Reviewerly/exHarmony huggingface dataset:
Real-Time Spatio-Temporal LiDAR Point Cloud Compression

Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40× to 90× compression rate, significantly higher than the MPEG's LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.
Multilevel analysis of the educational use of technology: Quantity and versatility of digital technology usage in Finnish basic education schools

Abstract The adoption of technology in teaching has been identified to relate to various factors from attitudes and self‐efficacy to subjective norms and digital references. The aim of this study is to broaden the perspective to hierarchical grouping effects. Multilevel modelling of the study utilizes the data of 2355 Finnish basic education teachers. The results show that, before the coronavirus pandemic, Finnish teachers used digital devices in teaching at least once a week, on average, and many times on a daily basis, varying according to the subject being taught. The variation in teachers' technology usage occurs mainly at the individual level, with a small proportion between schools; higher‐level hierarchies proved redundant in the context of Finland. At the teacher level, digital skills, age, and digital self‐efficacy increase technology usage in teaching. At the end, the significance and limitations of the research and the direction of future research in the post‐pandemic era are discussed.
Spatiotemporal Generative Adversarial Network-Based Dynamic Texture Synthesis for Surveillance Video Coding

Dynamic texture refers to the content in video sequences that is characterized by spatial repetition and temporal variation, such as swaying foliage and flowing water. It is a great challenge to compress the dynamic textures efficiently in the current prediction/transform hybrid video coding framework. However, these textures have little information for machine vision, and human visual perception is less sensitive to the textures than to the structures. Thus, we propose a spatiotemporal generative adversarial network (GAN) based dynamic texture synthesis method for surveillance video coding. We detect and remove the dynamic texture content at encoder side, which is irrelevant to machine vision. We generate the dynamic texture content using the proposed GAN at decoder side, so that the reconstructed videos can be observed by human without deteriorating perceptual quality. Specifically, we design a GAN network to synthesize dynamic textures by exploiting the correlation between spatial and temporal neighbors; we present a surveillance video coding scheme with the dynamic texture detection/synthesis method; we build a high-quality dynamic texture dataset, and we collect a dynamic texture testing dataset that goes beyond the existing video coding test datasets by focusing on surveillance scenes. The proposed video coding scheme has been implemented on top of the High Efficiency Video Coding (HEVC) reference software. Experiments have been conducted to evaluate the quantitative and qualitative performance of the proposed coding scheme. Our method achieves 7.4% and 7.6% bit-rate savings in low-delay-B and low-delay-P settings, respectively, at similar visual quality levels in comparison with HEVC.
Stable Linear Structures and Seam Measurements for Parallax Image Stitching

Parallax tolerance is a fundamental problem in image stitching. To solve this problem, increasing effort has been devoted to the spatially varying and seam cutting. However, there still exist some issues that need to be adequately addressed. First, the implementation of the spatially varying warping requires a restricted premise that the overlapping region can be aligned: if this premise is not met, ghosting caused by misalignment will emerge. In addition, the spatially varying warping may cause distortion due to the issue of inconsistent homographies. Second, conventional seam cutting will lead to objects being cropped and duplicated. Therefore, in this paper, we propose a stable framework for stitching images: the framework consists of a uniform linear structure model that is able to mitigate the distortion of projection and perspective, while preserving the structures of objects in the non-overlapping region; and a stable hybrid actor-critic that estimates stable seam measurements in the overlapping region to diminish the parallax. Comparison experiments show that the proposed method is superior to some conventional methods with respect to mitigating ghosting and preserving structure.
Modular Framework and Instances of Pixel-Based Video Quality Models for UHD-1/4K

The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods. Today's movies and TV shows are typically recorded in UHD-1/4K and streamed using settings attuned to the end-device and current network conditions. Video quality prediction models can be used to perform an extensive analysis of video codec settings to ensure high quality. Hence, we present a framework for the development of pixel-based video quality models. We instantiate four different model variants (hyfr, hyfu, fume and nofu) for short-term video quality estimation targeting various use cases. Our models range from a no-reference video quality model to a full-reference model including hybrid model extensions that incorporate client accessible meta-data. All models share a similar architecture and the same core features, depending on their mode of operation. Besides traditional mean opinion score prediction, we tackle quality estimation as a classification and multi-output regression problem. Our performance evaluation is based on the publicly available AVT-VQDB-UHD-1 dataset. We further evaluate the introduced center-cropping approach to speed up calculations. Our analysis shows that our hybrid full-reference model (hyfr) performs best, e.g. 0.92 PCC for MOS prediction, followed by the hybrid no-reference model (hyfu), full-reference model (fume) and no-reference model (nofu). We further show that our models outperform popular state-of-the-art models. The introduced features and machine-learning pipeline are publicly available for use by the community for further research and extension.
Autonomous Exploring Map and Navigation for an Agricultural Robot

In this paper, the robot is exploring and creating a map of the environment for autonomous navigation. Exploration forms an important role in creating the map and locating the obstacles for path planning. Autonomous Frontier Based Exploration is implemented on both hardware and software of the Turtlebot3 Burger platform. For Simultaneous Localization and Mapping (SLAM), the Breadth-First Search (BFS) algorithm is used. The path planning uses the A * algorithm for the global planning and for local planning uses the Dynamic Window Approach (DWA). The software used in this work is Ubuntu 18.04, along with ROS Melodic on virtual box. This is a work in progress, and the results will be used in further research work for autonomous monitoring of the agricultural fields.
Can financial incentives help with the struggle for security policy compliance?

