated evaluation of user's memory recall performances (recall time, accuracy, and error) by mapping a sequence of user's behaviors and events as a string. Results revealed that ANN group showed significantly more accurate performance (1.3 times) in ME and time performance (1.64 times) in SE than TUT group, while no statistical main difference was found in CM. Interestingly, although ANN showed statistically shorter execution time, the recalling time pattern of TUT group showed a steep convergence after initial trial. The results can be used in the field in terms of informing designers of IVR on what types of visual instruction are best for different task purpose.
Kollector: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning

With the rapid growth in smartphone usage, preventing leakage of personal information and privacy has become a challenging task. One major consequence of such leakage is impersonation. This type of illegal usage is nearly impossible to prevent as existing preventive mechanisms (e.g., passcode and fingerprinting), are not capable of continuously monitoring usage and determining whether the user is authorized. Once unauthorized users can defeat the initial protection mechanisms, they would have full access to the devices including using stored passwords to access high-value websites. We present Kollector, a new framework to detect impersonation based on a multi-view bagging deep learning approach to capture sequential tapping information on the smart-phone's keyboard. We construct a sequential-tapping biometrics model to continuously authenticate the user while typing. We empirically evaluated our system using real-world phone usage sessions from 26 users over eight weeks. We then compared our model against commonly used shallow machine techniques and find that our system performs better than other approaches and can achieve an 8.42 percent equal error rate, a 94.24 percent accuracy and a 94.41 percent H-mean using only the accelerometer and only five keyboard taps. We also experiment with using only three keyboard taps and find that the system still yields high accuracy while giving additional opportunities to make more decisions that can result in more accurate final decisions.
Use of pre-industrial baselines to monitor anthropogenic enrichment of metals concentrations in recently deposited sediment of floodplain lakes in the Peace-Athabasca Delta (Alberta, Canada)

Abstract Well-designed monitoring approaches are needed to assess effects of industrial development on downstream aquatic environments and guide environmental stewardship. Here, we develop and apply a monitoring approach to detect potential enrichment of metals concentrations in surficial lake sediments of the Peace-Athabasca Delta (PAD), northern Alberta, Canada. Since the ecological integrity of the PAD is strongly tied to river floodwaters that replenish lakes in the delta, and the PAD is located downstream of the Alberta oil sands, concerns have been raised over the potential transport of industry-supplied metals to the PAD via the Athabasca River. Surface sediment samples were collected in September 2017 from 61 lakes across the delta, and again in July 2018 from 20 of the same lakes that had received river floodwaters 2 months earlier, to provide snapshots of metals concentrations (Be, Cd, Cr, Cu, Ni, Pb, V, and Zn) that have recently accumulated in these lakes. To assess for anthropogenic enrichment, surficial sediment metals concentrations were normalized to aluminum and compared to pre-industrial baseline (i.e., reference) metal-aluminum linear relations for the Athabasca and Peace sectors of the PAD developed from pre-1920 measurements in lake sediment cores. Numerical analysis demonstrates no marked enrichment of these metals concentrations above pre-1920 baselines despite strong ability (&gt; 99% power) to detect enrichment of 10%. Measurements of river sediment collected by the Regional Aquatics- and Oil Sands-Monitoring Programs (RAMP/OSM) also did not exceed pre-1920 concentrations. Thus, results presented here show no evidence of substantial oil sands-derived metals enrichment of sediment supplied by the Athabasca River to lakes in the PAD and demonstrate the usefulness of these methods as a monitoring framework.
Social relations versus near neighbours

Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.
A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images

Accurate assessment of 3D models of patient-specific anatomy of the liver, including underlying hepatic and biliary tree, is critical for preparation and safe execution of complex liver resections, especially due to high variability of biliary and hepatic artery anatomies. Dynamic MRI with hepatospecific contrast agents is currently the only type of diagnostic imaging that provides all anatomical information required for generation of such a model, yet there is no information in the literature on how the complete 3D model can be generated automatically. In this work, a new automated segmentation workflow for extraction of patient-specific 3D model of the liver, hepatovascular and biliary anatomy from a single multiphase MRI acquisition is developed and quantitatively evaluated. The workflow incorporates course 4D k-means clustering estimation and geodesic active contour refinement of the liver boundary, based on organ's characteristic uptake of gadolinium contrast agents overtime. Subsequently, hepatic vasculature and biliary ducts segmentations are performed using multiscale vesselness filters. The algorithm was evaluated using 15 test datasets of patients with liver malignancies of various histopathological types. It showed good correlation with expert manual segmentation, resulting in an average of 1.76 ± 2.44 mm Hausdorff distance for the liver boundary, and 0.58 ± 0.72 and 1.16 ± 1.98 mm between centrelines of biliary ducts and liver veins, respectively. A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed. This enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitating better perception of organ's anatomy during preparation and execution of liver surgeries. Additionally, it may help to reduce the incidence of intraoperative biliary duct damage due to an unanticipated variation in the anatomy.
Fairness in Learning-Based Sequential Decision Algorithms: A Survey

