Reviewerly/exHarmony huggingface dataset:
Efficient vision ray calibration of multi-camera systems

Vision ray calibration provides imaging properties of cameras for application in optical metrology by identifying an independent vision ray for each sensor pixel. Due to this generic description of imaging properties, setups of multiple cameras can be considered as one imaging device. This enables holistic calibration of such setups with the same algorithm that is used for the calibration of a single camera. Obtaining reference points for the calculation of independent vision rays requires knowledge of the parameters of the calibration setup. This is achieved by numerical optimization which comes with high computational effort due to the large amount of calibration data. Using the collinearity of reference points corresponding to individual sensor pixels as the measure of accuracy of system parameters, we derived a cost function that does not require explicit calculation of vision rays. We analytically derived formulae for gradient and Hessian matrix of this cost function to improve computational efficiency of vision ray calibration. Fringe projection measurements using a holistically vision ray calibrated system of two cameras demonstrate the effectiveness of our approach. To the best of our knowledge, neither any explicit description of vision ray calibration calculations nor the application of vision ray calibration in holistic camera system calibration can be found in literature.
Overview of Block Chain Cross Chain Technology

With the continuous development of block chain technology, block chain is widely used in payment and settlement, product traceability, identity authentication and other fields. A large number of block chain networks with different characteristics and different scenarios have formed many value islands. The cross chain technology of block chain is an important technical means to realize the interconnection and value transfer between chains. This paper systematically summarizes the current mainstream cross chain technologies of block chain, analyzes the characteristics of different cross chain technologies, points out the challenges faced by cross chain technology, and forecasts its future development prospects.
The Role of Information and Communication Technology on Knowledge Sharing among the Academic Staff during COVID-19 Pandemic

Knowledge Sharing and Information and Communication Technology (ICT) has attracted the attention of researchers during the COVID 19 pandemic and there were two factors considered as essential elements for universities during the COVID 19 situation. This is because most of the universities postponed the physical attendance at universities and used technology tools to keep the communication among the academic staff and students. ICT allows for the rapid search, access, and retrieval of information and collaboration between employees, while ICT infrastructure refers to the equipment, software, communication systems, and the human component such as experience, skills, command, principles. The purpose of this study was to investigate the impact of ICT and ICT infrastructure on Knowledge Sharing among academic staff. The study was conducted at nine Iraqi public universities located in Baghdad and the sample was the academic staff at these universities. The study employed the stratified sample method to ensure equal distribution among the nine universities, and the data was collected using the questionnaire. Structural equation modelling was utilized to analyze 267 valid questionnaires. After testing the measurement model and ensuring the model fit, the study examined the structural model to find the results. The result showed that ICT and ICT infrastructure had positive and significant impact on Knowledge Sharing among the academic staff at Iraqi public universities. However, the result indicated the Knowledge Sharing was highly impacted by ICT infrastructure. The government and universities should enhance and improve the ICT infrastructure that would provide a great benefit to the Iraqi universities' reputation as well as the universities' rank.
Anomaly detection and classification in traffic flow data from fluctuations in the flow–density relationship

We describe and validate a novel data-driven approach to the real time detection and classification of traffic anomalies based on the identification of atypical fluctuations in the relationship between density and flow. For aggregated data under stationary conditions, flow and density are related by the fundamental diagram. However, high resolution data obtained from modern sensor networks is generally non-stationary and disaggregated. Such data consequently show significant statistical fluctuations. These fluctuations are best described using a bivariate probability distribution in the density–flow plane. By applying kernel density estimation to high-volume data from the UK National Traffic Information Service (NTIS), we empirically construct these distributions for London’s M25 motorway. Curves in the density–flow plane are then constructed, analogous to quantiles of univariate distributions. These curves quantitatively separate atypical fluctuations from typical traffic states. Although the algorithm identifies anomalies in general rather than specific events, we find that fluctuations outside the 95% probability curve correlate strongly with the spikes in travel time associated with significant congestion events. Moreover, the size of an excursion from the typical region provides a simple, real-time measure of the severity of detected anomalies. We validate the algorithm by benchmarking its ability to identify labelled events in historical NTIS data against some commonly used methods from the literature. Detection rate, time-to-detect and false alarm rate are used as metrics and found to be generally comparable except in situations when the speed distribution is bi-modal. In such situations, the new algorithm achieves a much lower false alarm rate without suffering significant degradation on the other metrics. This method has the additional advantages of being self-calibrating and adaptive: the curve marking atypical behaviour is different for each section of road and can evolve in time as the data changes, for example, due to long-term roadworks.
An explainable ensemble feedforward method with Gaussian convolutional filter

