Reviewerly/exHarmony huggingface dataset:
Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features

The information acquisition and automatic processing technology based on visual surveillance sensors in intelligent transportation system (ITS) has become an important application field of computer vision technology.The first step of a visual traffic surveillance system usually needs to correctly detect objects from videos and classify them into different categories.In this paper, the improved spatiotemporal sample consistency algorithm (STSC) is proposed, to enhance the robustness of background subtraction in complex scenes.To address this challenge of classifying acquired from visual traffic surveillance sensors in a particular area in China, improved spatiotemporal sample consistency algorithm is proposed, which consists of two main stages.In the first stage, the robustness of moving object detection is further provided by the method we proposed based spatiotemporal sample consistency; in the second stage, we propose the target classification method based prior knowledge, in addition correcting in tracking progress.The experiments on the CDnet 2014, MIO-TCD, and BIT-Vehicle show that the method we proposed successfully overcomes the adverse effects in the complex environment with different shooting angle and resolution taken by single fixed cameras, besides effectively reduces the false alarm rate of classification.
Restoration of an ancient temple mural by a local search algorithm of an adaptive sample block

Long-term influences in the external environment, including light, temperature and humidity, have caused varying degrees of damage to ancient Chinese murals. Allowing people to appreciate the original style of the murals has become important to experts, and the development of image processing and machine learning technology has allowed intelligent restoration of ancient murals. This paper proposed the adaptive sample block and local search (ASB–LS) algorithm based on the Criminisi algorithm to address the flaking deterioration of Kaihua Temple murals from the Song Dynasty. ASB–LS achieved virtual restoration of damaged areas. First, the mural's compositional characteristics were analyzed, the structure tensor was introduced, and the data items were redefined using eigenvalues to ensure accurate transmission of the image's structural information. Then, the data item was used to form a new priority function to improve the image filling order. Finally, the sample block size was adaptively selected by the average correlation of the structure tensor, and a local search strategy was used to improve matching efficiency, which effectively avoided mispropagation of the restored image structure and blinded search of the matching block. Experiments were performed on the Song Dynasty murals in the Kaihua Temple for two types of deterioration: flaking deterioration and artificial destruction. Compared with the Criminisi algorithm and two improved algorithms, the proposed ASB–LS algorithm had better subjective analysis and objective evaluation. Subjective visuals significantly improved and conformed to the image's compositional characteristics, and the inpainting time efficiency improved, establishing a good foundation for restoring ancient murals.
Just “OneSeC” for Producing Multilingual Sense-Annotated Data

The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages.In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning.Our automaticallygenerated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods.Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains.All the training data are available for research purposes at http://trainomatic.org/onesec.
PrivPy

Privacy is a big hurdle for collaborative data mining across multiple parties. We present multi-party computation (MPC) framework designed for large-scale data mining tasks. PrivPy combines an easy-to-use and highly flexible Python programming interface with state-of-the-art secret-sharing-based MPC backend. With essential data types and operations (such as NumPy arrays and broadcasting), as well as automatic code-rewriting, programmers can write modern data mining algorithms conveniently in familiar Python. We demonstrate that we can support many real-world machine learning algorithms (e.g. logistic regression and convolutional neural networks) and large datasets (e.g. 5000-by-1-million matrix) with minimal algorithm porting effort.
Bias Misperceived:The Role of Partisanship and Misinformation in YouTube Comment Moderation

Social media platforms have been the subject of controversy and scrutiny due to the spread of hateful content. To address this problem, the platforms implement content moderation using a mix of human and algorithmic processes. However, content moderation itself has lead to further accusations against the platforms of political bias. In this study, we investigate how channel partisanship and video misinformation affect the likelihood of comment moderation on YouTube. Using a dataset of 84,068 comments on 258 videos, we find that although comments on right-leaning videos are more heavily moderated from a correlational perspective, we find no evidence to support claims of political bias when using a causal model that controls for common confounders (e.g., hate speech). Additionally, we find that comments are more likely to be moderated if the video channel is ideologically extreme, if the video content is false, and if the comments were posted after a fact-check.
Android-Based Computer Assisted Instruction Development as a Learning Resource for Supporting Self-Regulated Learning

Computer Assisted Instruction (CAI) is one form of advances in information technology and computers that are proliferating today that can access anywhere and can be utilised as innovative learning resources.This research aims to develop Android-based Computer Assisted Instruction and evaluate its effectiveness.The Android-based CAI media were designed to improve students' ability both in Statistics and self-regulated learning.The development stages of this media referred to Borg & Gall R & D procedures that consist of 10 stages.Waterfall method was used to develop the Android-based CAI programming.Population in this research was the student of the first year in the mathematics education program.The sample for a small trial class and operational field testing was carried out using purposive sampling.The research used two non-test instruments: validation sheet and questionnaire to collect the data while descriptive analysis was used to analyse the data.At the end of the research, an application called Statistical Data Analysis (SDA) was produced, and it can be downloaded free on Google Play.Based on the results, it can be concluded that Android-based CAI is valid to be used as a learning resource, flexible, and supporting students' self-regulated learning.
Extracting API Tips from Developer Question and Answer Websites

