Reviewerly/exHarmony huggingface dataset:
Automatic On-Road Object Detection in LiDAR-Point Cloud Data Using Modified VoxelNet Architecture

Automatic detection of objects play an important and key role in developing real time applications related to robotics and autonomous driving vehicles. The latest research trend in computer vision is to detect objects in the 3D point cloud data produced by LiDAR (Light Detection and Ranging) sensors mounted on the self driving cars. This research paper aims at proposing modifications to the existing VoxelNet architecture for object detection. The proposed models perform direct 3D convolution on the point cloud data. Firstly, the point cloud is encoded into a suitable format in the detection pipeline, and next, the feature maps are extracted from the encoded output of the encoder, and lastly, object detection is done using this learnt feature maps in the final stage. Experimental results on the benchmark KITTI dataset show that the proposed modifications outperform the existing VoxelNet based models and other fusion based methods in terms of accuracy as well as time.
Probabilistic justification logic

Abstract We present a probabilistic justification logic, $\mathsf{PPJ}$, as a framework for uncertain reasoning about rational belief, degrees of belief and justifications. We establish soundness and strong completeness for $\mathsf{PPJ}$ with respect to the class of so-called measurable Kripke-like models and show that the satisfiability problem is decidable. We discuss how $\mathsf{PPJ}$ provides insight into the well-known lottery paradox.
Enhancement of entanglement concentration using catalysts

Auxiliary pure quantum entangled states shared between two parties can act as catalysts in bipartite entanglement transformations. The participation of a catalyst state in the transformation can enhance its success probability. We consider transformations involving entanglement concentration of a finite number of copies of arbitrary two-qubit pure states into a single copy of a maximally entangled two-qubit pure state using bipartite local quantum operations and classical communication aided by a catalyst state. We show the enhancement of success probabilities of the catalyst-assisted procedure compared to the case where no catalysts are used. The optimal two-qubit catalyst and the maximum probability of the transformation using such catalysts can be obtained analytically. The enhancement of concentration success probability using catalysts of higher dimensions is demonstrated numerically. Finally, catalyst aided strategies for obtaining multiple maximally entangled two-qubit states are discussed.
WITHDRAWN: Towards digital game-based learning content with multi-objective reinforcement learning

Game-based learning experiences establish immersive, effective and engaging learning activities. The interest in data-driven learning growth approaches, which personalizes learning experience automatically, has grown over the past years through tips, feedback, and problem scenarios. Learning creators are designed in game-based learning environments to modify simulated activities to meet different goals, such as better learning or cooperation with the pupils, which could be complementary or contradictory. This paper proposes a multi-target enrichment framework to induce game-based learning planners who compromise learning and engagement enhancement in game-based learning environments. To induce a key planner from various student knowledge with a game-based learning experience for the higher education students, we analyze a linear multi-political algorithm, Convex Hull Value Iteration. The findings suggest that a multi-target strengthening curriculum creates more effective policies than single-target interventions in combining multiple rewards. A contextual analysis of basic policy and multifunctional preference vectors shows how an MLS affects the selection of learning activity in student game-based learning experiences. Optimal learning and engagement results are accomplished.
Applying Blockchain Technology to Secure Object Detection Data

Recently, blockchain, deep learning, and computer vision are considered the most fundamental technologies in the world. The researchers have been paying much attention to use them. The centralized database system's problem has low security because it must be transferred through a trusted third party, while the data transmitted might be hacked. However, blockchain provides a distributed database that makes the network secure, flexible, and capable of supporting real-time services. This article proposes a blockchain-based object detection model. The blockchain system makes files secure using the Interplanetary Files System (IPFS) that receives our files, stores them on the decentralized application, and sends the hash to Ethereum to be stored. A YOLOv3 object detection model receives our encrypted data as a hash value and detects objects from images that consist of a huge dataset of different objects.
Super Resolution Based Underwater Image Enhancement by Illumination Adjustment and Color Correction with Fusion Technique