The emerging deep learning technologies are leading to a new wave of artificial intelligence, but in some critical applications such as medical image processing, deep learning is inapplicable due to the lack of interpretation, which is essential for a critical application. This work develops an explainable feedforward model with Gaussian kernels, in which the Gaussian mixture model is leveraged to extract representative features. To make the error within the allowable range, we calculate the lower bound of the number of samples through the Chebyshev inequality. In the training processing, we discuss both the deterministic and stochastic feature representations, and investigate the performance of them and the ensemble model. Additionally, we use Shapely additive explanations to analyze the experiment results. The proposed method is interpretable, so it can replace the deep neural network by working with shallow machine learning technologies, such as the Support Vector Machine and Random Forest. We compare our method with baseline methods on Brain Tumor and Mitosis dataset. The experimental results show our method outperforms the RAM (Recurrent Attention Model), VGG19 (Visual Geometry Group 19), LeNET-5, and Explainable Prediction Framework while having strong interpretability.
Towards Efficient Hardware Implementation of NTT for Kyber on FPGAs

Kyber is a promising lattice-based post-quantum cryptography (PQC) for key encapsulation mechanisms. Since number theoretic transform (NTT) is the most computationally expensive operation in Kyber, this paper focuses on novel optimization techniques for efficient hardware implementation of NTT for Kyber. Benefiting from the proposed fast modular multiplication method and a doubled bandwidth ping-pong memory access scheme, our NTT architecture can complete an NTT operation in Kyber in 490 cycles using only 609 LUTs, 640 FFs, 2 DSPs on a Xlinx Artix-7 FPGA. The proposed NTT architecture is 3.95 times faster than the state-of-the-art design for Kyber while achieves an improvement of more than 1.5 times in the area time product (ATP). Compared with the state-of-the- art NTT designs for other algorithms, our NTT architecture reduces 24% FFs and 50% DSPs and ranks second smallest in ATPs, which can also confirm the high efficiency of our design.
How to Use Indistinguishability Obfuscation: Deniable Encryption, and More

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 10 July 2015Accepted: 17 November 2020Published online: 06 May 2021Keywordscryptography, obfuscation, indistinguishability obfuscationAMS Subject Headings68Q01Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Scale-balanced loss for object detection

Object detection is an important field in computer vision. Nevertheless, a research area that has so far not received much attention is the study into the effectiveness of anchor matching strategy and imbalance in anchor-based object detection, in particular small object detection. It is clear that the objects with larger sizes tend to match more anchors than smaller ones. This matching imbalance may result in poor performance in detecting small objects. It can be alleviated by paying more attention to the objects that match with fewer anchors. We propose an innovative flexible loss function for object detection, which is compatible with popular anchor-based detection methods. The proposed method, called the scale-balanced loss, does not add any extra computational cost to the original pipelines. By re-weighting strategy, the proposed method significantly improves the accuracy of multi-scale object detection, especially for small objects. Comprehensive experiments indicate that the scale-balanced loss achieved excellent generalization performance when separately applied to some popular detection methods. The scale-balanced loss attained up to 15% improvements on recall rates of small and medium objects in both the PASCAL VOC and MS COCO dataset. It is also beneficial to the AP result on MS COCO with an improvement of more than 1.5%.
Advantages of Print Reading over Screen Reading: A Comparison of Visual Patterns, Reading Performance, and Reading Attitudes across Paper, Computers, and Tablets

(2021). Advantages of Print Reading over Screen Reading: A Comparison of Visual Patterns, Reading Performance, and Reading Attitudes across Paper, Computers, and Tablets. International Journal of Human–Computer Interaction: Vol. 37, No. 17, pp. 1674-1684.
Recognition of overlapping elliptical objects in a binary image

