Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information

Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized that the main limitations are due to the inaccuracy of the target position and the uncertainty in the depth distribution of the foreground target. These two problems arise from the inaccurate depth estimation. To deal with the aforementioned problems, we propose two innovative solutions. The first is a novel method based on joint image segmentation and geometric constraints, used to predict the target depth and provide the depth prediction confidence measure. The predicted target depth is fused with the overall depth of the scene and results in the optimal target position. For the second, we utilize the target scale, normalized with the Gaussian function, as a priori information. The uncertainty of depth distribution, which can be visualized as long-tail noise, is reduced. With the refined depth information, we convert the optimized depth map into the point cloud representation, called a pseudo-LiDAR point cloud. Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset. The results demonstrate that our proposed framework outperforms various state-of-the-art methods by more than 12.37% and 5.34% on the easy and hard settings of the KITTI validation subset, respectively. On the KITTI test set, our framework also outperformed state-of-the-art methods by 5.1% and 1.76% on the easy and hard settings, respectively.
Enforcement for infinite-step opacity and K-step opacity via insertion mechanism

Opacity is an important information-flow property concerning the security and privacy of cyber–physical systems. We investigate the synthesis problem of infinite-step opacity and K-step opacity by using insertion function. An insertion function is a monitoring interface placed between the system and the outside observer that inserts fictitious events to the system output if necessary. To successfully enforce infinite-step opacity and K-step opacity, in this paper, we first review the insertion mechanism without considering the opacity enforcement issue, and propose two new automata for recognizing the safe languages for infinite-step opacity and K-step opacity respectively. Then, we enforce the infinite-step opacity and the K-step opacity with the reviewed insertion mechanism. Computational complexity issues are also discussed. Our results extend the prior results about the synthesis of insertion functions from the notion of current-state opacity to the notions of infinite-step opacity and K-step opacity.
Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0

Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.
Experimenting hybrid reality in cultural heritage reconstruction. The Peasant Civilisation Park and the ‘Vicinato a Pozzo’ museum of Matera (Italy)

Hybrid reality (HR) in cultural heritage is mainly conceptual, and its exploitation in cultural heritage and museums remains an under-investigated topic. This research aims to shed light on the experimenting role of HR as an alternative soft-technological approach in cultural heritage virtual reconstruction, which enhance visitors’ experience and satisfaction. A quantitative survey methodology was employed to analyse the ‘Peasant Civilization Park’, a project based on HR invisible projections installed in the Vicinato a Pozzo Museum, in the thousand-year-old city Matera (Italy) – European Capital of Culture 2019. Findings and conclusions open up innovative scenarios on the HR in cultural heritage, driving the identification of alternative museum business models and new museum immersive experiences. Managerial implications suggest how HR improves heritage exhibition management, rethinking experiential value proposition and visitors’ satisfaction.
HSELL-Net: A Heterogeneous Sample Enhancement Network With Lifelong Learning Under Industrial Small Samples

Small sample size leads to low accuracy and poor generalization of industrial fault diagnosis modeling. Domain adaptation (DA) attempts to enhance small samples by transferring samples in other similar domains, but it has limited application in industrial fault diagnosis, since the differences in working conditions lead to large variations of fault samples. To address the above issues, this article proposes a heterogeneous sample enhancement network with lifelong learning (HSELL-Net). First, a heterogeneous DA subnet (HDA-subnet) is presented, in which the designed heterogeneous supporting domain ensures dimension alignment and the designed distribution jointly matching improves the performance of distribution matching; thus, fault samples from other working conditions can be employed to reliably enhance small samples. Second, a lifelong learning subnet (LL-subnet) is designed, in which the proposed Admixup and shared knowledge repository enable incremental samples to further enhance small samples without retraining the network. The two subnets are mutually embedded and reinforced to enhance the number and types of small samples; thus, the accuracy and generalization of fault diagnosis under industrial small samples are improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experimental results show the HSELL-Net outperforms the existing works under industrial small samples.
Multi-scale feature aggregation and boundary awareness network for salient object detection

