Reviewerly/exHarmony huggingface dataset:
Artifact Handling Based on Depth Image for View Synthesis

The depth image based rendering (DIBR) is a popular technology for 3D video and free viewpoint video (FVV) synthesis, by which numerous virtual views can be generated from a single reference view and its depth image. However, some artifacts are produced in the DIBR process and reduce the visual quality of virtual view. Due to the diversity of artifacts, effectively handling them becomes a challenging task. In this paper, an artifact handling method based on depth image is proposed. The reference image and its depth image are extended to fill the holes that belong to the out-of-field regions. A depth image preprocessing method is applied to project the ghosts to their correct place. The 3D warping process is optimized by an adaptive one-to-four method to deal with the cracks and pixel overlapping. For disocclusions, we calculate depth and background terms of the filling priority based on depth information. The search for the best matching patch is performed simultaneously in the reference image and the virtual image. Moreover, adaptive patch size is used in all hole-filling processes. Experimental results demonstrate the effectiveness of the proposed method, which has better performance compared with previous methods in subjective and objective evaluation.
Context-aware Systems Testing and Validation

Newer systems are still tested and validated following techniques which have been developed decades ago when systems were of a different nature. We report on an attempt to define a new method which is practical and focus on the concept of `context' as a system aspect which have become more relevant in the development of the subsystem category called Intelligent Environments.
User-Generated Gestures for Voting and Commenting on Immersive Displays in Urban Planning

Traditional methods of public consultation offer only limited interactivity with urban planning materials, leading to a restricted engagement of citizens. Public displays and immersive virtual environments have the potential to address this issue, enhance citizen engagement and improve the public consultation process, overall. In this paper, we investigate how people would interact with a large display showing urban planning content. We conducted an elicitation study with a large immersive display, where we asked participants (N = 28) to produce gestures to vote and comment on urban planning material. Our results suggest that the phone interaction modality may be more suitable than the hand interaction modality for voting and commenting on large interactive displays. Our findings may inform the design of interactions for large immersive displays, in particular, those showing urban planning content.
A blockchain for media: Survey

As the Internet has developed, media distribution services have spread. However, these distribution services have many problems. The blockchain technology can solve these problems. In this paper, this blockchain is defined as media blockchain and is classified into two categories. One is content security that means securing content using blockchain. The other is content right information that means tracing and managing right information using blockchain. We introduce and analyze the media blockchain.
Collaborative Filtering with Noisy Ratings

User ratings on items are noisy in real-world recommender systems, which raises challenges to matrix approximation (MA)-based collaborative filtering (CF) algorithms -the learned models will be easily biased to the noisy training data and yield low generalization performance.This paper proposes a noise-resilient matrix approximation (NORMA) method, which can achieve less biased matrix approximation and thus more accurate collaborative filtering.In NORMA, an adaptive weighting strategy is proposed to decrease the gradient updates of noisy ratings, so that the learned MA models will be less prone to the noisy ratings.Theoretical analyses show that NORMA can achieve better generalization performance than standard matrix approximation methods.Experimental studies on real-world datasets demonstrate that NORMA can outperform state-of-the-art matrix approximation-based collaborative filtering methods in recommendation accuracy.
Study on 3D Action Recognition Based on Deep Neural Network

Recognition of the detailed human action in a harsh environment such as low light environment is an issue that needs to be constantly resolved in a video surveillance system. Action recognition technology using 3D depth map information is understood as a way to solve this issue. In this paper, we propose efficient action recognition using 3D image based on convolution neural network (CNN). And we apply it to the intelligent video surveillance system and measure the detection accuracy by the action. Experimental results show that the security action recognition accuracy of 61.5%.
Towards better traceability of field sampling data

Ensuring traceability of both field experimental data and laboratory sampling data for a reproducible research remains a challenge nowadays. Between the time when geolocalized specimens are taken, and the time the resulting data ends up in analysis published within a study, many manual operations take place that are prone to generate errors. The French nodes of the European Long-Term Socio-Ecological Research Infrastructure called ”Zones Ateliers” propose a solution as generic as possible to this problem of monitoring of the samples and the data associated with them. Compared to existing solutions such as Laboratory Information Management Systems, we target a robust solution for labelling adapted to outdoor working conditions, with the management of storages and movements of samples. We designed and realized a software package tested from end to end, using open source licenses and cheap hardware, including small printers (mobile or not) and Raspberry Pis. This system provides sufficient flexibility so that it can facilitate working with a wide variety of existing protocols. One of the most interesting feature consists to record all contextual data associated with the samples, which constitute important parameters of the subsequent analyses. Furthermore, not only traceability is thus guaranteed, but also we can expect a reduced handling times and an increased streamlining of the storage of samples that will improve the return on investment.
Loops of isogeny graphs of supersingular elliptic curves at j = 0