Abstract Recognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.
Feature Selection in Machine Learning: Methods and Comparison

Nowadays, a huge amount of data is generated every day in continuous manner in every hour and if the data is not utilized in the right or meaningful manner then this is just like garbage. Therefore, the meaningful information from the data can be represented through the feature selection. Feature selection refers to various methods which selects the most appropriate from the data according to the problem. This paper provides an insight into various feature selection methods which include filter, wrapper, embedded, hybrid and a detailed explanation of various techniques utilized by these methods. Further, a comparison among these feature selection methods have also been made.
Measurement of Nonlinear Poisson’s Ratio of Thermoplastic Polyurethanes under Cyclic Softening Using 2D Digital Image Correlation

Thermoplastic polyurethanes (TPUs) and other elastomers are widely used in many applications for the advantages they provide in terms of high elasticity, lightness, resistance to breakage, and impact resistance. These materials exhibit strong hysteresis in the large strain stress-strain behavior, known as cyclic softening or the Mullins effect. Despite the extensive studies on this phenomenon and the importance of Poisson’s ratio, how the Poisson’s ratio of these materials changes during cyclic uniaxial tests is still unclear. Here, we measure the nonlinear Poisson’s ratio of TPU and investigate its correlation with cyclic softening using two-dimensional digital image correlation (2D-DIC) combined with the reference sample compensation (RSC) method. This accuracy-enhanced method can effectively eliminate the measurement errors induced by the unavoidable out-of-plane displacements and lens distortion. We find that the Poisson’s ratio of TPUs also exhibits large hysteresis in the first cycle and then approaches a steady state in subsequent cycles. Specifically, it starts from a relatively low value of 0.45 ± 0.005 in the first loading, then increases to 0.48 ± 0.005 in the first unloading, and remains largely constant afterward. Such a change in the Poisson’s ratio results in a slight volume increase (≈1%) at a maximum strain of 17.5%. Our findings are useful for those who use finite element method to analyze the mechanical behavior of TPU, and shed new light on understanding the physical origin of cyclic softening.
Preventing DNN Model IP Theft via Hardware Obfuscation

Training accurate deep learning (DL) models require large amounts of training data, significant work in labeling the data, considerable computing resources, and substantial domain expertise. In short, they are expensive to develop. Hence, protecting these models, which are valuable storehouses of intellectual properties (IP), against model stealing/cloning attacks is of paramount importance. Today's mobile processors feature Neural Processing Units (NPUs) to accelerate the execution of DL models. DL models executing on NPUs are vulnerable to hyperparameter extraction via side-channel attacks and model parameter theft via bus monitoring attacks. This paper presents a novel solution to defend against DL IP theft in NPUs during model distribution and deployment/execution via lightweight, keyed model obfuscation scheme. Unauthorized use of such models results in inaccurate classification. In addition, we present an ideal end-to-end deep learning trusted system composed of: 1) model distribution via hardware root-of-trust and public-key cryptography infrastructure (PKI) and 2) model execution via low-latency memory encryption. We demonstrate that our proposed obfuscation solution achieves IP protection objectives without requiring specialized training or sacrificing the model's accuracy. In addition, the proposed obfuscation mechanism preserves the output class distribution while degrading the model's accuracy for unauthorized parties, covering any evidence of a hacked model.
Robust Video-Based Person Re-Identification by Hierarchical Mining

