Reviewerly/exHarmony huggingface dataset:
ON THE FUSION OF CAMERA AND LIDAR FOR 3D OBJECT DETECTION AND CLASSIFICATION

Abstract. In this paper we present a sensor fusion framework for the detection and classification of objects in autonomous driving applications. The presented method uses a state-of-the-art convolutional neural network (CNN) to detect and classify object from RGB images. The 2D bounding boxes calculated by the CNN are fused with the 3D point cloud measured by Lidar sensors. An accurate sensor cross-calibration is used to map the Lidar points into the image, where they are assigned to the 2D bounding boxes. A one-dimensional K-means algorithm is applied to separate object points from foreground and background and to calculated accurate 3D centroids for all detected objects. The proposed algorithm is tested based on real world data and shows a stable and reliable object detection and centroid estimation in different kind of situations.
The Low-Area FPGA Design for the Post-Quantum Cryptography Proposal Round5

Post-Quantum Cryptography (PQC) is getting attention recently.The main reason of this situation is the announcement by the U.S. National Institute for Standard and Technology (NIST) about an opening of the standardization process for PQC.Recently NIST published a list of submissions qualified to the second round of this process.One of the selected algorithms is Round5, offering a key encapsulation mechanism (KEM) and public key encryption (PKE).Due to high complexity of postquantum cryptosystems, only a few FPGA implementations have been reported to date.In this paper, we report results for lowarea purely-hardware implementation of Round5 targeting lowcost FPGAs.
Font Design—Shape Processing of Text Information Structures in the Process of Non-Invasive Data Acquisition

Computer fonts can be a solution that supports the protection of information against electromagnetic penetration; however, not every font has features that counteract this process. The distinctive features of a font’s characters define the font. This article presents two new sets of computer fonts. These fonts are fully usable in everyday work. Additionally, they make it impossible to obtain information using non-invasive methods. The names of these fonts are directly related to the shapes of their characters. Each character in these fonts is built using only vertical and horizontal lines. The differences between the fonts lie in the widths of the vertical lines. The Safe Symmetrical font is built from vertical lines with the same width. The Safe Asymmetrical font is built from vertical lines with two different line widths. However, the appropriate proportions of the widths of the lines and clearances of each character need to be met for the safe fonts. The structures of the characters of the safe fonts ensure a high level of similarity between the characters. Additionally, these fonts do not make it difficult to read text in its primary form. However, sensitive transmissions are free from distinctive features, and the recognition of each character in reconstructed images is very difficult in contrast to traditional fonts, such as the Sang Mun font and Null Pointer font, which have many distinctive features. The usefulness of the computer fonts was assessed by the character error rate (CER); an analysis of this parameter was conducted in this work. The CER obtained very high values for the safe fonts; the values for traditional fonts were much lower. This article aims to presentat of a new solution in the area of protecting information against electromagnetic penetration. This is a new approach that could replace old solutions by incorporating heavy shielding, power and signal filters, and electromagnetic gaskets. Additionally, the application of these new fonts is very easy, as a user only needs to ensure that either the Safe Asymmetrical font or the Safe Symmetrical font is installed on the computer station that processes the text data.
Polarization of Quantum Channels Using Clifford-Based Channel Combining

We provide a purely quantum version of polar codes, achieving the symmetric coherent information of any qubit-input quantum channel. Our scheme relies on a recursive channel combining and splitting construction, where a two-qubit gate randomly chosen from the Clifford group is used to combine two single-qubit channels. The inputs to the synthesized bad channels are frozen by preshared EPR pairs between the sender and the receiver, so our scheme is entanglement assisted. We further show that quantum polarization can be achieved by choosing the channel combining Clifford operator randomly, from a much smaller subset of only nine two-qubit Clifford gates. Subsequently, we show that a Pauli channel polarizes if and only if a specific classical channel over a four-symbol input set polarizes. We exploit this equivalence to prove fast polarization for Pauli channels, and to devise an efficient successive cancellation based decoding algorithm for such channels. Finally, we present a code construction based on chaining several quantum polar codes, which is shown to require a rate of preshared entanglement that vanishes asymptotically.
Effects of Information Security Management Systems on Firm Performance