We improve Adj et al.'s bound in [1, Theorem 12] from p > 4 ℓ to p > 3 ℓ for the loops of E 0 : y 2 = x 3 + 1 in the ℓ -isogeny graph G ℓ ( F p 2 , − 2 p ) of supersingular elliptic curves over F p 2 with trace − 2 p .
Kalman filter-based phase estimation of continuous-variable quantum key distribution without sending local oscillator

Phase estimation is one of the crucial procedures in continuous-variable quantum key distribution without sending local oscillator (LLO-CVQKD), aiming at compensating the phase drift in post-processing. To decrease the phase noise caused by the phase drift and measurement error, we propose Kalman filter-based phase noise estimation of LLO-CVQKD, which involves the measurements and filtering through exploiting the small amplitude reference pulses, achieving the accurate phase noise estimation. Simulation results indicate that Kalman filter-based phase noise estimation is capable of reducing measurement error and phase noise, while increasing the secret key rate by 7 times.
$${\textsf {ACUOS}}^\mathbf {2}$$ : A High-Performance System for Modular ACU Generalization with Subtyping and Inheritance

Generalization in order-sorted theories with any combination of associativity (A), commutativity (C), and unity (U) algebraic axioms is finitary. However, existing tools for computing generalizers (also called "anti-unifiers") of two typed structures in such theories do not currently scale to real size problems. This paper describes the $${\textsf {ACUOS}}^\mathbf {2}$$ system that achieves high performance when computing a complete and minimal set of least general generalizations in these theories. We discuss how it can be used to address artificial intelligence (AI) problems that are representable as order-sorted ACU generalization, e.g., generalization in lists, trees, (multi-)sets, and typical hierarchical/structural relations. Experimental results demonstrate that $${\textsf {ACUOS}}^\mathbf {2}$$ greatly outperforms the predecessor tool ACUOS by running up to five orders of magnitude faster.
A Neural Semantic Parser for Math Problems Incorporating Multi-Sentence Information

In this article, we study the problem of parsing a math problem into logical forms. It is an essential pre-processing step for automatically solving math problems. Most of the existing studies about semantic parsing mainly focused on the single-sentence level. However, for parsing math problems, we need to take the information of multiple sentences into consideration. To achieve the task, we formulate the task as a machine translation problem and extend the sequence-to-sequence model with a novel two-encoder architecture and a word-level selective mechanism. For training and evaluating the proposed method, we construct a large-scale dataset. Experimental results show that the proposed two-encoder architecture and word-level selective mechanism could bring significant improvement. The proposed method can achieve better performance than the state-of-the-art methods.
A Hardware Implementation of SHA3 Hash Processor using Cortex-M0

This paper describes a SHA3 hash processor that is interfaced with Cortex-M0 to be used as an IP. The SHA3 hash processor was designed with a round-iterative structure of 1600-bit data-path, and it supports four different message digest sizes of 512, 384, 256, and 224 bits depending on the hash function used. The SHA3 processor interfaced with Cortex-M0 was verified by FPGA implementation. It was estimated to have a throughput of 5 Gbps at the maximum clock frequency of 289 MHz, and it occupied 1,692 slices of Virtex5 FPGA device.
CAPTCHA

As currently many people use the Internet to access websites, Internet security becomes an important topic. One popular security mechanism is Captcha or Completely Automated Public Turing Computer and Humans Apart, which determine whether or not the user is human. Nowadays there exist many forms of Captcha with different testing mechanisms. In this study, the researcher would like to determine a form of Captcha that improves user experience on websites. Captcha is divided into 5 types in this study: Invisible Captcha, reCaptcha by Google, Text-based Captcha, Game-based Captcha and Math Captcha, which were tested on 40 people aged 25-34 years as according to surveys people in this age group were the largest group of Internet users. After passing all 5 Captcha tests, the researcher was able to conclude that reCaptcha by Google was the fastest to pass (3.09 seconds), while Captcha that granted the best user experience was Math Captcha because of its entertainment and intuitiveness. Another interesting issue is Invisible Captcha, although being convenient, was not trusted by the majority of the sample users due to its perceived insecurity. Conversely, reCaptcha by Google was deemed the safest.
Contrast-Enhancing Snapshot Narrow-Band Imaging Method for Real-Time Computer-Aided Cervical Cancer Screening