Video-based person re-identification (Re-ID) aims at retrieving the person through the video sequences across non-overlapping cameras. Some characteristics of pedestrians are not consecutive across frames due to the variations of viewpoints, postures, and occlusions over time. However, existing methods ignore such data peculiarity and the networks tend to only learn those salient consecutive characteristics among frames in video sequences. As a result, the learned representations fail to cover all the characteristics of pedestrians, thus lacking integrity and discrimination. To tackle this problem, we present a novel deep architecture termed Hierarchical Mining Network (HMN), which mines as many pedestrians’ characteristics by referring to the temporal and intra-class knowledge. It consists of a novel Attentive Temporal Module (ATM) and a Dynamic Supervising Branch (DSB), with a Balancing Triplet Loss (BTL) assisting the training. The proposed ATM, with pedestrian perceiving capacity, is capable of evaluating each activation of features through temporal analysis, so that the temporally scattered characteristics of pedestrians can be better aggregated and the contaminated ones can be eliminated. Then, the DSB along with the BTL further enhances the integrity of representations by multiple supervision. Specifically, the DSB perceives the diversities of intra-class samples in each mini-batch and generates targeted supervising signals for them, in which process the BTL guarantees the signals with smaller intra-class variations and larger inter-class variations. Comprehensive experiments on two video-based datasets, i.e., MARS, and DukeMTMC-VideoReID, demonstrate the contribution of each component and the superiority of the proposed HMN over the state-of-the-arts. Benchmarking our model on three popular image-based datasets, i.e., Market1501, DukeMTMC-Reid, and MSMT17 additionally verifies the promising generalizability of the proposed DSB and BTL.
Selecting practices in complex technical planning projects: A pathway for tailoring agile project management into the manufacturing industry

Innovation is the key to the success in an increasingly complex business environment. In manufacturing companies, innovation involves the (re)designing business processes and structures. Conventional, plan-driven project management approaches are stretched to their limits. Hybrid models, adopting elements from agile project management domain, gained in importance in the manufacturing area, since they achieve excellent progress in such complex environments. In this contribution, we present a customizing procedure for conventional project management approaches in the manufacturing industry. The method helps practitioners to solve emerging technical problems more effectively by deploying individual agile practices and, therefore, by achieving an individual and dynamic hybridism between stability and agility. We applied the approach in the automotive industry during a filed study at a premium manufacturer of electric and conventional cars.
My Bad! Repairing Intelligent Voice Assistant Errors Improves Interaction

One key technique people use in conversation and collaboration is conversational repair. Self-repair is the recognition and attempted correction of one's own mistakes. We investigate how the self-repair of errors by intelligent voice assistants affects user interaction. In a controlled human-participant study (N =101), participants asked Amazon Alexa to perform four tasks, and we manipulated whether Alexa would "make a mistake'' understanding the participant (for example, playing heavy metal in response to a request for relaxing music) and whether Alexa would perform a correction (for example, stating, "You don't seem pleased. Did I get that wrong?'') We measured the impact of self-repair on the participant's perception of the interaction in four conditions: correction (mistakes made and repair performed), undercorrection (mistakes made, no repair performed), overcorrection (no mistakes made, but repair performed), and control (no mistakes made, and no repair performed). Subsequently, we conducted free-response interviews with each participant about their interactions. This study finds that self-repair greatly improves people's assessment of an intelligent voice assistant if a mistake has been made, but can degrade assessment if no correction is needed. However, we find that the positive impact of self-repair in the wake of an error outweighs the negative impact of overcorrection. In addition, participants who recently experienced an error saw increased value in self-repair as a feature, regardless of whether they experienced a repair themselves.
Meta Learning via Learned Loss

Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper, we take the first step towards automating this process, with the view of producing models which train faster and more robustly. Concretely, we present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures. We develop a pipeline for “meta-training” such loss functions, targeted at maximizing the performance of the model trained under them. The loss landscape produced by our learned losses significantly improves upon the original task-specific losses in both supervised and reinforcement learning tasks. Furthermore, we show that our meta-learning framework is flexible enough to incorporate additional information at meta-train time. This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time. We make our code available at https://sites.google.com/view/mlthree
Motion-transformer

With the development of deep learning, skeleton-based action recognition has achieved great progress in recent years. However, most of the current works focus on extracting more informative spatial representations of the human body, but haven't made full use of the temporal dependencies already contained in the sequence of human action. To this end, we propose a novel transformer-based model called Motion-Transformer to sufficiently capture the temporal dependencies via self-supervised pre-training on the sequence of human action. Besides, we propose to predict the motion flow of human skeletons for better learning the temporal dependencies in sequence. The pre-trained model is then fine-tuned on the task of action recognition. Experimental results on the large scale NTU RGB+D dataset shows our model is effective in modeling temporal relation, and the flow prediction pre-training is beneficial to expose the inherent dependencies in time dimensional. With this pre-training and fine-tuning paradigm, our final model outperforms previous state-of-the-art methods.