Reviewerly/exHarmony huggingface dataset:
Fundamental Properties of the Layer Below a Payment Channel Network

Payment channel networks are a highly discussed approach for improving scalability of cryptocurrencies such as Bitcoin. As they allow processing transactions off-chain, payment channel networks are referred to as second layer technology, while the blockchain is the first layer. We uncouple payment channel networks from blockchains and look at them as first-class citizens. This brings up the question what model payment channel networks require as first layer. In response, we formalize a model (called RFL model) for a first layer below a payment channel network. While transactions are globally made available by a blockchain, the RFL model only provides the reduced property that a transaction is delivered to the users being affected by a transaction. We show that the reduced model’s properties still suffice to implement payment channels. By showing that the RFL model can not only be instantiated by the Bitcoin blockchain but also by trusted third parties like banks, we show that the reduction widens the design space for the first layer. Further, we show that the stronger property provided by blockchains allows for optimizations that can be used to reduce the time for locking collateral during payments over multiple hops in a payment channel network.
LR-BCA: Label Ranking for Bridge Condition Assessment

Bridge condition assessment (BCA) plays an important role in modern bridge management.Existing assessment methods are time-consuming, labor-intensive and error-prone.The use of machine learning for BCA can effectively solve the above problems.However, the large amount of label noise in the dataset severely affected the performance of the BCA model.In this paper, we present an effective label ranking approach for BCA (LR-BCA).Our proposed LR-BCA method considers the natural order relationship between bridge condition ratings.Moreover, a heuristic data cleaning (HDC) approach is proposed for cleaning bridge condition dataset.The HDC method firstly identifies all the label conflict examples, then iteratively filters out the noise.Experimental results on real-world dataset confirm the effectiveness of the HDC method and demonstrate that our proposed LR-BCA method achieves 99% Top-2 accuracy, which is highly competitive compared to baseline methods.
Memory Error Detection Based on Dynamic Binary Translation

Dynamic memory error detection technology is widely utilized in locating and mitigating memory-corruption vulnerabilities. High false negative in existing technologies impairs the effectiveness of detection, especially when the detector has no source code. We proposed a dynamic memory error detection method based on dynamic binary translation. It realized the detection by combining a call stack tracing method and an IR language level memory error detection method. Experiments show that the detection success rate of this method in the heap and stack memory destruction is more than 80%.
Variational Autoencoder for 3D Voxel Compression

3D scene sensing and understanding is a fundamental task in the field of computer vision and robotics. One widely used representation for 3D data is a voxel grid. However, explicit representation of 3D voxels always requires large storage space, which is not suitable for light-weight applications and scenarios such as robotic navigation and exploration. In this paper we propose a method to compress 3D voxel grids using an octree representation and Variational Autoencoders (VAEs). We first capture a 3D voxel grid -in our application with collaborating Realsense D435 and T265 cameras. The voxel grid is decomposed into three types of octants which are then compressed by the encoder and reproduced by feeding the latent code into the decoder. We demonstrate the efficiency of our method by two applications: scene reconstruction and path planning.
Intelligent analysis of classroom student state based on neural network algorithm and emotional feature recognition

Classroom teaching in the context of artificial intelligence needs to be combined with modern intelligent recognition technology to improve classroom teaching efficiency. In order to study the auxiliary teaching system for classroom student management, this article is based on neural network technology and emotional feature recognition algorithm, and according to the actual situation of classroom teaching, an intelligent analysis system for classroom student status is constructed. The system simulates the RFID mode to tag the students. Moreover, this article sets the system function module according to the actual teaching management needs and designs the learning algorithm of the quantitative assessment model. In addition, this study uses machine learning methods to design the quantitative evaluation index system, logistic regression scoring algorithm and model training algorithm. Finally, this study uses the neural network algorithm as the comparison algorithm to verify the performance of the constructed model and analyzes the comparison results through chart comparison. The research results show that the model proposed in this paper has good performance and can be applied to practical classrooms.
Two-stream neural architecture for unsafe maneuvers classification from dashcam videos and GPS/IMU sensors

