Reviewerly/exHarmony huggingface dataset:
A static and fast calibration method for line scan camera based on cross-ratio invariance

AbstractThis paper presents a new calibration method of line scan camera model based on cross-ratio invariance using a calibration pattern on the top of the gauge block. Instead of using the 3D pattern, planner pattern with precise motion platform, or pre-calibrated matrix camera, only a planner pattern with gauge blocks is used in this new method. A group of data on the line scan camera projection plane is obtained by placing the planner pattern at a different height. Line scan camera parameters were calibrated through a derived mathematical model. This method is simple, inexpensive and easy to use. The experimental results demonstrated that the average reprojection error of this method is 0.0431, which is 62.2% lower than the traditional method, which proves the feasibility and effectiveness of this method.KEYWORDS: Line scan cameracalibrationgauge blockscross-ratio invariance Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Key Research and Development Program of China [grant number 2017YFA0701200]; Tianjin Natural Science Foundation of China [grant number 19JCZDJC39100]; Science Challenge Program [grant number TZ2018006-0203-01].
SIN: Semantic Inference Network for Few-Shot Streaming Label Learning

Streaming label learning aims to model newly emerged labels for multilabel classification systems, which requires plenty of new label data for training. However, in changing environments, only a small amount of new label data can practically be collected. In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels. We propose a meta-learning framework, semantic inference network (SIN), which can learn and infer the semantic correlation between new labels and past labels to adapt FSLL tasks from a few examples effectively. SIN leverages label semantic representation to regularize the output space and acquires labelwise meta-knowledge based on gradient-based meta-learning. Moreover, SIN incorporates a novel label decision module with a meta-threshold loss to find the optimal confidence thresholds for each new label. Theoretically, we illustrate that the proposed semantic inference mechanism could constrain the complexity of hypotheses space to reduce the risk of overfitting and achieve better generalizability. Experimentally, extensive empirical results and ablation studies demonstrate the performance of SIN is superior to the prior state-of-the-art methods on FSLL.
Personalization in Australian K-12 classrooms: how might digital teaching and learning tools produce intangible consequences for teachers’ workplace conditions?

Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's Human Rights and Technology final report, this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.
Integration of Deep Learned and Handcrafted Features for Image Retargeting Quality Assessment

AbstractThis paper proposed an image retargeting quality assessment. In the proposed method, a deep convolution network is trained on the pixel displacement patterns of different image retargeting methods to produce a measure for evaluating the quality of output images. Also, the method extracts three other measures that assess the geometric changes of important objects in the image, the bending of block lines, and the extent of information loss during the retargeting process. The tests performed on two well-known databases, RetargetMe and CUHK, demonstrate the excellent performance, stability, and reliability of the proposed method compared to the existing methods.In this paper, we present a new method for evaluating the quality of retargeted images. The innovations of the proposed method are: Using a deep learning method to identify the important regions of the image that contain foreground objects and peopleProviding a quality evaluation measure, obtained by training a CNN with regression output on the pixel displacement patterns in different image retargeting methodsExtracting the foreground objects in the original image and the corresponding objects in the retargeted image and determining the extent of geometric change in each object.Estimating the extent of information loss and distortion based on the extent to which the blocks of the original image have been bent.Estimating the quality score of the retargeted image using Gaussian process regression.Keywords: Deep learningimage quality assessmentimage retargetingimportance map
Mapping Co–Cr–Cu and Fe Occurrence in a Legacy Mining Waste Using Geochemistry and Satellite Imagery Analyses