This study examined the effects of financial incentives on security policy compliance. Participants were recruited for a computerized in-basket job simulation and randomly assigned to one of three groups: (1) control group with no financial incentives for compliance; (2) positive frame (gain) group where participants could gain up to $5 for compliance; (3) negative frame (loss) group where participants were awarded $5, but would lose a portion with each compliance failure. Incentives increased individuals' vigilance toward phishing emails and decreased likelihood of clicking on links, with the negative frame incentive having the strongest effects. Incentives worked better for non-contextualized emails.
Multi-level Alignment Network for Domain Adaptive Cross-modal Retrieval

Cross-modal retrieval is an important but challenging research task in the multimedia community. Most existing works of this task are supervised, which typically train models on a large number of aligned image-text/video-text pairs, making an assumption that training and testing data are drawn from the same distribution. If this assumption does not hold, traditional cross-modal retrieval methods may experience a performance drop at the evaluation. In this paper, we introduce a new task named as domain adaptive cross-modal retrieval, where training (source) data and testing (target) data are from different domains. The task is challenging, as there are not only the semantic gap and modality gap between visual and textual items, but also domain gap between source and target domains. Therefore, we propose a Multi-level Alignment Network (MAN) that has two mapping modules to project visual and textual modalities in a common space respectively, and three alignments are used to learn more discriminative features in the space. A semantic alignment is used to reduce the semantic gap, a cross-modality alignment and a cross-domain alignment are employed to alleviate the modality gap and domain gap. Extensive experiments in the context of domain-adaptive image-text retrieval and video-text retrieval demonstrate that our proposed model, MAN, consistently outperforms multiple baselines, showing a superior generalization ability for target data. Moreover, MAN establishes a new state-of-the-art for the large-scale text-to-video retrieval on TRECVID 2017, 2018 Ad-hoc Video Search benchmark.
Sentiment classification using hybrid feature selection and ensemble classifier

This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time.
A Convolutional Recurrent Neural Network for the Handwritten Text Recognition of Historical Greek Manuscripts

In this paper, a Convolutional Recurrent Neural Network architecture for offline handwriting recognition is proposed. Specifically, a Convolutional Neural Network is used as an encoder for the input which is a textline image, while a Bidirectional Long Short-Term Memory (BLSTM) network followed by a fully connected neural network acts as the decoder for the prediction of a sequence of characters. This work was motivated by the need to transcribe historical Greek manuscripts that entail several challenges which have been extensively analysed. The proposed architecture has been tested for standard datasets, namely the IAM and RIMES, as well as for a newly created dataset, namely EPARCHOS, which contains historical Greek manuscripts and has been made publicly available for research purposes. Our experimental work relies upon a detailed ablation study which shows that the proposed architecture outperforms state-of-the-art approaches.
Are Comments on Stack Overflow Well Organized for Easy Retrieval by Developers?

Many Stack Overflow answers have associated informative comments that can strengthen them and assist developers. A prior study found that comments can provide additional information to point out issues in their associated answer, such as the obsolescence of an answer. By showing more informative comments (e.g., the ones with higher scores) and hiding less informative ones, developers can more effectively retrieve information from the comments that are associated with an answer. Currently, Stack Overflow prioritizes the display of comments, and, as a result, 4.4 million comments (possibly including informative comments) are hidden by default from developers. In this study, we investigate whether this mechanism effectively organizes informative comments. We find that (1) the current comment organization mechanism does not work well due to the large amount of tie-scored comments (e.g., 87% of the comments have 0-score) and (2) in 97.3% of answers with hidden comments, at least one comment that is possibly informative is hidden while another comment with the same score is shown (i.e., unfairly hidden comments). The longest unfairly hidden comment is more likely to be informative than the shortest one. Our findings highlight that Stack Overflow should consider adjusting the comment organization mechanism to help developers effectively retrieve informative comments. Furthermore, we build a classifier that can effectively distinguish informative comments from uninformative comments. We also evaluate two alternative comment organization mechanisms (i.e., the Length mechanism and the Random mechanism) based on text similarity and the prediction of our classifier.
HD Map Change Detection with Cross-Domain Deep Metric Learning

High-definition (HD) maps are emerging as an essential tool for autonomous driving since they provide high-precision semantic information about the physical environment. To function as a reliable source of map information, HD maps must be constantly updated with changes that occur to the state of the road. In this paper, we propose a novel framework for HD map change detection that can be used to maintain an up-to-date HD map. More specifically, we design our HD map change detection algorithm based on deep metric learning, providing a unified framework that directly maps an input image to estimated probabilities of HD map changes. To reduce the discrepancy between input domains, i.e., camera image and HD map, we propose an effective learning scheme for metric space based on adversarial learning. Finally, we augment our framework with a pixel-level local change detector that specifies the region of changes in the image. We verify the effectiveness of our framework by evaluating it on a city-scale urban HD map dataset. Experimental results show that our method can robustly detect changes against noises due to dynamic objects and error in vehicle poses.
What has changed with neural machine translation? A critical review of human factors

