Reviewerly/exHarmony huggingface dataset:
Recurrent Comparator with Attention Models to Detect Counterfeit Documents

This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation.
Measuring subjectively experienced time in usability and user experience testing scenarios

Duration perception influences decision and evaluation processes in daily life. Nevertheless, subjectively experienced time is not commonly considered in usability and user experience (UX) testing scenarios. In this paper, we introduce an easy way to measure subjectively experienced time (SXT) and integrate it into a framework of UX, both theoretically and empirically. In two studies (N=62 and N=80), we show that SXT can be evaluated by users after solving tasks on a website. Moreover, we show that it correlates substantially with components of UX but that it is not redundant to common UX measures. Additionally, we show that varying aspects of the website (usability) and the evaluation task (think aloud) show similar effects on both SXT and UX measures. Our results suggest that SXT is partly based on the estimated clock time as distortions in estimations translate into subjectively experienced time. We conclude that users perceive time during an ongoing interaction with a technical device, that they evaluate it, and integrate this evaluation into their UX evaluation. Hence, the presented way of measuring SXT can be handy for UX practitioners to understand how objective temporal characteristics of the interaction translate into UX, especially when designing temporal aspects of interactions to elicit a positive UX.
ChainIDE: A Cloud-Based Integrated Development Environment for Cross-Blockchain Smart Contracts

Blockchain has become novel solutions for many traditional issues in computer science and finance. Recently, with the release of Libra blockchain from Facebook, the decentralized finance concepts have another huge development. However, there are already many different blockchain systems existing now and the development of blockchain systems have become more and more complicated since each kind of blockchain development environment will consume time to be built. To make the programming on different blockchain systems more easily, we propose a novel cloud-based solution, namely ChainIDE, for the development of blockchain-based smart contracts on multiple kinds of blockchain systems. With chainIDE, cross-chain developing of smart contracts on different blockchain systems can be easily done without any time consumed by building the environment. Based on the operation statistics in this paper, we served more than 310,000 compilations in the past 30 days which makes us the most popular cloud-based cross-chain development platform in the world.
Reactive Interaction Through Body Motion and the Phase-State-Machine

Between humans, body motion is an intuitive and ubiquitous means to coordinate interactions, but so far collaborative robots have not embraced this mode of communication. One reason for this is that conventional behavior generation systems use finite state machines which make it exceptionally difficult to ingest and produce the inherently continuous and concurrent information flow that body motion provides. We propose a new, reactive motion generator based on a dynamical system instead. It mimics a conventional state machine except that transitions are not instantaneous but time-extended, reversible, and have phases. Moreover, consecutive states and transitions may overlap during execution. Most notably, more than one transition can be active at the same time if they share predecessor state. Together, these unique features enable instantaneous and gradual responses to novel information and continuous decision processes without changing the state graph itself. We demonstrate the system's capabilities in an object handover task coordinated by body motion.
Quantum tomography by regularized linear regressions

In this paper, we study extended linear regression approaches for quantum state tomography based on regularization techniques. For unknown quantum states represented by density matrices, performing measurements under certain basis yields random outcomes, from which a classical linear regression model can be established. First of all, for complete or over-complete measurement bases, we show that the empirical data can be utilized for the construction of a weighted least squares estimate (LSE) for quantum tomography. Taking into consideration the trace-one condition, a constrained weighted LSE can be explicitly computed, being the optimal unbiased estimation among all linear estimators. Next, for general measurement bases, we show that ℓ2-regularization with proper regularization gain provides even a lower mean-square error under a cost in bias. The optimal regularization parameter is defined in terms of a risk characterization for any finite sample size and a resulting implementable estimator is proposed. Finally, a concise and unified formula is established for the regularization parameter with complete measurement basis under an equivalent regression model, which proves that the proposed implementable tuning estimator is asymptotically optimal as the number of copies grows to infinity. Additionally, several numerical examples are provided to validate the established results.
In-Bed Body Motion Detection and Classification System

In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.
Uncovering the failure of Agile framework implementation using SSM-based action research

Abstract It is undeniable that the Agile software development (SD) framework has been commonly used in information technology projects. However, many of the early adopters failed to implement it properly. Some researchers had proposed a solution by using the adaptation framework as a support, but they cannot clearly explain its development. Furthermore, the correlation between adaptation framework and problem solving is dubious. This study reviews and discusses the literature and empirical studies on the failure of Agile SD implementation by revisiting the supporting framework that used to comprehend the issues in Agile SD implementation. A comparative approach is used to review the literature available in academic databases, such as ProQuest, EBSCO, IEEE, and Scopus, to ensure research originality and novelty. By filtering 87 papers that met the initial criteria, only 4 researchers focused on the area of knowledge management and used the learning process as a supporting framework in Agile SD implementation. However, they did not clearly explain the development of the framework for Agile SD. The author’s initial research on a corporation reveals the failure of Agile SD implementation. Soft systems methodology (SSM)-based action research (AR) proposed to identify the real issues in Agile SD implementation. SSM-based AR provides a solution by clarifying the development of a framework using tacit and explicit knowledge to validate the development process. This study can help practitioners and academics understand the process of Agile SD implementation by early adopters who still need assistance with its application. This study is the first to explore Agile SD implementation in a corporation using the experience-based approach, contributing to knowledge improvement. It contributes to the scholarly literature by presenting concepts that combine human-centric aspects and knowledge management to improve the learning in the organization.
Eye-tracking Complementing Manual Vision Screening for Detecting Oculomotor Dysfunction