In underwater photographs are look like low-quality images, the main reason is behind that due to attenuation of the propagated light, absorption and scattering effect.The absorption significantly reduces the light energy, while the dispersion causes changes in the light propagation path.They result in foggy appearance and degradation of contrast, causing misty distant objects.So, for getting the most effective result from that type of image, there must be an enhancement technique that has to be applied.We propose an efficient technique to enhance the images captured underwater by applying a fusion-based technique using super-resolution.For enhancing images, we have followed two steps.The first one illumination adjustment and another one is color correction.Then fusion technique is applied to the resultant image from illumination adjustment and color correction as two inputs and combined them with their maximum coefficient value and received output from there.After that, on the fused output image, we used the Super-Resolution method.In the Super-resolution procedure, low resolution and high-resolution images are used then a bicubic interpolation algorithm and finally, VDSR (very-deep super-resolution) neural network has been used to get the most effective result from an obscure underwater image.For getting the most effective result from an obscure image, a new high-quality and efficient image enhancement method has been proposed in this paper.
The Hilbert Transform of B-Spline Wavelets

Both the Hilbert transform and the B-spline wavelets are important tools in signal processing, which makes a study of relation between these two subjects be of practical significance. In particular, because the B-spline wavelets have good properties including vanishing moments, symmetry, compact support and so on, we focus on the Hilbert transform of B-spline wavelets in this letter. For this purpose, the B-spline wavelets of order m is described in a piecewise polynomial form firstly. An important property of the Pascal triangle transform is then explored. Based on these results, an explicit form of the Hilbert transform of B-spline wavelet of order m is established. Furthermore, the vanishing moments, symmetry and asymptote behavior of the Hilbert transform of B-spline wavelets are also discussed. To demonstrate the effectiveness of these results, two examples in the case of m=3 and m=4 are given and the graphs of these two B-spline wavelets as well as their Hilbert transforms are presented. These two cases of m=3 and m=4 provide two Hilbert transform pairs of wavelets, which can be used in digital image processing.
Bayesian variable selection in clustering high-dimensional data via a mixture of finite mixtures

When clustering high-dimensional data, it is often important to identify variables that discriminate the clusters. Meanwhile, a common issue in clustering is to determine the number of clusters. In this study, we propose a new method that simultaneously performs clustering and variable selection, while inferring the number of clusters from the data. We formulate the clustering problem using a finite mixture model with a symmetric Dirichlet weights prior, while also placing a prior on the number of components. That is, we utilize a mixture of finite mixtures. We handle the variable selection problem by introducing a latent binary vector, which represents the inclusion/exclusion of variables. We update the binary vector for variable selection using a Metropolis algorithm and perform inference on the cluster structure using a split–merge Markov chain Monte Carlo technique. We demonstrate the advantage of our method using simulated and two real DNA microarray datasets.
Patience and financial development

Norms, behaviors, beliefs, and preferences have been accepted as some of the most important determinants of financial development. In this paper, as a contribution to the literature, we ask whether patience plays a role in promoting financial development. Our cross-country empirical results show that patience is positively and significantly associated with financial development. Our results are quite robust for stock market development. However, the effect of patience on banking sector development loses its significance once the quality of institutions is controlled. Our results imply that patience has more direct and decisive effects on stock market development.
Accelerating real‐time object detection in high‐resolution video surveillance

Summary As various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel‐level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first‐compression‐and‐then‐detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2‐tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real‐time video analytics while maintaining high detection accuracy.
A new view on the trend of solar radiation in mainland China — based on the optimized empirical model

As a kind of renewable energy, the development and utilization of solar energy are valued by many countries. To accurately provide a basis for the use of solar energy in mainland China, the optimized empirical model is adopted to analyze the variation trends and spatial patterns in solar radiation (SR) during 1961–2016 based on the data of 31 SR sites and 500 sunshine duration (SD) stations. The results indicate that there are obvious discrepancies in the variation trends of annual SR and SD during 1961–2016, with trend conversion occurring in 1992 (SR) and 1980 (SD), respectively. Overall, annual SR decreases at the rate of −3.68 MJ/m2·a in China. Notably, SR declines at the rate of −16.95 MJ/m2·a during 1961–1989 ("dimming" stage), while it increases at the rate of 5.34 MJ/m2·a for 1990–2016 ("brightening" period). In addition, all seasons show a tendency of dimming first and then brightening except for autumn. Compared with SD, SR is more sensitive to changes in pollution, leading to a marked recovery with the reduction of pollution after the 1990s. This study provides a new perspective for the trend difference between SR and SD after the 1990s.
Multi-Agent-Based Recommender Systems: A Literature Review