Composition of cervical precancerous lesions and carcinoma in situ is rich in hemoglobin, unlike healthy tissues. In this study, we aimed to utilize this difference to enhance the contrast between healthy and diseased tissues via snapshot narrow-band imaging (SNBI). Four narrow-band images centered at wavelengths of characteristic absorption/reflection peaks of hemoglobin were captured with zero-time delay in between by a custom-designed SNBI video camera. Then these spectral images were fused in real time into a single combined image to enhance the contrast between normal and abnormal tissues. Finally, a Euclidean distance algorithm was employed to classify the tissue into clinical meaningful tissue types. Two pre-clinical experiments were conducted to validate the proposed method. Experimental results indicate that contrast between different grades of diseased tissues in the SNBI generated image was indeed enhanced, as compared to conventional white light image (WLI). The computer-aided classification accuracy was 100% and 50% as compared to the gold standard histopathological diagnosis results with the SNBI and the conventional WLI methods, respectively. Further, the boundary contour between health tissue, cervical precancerous regions, and carcinoma in situ can be automatically delineated in SNBI. The proposed SNBI method was also fast, and it generated automatic diagnostic results with clear boundary contours at over 11 fps on a Pentium 1.6-GHz laptop. Hence, the proposed SNBI is of great significance to enlarge worldwide the coverage of regular cervical screening program, and to live guide surgeries such as biopsy sample collection and accurate cervical cancer treatment.
A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection

We propose a novel practical method for finding the optimal classifier parameter status corresponding to the Bayes error (minimum classification error probability) through the evaluation of estimated class boundaries from the perspective of Bayes boundary-ness. While traditional methods approach classifier optimality from the angle of minimization of the estimated classification error probabilities, we approach it from the angle of optimality of the estimated classification boundaries. The optimal classification boundary consists solely of uncertain samples, whose posterior probability is equal for the two classes separated by the boundary. We refer to this essential characteristic of the boundary as “Bayes boundary-ness”, and use it to measure how optimal the estimated boundary is. Our proposed method achieves the optimal parameter status using the training data only once, in contrast to such traditional methods as Cross-Validation (CV), which demand separate validation data and often require a number of repetitions of training and validation. Moreover, it can be directly applied to any type of classifier, and potentially to any type of sample. In this paper, we first elaborate on our proposed method that implements the Bayes boundary-ness with an entropy-based uncertainty measure. Next, we analyze the mathematical characteristics of the uncertainty measure adopted. Finally, we evaluate the method through a systematic experimental comparison with CV-based Bayes boundary estimation, which is known to be highly reliable in the Bayes error estimation. From the analysis, we rigorously show the theoretical validity of our adopted uncertainty measure. Moreover, from the experiment, we successfully demonstrate that our method can closely approximate the CV-based Bayes boundary estimate and its corresponding classifier parameter status with only a single-shot training over the data in hand.
Flexible Photogrammetric Computations Using Modular Bundle Adjustment: The Chain Rule and the Collinearity Equations

The main purpose of this article is to show that photogram-metric bundle-adjustment computations can be sequentially organized into modules. Furthermore, the chain rule can be used to simplify the computation of the analytical Jacobians needed for the adjustment. Novel projection models can be flexibly evaluated by inserting, modifying, or swapping the order of selected modules. As a proof of concept, two variants of the pinhole projection model with Brown lens distortion were implemented in the open-source Damped Bundle Adjustment Toolbox and applied to simulated and calibration data for a nonconventional lens system. The results show a significant difference for the simulated, error-free, data but not for the real calibration data. The current flexible implementation incurs a performance loss. However, in cases where flexibility is more important, the modular formulation should be a useful tool to investigate novel sensors, data-processing techniques, and refractive models.
Image-driven unsupervised 3D model co-segmentation

Segmentation of 3D models is a fundamental task in computer graphics and vision. Geometric methods usually lead to non-semantic and fragmentary segmentations. Learning techniques require a large amount of labeled training data. In this paper, we explore the feasibility of 3D model segmentation by taking advantage of the huge number of easy-to-obtain 2D realistic images available on the Internet. The regional color exhibited in images provides information that is valuable for segmentation. To transfer the segmentations, we first filter out inappropriate images with several criteria. The views of these images are estimated by our proposed texture-invariant view estimation Siamese network. The training samples are generated by rendering-based synthesis without laborious labeling. Subsequently, we transfer and merge the segmentations produced by each individual image by applying registration and a graph-based aggregation strategy. The final result is obtained by combining all segmentations within the 3D model set. Our qualitative and quantitative experimental results on several model categories validate effectiveness of our proposed method.
Seemingly unrelated extreme learning machine