Abandoned mining wastes are both an environmental challenge and a possible secondary raw material source. The characterization and monitoring of these sites are often expensive and cumbersome because of the need of repeated field surveys. Remote sensing data are a cost-effective alternative that helps in producing multiscale maps of mining wastes. These maps can be used to investigate and monitor the spatial patterns of different elements within the mining wastes. In this work, Sentinel-2 images are combined with the geochemical samples in order to map the distribution of iron, copper, chromium, and cobalt. The target area was the Vigonzano mining wastes in Northern Apennines (Italy) where there are a small number of geochemical analyses but a large amount of satellite image data. We used the multivariate geostatistical estimation method (Co-Kriging) that exploit the meaningful spatial correlation between the elements of interest and band ratios (obtained from Sentinel-2 images). The concentration maps highlighted subareas for Cu and Cr with an estimated grade of about 0.3% and 0.2%, respectively. In addition, the critical element Co showed an enrichment in the south-east part of the mining wastes, in a similar pattern as Cr. Instead, the obtained maps show Ce, La, Rb, and Nb depletion compared to the surrounding agricultural areas. The concentration maps were intended as a prefeasibility study to determine enriched areas for further detailed investigation.
Global and local interaction matching model for knowledge-grounded response selection in retrieval-based chatbots

Knowledge-grounded response selection, the task of selecting a proper response from a set of response candidates given the conversation context and its background knowledge, is important for building intelligent dialogue systems. Challenges for the task mainly lie in how to interact between the context and the knowledge, and how to match between the response and the context-knowledge pair. Existing work mainly focuses on exploiting global or local interaction matching information, and hence cannot fully utilize the information. In this paper, we propose a global and local interaction matching model (GLIMM) that matches a response with the context-knowledge pair from global and local views. For global interaction matching, GLIMM takes the context and the knowledge as long sequences. For local interaction matching, GLIMM treats each context utterance and knowledge sentence separately. GLIMM integrates information from two different views that could provide complementary information for each other. Experiments on two datasets show that the proposed model outperforms the state-of-the-art baselines.
Hybridized improvement of the chaotic Harris Hawk optimization algorithm and Aquila Optimizer

In the improvement of many meta-heuristic algorithms, two or more algorithms are often mixed into one algorithm to improve the convergence speed and accuracy of the algorithm. The search ability of Harris Hawk optimization (HHO) algorithm <sup>[1]</sup> is relatively weak, and the search ability of Aquila Optimizer (AO) is relatively strong. So, in this article, the Aquila Optimizer (AO) algorithm is mixed on the basis of the piecewise linear map enabled Harris Hawk optimization (HHO) algorithm, and the advantages of the AO algorithm are used to complement the disadvantages of the piecewise linear map enabled HHO algorithm. This hybrid algorithm is called CHHOAO. The results show that this hybrid improvement is effective and can significantly improve the optimization ability of the algorithm.
Software Productivity in Practice: A Systematic Mapping Study

Practitioners perceive software productivity as one of the most important subjects of software engineering (SE) because it connects technical to social and economic aspects. Nonetheless, software processes are complex and productivity means different things to different people. In order to realize the full contribution of software productivity research to the industrial practice of SE, the analysis and synthesis of existing practitioner viewpoints and concerns are required. A systematic mapping study is developed here to investigate the existence of diverse empirical perceptions of productivity within the distinct business sectors and knowledge areas covered by the industrial practice of SE, also identifying the commonalities among them. This study adopts the DBLP and Scopus search engines to identify bibliographic references from 1987 to 2021 related to software productivity. References that do not correspond to complete not-later-subsumed articles published in peer-reviewed journals and proceedings are excluded from the analyses. Only papers reporting on empirical studies based on software industry data or that present industry practitioner viewpoints are included in these analyses. In total, 99 papers are analyzed. The mapping found great variability in study findings, particularly concerning the impacts of agile development practices on software productivity. The systematic mapping also drew methodological recommendations to help industry practitioners address this subject and develop further research.
Relational Representation Learning in Visually-Rich Documents

Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive contextual representations and exploit them as prior knowledge for downstream tasks. In spite of their impressive results, we observe that the widespread relational hints (e.g., relation of key/value fields on receipts) built upon contextual knowledge are not excavated yet. To mitigate this gap, we propose DocReL, a Document Relational Representation Learning framework. The major challenge of DocReL roots in the variety of relations. From the simplest pairwise relation to the complex global structure, it is infeasible to conduct supervised training due to the definition of relation varies and even conflicts in different tasks. To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views. RCM provides relational representations which are more compatible to the urgent need of downstream tasks, even without any knowledge about the exact definition of relation. DocReL achieves better performance on a wide variety of VRD relational understanding tasks, including table structure recognition, key information extraction and reading order detection.
Robust Multi-Mode Synchronization of Chaotic Fractional Order Systems in the Presence of Disturbance, Time Delay and Uncertainty with Application in Secure Communications

This paper investigates the robust adaptive synchronization of multi-mode fractional-order chaotic systems (MMFOCS). To that end, synchronization was performed with unknown parameters, unknown time delays, the presence of disturbance, and uncertainty with the unknown boundary. The convergence of the synchronization error to zero was guaranteed using the Lyapunov function. Additionally, the control rules were extracted as explicit continuous functions. An image encryption approach was proposed based on maps with time-dependent coding for secure communication. The simulations indicated the effectiveness of the proposed design regarding the suitability of the parameters, the convergence of errors, and robustness. Subsequently, the presented method was applied to fractional-order Chen systems and was encrypted using the chaotic masking of different benchmark images. The results indicated the desirable performance of the proposed method in encrypting the benchmark images.
Complete coverage path planning algorithm based on energy compensation and obstacle vectorization

A complete coverage path planning (CCPP) algorithm based on energy compensation and obstacle vectorization (ECOV) is proposed. The algorithm can be used in demanding fields such as disinfection robots due to its advantages, such as a low path coverage repetition rate and high coverage rate. The algorithm builds an energy map, classifies various obstacles, and proposes separate special area definitions and corresponding energy reconstruction methods for various types of obstacles. Through the energy compensation of the path and the real-time and non-real-time energy reconstruction of special areas of various obstacles, the robot can adapt to more complicated map models and obtain improved results. The proposed algorithm has strong sensitivity to various complex obstacles. Furthermore, the concept of map parity is proposed. Experimental analysis showed that the algorithm is not sensitive to the map parity of the map model, and the parity will not have a large impact on the algorithm due to the minor changes in the actual environment.
Coherence weight of quantum channels

Quantifying coherence has received significant attention in recent years. Many research is focus on the resource theory of static coherence. In this paper, we introduce the coherence weight of quantum channel to study the quantum resource theory of dynamical coherence. The free channel is classical channel and the free super-channel are the maximally incoherent superchannels (MISC) and incoherent superchannels (ISC). We first study some basic properties (such as convexity and monotonicity) of coherence weight of quantum channel. By introducing entanglement-assisted super-subchannel exclusion task, we show that the advantage of coherent channels in such task. Finally, we compute the coherence weight of quantum channel for some channels.
Multiple-feature-based zero-watermarking for robust and discriminative copyright protection of DIBR 3D videos

Zero-watermarking is a key technique for achieving lossless and flexible copyright protection of depth image-based rendering (DIBR) videos. Existing approaches extract features of both 2D frames and depth maps via a single mechanism to protect them simultaneously. However, it is difficult for these schemes to fully satisfy the copyright protection requirements of the two components, including the remarkable discriminative capability of 3D videos and robustness against various attacks. Hence, in this paper, we propose a novel multiple-feature-based zero-watermarking scheme to protect the copyright of DIBR 3D videos. To the best of our knowledge, this is the first scheme that integrates multiple features to improve both the discriminative capability and robustness against various attacks. Specifically, dual-tree complex wavelet transform and discrete cosine transform features enhance the robustness against DIBR conversion and noise addition, respectively, while ring-partition statistical residual features ensure robustness against geometric attacks and provide sufficient discriminative capacity. In addition, we use a logistic-logistic chaotic system to encrypt these multiple features for enhanced security and design an attention-based fusion approach to offer an optimal copyright protection solution. Extensive experimental results demonstrate that our proposed scheme has stronger robustness and discriminative capacity compared to state-of-the-art zero-watermarking methods.
Vehicle Re-Identification in Aerial Imagery Based on Normalized Virtual Softmax Loss