Algorithmic fairness in decision-making has been studied extensively in static settings where one-shot decisions are made on tasks such as classification. However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data. This is particularly the case when decisions affect the individuals or users generating the data used for future decisions. In this survey, we review existing literature on the fairness of data-driven sequential decision-making. We will focus on two types of sequential decisions: (1) past decisions have no impact on the underlying user population and thus no impact on future data; (2) past decisions have an impact on the underlying user population, and therefore the future data, which can then impact future decisions. In each case the impact of various fairness interventions on the underlying population is examined.
Bottom-up unsupervised image segmentation using FC-Dense u-net based deep representation clustering and multidimensional feature fusion based region merging

Recent advances in system resources provide ease in the applicability of deep learning approaches in computer vision. In this paper, we propose a deep learning-based unsupervised image segmentation approach for natural image segmentation. Image segmentation aims to transform an image into regions, representing various objects in the image. Our method consists of a fully convolutional dense network-based unsupervised deep representation oriented clustering, followed by shallow features based high-dimensional region merging to produce the final segmented image. We evaluate our proposed approach on the BSD300 database and perform a comparison with several classical and some recent deep learning-based unsupervised segmentation methods. The experimental results represent that the proposed method is comparable and confirm the efficacy of the proposed approach.
A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.
AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation

Despite being adopted in software engineering tasks, deep neural networks are treated mostly as a black box due to the difficulty in interpreting how the networks infer the outputs from the inputs. To address this problem, we propose AutoFocus, an automated approach for rating and visualizing the importance of input elements based on their effects on the outputs of the networks. The approach is built on our hypotheses that (1) attention mechanisms incorporated into neural networks can generate discriminative scores for various input elements and (2) the discriminative scores reflect the effects of input elements on the outputs of the networks. This paper verifies the hypotheses by applying AutoFocus on the task of algorithm classification (i.e., given a program source code as input, determine the algorithm implemented by the program). AutoFocus identifies and perturbs code elements in a program systematically, and quantifies the effects of the perturbed elements on the network's classification results. Based on evaluation on more than 1000 programs for 10 different sorting algorithms, we observe that the attention scores are highly correlated to the effects of the perturbed code elements. Such a correlation provides a strong basis for the uses of attention scores to interpret the relations between code elements and the algorithm classification results of a neural network, and we believe that visualizing code elements in an input program ranked according to their attention scores can facilitate faster program comprehension with reduced code.
Collective Sports: A multi-task dataset for collective activity recognition

Collective activity recognition is an important subtask of human action recognition, where the existing datasets are mostly limited. In this paper, we look into this issue and introduce the “Collective Sports (C-Sports)” dataset, which is a novel benchmark dataset for multi-task recognition of both collective activity and sports categories. Various state-of-the-art techniques are evaluated on this dataset, together with multi-task variants which demonstrate increased performance. From the experimental results, we can say that while sports categories of the videos are inferred accurately, there is still room for improvement for collective activity recognition, especially regarding the generalization ability beyond previously unseen sports categories. In order to evaluate this ability, we introduce a novel evaluation protocol called unseen sports, where the training and test are carried out on disjoint sets of sports categories. The relatively lower recognition performances in this evaluation protocol indicate that the recognition models tend to be influenced by the surrounding context, rather than focusing on the essence of the collective activities. We believe that C-Sports dataset will stir further interest in this research direction.
Towards stabilizing facial landmark detection and tracking via hierarchical filtering: A new method

Many facial landmark detection and tracking methods suffer from instability problems that have a negative influence on real-world applications, such as facial animation, head pose estimation and real-time facial 3D reconstruction. The instability results of landmark tracking cause face pose shaking and face movement that is not fluent enough. However, most of the existing landmark detection and tracking methods only consider the stability of face location but neglect the stability of local landmark movement. To solve the problem of landmark local shaking, we present a novel hierarchical filtering method for stabilized facial landmark detection and tracking in video frames. The proposed method addresses the challenging landmark local shaking problem and provides effective remedies to solve them. The main contribution within our solution is a novel hierarchical filtering strategy, which guarantees the robustness of global whole facial shape tracking and the adaptivity of local facial parts tracking. The proposed solution does not depend on specific face detection and alignment algorithms, and thus, can be easily deployed into existing systems. Extensive experimental evaluations and analyses on benchmark datasets and 3D head pose datasets verify the effectiveness of our proposed stabilizing method.
Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar's test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.
C2FHAR: Coarse-to-Fine Human Activity Recognition With Behavioral Context Modeling Using Smart Inertial Sensors