In recent years, the push towards automation and translation productivity led to great efforts dedicated to the development of machine translation (MT) systems. Neural machine translation (NMT) represents the latest of these efforts. In this paper we present a critical review of human factors in NMT research with two goals: to provide a snapshot of research in NMT involving human stakeholders, and to appraise how professional translators have been included in discourses around NMT. We report four key findings. First, from translators’ perspective, changes brought about by the neural paradigm are not as much to do with workflows, but rather with the NMT editing process and its specifics. Second, the majority of NMT research involving human stakeholders is directed towards advancing the state of MT development rather than ensuring the usefulness of NMT as a tool for professionals. Third, the review suggested overall narrow conceptualisations of translation productivity that were often based solely on measures of processing time or throughput. Fourth, it emerged that NMT investigations involving end-users are still relatively scarce. We present and discuss these findings, and make recommendations for future research on topics including the concept of productivity and the role of NMT as a professional tool.
Segmentation of High Spatial Resolution Remote Sensing Image based On U-Net Convolutional Networks

With the rapid development of deep learning in recent years, the field of remote sensing image processing has gradually started to use some deep learning algorithms to achieve intelligent and fast processing of images, and the results have improved to a certain extent compared to traditional methods. The U-Net convolutional neural network was proposed used in medical image segmentation in 2015. Based on the previous work, we transferred the U-Net to remote sensing image segmentation to realize the pixel level semantic segmentation of remote sensing image end-to-end. Through U-Net training and learning on GF-2 remote sensing image, the overall accuracy of training sets is 93.83%, while the overall accuracy of the test data is 82.27%, the kappa coefficient is 0.7721, and the Mean intersection Over Union (MiOU) is 0.6405. The results showed that the experiment has high segmentation accuracy and generalization ability.
The Cybersecurity Focus Area Maturity (CYSFAM) Model

The cost of recovery after a cybersecurity attack is likely to be high and may result in the loss of business at the extremes. Evaluating the acquired cybersecurity capabilities and evolving them to a desired state in consideration of risks are inevitable. This research proposes the CYberSecurity Focus Area Maturity (CYSFAM) Model for assessing cybersecurity capabilities. In this design science research, CYSFAM was evaluated at a large financial institution. From the many cybersecurity standards, 11 encompassing focus areas were identified. An assessment instrument—containing 144 questions—was developed. The in-depth single case study demonstrates how and to what extent cybersecurity related deficiencies can be identified. The novel scoring metric has been proven to be adequate, but can be further improved upon. The evaluation results show that the assessment questions suit the case study target audience; the assessment can be performed within four hours; the organization recognizes itself in the result.
Comparing Order Picking Guidance with Microsoft Hololens, Magic Leap, Google Glass XE and Paper

Head-worn displays (HWDs) are an efficient and cost-effective means to guide users in order picking, a task that requires users to alternate their attention between the physical environment and the HWD's virtual image. After training 12 participants to expertise in picking, we compare three significantly different HWDs: Magic Leap One, Microsoft Hololens, and Google Glass Explorer Edition against paper pick lists (the industry standard). We find that previous findings on HWD benefits during such tasks are not reflected in all HWDs, suggesting that hardware design significantly influences efficacy. Based on experimental results and observations, we highlight challenges such as head weight, mounting, display clarity, field of view (FOV), and display position and discuss their possible effects on user comfort, user preference, task speed, and task accuracy.
Practical and Secure Circular Range Search on Private Spatial Data

With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search. In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes.
TKC: Mining Top-K Cross-Level High Utility Itemsets

High utility itemset mining is a well-studied data mining task for analyzing customer transactions. The goal is to find all high utility itemsets, that is items purchased together that generate a profit equal to or greater than a user-defined minimum utility threshold. However, a limitation of traditional high utility itemset mining algorithms is that item categories (e.g. drinks, dairy products) are ignored. Recently, two algorithms were designed to find multi-level and cross-level high utility itemsets to reveal relationships between items and/or categories of items. This is achieved by considering a product taxonomy, where items are organized into a hierarchy. Though these algorithms can reveal interesting patterns, a problem is that setting the minimum utility threshold is not intuitive and greatly influences the number of patterns found and the algorithms' performance. If the user sets the threshold too low, a huge number of patterns is found and runtimes can be very long, while if the threshold is set too high, few patterns are found. Hence, a user often have to run an algorithm numerous times to find an appropriate threshold value to obtain just enough patterns. This paper addresses this issue by presenting a novel algorithm called TKC (Top-K Cross-level high utility itemset miner), which let the user directly set the number of patterns <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> to be discovered. TKC performs a depth-first search and include search space pruning techniques and an optimization to enhance its performance. Experiments were done on retail data with taxonomy information. Results indicate that the algorithm is efficient and the optimization improves its performance.