In this paper, we propose a novel deep learning architecture for the end-to-end classification of unsafe maneuvers from dashcam data; the proposed model is based on an innovative two-stream architecture capable of processing both video and GPS/IMU signals as input streams. A wide experimentation on a well known naturalistic driving dataset (SHRP2 NDS) shows that the two sources of information complement each other in the classification task and proves the effectiveness of the proposed approach. As a by-product of this research, we propose and make available a novel classification of safety-critical events based on the unsafe maneuver leading to them, which is representative of the real distribution of car crashes and near crashes.
Surface Lightfield Support in Video-based Point Cloud Coding

Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.
Greedy weights for matroids

Abstract We introduce greedy weights of matroids, inspired by those for linear codes. We show that a Wei duality holds for two of these types of greedy weights for matroids. Moreover we show that in the cases where the matroids involved are associated to linear codes, our definitions coincide with those for codes. Thus our Wei duality is a generalization of that for linear codes given by Schaathun. In the last part of the paper we show how some important chains of cycles of the matroids appearing, correspond to chains of component maps of minimal resolutions of the independence complex of the corresponding matroids. We also relate properties of these resolutions to chainedness and greedy weights of the matroids, and in many cases codes, that appear.
Preference-aware Heterogeneous Graph Neural Networks for Recommendation

Knowledge graph is considered as a significant information structure for recommendation, therefore the graph based recommendation has attracted increasing attention in recent years. However, the existing methods face two major challenges. First, the users' preferences should be well considered in the algorithmic model and explicitly shown after model training. Second, there need a better solution to simultaneously learn and combine the information on multiple graphs from different aspects rather than the methods designed for single homogeneous graph. In this paper, we propose the preferences embeddings, which are able to learn the explicit representations for the preferences that influence the users' choices. Further, we innovatively design three channels in a new graph neural network that contains different graph convolutions specifically for the recommendation scenario. This framework can effectively excavate and combine heterogeneous information among user graph, item graph and interaction graph. Extensive experiments on real-world datasets demonstrate the effectiveness and good interpretability of the proposed framework.
Inference Benchmarking on HPC Systems

As deep learning on edge computing systems has become more prevalent, investigation of architectures and configurations for optimal inference performance has become a critical step for proposed artificial intelligence solutions. While there has been considerable work in the development of hardware and software for high performance inferencing, there is little known about the performance of such systems on HPC architectures. In this paper, we address outstanding questions on the parallel inference performance on HPC systems. We report results and recommendations derived from evaluating iBench on multiple platforms in a variety of HPC configurations. We systematically benchmark single-GPU performance, single-node performance, and multi-node performance for maximum client-side and server-side inference throughput. In order to achieve linear speedup, we show that concurrent sending clients must be used, as opposed to sending large batch payloads parallelized across multiple GPUs. We show that client/server inferencing architectures add a considerable data transfer component that needs to be taken into consideration when benchmarking HPC system that benchmarks such as MLPerf do not measure. Finally, we investigate energy efficiency of GPUs for different levels of concurrency and batch sizes to report optimal configurations that minimize cost per inference.
Revisiting Keyword Analysis in a Specialized Corpus: Religious Terminology Extraction

This study investigates keyword extraction using a compiled Buddhist corpus. It sets out the fundamental mode of generation and refinement of keywords with statistical measures and manual screening with specific criteria. The Buddhist Word List contains 1244 keywords with 375 Pali words in Buddhist literacy. We compared the results of applying occurring frequency, log-likelihood (LL), and odds ratio (OR) in keyword analyses, each of which resulted in different keyword rankings. Our results show that statistical measures are useful for the identification of particular keywords in specific fields and OR is more effective in identifying technical terms. We demonstrate that multilevel keyword analysis is more effective at the identification of high-frequency technical words than either of these methods used alone. Multilevel methods are recommended for the creation of future domain-specific vocabulary lists to overcome the inherent flaws of individual analytic methods.
Is Lecture as a Dominant Form of Teaching Dying?