Salient object detection aims to detect the most visually distinctive objects in an image. Although existing FCN-based methods have shown strong advantages in this field, scale variation and complex boundary are still great challenges. In this paper, we propose a multi-scale feature aggregation and boundary awareness network to overcome the problems. Multi-scale feature aggregation module is proposed to integrate adjacent hierarchical features and the multiple aggregation strategy solves the problem of scale variation. To obtain more effective multi-scale features from integrated features, a cross feature refinement module is proposed to compose the decoder. For the issue of complex boundary, we design a boundary pixel awareness loss function to enable the network to acquire boundary information and generate high-quality saliency maps with better boundary. Experiments on five benchmark datasets show that our network outperforms recent state-of-the-art detectors quantitatively and qualitatively.
Secured Storing and Sharing of Medical Records Based on Blockchain

Electronic Medical Records(EMRs) are files that contain very sensitive and private information regarding a patient&#x0027;s diagnosis and treatment that must be exchanged often across peers. Sharing medical records amongst participants is highly difficult due to the risk of data leakage or alteration throughout the operational procedure. The key solution to these issues is a blockchain-based electronic medical records system. A blockchain-based records management system is proposed for the maintenance and sharing of electronic medical information. The doctor and the patient can use this technology to save and access medical data in the blockchain network. The metamask generates the amount required to mine the data and store it in the blockchain when any data is to be saved. The amount generated is expressed in gas form. The data is subsequently converted into ethereum currency using metamask. The blockchain network then mines the data, which is then put to the blockchain. When a doctor wishes to see the data, they must input the OTP that was issued to the patient&#x0027;s registered email. Medical data of patients is extremely sensitive and must be safeguarded. Blockchain technology could be a viable solution to the issue. The hospital&#x0027;s electronic health system will benefit from a medical data exchange and protection plan based on the blockchain.
Sequestration of imaging studies in MIDRC: a multi-institutional data commons

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available image repository/commons as well as a sequestered database for performance evaluation and benchmarking of algorithms. After de-identification, approximately 80% of the medical images and associated meta-data will become part of the open repository and 20% will be sequestered and kept separate from the open commons. To ensure that both the public, open dataset and the sequestered dataset are representative of the population available, demographic characteristics across the two datasets must be balanced. Our method uses multidimensional stratified sampling where several demographic variables of interest are sequentially used to separate the data into individual strata, each representing a unique combination of variables. Within each stratum, patients are randomly assigned to the open set (80%) or the sequestered set (20%). Thus, for p variables of interest, the balance of the pdimensional distribution of variable combinations can be controlled. This algorithm was used on an example COVID-19 dataset containing image exams of 4662 patients using the variables of race, age, sex at birth, and ethnicity, each containing 8, 8, 2, and 4 categories, respectively. After stratification of this dataset into the two subsets, resulting distributions of each variable matched the distribution from the original dataset with a maximum percent difference from its original fraction of 0.4%. These results demonstrate that the implemented process of multi-dimensional sequential stratified sampling can partition a large database while maintaining balance across several variables.
Image Matting With Deep Gaussian Process

We observe a common characteristic between the classical propagation-based image matting and the Gaussian process (GP)-based regression. The former produces closer alpha matte values for pixels associated with a higher affinity, while the outputs regressed by the latter are more correlated for more similar inputs. Based on this observation, we reformulate image matting as GP and find that this novel matting-GP formulation results in a set of attractive properties. First, it offers an alternative view on and approach to propagation-based image matting. Second, an application of kernel learning in GP brings in a novel deep matting-GP technique, which is pretty powerful for encapsulating the expressive power of deep architecture on the image relative to its matting. Third, an existing scalable GP technique can be incorporated to further reduce the computational complexity to O(n) from O(n3) of many conventional matting propagation techniques. Our deep matting-GP provides an attractive strategy toward addressing the limit of widespread adoption of deep learning techniques to image matting for which a sufficiently large labeled dataset is lacking. A set of experiments on both synthetically composited images and real-world images show the superiority of the deep matting-GP to not only the classical propagation-based matting techniques but also modern deep learning-based approaches.
The origin of Franson-type nonlocal correlation