Considering the growing volume of information and services available on the web, it has become essential to provide websites and applications with tools, such as recommender systems, capable of helping users to obtain the information and services appropriate to their interests. Due to the complexity of web adaptation and the ability of multi-agent systems to deal with complex problems, the use of multi-agent approaches in recommender systems has been increasing. In the present work, we make a thorough review of the use of multi-agent-based recommender systems. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns the diversity of domains, different types of approaches and contribution to the performance improvement of the recommender systems.
Application of Association Rule:Apriori Algorithm in E-Commerce

Associative classification frameworks have been effectively used to build classification frameworks. The significant strength of such strategies is that they can utilize the most exact guidelines among a comprehensive rundown of class-affiliation rules. This clarifies their great exhibition as a rule, however to the inconvenience of a costly figuring cost, acquired from affiliation rules revelation calculations. We address this issue by proposing an appropriate procedure dependent on FP-development calculation. In a shared nothing design, subsets of arrangement rules are created in equal amounts from a few information segments. A between processor correspondence is set up to settle on worldwide choices. This trade is made distinctly in the main degree of recursion, permitting each machine to thus deal with all its allotted errands autonomously. The last classifier is worked by a lion's share vote. This methodology is outlined by a definite model, and an examination of correspondence cost.
Generalisation of Cyberbullying Detection

Filtering out Cyberbullying of communities has proven to be a challenge, and efforts have led to the creation of many different datasets to train classifiers. Through these datasets, we will explore the variety of definitions of cyberbullying behaviors and the impact of these differences on the portability of one classifier to another community. We also gain insight on the generalization power of these classifiers. A study of ensemble models combining these classifiers will help us understand how they interact with each other.
On tight bounds for function approximation error in risk-sensitive reinforcement learning

In this letter we provide several informative tight error bounds when using value function approximators for the risk-sensitive cost setting for a given policy represented using exponential utility. The novelty of our approach is that we make use of the irreducibility of the underlying Markov chain (resulting in better bounds using Perron–Frobenius eigenvectors) to derive new bounds whereas the earlier work used primarily the spectral variation bound which holds for any matrix, hence did not make use of the irreducibility. All our bounds have a perturbation term for large state spaces. We also present examples where we show that the new bounds perform 90-100% better than the earlier proposed spectral variation bound.
Multitarget Decryption Failure Attacks and Their Application to Saber and Kyber

Many lattice-based encryption schemes are subject to a very small probability of decryption failures. It has been shown that an adversary can efficiently recover the secret key using a number of ciphertexts that cause such a decryption failure. In PKC 2019, D'Anvers et al. introduced 'failure boosting', a technique to speed up the search for decryption failures. In this work we first improve the state-of-the-art multitarget failure boosting attacks. We then improve the cost calculation of failure boosting and extend the applicability of these calculations to permit cost calculations of real-world schemes. Using our newly developed methodologies we determine the multitarget decryption failure attack cost for all parameter sets of Saber and Kyber, showing among others that the quantum security of Saber can theoretically be reduced from 172 bits to 145 bits in specific circumstances. We then discuss the applicability of decryption failure attacks in real-world scenarios, showing that an attack might not be practical to execute.
Geochemistry of fracture coatings in Athabasca Group sandstones as records of elemental dispersion from the McArthur River Uranium deposit

A mineralogical and geochemical study of minerals on seven types of fracture fillings from near and above the McArthur River unconformity-related uranium deposit has been carried out to determine if fracture filling materials and wall rocks record evidence of primary and secondary dispersion of elements from the deposit. This study shows that Pb extracted by weak acid leaching (2% HNO3) provides Pb-Pb model ages near 1100 Ma for fractures with any of the three REE patterns: 1) LREE-enriched patterns, 2) REE patterns that are concave and similar to uraninite in the deposit, and 3) fractures with flat REE patterns. The REE patterns and ages indicate the fractures are an open system to post-mineralization dispersion of radiogenic Pb from a U-rich source, with a major mobilization of Pb from the uranium deposit at ca. 1100 Ma. Brown (Type 1) fractures, which host Mn-Fe oxides and clay minerals, provide the best evidence of U mineralization through post-mineralization dispersion, containing elevated concentrations of pathfinder elements Co, Ba, Tl, Mn and radiogenic Pb near the surface and at depth. Post-mineralization dispersion is also observed within brown (Type 1) and pink (Type 7; clay minerals + hematite) fractures and wall rock, showing low 207Pb/206Pb values of 0.29–0.50 that were subsequently overprinted by fluids with more common Pb, as recorded in fractures with higher 207Pb/206Pb values of 0.51–0.84. Fracture fillings were analyzed by continuous leach inductively coupled plasma mass spectrometry (CL-ICP-MS) to determine the relationship between trace elements and elemental release from specific mineral phases. CL-ICP-MS results indicate that common Pb (with 207Pb/206Pb values > 0.7) and anomalous Co (360 ppm) and V (220 ppm) contents are resident in organic phases that are dissolved during the 10% H2O2 leach phase. CL-ICP-MS also indicates that radiogenic Pb, as well as Ni and V, are resident in clay minerals and Fe-Mn oxy-hydroxides that break down during leaching with 30% HNO3. Uranium contents are low in the leachate during release of Fe, Al, Mn, and Pb using the 10% and 30% HNO3 leaches, reflecting post-mineralization dispersion of radiogenic Pb, but not U. Thus, secondary dispersion of elements into fractures enhances the size of the deposit footprint, particularly in deep fractures near the P2 fault, but also in some fractures near the surface. The results of this study strongly suggest that fractures did indeed act as conduits for dispersion of elements from the deposit, reaching all the way from deposit to the surface, providing viable exploration targets for U mineralization in sedimentary basins.
Understanding the Benefit of Being Patient in Payment Channel Networks