With the development and popularization of unmanned aerial vehicles (UAVs) and surveillance cameras, vehicle re-identification (ReID) task plays an important role in the field of urban safety. The biggest challenge in the field of vehicle ReID is how to robustly learn the common visual representation of vehicle from different viewpoints while discriminate different vehicles with similar visual appearance. In order to solve this problem, this paper designs the normalized virtual softmax loss to enlarge the inter-class distance and decrease the intra-class distance, and a vehicle ReID model is proposed by jointly training the network with the proposed loss and triplet loss. In addition, we contribute a novel UAV vehicle ReID dataset from multiple viewpoint images to verify the robustness of methods. The experimental results show that comparing with the other softmax-based losses, our method achieves better performance and gets 76.70% and 98.95% in Rank-1 on VRAI and VRAI_AIR dataset, respectively.
Learning Multifeature Correlation Filter and Saliency Redetection for Long-Term Object Tracking

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods.
Digital Transformation-Oriented Innovation in Museum Settings via Digital Engagement

The digital era has been one of the most popular topics that has drawn a vast amount of attention over the past several decades. Digitalization-oriented innovative approaches to unleash the business potential and improve consumer satisfaction with immersive technologies have been another hot topic among scholars. Although theoretical-based discussion dates back to the 1930s regarding the development of virtual reality, practical aspects of immersive technology are still scant in the current literature. Thus, this study proposes several objectives: (1) how advanced immersive technology can revolutionize the accessibility of the museums, (2) how VR technology influences visitors' motivation, (3) the linkage between VR technology and visit and revisit intentions. Due to the lack of supporting evidence in a museum setting, this chapter also aims to enhance the extant literature by exploring the critical aspects of VR technology. Lastly, this study also intends to come up with constructive, innovative, and creative suggestions for the local and national authorities and museum management teams.
RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model

Cervical cancer is one of the main causes of death from cancer in women. However, it can be treated successfully at an early stage. This study aims to propose an image processing algorithm based on acetowhite, which is an important criterion for diagnosing cervical cancer, to increase the accuracy of the deep learning classification model. Then, we mainly compared the performance of the model, the original image without image processing, a mask image made with acetowhite as the region of interest, and an image using the proposed algorithm. In conclusion, the deep learning classification model based on images with the proposed algorithm achieved an accuracy of 81.31%, which is approximately 9% higher than the model with original images and approximately 4% higher than the model with acetowhite mask images. Our study suggests that the proposed algorithm based on acetowhite could have a better performance than other image processing algorithms for classifying stages of cervical images.
Undermining Deep Learning Based Channel Estimation via Adversarial Wireless Signal Fabrication

Channel estimation is a crucial step in wireless communications. The estimator identifies the wireless channel distortions during the signal propagation and this information is further used for data precoding and decoding. Recent studies have shown that deep learning techniques can enhance the accuracy of conventional channel estimation algorithms. However, the reliability and security aspects of these deep learning algorithms have not yet been well investigated in the context of wireless communications. With no exceptions, channel estimation based on deep learning may be vulnerable to the adversarial machine learning attacks. However, close examination shows that we cannot simply adapt the traditional adversarial learning mechanisms to effectively manipulate channel estimation. In this paper, we propose a novel attack strategy that crafts a perturbation to fool the receiver with wrong channel estimation results. This attack is launched without knowing the current input signals and by only requiring a loose form of time synchronization. Through the over-the-air experiments with software-defined radios in our multi-user MIMO testbed, we show that the proposed strategy can effectively reduce the performance of deep learning-based channel estimation. We also demonstrate that the proposed attack can hardly be detected with the detection rate of 8% or lower.