Franson-type nonlocal correlation results in a second-order intensity fringe between two remotely separated parties via coincidence measurements, whereas the corresponding local measurements show a perfect incoherence feature. This nonlocal correlation fringe between paired photons is mysterious due to the local randomness in both parties. Here, the Franson nonlocal correlation fringe is analytically investigated using the wave nature of photons to understand the mysterious quantum feature. As a result, the nonlocal intensity fringe is turned out to be a measurement selection-based coherence feature, while the local randomness is from effective decoherence among broad bandwidth-distributed photon pairs. As a result, a coherence version of Franson nonlocal correlation is suggested for macroscopic quantum applications with a commercial laser. The local and nonlocal correlations of the proposed scheme show the same results as entangled photon-pair based Franson correlation. At the request of the authors, this article is being retracted effective 28 October 2022.
The Role of Latent Semantic Categories and Clustering in Enhancing the Efficiency of Human Sensitivity Review

Government documents must be manually sensitivity reviewed to identify and protect any sensitive information (e.g. personal information) in the documents before the documents can be opened to the public. However, due to the large volume of born-digital documents that need to be reviewed, there is a growing need for technologies to assist human reviewers and improve the efficiency of the review process. For example, in sensitivity review, a reviewer needs to be able to quickly find documents that belong to specific latent semantic categories (e.g., documents about criminality that contain the personal details of victims). However, manually identifying such document categories is a challenging task when reviewing digital documents, due to the size of, and lack of structure in the collections. We hypothesise that reviewing documents that are clustered by their latent semantic categories will increase the efficiency of the human reviewers, since the reviewers will be able to review related documents in sequence. In this work, we conduct a user study to evaluate the effectiveness of different clustering techniques, document metadata and automatic sensitivity classification, for grouping and prioritising documents for review, to increase the efficiency of the review process. Our study shows that reviewing documents in semantic clusters can significantly improve the efficiency (i.e., speed) of the sensitivity reviewers (+15.65%, T-Test, p<0.05) while maintaining the reviewers' accuracy. Moreover, we propose a novel strategy for prioritising document clusters for review to maximise the number of documents that are opened to the public within a fixed reviewing time budget. Our proposed prioritisation strategy results in a significant increase in the number of documents that are opened to the public (+37.99%, T-Test, p<0.05) compared to prioritising documents without clusters.
Design and error calibration of an on-axis deflectometric microscope system

An on-axis deflectometric microscope system (ODMS) is proposed for the microscopic surface measurement with high accuracy and a large slope dynamic range. To reduce the geometry sensitivity, a beam splitter is employed to build the coaxial configuration among the illumination screen, camera, and tested sample, which facilitates the calibration of system geometrical parameters. Due to the small working distance, the system model miscalibration in the model-ray-tracing-based "null" testing could cause obvious geometrical aberrations. In this paper, the geometrical aberrations due to the system model miscalibration are analyzed, and the corresponding calibration method based on computer-aided reverse optimization is applied to achieve accurate measurement. In addition, the systematic error introduced by the system components in the ODMS are also discussed. Both the simulation and experiment have been carried out to demonstrate the feasibility and high accuracy of the proposed measurement method. The proposed system is compact in structure, large in measurable slope range, and high in spatial resolution, providing a viable metrological tool for the microscopic testing of various freeform surfaces, microstructural elements, and micro-devices.
Knowledge management and community-based enterprise: an initiative to preserve the shadow puppet traditional knowledge in Yogyakarta, Indonesia