A seemingly unrelated regression (SUR) system simultaneously studies the groups of samples that are related with each other through the covariance of decision attributes. Each group of samples is studied by a regression equation, and the error terms of the regression equations are correlated in SUR system. Extreme Learning Machine (ELM) is a training method for single-hidden layer feedforward neural network, it is widely used in many machine learning domains due to the good generalization capability and fast training speed. Since a single ELM ignores the correlated information among different groups of samples, it fails to solve the SUR problem effectively. This ineffectiveness becomes more obvious with the correlation among equations going up. In order to overcome this problem, an extended ELM model is proposed in this paper, described as Seemingly Unrelated ELM (SUELM). SUELM simultaneously learns multiple ELMs by sufficiently using the correlated information among different groups of samples, thus it can solve the SUR problem effectively. In comparison with a single ELM, SUELM significantly improves the performance. Simulation results show that SUELM performs better than the single ELM with respect to mean square error and generalization ability, especially when significant correlations exist among different groups of samples. This paper provides an effective way for solving the SUR problem by adopting ELM as the learning model.
Geological and Fluid Inclusion Constraints on Gold Deposition Processes of the Dayingezhuang Gold Deposit, Jiaodong Peninsula, China

The Dayingezhuang gold deposit, hosted mainly by Late Jurassic granitoids on Jiaodong Peninsula in eastern China, contains an estimated 170 t of gold and is one of the largest deposits within the Zhaoping fracture zone. The orebodies consist of auriferous altered pyrite–sericite–quartz granites that show Jiaojia‐type (i.e., disseminated and veinlet) mineralization. Mineralization and alteration are structurally controlled by the NE‐ to NNE‐striking Linglong detachment fault. The mineralization can be divided into four stages: (K‐feldspar)–pyrite–sericite–quartz, quartz–gold–pyrite, quartz–gold–polymetallic sulfide, and quartz–carbonate, with the majority of the gold being produced in the second and third stages. Based on a combination of petrography, microthermometry, and laser Raman spectroscopy, three types of fluid inclusion were identified in the vein minerals: NaCl–H 2 O (A‐type), CO 2 –H 2 O–NaCl (AC‐type), and pure CO 2 (PC‐type). Quartz crystals in veinlets that formed during the first stage contain mainly AC‐type fluid inclusions, with rare PC‐type inclusions. These fluid inclusions homogenize at temperatures of 251°C–403°C and have low salinities of 2.2–9.4 wt% NaCl equivalent. Quartz crystals that formed in the second and third stages contain all three types of fluid inclusions, with total homogenization temperatures of 216°C–339°C and salinities of 1.8–13.8 wt% NaCl equivalent for the second stage and homogenization temperatures of 195°C–321°C and salinities of 1.4–13.3 wt% NaCl equivalent for the third stage. In contrast, quartz crystals that formed in the fourth stage contains mainly A‐type fluid inclusions, with minor occurrences of AC‐type inclusions; these inclusions have homogenization temperatures of 106°C–287°C and salinities of 0.5–7.7 wt% NaCl equivalent. Gold in the ore‐forming fluids may have changed from Au(HS) 0 as the dominant species under acidic conditions and at relatively high temperatures and f O 2 in the early stages, to Au(HS) 2– under neutral‐pH conditions at lower temperatures and f O 2 in the later stages. The precipitation of gold and other metals is inferred to be caused by a combination of fluid immiscibility and water–rock interaction.
Applying Computer Vision to Track Tool Movement in an Automotive Assembly Plant

In the context of automobile assembly, torque tools are used by technicians to mount bolts to parts of a vehicle. Different bolts require varying torque levels to be fastened correctly. An intelligent tool can be programmed to deliver torque levels in a specified order that must be strictly followed by the technician as they secure the bolts. Because of the pre-specified order, it is imperative that the technician follows the correct order precisely. This paper describes our investigation into the use of computer vision to identify, correct, and document the human error involved in the bolt securing process (i.e., incorrect ordering) on an automotive factory assembly line. We built a computer vision application to select a desired order of bolts, detect visitation of a torque tool to each bolt location, and report errors made in the sequence of actions in a vehicle assembly line. The application is used to determine the accuracy of error detection for varying degrees of distance between bolt locations and sizes in order to determine which assembly line stations are appropriate for this type of monitoring. The context for application of our project is a large automotive manufacturing facility in the Southeastern United States -- the Mercedes-Benz US International (MBUSI) factory in Vance, Alabama. The limitations of computer vision for determining errors in assembly order using the torque tool are reported with a discussion of lessons learned.
Reprint of: Introduction to special issue on geologic remote sensing

Herein we provide an overview of science and technology involved in remote sensing, and outlines some practical constraints in applications to geological problems. We further summarize diagnostic spectral features of important geological material that can be detected using satellite- and air-borne remote sensing. Finally, the papers contained in the special issue are briefly introduced.