The purpose of this paper is to determine the aspects of the information security management system (ISMS) on which decision-makers must act to achieve the performance targets.Information assets as core of any Information systems (IS) should be taken seriously by a custom security.In this research, we conduct a case study specially using the Delone and McLean's IS success model.The hypotheses were tested by PLS-SEM of theSmartPLS software using survey data collected among 136 IS and IT professionals.We found that the ISMS (system, service and information qualities, maturity level of information security risk management process) and performance are directly related on one hand, and indirectly by the IT capabilities of the company in the other hand.This shows that mastering security information management risks process is crucial for an enterprise because it greatly contributes to organizational performance, improve the IS' support such as IT management, IT personal skills and IT infrastructure.This work has explored the feasibility of using the IS success model on ISMS, a key world know element, where Africa is both the target of the informational mobility, hackers and especially in the global economy.We consider the IS success model with 4 dependent variables including maturity level of process.
Comparative assessment of five metaheuristic methods on distinct problems

Metaheuristic algorithms belong to the non-gradient based optimization methods. Accomplished studies in this area reveal that each of these methods mostly has its own affirmative and inconvenient aspects. So that, one might provide a high level of exploration while the other can perform a great level of exploitation. Thus, selecting the proper and efficient algorithm for a problem can highly affect both the convergence rate and the accuracy level. There are several different metaheuristic algorithms have been announced in the technical literature in the last decade. Therefore, performing an objective comparative assessment over some of these methods can provide a fundamental and fair attitude for researchers either to select an algorithm which is more fitted with their target(s) or to develop even more efficient methods. So, the current investigation deals with evaluating and comparing of five different metaheuristic techniques emerged from ten years ago up to now. The selected methods can be sorted chronologically as Firefly Algorithm (FA), Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO) method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm (BOA). Different properties of these algorithms as convergence rate, diversity variation, complexity and accuracy level of the final solutions are compared on both constrained and non-constrained optimization problems include mathematical functions, mechanical and structural problems. The results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.
Relationships of Cohen's Kappa, Sensitivity, and Specificity for Unbiased Annotations

For the binary classification tasks in supervised learning, the labels of data have to be available for classifier development. Cohen's kappa is usually employed as a quality measure for data annotation, which is inconsistent with its true functionality of assessing the inter-annotator consistency. However, the derived relationship functions of Cohen's kappa, sensitivity, and specificity in the literature are complicated, thus are unable to be employed to interpret classification performance from kappa values. In this study, based on an annotation generation model, we develop simple relationships of kappa, sensitivity, and specificity when there is no bias in the annotations. A relationship between kappa and Youden's J statistic, a performance metric for binary classification, is further obtained. The derived relationships are evaluated on a synthetic dataset using linear regression analysis. The results demonstrate the accuracy of the derived relationships. It suggests the potential of estimating classification performance from kappa values when bias is absent in the annotations.
ARFurniture: Augmented Reality Interior Decoration Style Colorization

Augmented Reality (AR) can seamlessly create an illusion of virtual elements blended into the real world scene, which is one of the most fascinating human-machine interaction technologies. AR has been utilized in a variety of real-life applications including immersive collaborative gaming, fashion appreciation, interior design, and assistive devices for individuals with vision impairments. This paper contributes a real-time AR application, ARFurniture, which will allow the users to envision furniture-ofinterests in different colors and different styles, all from their smart devices. The core software architecture consists of deep-learning based semantic segmentation and fast-speed color transformation. Our software architecture allows the user prompt the system to colorize the style of the furniture-of-interest within the scene on their mobile devices, and has been successfully deployed on mobile devices. In addition, using eye gaze as a pointing indicator, a head-mounted user-centric augmented reality based indoor decoration style colorization concept is discussed. Related algorithms, system design, and simulation results for ARFurniture are presented. Furthermore, a no-reference image quality measure, Naturalness Image Quality Evaluator (NIQE), was utilized to evaluate the immersiveness and naturalness of ARFuniture. The results demonstrate that ARFurniture has game-changing value to enhance user experience in indoor decoration.
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis

Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter-conditional image generation and depth image generation without any 3D annotations, such as camera poses or depth. We use an explicit 3D consistency loss for two RGBD images generated from different camera parameters, in addition to the ordinal GAN objective. The loss is simple yet effective for any type of image generator such as DCGAN and StyleGAN to be conditioned on camera parameters. Through experiments, we demonstrated that the proposed method could learn 3D representations from 2D images with various generator architectures.
Event Temporal Relation Classification Based on Graph Convolutional Networks

Classifying temporal relations between events is an important step of understanding natural language, and a significant subsequent study of event extraction. With the development of deep learning, various neural network frameworks have been applied to the task of event temporal relation classification. However, current studies only consider semantic information in local contexts of two events and ignore the syntactic structure information. To solve this problem, this paper proposes a neural architecture combining LSTM and GCN. This method can automatically extract features from word sequences and dependency syntax. A series of experiments on the Timebank-Dense corpus also show the superiority of the model presented in this paper.
Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs

In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than the largest dataset to date by orders of magnitude. Unprecedented challenges emerge in this situation compared to previous NMT work, including severe noise in the data and prohibitively long training time. We propose practical solutions to handle these issues and demonstrate that large-scale pretraining significantly improves NMT performance. We are able to push the BLEU score of WMT17 Chinese-English dataset to 32.3, with a significant performance boost of +3.2 over existing state-of-the-art results.
Combining Quality Metrics using Machine Learning for improved and robust HDR Image Quality Assessment

We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework using simple linear regression techniques and other non-linear machine learning (ML) based approaches. We find that using a non-linear combination of scores from different quality metrics using support vector machine is better at prediction than the other techniques such as random forest, random trees, multilayer perceptron or a radial basis function network. To improve performance and reduce complexity of the proposed approach, we use the Sequential Floating Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available calibrated databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.
Persistent and Unforgeable Watermarks for Deep Neural Networks.

As companies continue to invest heavily in larger, more accurate and more robust deep learning models, they are exploring approaches to monetize their models while protecting their intellectual property. Model licensing is promising, but requires a robust tool for owners to claim ownership of models, i.e. a watermark. Unfortunately, current designs have not been able to address piracy attacks, where third parties falsely claim model ownership by embedding their own into an already-watermarked model. We observe that resistance to piracy attacks is fundamentally at odds with the current use of incremental training to embed watermarks into models. In this work, we propose null embedding, a new way to build piracy-resistant watermarks into DNNs that can only take place at a model's initial training. A null embedding takes a bit string (watermark value) as input, and builds strong dependencies between the model's normal classification accuracy and the watermark. As a result, attackers cannot remove an embedded watermark via tuning or incremental training, and cannot add new pirate watermarks to already watermarked models. We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models. Finally, we explore a number of adaptive counter-measures, and show our watermark remains robust against a variety of model modifications, including model fine-tuning, compression, and existing methods to detect/remove backdoors. Our watermarked models are also amenable to transfer learning without losing their watermark properties.
Controllable Sentence Simplification

Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on attributes such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these attributes allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), establishes the state of the art at 41.87 SARI on the WikiLarge test set, a +1.42 improvement over the best previously reported score.
Fluid Inclusion and H–O–S–Pb Isotope Geochemistry of the Yuka Orogenic Gold Deposit, Northern Qaidam, China