The article is devoted to the actively debated, difficult and for many teachers a painful problem addressing lecture as a dominant form of teaching. Nowadays, lecture is increasingly seen as an inefficient form of education, hopelessly outdated, with little interest and response among students, useless and, in principle, dying. The authors are firmly convinced that lecture was, continues to be and will be the main and indispensable form of instruction for the reproduction of an intellectual elite. To substantiate this position, the article presents an argumentation system that unfolds in three stages. In the first part of the article, the essence of lecture is revealed in the light of Randall Collins' theory of intellectual interactive rituals. The subject's intellectual creativity is conditioned by his personal contacts “face to face” with other intellectuals, as well as the position of this subject in the intellectual network. There is a demonstration that lecture has all the hallmarks of an interactive ritual, and it is its archetypal case, which ensures the transmission of cultural capital from the older generation of intellectuals to the young one. During this process, its successful participants feel surge of emotional energy necessary for intellectual creativity. In the second part of the article, the distinctive positive aspects of lecture are systematized and discussed, which together make it a unique form of training that gives indispensable experience to both lecturer and participants. The third part of the article, based on the real history of education and providing illustrative examples, is a kind of empirical confirmation of the first two parts. The article may be of interest to teachers, students, as well as anyone who is not indifferent to the current state of education in Russia.
The Heuristic of Directional Qualitative Semantic: A New Heuristic for Making Decisions about Spinning with Qualitative Reasoning

Multifunctional Robot On Topological Notions (MROTN) is a research program that has as one of its goals to develop qualitative algorithms that make navigation decisions. This article presents new research from MROTN that extends previous results by allowing an agent to carry out qualitative reasoning about direction and spinning. The main result is a new heuristic, the Heuristic of Directional Qualitative Semantic (HDQS), which allows for selecting a spinning action to establish a directional relation between an agent and an object. The HDQS is based on the key idea of encoding directional information into topological relations. The new heuristic is important to the MROTN because it permits the continued development of qualitative navigation methods based on topological notions. We show this by presenting a new version of the Topological Qualitative Architecture of Navigation that uses the HDQS to address situations that require spinning.
Through the Cognitive Functions Lens - A Socio-technical Analysis of Predictive Maintenance

The effective use of artificial intelligence promises significant business value. Effective use, however, requires a thorough exploration of its strengths and weaknesses from different perspectives. Information systems research is particularly invested in the management and use of artificial intelligence in organizations. It has proposed the use of cognitive functions to guide this exploration. In this paper, we evaluate the usefulness of such a cognitive functions lens for a relatively mature application of artificial intelligence, predictive maintenance. Our evaluation is informed by the insights we collected from an embedded single-case study. We find that a cognitive functions lens can indeed be a useful tool to explore artificial intelligence. In particular, it can aid the allocation of tasks between human agents and artificial intelligence-based systems and the design of human-AI hybrids. It is particularly helpful for those who investigate the management of artificial intelligence.
RETRACTED: Cloud computing database and remote system for real-time image acquisition application in english classroom teaching

Under the impetus of information technology, English teaching is gradually becoming diversified and informationalized. And how to use modern information technology to make English classroom teaching more effective is important. Students often miss knowledge points in class, and teaching can only obtain students' mastery of knowledge through exams, and No real-time feedback was obtained. Therefore, classroom behavior image acquisition has become an urgent need for in-depth research in intelligent teaching and improving teaching quality. This article discusses about the real-time image acquisition application in English classroom teaching based on the cloud computing database and remote system. Image acquisition is accomplished through the proper camera, utilizing distinctive diverse camera applications. On the off chance that we need beam image the camera (film) use, which is light-touchy in the event that we need to infrared image utilize this camera delicate to infrared radiation. For normal image (family image, and so forth), we utilize a camera that is delicate to the obvious range.
Adaptive Deconvolution-Based Stereo Matching Net for Local Stereo Matching

In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy. However, it is unrealistic to increase the size of the image patch size without restriction. Arbitrarily extending the patch size will change the local stereo matching method into the global stereo matching method, and the matching accuracy will be saturated. We simplified the existing Siamese convolutional network by reducing the number of network parameters and propose an efficient CNN based structure, namely adaptive deconvolution-based disparity matching net (ADSM net) by adding deconvolution layers to learn how to enlarge the size of input feature map for the following convolution layers. Experimental results on the KITTI2012 and 2015 datasets demonstrate that the proposed method can achieve a good trade-off between accuracy and complexity.
RP-LGMC: Rating prediction based on local and global information with matrix clustering