Purpose This study aims to examine the dynamics of traditional wayang kulit or shadow puppet knowledge management in a community-based enterprise (CBE) known as “Wisata Wayang” in Wukirsari Village, Yogyakarta, Indonesia. Design/methodology/approach A qualitative case study was adopted, which allows the author to explore the dynamics or uniqueness of an event or cultural phenomenon more deeply. Findings The shadow puppet is an artefact of Javanese culture with rich life philosophy and wisdom. It guides people the pursuit of harmony with themselves, others, the universe and God. The success of knowledge management of the shadow puppet at CBE was supported by the high entrepreneurial orientation of the administrators. This study showed that entrepreneurial orientation should be extended into sociopreneurial with additional aspects, including preservation mission and communality, promoting the emergence of grassroots innovations. The knowledge of shadow puppet craft in this village is passed through nyantrik , also known as apprenticeship. Originality/value No previous research has explored the dynamics of traditional knowledge management in the context of CBE in Indonesia. As Indonesia has rich traditional knowledge from hundreds of tribes and prominent communal cultures, this study of community-based knowledge management contributes new insights in the knowledge management literature.
Linguistically-Based Comparison of Different Approaches to Building Corpora for Text Simplification: A Case Study on Italian

In this paper, we present an overview of existing parallel corpora for Automatic Text Simplification (ATS) in different languages focusing on the approach adopted for their construction. We make the main distinction between manual and (semi)-automatic approaches in order to investigate in which respect complex and simple texts vary and whether and how the observed modifications may depend on the underlying approach. To this end, we perform a two-level comparison on Italian corpora, since this is the only language, with the exception of English, for which there are large parallel resources derived through the two approaches considered. The first level of comparison accounts for the main types of sentence transformations occurring in the simplification process, the second one examines the results of a linguistic profiling analysis based on Natural Language Processing techniques and carried out on the original and the simple version of the same texts. For both levels of analysis, we chose to focus our discussion mostly on sentence transformations and linguistic characteristics that pertain to the morpho-syntactic and syntactic structure of the sentence.
Reaching articular limits can negatively impact embodiment in virtual reality

Providing Virtual Reality(VR) users with a 3D representation of their body complements the experience of immersion and presence in the virtual world with the experience of being physically located and more personally involved. A full-body avatar representation is known to induce a Sense of Embodiment (SoE) for this virtual body, which is associated with improvements in task performance, motivation and motor learning. Recent experimental research on embodiment provides useful guidelines, indicating the extent of discrepancy tolerated by users and, conversely, the limits and disruptive events that lead to a break in embodiment (BiE). Based on previous works on the limit of agency under movement distortion, this paper describes, studies and analyses the impact of a very common yet overlooked embodiment limitation linked to articular limits when performing a reaching movement. We demonstrate that perceiving the articular limit when fully extending the arm provides users with an additional internal proprioceptive feedback which, if not matched in the avatar's movement, leads to the disruptive realization of an incorrect posture mapping. This study complements previous works on self-contact and visuo-haptic conflicts and emphasizes the risk of disrupting the SoE when distorting users' movements or using a poorly-calibrated avatar.
Optimization-Based Post-Training Quantization With Bit-Split and Stitching

Deep neural networks have shown great promise in various domains. Meanwhile, problems including the storage and computing overheads arise along with these breakthroughs. To solve these problems, network quantization has received increasing attention due to its high efficiency and hardware-friendly property. Nonetheless, most existing quantization approaches rely on the full training dataset and the time-consuming fine-tuning process to retain accuracy. Post-training quantization does not have these problems, however, it has mainly been shown effective for 8-bit quantization. In this paper, we theoretically analyze the effect of network quantization and show that the quantization loss in the final output layer is bounded by the layer-wise activation reconstruction error. Based on this analysis, we propose an Optimization-based Post-training Quantization framework and a novel Bit-split optimization approach to achieve minimal accuracy degradation. The proposed framework is validated on a variety of computer vision tasks, including image classification, object detection, instance segmentation, with various network architectures. Specifically, we achieve near-original model performance even when quantizing FP32 models to 3-bit without fine-tuning.