The success of question and answer (Q&A) websites attracts massive user-generated content for using and learning APIs, which easily leads to information overload: many questions for APIs have a large number of answers containing useful and irrelevant information, and cannot all be consumed by developers. In this work, we develop DeepTip, a novel deep learning-based approach using different Convolutional Neural Network architectures, to extract short practical and useful tips from developer answers. Our extensive empirical experiments prove that DeepTip can extract useful tips from a large corpus of answers to questions with high precision (i.e., avg. 0.854) and coverage (i.e., 0.94), and it outperforms two state-of-the-art baselines by up to 56.7% and 162%, respectively, in terms of Precision. Furthermore, qualitatively, a user study is conducted with real Stack Overflow users and its results confirm that tip extraction is useful and our approach generates high-quality tips.
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed using an artificial dataset. The TM is further applied to forecast dengue outbreaks of all the seventeen regions in Philippines using the spatio-temporal properties of the data. Experimental results show that dengue outbreak forecasts made by the TM are more accurate than those obtained by a Support Vector Machine (SVM), Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting precision and F1-score.
A weighted feature extraction method based on temporal accumulation of optical flow for micro-expression recognition

Spatiotemporal features are widely used in micro-expression (ME) recognition to represent facial appearance and action. The features extracted from different face regions are usually given different weights according to the motion intensities in the corresponding regions. The weighted features are reported to be more discriminative than the unweighted ones for ME recognition. However, MEs are so subtle that their motion intensities are usually as low as noises, therefore small image noises can cause similar weights with MEs and degenerate the effectiveness of these weights. To address this issue, a novel weighted feature extraction method is proposed in this paper, whereby the neighboring optical flows in a time interval are accumulated to compute motion intensities. In this manner, the displacements caused by image noises in optical flow are decreased because these displacements are random and direction-inconsistent. Meanwhile, the displacements caused by facial expressions are enhanced because the displacements caused by facial expressions are usually direction-consistent among neighboring frames. The weights computed from the accumulated optical flows are multiplied with the spatiotemporal features, then the weighted features are fed to SVM to classify MEs. The experimental results demonstrate that our method achieves comparable recognition performances with the state-of-the-art methods on SMIC-HS and outperforms the state-of-the-art methods on CASME II.
BND*-DDQN: Learn to Steer Autonomously Through Deep Reinforcement Learning

It is vital for mobile robots to achieve safe autonomous steering in various changing environments. In this paper, a novel end-to-end network architecture is proposed for mobile robots to learn steering autonomously through deep reinforcement learning. Specifically, two sets of feature representations are first extracted from the depth inputs through two different input streams. The acquired features are then merged together to derive both linear and angular actions simultaneously. Moreover, a new action selection strategy is also introduced to achieve motion filtering by taking the consistency in angular velocity into account. Besides, in addition to the extrinsic rewards, the intrinsic bonuses are also adopted during training to improve the exploration capability. Furthermore, it is worth noting the proposed model is readily transferable from the simple virtual training environment to much more complicated real-world scenarios so that no further fine-tuning is required for real deployment. Compared to the existing methods, the proposed method demonstrates significant superiority in terms of average reward, convergence speed, success rate, and generalization capability. In addition, it exhibits outstanding performance in various cluttered real-world environments containing both static and dynamic obstacles. A video of our experiments can be found at https://youtu.be/19jrQGG1oCU.
Unscented Particle Filter for Online Total Image Jacobian Matrix Estimation in Robot Visual Servoing

The main purpose of visual servoing is to control the motion of a robot system based on visual information provided by one or more cameras.It is an important research topic in the robotics community.In uncalibrated visual servoing, the image Jacobian matrix estimation is of great importance to the success of visual servoing control.This paper addresses the online estimation of the total Jacobian matrix for robot visual servoing using the unscented particle filter.We first give the definition of the total Jacobian matrix and formulate the total Jacobian matrix estimation problem into Bayesian filtering framework.Then, we propose to estimate the total Jacobian matrix using the unscented particle filter.Each particle is propagated and updated using the unscented Kalman filter equations.Such an update can make full use of the image feature measurements and consequently generate more accurate estimation results.The simulation results on a 2DOF robot visual servoing platform show that the proposed method provides accurate and reliable performance in the object tracking task.
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples—sometimes only one—from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants’ ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.
Parallel Algorithms for Butterfly Computations