The Yuka gold deposit, located in the western part of northern Qaidam, contains Au orebodies hosted in early Paleozoic metamorphic basic volcaniclastic rocks. The Yuka mineralization can be divided into three stages: early quartz-pyrite (stage-I), middle quartz-gold-polymetallic sulfide (stage-II), and late quartz-carbonate (stage-III). Gold deposition is primarily contained within stage-II. Three types of fluid inclusions were identified in the vein mineral assemblages using petrography and laser Raman spectroscopy: H 2 O-CO 2 -NaCl (C-type), H 2 O-NaCl (W-type), and pure CO 2 (PC-type). Stage-I fluids record medium temperatures (205.2°C to 285.5°C) and H 2 O-CO 2 -NaCl±CH 4 fluids with variable salinities (0.6–8.5 wt.% NaCl equiv.). Stage-II fluids evolved towards a more H 2 O-rich composition within a H 2 O-CO 2 -NaCl±CH 4 hydrothermal system at medium temperatures (193.1°C to 271.1°C), with variable salinities (0.4–11.7 wt.% NaCl equiv.). Stage-III fluids are almost pure H 2 O and characterized by low temperatures (188.1°C to 248.5°C) and salinities (0.4–16.1 wt.% NaCl equiv.). These data indicate that ore-forming fluids are characterized by low to medium homogenization temperatures and low salinity and are evolved from a CO 2 -rich metamorphogenic fluid to a CO 2 -poor fluid due to inputs of meteoric waters, which is similar to orogenic-type gold deposits. The average <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>δ</mml:mi></mml:mrow><mml:mrow><mml:mn>18</mml:mn></mml:mrow></mml:msup><mml:msub><mml:mrow><mml:mtext>O</mml:mtext></mml:mrow><mml:mrow><mml:mtext>W</mml:mtext></mml:mrow></mml:msub></mml:math> of quartz varies from 3.3‰ in stage-I to 2.1‰ in stage-II and to 1.4‰ in stage-III, with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>δ</mml:mi><mml:mtext>D</mml:mtext></mml:math> values ranging from −41.6‰ to −58.5‰, suggesting that ore-forming fluids formed from metamorphic fluids mixed with meteoric waters. Auriferous pyrite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msub><mml:mrow><mml:mi>δ</mml:mi></mml:mrow><mml:mrow><mml:mn>34</mml:mn></mml:mrow></mml:msub><mml:mtext>S</mml:mtext></mml:math> ranges from 0.5 to 7.4‰ with a mean value of 4.43‰, suggesting that fluids were partially derived from Paleozoic rocks via fluid-wall rock interactions. Auriferous pyrites have 206 Pb/ 204 Pb of 18.238–18.600 (average of 18.313), 207 Pb/ 204 Pb of 15.590–15.618 (average of 15.604), and 208 Pb/ 204 Pb of 38.039–38.775 (average of 38.1697) and stem from the upper crust. Basing on geological characteristics of the ore deposit as well as new data from the ore-forming fluids, and H-O-S-Pb isotopes, the Yuka gold deposit is best described as an orogenic-type gold deposit.
DenseNet+Inception and Its Application for Electronic Transaction Fraud Detection

With the development of online payment, electronic transaction fraud events also take place often and result in huge financial losses. Recently, DenseNet as one of the most prominent Convolutional Neural Networks (CNNs) has achieved a remarkable success in many applications such as image recognition. However, when we apply DenseNet to the electronic transaction fraud detection in which the original data is directly as the input of DenseNet, a perfect performance cannot be obtained since the transaction data is different from the image data and the features of the original transaction data are not rich. The Inception module used in GoogLeNet (a kind of CNN) has different convolution kernels of different sizes and thus can enhance the feature mining ability. In this paper, we improve DenseNet (iDenseNet for short) in order to suit electronic transaction data and then propose two integration patterns of iDenseNet and Inception module. Experiments are done on two large datasets with millions of transaction records and compare our methods with CNN, DenseNet and Random Forest that are the state-of-the-art methods used in electronic transaction fraud detection. Results illustrate better performances of our methods.
Novel Data Sharing Agreement to Accelerate Big Data Translational Research Projects in the One Health Sphere

When conducting translational research, the ability to share data generated by researchers and clinicians working with for-profit companies is essential, particularly in cases that involve "one health" data (i.e., data that could come from human, animal, or environmental sources). The 1DATA Project, a collaboration between Kansas State University and the University of Missouri, has examined and overcome some of the barriers to sharing this information for "big data" projects. This article discusses some of the obstacles we encountered, and the ways those obstacles can be surmounted via a novel form of Master Sharing Agreement. Developed in collaboration with industry partners, it is presented here as a template for expediting future one health work.
Multilingual End-to-End Speech Translation

In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic.
Seismic Monitoring at the Turkish National Seismic Network (TNSN)

Fatih Alver, Ömer Kılıçarslan, Kerem Kuterdem, Meltem Türkoğlu and M.Doruk Şentürk report on the Turkish National Seismic Network for the Summary of the Bulletin of the International Seismological Centre.&#x0D; &#x0D;