Smart sensing devices are furnished with an array of sensors, including locomotion sensors, which enable continuous and passive monitoring of human activities for the ambient assisted living.As a result, sensor-based human activity recognition has earned significant popularity in the past few years.A lot of successful research studies have been conducted in this regard.However, the accurate recognition of in-the-wild human activities in real-time is still a fundamental challenge to be addressed as human physical activity patterns are adversely affected by their behavioral contexts.Moreover, it is essential to infer a user's behavioral context along with the physical activity to enable context-aware and knowledge-driven applications in real-time.Therefore, this research work presents ''C2FHAR'', a novel approach for coarseto-fine human activity recognition in-the-wild, which explicitly models the user's behavioral contexts with activities of daily living to learn and recognize the fine-grained human activities.For addressing realtime activity recognition challenges, the proposed scheme utilizes a multi-label classification model for identifying in-the-wild human activities at two different levels, i.e., coarse or fine-grained, depending upon the real-time use-cases.The proposed scheme is validated with extensive experiments using heterogeneous sensors, which demonstrate its efficacy.
Gastric Cancer Diagnosis with Mask R-CNN

It is a reliable way to judge gastric cancer by pathological section. Using deep learning method to detect medical images, as an auxiliary diagnosis method, it can improve the speed and accuracy of doctors to diagnose gastric cancer, and reduce misdiagnosis and missed diagnosis. Mask R-CNN is the latest method in the related field at the beginning of the research. It is mainly used to segment the objects in daily life and achieve good results. The medical image is very different from the scene in life, and the detection effect is also weakened. We use the Mask R-CNN method to detect the pathological sections of gastric cancer, and segment the cancer nest, and then optimize it by adjusting parameters. The method finally allows it to obtain a test result with an AP value of 61.2 when detecting medical images.
Robust 2½D Visual Servoing of A Cable-Driven Parallel Robot Thanks to Trajectory Tracking

Cable-driven parallel robots (CDPRs) are a kind of parallel robots that have cables instead of rigid links. Implementing vision-based control on CDPRs leads to a good final accuracy despite modeling errors and other perturbations in the system. However, unlike final accuracy, the trajectory to the goal can be affected by the perturbations in the system. This letter proposes the use of trajectory tracking to improve the robustness of 21/2D visual servoing control of CDPRs. Lyapunov stability analysis is performed and, as a result, a novel workspace, named control stability workspace, is defined. This workspace defines the set of moving-platform poses where the robot is able to execute its task while being stable. The improvement of robustness is clearly shown in experimental validation.
An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison.
Spaceborne mapping of hydrothermal alteration zones associated with the Mundiyawas-Khera copper deposit, Rajasthan, India, using SWIR bands of ASTER: Implications for exploration targeting

This paper discusses the effectiveness of spectroscopic techniques for identifying hydrothermal alteration zones associated with the Mundiyawas-Khera copper deposit, Alwar Basin, western India, and assesses implications for mineral exploration targeting in the greater Alwar basin. Mineral specific SWIR bands of ASTER were spectrally enhanced to highlight areas of anomalous Al-OH and Mg-OH responses, representing key mappable expressions of the Mundiyawas-Khera copper deposit. The hydroxyl-bearing mineral zones were identified at 98% and 95% threshold pixels, respectively. Kaolinite ± scapolite, sericite (muscovite), tremolite and dolomite, contained in the felsic volcanic and dolomitic copper host rocks, were identified from spectroscopic data and corroborated with petrographic and XRD data. On this basis, distinct argillic, phyllic and propylitic hydrothermal alteration zones could be identified at Mundiyawas-Khera. The integrated approach presented here (i) identified a well defined phyllic alteration pattern in the felsic volcanic rocks, (ii) resulted in a comprehensive remotely sensed hydrothermal alteration map illustrating the spatial distribution of the copper-related hydrothermal alteration zones (i.e., an eastern argillic, a central phyllic, and a western propylitic zone), (iii) helped to delineate potential copper-rich zones coincident with a known lithostructural trend, and (iv) has implications for copper exploration in the greater Alwar basin.