Butterflies are the smallest non-trivial subgraph in bipartite graphs, and therefore having efficient computations for analyzing them is crucial to improving the quality of certain applications on bipartite graphs. In this paper, we design a framework called ParButterfly that contains new parallel algorithms for the following problems on processing butterflies: global counting, per-vertex counting, per-edge counting, tip decomposition (vertex peeling), and wing decomposition (edge peeling). The main component of these algorithms is aggregating wedges incident on subsets of vertices, and our framework supports different methods for wedge aggregation, including sorting, hashing, histogramming, and batching. In addition, ParButterfly supports different ways of ranking the vertices to speed up counting, including side ordering, approximate and exact degree ordering, and approximate and exact complement coreness ordering. For counting, ParButterfly also supports both exact computation as well as approximate computation via graph sparsification. We prove strong theoretical guarantees on the work and span of the algorithms in ParButterfly.We perform a comprehensive evaluation of all of the algorithms in ParButterfly on a collection of real-world bipartite graphs using a 48-core machine. Our counting algorithms obtain significant parallel speedup, outperforming the fastest sequential algorithms by up to 13.6× with a self-relative speedup of up to 38.5×. Compared to general subgraph counting solutions, we are orders of magnitude faster. Our peeling algorithms achieve self-relative speedups of up to 10.7× and outperform the fastest sequential baseline by up to several orders of magnitude.
Stereo calibration with absolute phase target

Stereo cameras have been widely used for three-dimensional (3D) photogrammetry, and stereo calibration is a crucial process to estimate the intrinsic and extrinsic parameters. This paper proposes a stereo calibration method with absolute phase target by using horizontal and vertical phase-shifting fringes. The one-to-one mapping from the world points to the image points that can be recovered by referring to the absolute phase and then used to calibrate the stereo cameras. Compared with traditional methods that only use feature points within the overlapping field-of-view (FOV), the proposed method can use all feature points within the overlapping and non-overlapping FOVs. Besides, since phase is more robust against camera defocusing than intensity, the target images can be captured regardless of the depth-of-field (DOF). With the advantages of whole-field capability and defocusing tolerability, the target placement becomes very flexible. Both simulations and experiment results demonstrate the robustness and accuracy of the proposed method.
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering

When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as Spectral Clustering, and theoretically founded to optimize a proposed density criterion of clusterings. Through experiments on synthetic and real-world data, we demonstrate that our approach provides robust and reliable clusterings.
Diffractive Coupling For Photonic Networks: How Big Can We Go?

Photonic networks are considered a promising substrate for high-performance future computing systems.Compared to electronics, photonics has significant advantages for a fully parallel implementation of networks.A promising approach for parallel large-scale photonic networks is realizing the connections using diffraction.Here, we characterize the scalability of such diffractive coupling in great detail.Based on experiments, analytically obtained bounds and numerical simulations considering real-world optical imaging setups, we find that the concept in principle enables networks hosting over a million optical emitters.This would be a breakthrough in multiple areas, illustrating a clear path toward future, large scale photonic networks.
Omnidirectional Feature Learning for Person Re-Identification

Person re-identification (PReID) has received increasing attention due to it being an important role in intelligent surveillance.Many state-of-the-art PReID methods are part-based deep models.Most of these models focus on learning the part feature representation of a person's body from the horizontal direction.However, the feature representation of the body from the vertical direction is usually ignored.In addition, the relationships between these part features and different feature channels are not considered.In this paper, we introduce a multi-branch deep model for PReID.Specifically, the model consists of five branches.Among the five branches, two branches learn the part features with spatial information from horizontal and vertical orientations; one branch aims to learn the interdependencies between different feature channels generated by the last convolution layer of the backbone network; the remaining two branches are identification and triplet sub-networks in which the discriminative global feature and a corresponding measurement can be learned simultaneously.All five branches can improve the quality of representation learning.We conduct extensive comparison experiments on three benchmarks, including Market-1501, CUHK03, and DukeMTMC-reID.The proposed deep framework outperforms other competitive state-of-the-art methods.The code is available at https://github.com/caojunying/person-reidentification.
Revisiting CFR+ and Alternating Updates

&#x0D; &#x0D; &#x0D; The CFR+ algorithm for solving imperfect information games is a variant of the popular CFR algorithm, with faster empirical performance on a range of problems. It was introduced with a theoretical upper bound on solution error, but subsequent work showed an error in one step of the proof. We provide updated proofs to recover the original bound.&#x0D; &#x0D; &#x0D;