Scaling blockchain efficiency is crucial to its widespread usage in which the payment channel is one of the most prominent approaches. With payment channels and the network they construct, two users can move some transactions off-chain to avoid expensive and time-consuming on-chain settlements. Existing works are devoted to designing high-throughput payment channel networks (PCNs) or efficient PCN routing policies to reduce the fee charged by intermediate nodes. In this paper, we investigate the PCN routing from a different perspective by answering whether the routing fee of transactions can be saved through being a bit more patient. The key idea is to reorder the processing sequence of atomic transactions, other than to handle each of them separately and immediately. We present two mechanisms, one is periodic transaction processing and the other is purely strategic waiting. In the former, the incoming transactions in a short time interval are processed collectively. We formulate an optimization model to minimize their total routing fee and derive the optimal permutation of processing transactions as well as the routing policy for each of them. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shapley value</i> based scheme is presented to redistribute the benefit of reordering among the transactions efficiently and fairly. In the latter, we model the waiting time of a strategic transaction on a single payment channel as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first passage time</i> problem in queuing theory when the transaction value is higher than the channel balance upon its arrival. By capturing the balance dynamics, we are able to calculate the recursive expression of waiting time distribution that is useful to gauge a user's cost of patience. Experimental results manifest that our cost redistribution mechanism can effectively save routing fees for all the transactions, and the waiting time distribution coincides with the model well.
Poetic Expression Through Scenery: Sentimental Chinese Classical Poetry Generation from Images

Most Chinese poetry generation methods only accept texts or user-specified words as input, which contradicts with the fact that ancient Chinese wrote poems inspired by visions, hearings and feelings. This paper proposes a method to generate sentimental Chinese classical poetry automatically from images based on convolutional neural networks and the language model. First, our method extracts visual information from the image and maps it to initial keywords by two parallel image classification models, then filters and extends these keywords to form a keywords set which is finally input into the poetry generation model to generate poems of different genres. A bi-directional generation algorithm and two fluency checkers are proposed to ensure the diversity and quality of generated poems, respectively. Besides, we constrain the range of optional keywords and define three sentiment-related keywords dictionary to avoid modern words that lead to incoherent content as well as ensure the emotional consistency with given images. Both human and automatic evaluation results demonstrate that our method can reach a better performance on quality and diversity of generated poems.
Optimized Scale Invariant HOG Descriptors for Object and Human Detection

Abstract This article presents a scale variation approach to identify objects and humans in video sequences using histogram of gradient descriptor. A significant restriction in HOG descriptors is its variations with scale changes and illumination changes, as is frequently the considered case. We recommend unique SIO-HOG descriptors that are figured to be invariant to scale changes. The system associates the benefits of adoptive bin selections and sample resizing in the object recognition process. We analyze the effect of PCA transform based feature selection process on object detection performance, ultimately the finite scale range, adoptive orientation binning in non-overlapping descriptors is all main thing for nominal detection rate. HOG feature vector over complete search window is computationally more exclusive, to acquire more precise set of features with finite Euclidean distance to classify them using KNN classifier. This new approach provides near-perfect ways of separating humans from other objects. The whole object detection system was assessed on a few test samples from real-world data sets and compared beside a publicly available pedestrian detection data base.