Recommendation system has attracted large amount of attention in the field of E-commerce research. Traditional MF (Matrix Factorization) methods take a global view on the user-item rating matrix to derive latent user vectors and latent item vectors for rating prediction. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. Motivated by this observation, this paper proposes a novel rating prediction approach called RP-LGMC (Rating Prediction based on Local and Global information with Matrix Clustering) based on matrix factorization by making use of the local correspondence between user clusters and item clusters. The RP-LGMC approach consists of three components. The first component is to partition the user-item rating matrix into small blocks by the sparse subspace clustering (SCC) algorithm with co-clustering its rows (users) and columns (items) simultaneously. The second component is local distillation to extract those dense and stable blocks by thresholding block density and standard deviation. The third component is to predict the ratings with residual approximation on the local blocks and SVD++ on the global blocks of the original user-item matrixR. The RP-LGMC approach can not only reduce the data sparsity but also increase the computation scalability. Experiments on the MovieLens-25 M dataset demonstrate that the proposed RP-LGMC approach performs better than most state-of-the-art methods in terms of recommendation accuracy and has lower computation complexity than the SVD++ algorithm.
Tyre Inspection through Multi-State Convolutional Neural Networks

Road accident is a potential risk to the lives of both drivers and passers-by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed ... | Find, read and cite all the research you need on Tech Science Press
Differentiated Explanation of Deep Neural Networks With Skewed Distributions

Over the last decade, deep neural networks (DNNs) are regarded as black-box methods, and their decisions are criticized for the lack of explainability. Existing attempts based on local explanations offer each input a visual saliency map, where the supporting features that contribute to the decision are emphasized with high relevance scores. In this paper, we improve the saliency map based on differentiated explanations, of which the saliency map not only distinguishes the supporting features from backgrounds but also shows the different degrees of importance of the various parts within the supporting features. To do this, we propose to learn a differentiated relevance estimator called DRE, where a carefully-designed distribution controller is introduced to guide the relevance scores towards right-skewed distributions. DRE can be directly optimized under pure classification losses, enabling higher faithfulness of explanations and avoiding non-trivial hyper-parameter tuning. The experimental results on three real-world datasets demonstrate that our differentiated explanations significantly improve the faithfulness with high explainability. Our code and trained models are available at https://github.com/fuweijie/DRE.
Extending de Bruijn sequences to larger alphabets

A de Bruijn sequence of order n over a k-symbol alphabet is a circular sequence where each length-n sequence occurs exactly once. We present a way of extending de Bruijn sequences by adding a new symbol to the alphabet: the extension is performed by embedding a given de Bruijn sequence into another one of the same order, but over the alphabet with one more symbol, while ensuring that there are no long runs without the new symbol. Our solution is based on auxiliary graphs derived from the de Bruijn graph and solving a problem of maximum flow.
Estimating Human Body Orientation using Skeletons and Extreme Gradient Boosting

Human body orientation is a valuable information for social robots due to its use in social path planning and person (or group) approaching. Multi-sensory is an alternative for orientation estimation but it is not available for in-the-wild applications. To estimate orientation using only a single image, several computer vision techniques have demonstrated insufficient accuracy. We propose combining 2D skeleton information with extreme gradient boosting algorithm to detect orientation. We obtain person's skeleton using the OpenPose deep architecture, and extract its distances and angle features. These attributes are used to train a gradient boosting learning system by XGBoost. To evaluate predictions considering real situations based on a single camera, the TUD Multiview Pedestrian dataset is used. We compared the proposed approach against various state-of-the-art methods and our results indicate better classification performance. Furthermore, we prove that our method is viable for body orientation estimation on real-life scenarios by presenting case studies on simulated scenes.
On the Complexities of Federating Research Data Infrastructures

Federated Research Data Infrastructures aim to provide seamless access to research data along with services to facilitate the researchers in performing their data management tasks. During our research on Open Science (OS), we have built cross-disciplinary federated infrastructures for different types of (open) digital resources: Open Data (OD), Open Educational Resources (OER), and open access documents. In each case, our approach targeted only the resource “metadata”. Based on this experience, we identified some challenges that we had to overcome again and again: lack of (i) harvesters, (ii) common metadata models and (iii) metadata mapping tools. In this paper, we report on the challenges we faced in the federated infrastructure projects we were involved with. We structure the report based on the three challenges listed above.