Visually demanding tasks such as reading, writing or playing, depend on our ability to maintain accurate and steady eye movements and coordinate the muscles controlling our eyes. Problems in our eye-muscle coordination, oculomotor dysfunctions (OMD) are common visual disturbances and can be trained to be better. The aim of this paper is to illustrate how a software program (C&Look), based on affordable eye-tracking (ET) technology, is constructed and can be used to help the existing detection methods. The results present the development of C&Look and discuss how it can supplement manual testing. By discussing the advantages and limitations of C&Look, the paper contributes to a better understanding of supplementing current detection methods for OMDs.
Saliency Mapping for Processing 3D Meshes in Industrial Modeling Applications

The latest advancements in 3D scanning technologies have facilitated the generation and adoption of 3D models in several industrial applications ranging from manufacturing inspection and repair to digital twins and medical industry. All these applications require an accurate representation of physical objects through saliency mechanisms identifying certain areas of the 3D model that are considered as important information by humans. Hence, 3D saliency mapping is an essential mechanism in a number of 3D processing applications including denoising, compression, simplification, registration, viewpoint selection, etc. In this work, we propose a robust 3D saliency mapping method ideally suited for industrial 3D models with sharp and small scale geometric features. An extensive simulation study using a variety of 3D scanned and CAD models, verify the effectiveness of the proposed method as compared to other relevant approaches.
DV-Net: Dual-view network for 3D reconstruction by fusing multiple sets of gated control point clouds

Deep learning for 3D reconstruction have just shown some promising advantanges, where 3D shapes can be predicted from a single RGB image. However, such works are often limited by single feature cue, which does not capture the 3D shape of objects well. To address this problem, an end-to-end 3D reconstruction approach that predicts 3D point cloud from dual-view RGB images is proposed in this paper. It consists of several processing parts. A dual-view 3D reconstruction network is proposed for 3D reconstruction, which predicts object's point clouds by exploiting two RGB images with different views, and avoids the limitation of single feature cue. Another structure feature learning network is performed to extract the structure features with stronger representation ability from point clouds. A gated control network for data fusion is proposed to gather point clouds. It takes two sets of point clouds with different views as input and fuses them. The proposed approach is thoroughly evaluated with extensive experiments on the widely-used ShapeNet dataset. Both the qualitative results and quantitative analysis demonstrate that this method not only captures the detailed geometric structures of 3D shapes for different object categories with complex topologies, but also achieves state-of-the-art performance.
Focal Stack Based Light Field Coding for Refocusing Applications

Since a light field camera generates huge volume of image data, it requires powerful compression for efficient transmission and storage of its data. In its refocusing applications, users want to see their objects of interest at different depth levels. To address this, we first render a focal stack that contains multi-focus images, then compress it instead of original light field data. By doing so, we can minimize the amount of data to process for its target refocusing applications. Our experiment results show that the proposed method can save 54.09% of bitrate and have PSNR gain of 2.08 dB at the same bitrate.
Identification of Dog Breeds Using Deep Learning

In this forecourt competition, we are provided a stringently mongrel division of ImageNet in order to exercise fine-grained image cataloguing. The dataset contains images of dogs of different breeds. Deep Learning is a technique by which a computer program learns statistical patterns within the data that will enable it to recognize or help us to distinguish between the different breeds of dogs. The model trains itself on the different features based on the images present and represent the data numerically, organizing the data in space. Initially, the image is divided into numerous lattices and a training batch size is set accordingly, then an algorithm is used to split and combine the descriptors, and the channel information of the image is extracted as the input of the convolutional neural network and finally, we design a convolutional neural network-based to identify the dog species.
Riverine Coverage with an Autonomous Surface Vehicle over Known Environments

Environmental monitoring and surveying operations on rivers currently are performed primarily with manually-operated boats. In this domain, autonomous coverage of areas is of vital importance, for improving both the quality and the efficiency of coverage. This paper leverages human expertise in river exploration and data collection strategies to automate and optimize these processes using autonomous surface vehicles (ASVs). In particular, three deterministic algorithms for both partial and complete coverage of a river segment are proposed, providing varying path length, coverage density, and turning patterns. These strategies resulted in increases in accuracy and efficiency compared to human performance. The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the algorithms were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in total of more than 35km of coverage trajectories in the field.
Group Split and Merge Prediction With 3D Convolutional Networks

Mobile robots in crowds often have limited navigation capability due to insufficient evaluation of pedestrian behavior. We strengthen this capability by predicting splits and merges in multi-person groups. Successful predictions should lead to more efficient planning while also increasing human acceptance of robot behavior. We take a novel approach by formulating this as a video prediction problem, where group splits or merges are predicted given a history of geometric social group shape transformations. We take inspiration from the success of 3D convolution models for video-related tasks. By treating the temporal dimension as a spatial dimension, a modified C3D model successfully captures the temporal features required to perform the prediction task. We demonstrate performance on several datasets and analyze transfer ability to other settings. While current approaches for tracking human motion are not explicitly designed for this task, our approach performs significantly better at predicting the occurrence of splits and merges. We also draw human interpretations from the model's learned features.
Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion

We tackle the INS/GPS sensor fusion problem for pose estimation, particularly in the common setting where the INS components (IMU and magnetometer) function at much higher frequencies than GPS, and where the magnetometer and GPS are prone to giving erroneous measurements (outliers) due to magnetic disturbances and glitches. Our main contribution is a novel non-linear optimization framework that (1) fuses pre-integrated IMU and magnetometer measurements with GPS, in a manner that respects the manifold structure of the state space; and (2) supports the usage of robust norms and efficient large scale optimization to effectively mitigate the effects of outliers. Through extensive experiments, we demonstrate the superior accuracy and robustness of our approach over filtering methods (which are customarily applied in the target setting) with minimal impact to computational efficiency. Our work further illustrates the strength of optimization approaches in state estimation problems and paves the way for their adoption in the control and navigation communities.
Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

Summary We consider the problem of quantifying the degree of association between pairs of discrete event time series, with potential applications in forensic and cybersecurity settings. We focus in particular on the case where two associated event series exhibit temporal clustering such that the occurrence of one type of event at a particular time increases the likelihood that an event of the other type will also occur nearby in time. We pursue a non-parametric approach to the problem and investigate various score functions to quantify association, including characteristics of marked point processes and summary statistics of interevent times. Two techniques are proposed for assessing the significance of the measured degree of association: a population-based approach to calculating score-based likelihood ratios when a sample from a relevant population is available, and a resampling approach to computing coincidental match probabilities when only a single pair of event series is available. The methods are applied to simulated data and to two real world data sets consisting of logs of computer activity and achieve accurate results across all data sets.
Automatic Detection of Wireless Transmissions

The current understanding of activity in the wireless spectrum is limited to mostly punctual studies of aggregated energy values. However, there is a need and increasing technological means for a better understanding of spectrum usage by automatically detecting and recognizing wireless transmissions in an unlicensed or shared frequency band. In this paper we propose, implement and evaluate a framework for automatic detection of wireless transmissions. Our framework includes a manual component as our assessment suggests manual labor has a paramount impact on tuning and maintaining good performance of an automatic transmission detection system. However, a considerable problem in this aspect is represented by the disagreement amongst human annotations which is a universally recognized issue. To this end, we discuss and evaluate challenges in generating labeled datasets that can then be used as ground truth for evaluating and possibly training automatic transmission detection systems. We also propose two methods for automatic transmission detection that are not based on machine learning and therefore do not need training data and evaluate their performance against each other and manually labeled data. Our results show that generating human-labeled ground truth data is an expensive and imperfect process. Humans on average require 90 minutes to label 56 minutes of unlicensed European narrowband spectrum. The experts that generate the ground truth sometimes only agree on as little as 40.18% of the labeled cases.
Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognition

Human falls are common source of injury among the elderly, because often the elderly person is injured and cannot call for help. In the literature this is addressed by various fall-detection systems, of which most common are the ones that use wearable sensors. This paper describes the winning method developed for the Challenge Up: Multimodal Fall Detection competition. It is a multi-sensor data-fusion machine-learning method that recognizes human activities and falls using 5 wearable inertial sensors: accelerometers and gyroscopes. The method was evaluated on a dataset collected by 12 subjects of which 3 were used as a test-data for the challenge. In order to optimally adapt the method to the 3 test subjects, we performed an unsupervised similarity search—that finds the three most similar users to the three users in the test data. This helped us to tune the method and its parameters to the 3 most similar users as the ones used for the test. The internal evaluation on the 9 users showed that with this optimized configuration the method achieves 98% accuracy. During the final evaluation for the challenge, our method achieved the highest results (82.5% F1-score, and 98% accuracy) and won the competition.
Multiple Comparative Attention Network for Offline Handwritten Chinese Character Recognition

Recent advances in deep learning have made great progress in offline Handwritten Chinese Character Recognition (HCCR). However, most existing CNN-based methods only utilize global image features as contextual guidance to classify characters, while neglecting the local discriminative features which is very important for HCCR. To overcome this limitation, in this paper, we present a convolutional neural network with multiple comparative attention (MCANet) in order to produce separable local attention regions with discriminative feature across different categories. Concretely, our MCANet takes the last convolutional feature map as input and outputs multiple attention maps, a contrastive loss is used to restrict different attention selectively focus on different sub-regions. Moreover, we apply a region-level center loss to pull the features that learned from the same class and different regions closer to further obtain robust features invariant to large intra-class variance. Combining with classification loss, our method can learn which parts of images are relevant for recognizing characters and adaptively integrates information from different regions to make the final prediction. We conduct experiments on ICDAR2013 offline HCCR competition dataset with our proposed approach and achieves an accuracy of 97.66%, outperforming all single-network methods trained only on handwritten data.