Reviewerly/exHarmony huggingface dataset:
People Counting in Dense Crowd Images Using Sparse Head Detections

People counting in extremely dense crowds is a challenging problem due to severe occlusions, few pixels per head, cluttered environments, and skewed camera perspectives. In this paper, we present a novel algorithm for people counting in highly dense crowd images. Our approach relies on the fact that the head is the most visible part of an individual in a dense crowd. As such, a head detector can be used to estimate the spatially varying head size, which is the key feature used in our head counting procedure. We leverage the state-of-the art convolutional neural network for the sparse head detection in a dense crowd. After sub-dividing the image into rectangular patches, we first use an speeded-up robust features-based support vector machine binary classifier to label each patch as crowd/not-crowd and eliminate all not-crowd patches. Regression is then performed on each crowd patch to estimate average head size. The number of individuals in each patch is estimated by dividing the patch area with the estimated head size. For the crowd patches where no heads are detected, the counts are estimated based on distance-based weighted averaging over the counts from neighboring patches. Finally, the individual patch counts are summed up to obtain the total count. We evaluate our approach on three publicly available datasets for extremely dense crowds: UCF_CC_50, ShanghaiTech, and AHU-Crowd. Our approach gives comparable results on these challenging datasets to other state of the art algorithms but, unlike other algorithms, our proposed method does not require the laborious task of obtaining labeled training data of dense crowd images.
MoveIt! Task Constructor for Task-Level Motion Planning

A lot of motion planning research in robotics focuses on efficient means to find trajectories between individual start and goal regions, but it remains challenging to specify and plan robotic manipulation actions which consist of multiple interdependent subtasks. The Task Constructor framework we present in this work provides a flexible and transparent way to define and plan such actions, enhancing the capabilities of the popular robotic manipulation framework MoveIt!. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The Task Constructor framework is publicly available at https://github.com/ros-planning/moveit_task_constructor Subproblems are solved in isolation in black-box planning stages and a common interface is used to pass solution hypotheses between stages. The framework enables the hierarchical organization of basic stages using containers, allowing for sequential as well as parallel compositions. The flexibility of the framework is illustrated in multiple scenarios performed on various robot platforms, including bimanual ones.
Asymptotic theory for clustered samples

We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.
Blind Quality Assessment of Camera Images Based on Low-Level and High-Level Statistical Features

Camera images in reality are easily affected by various distortions, such as blur, noise, blockiness, and the like, which damage the quality of images. The complexity of distortions in camera images raises significant challenge for precisely predicting their perceptual quality. In this paper, we present an image quality assessment (IQA) approach that aims to solve this challenging problem to some extent. In the proposed method, we first extract the low-level and high-level statistical features, which can capture the quality degradations effectively. On the one hand, the first kind of statistical features are extracted from the locally mean subtracted and contrast normalized coefficients, which denote the low-level features in the early human vision. On the other hand, the recently proposed brain theory and neuroscience, especially the free-energy principle, reveal that the human brain tries to explain its encountered visual scenes through an inner creative model, with which the brain can produce the projection for the image. Then, the quality of perceptions can be reflected by the divergence between the image and its brain projection. Based on this, we extract the second type of features from the brain perception mechanism, which represent the high-level features. The low-level and high-level statistical features can play a complementary role in quality prediction. After feature extraction, we design a neural network to integrate all the features and convert them to the final quality score. Extensive tests performed on two real camera image datasets prove the validity of our method and its advantageous predicting ability over the competitive IQA models.
Blind Quality Assessment of Tone-Mapped Images Considering Colorfulness, Naturalness, and Structure

Nowadays, high-dynamic-range (HDR) imaging represents a prevailing trend and attracts much attention from both academic and industrial scholars. Since HDR images cannot be properly produced on the mainstream low-dynamic-range (LDR) displays, various tone-mapping operators or postprocessing technologies have been designed to transform HDR images into LDR images for visualization on LDR displays. However, it inevitably induces artifacts and distortions due to dynamic range compression. Besides, existing tone-mapped (TM) technologies cannot effectively handle all kinds of images with diverse contents and structures, leaving to a very challenging and urgent image quality assessment (IQA) problem. To cope with this challenge, in this paper, an effective blind quality assessment approach for TM images is proposed through a comprehensive consideration of their characteristics. More specifically, to dig out sufficient information from TM images, multiple quality-sensitive features are captured to fully represent different attributes, including colorfulness, naturalness, and structure. The connection between feature space and associated subjective ratings is established via a regression model. Extensive experiments on a recently released TM image database prove that the proposed approach is superior to the state-of-the-art no-reference IQA approaches.
Smart Contracts for Bribing Miners

We present three smart contracts that allow a briber to fairly exchange bribes to miners who pursue a mining strategy benefiting the briber. The first contract, CensorshipCon, highlights that Ethereum's uncle block reward policy can directly subsidise the cost of bribing miners. The second contract, HistoryRevisionCon, rewards miners via an in-band payment for reversing transactions or enforcing a new state of another contract. The third contract, GoldfingerCon, rewards miners in one cryptocurrency for reducing the utility of another cryptocurrency. This work is motivated by the need to understand the extent to which smart contracts can impact the incentive mechanisms involved in Nakamoto-style consensus protocols.
Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network

In intelligent transportation systems, there are many tasks that rely on the detection of road congestion, such as traffic signal scheduling and traffic accident detection. As traditional methods for traffic congestion detection are difficult to use, expensive, and may cause damage to the road surface, this paper presents a method for road congestion detection that is based on multidimensional visual features and a convolutional neural network (CNN). This method first detects the density of foreground objects by using a gray-level co-occurrence matrix; second, the speed of moving objects is detected by using the Lucas-Kanade optical flow with pyramid implementation. Third, a Gaussian mixture model is used to model the background, and the CNN is then used to accurately detect the final foreground from the candidate foregrounds. Finally, the proposed method performs road congestion detection in terms of a multidimensional feature space, including traffic density, traffic velocity, road occupancy, and traffic flow. Furthermore, we propose an information entropy method using a histogram of optical flow to enhance the accuracy and reliability of road congestion detection. Simulation results via quantitative and qualitative assessment indicate that the proposed method is able to significantly outperform the state-of-the-art road-traffic congestion detection methods due to the fusion of multidimensional features using the CNN.
Unpacking and describing interaction on Chinese WeChat: A methodological approach

WeChat is a popular social media application developed in China and designed for the mobile phone. Users may send and receive messages in a variety of systems, including text, emoji, gif files, pictures, and audio messages. They may create messages in one or more languages and scripts, and may interact asynchronously, or synchronously. We examined data in the form of messages shared using the synchronous chat function of WeChat. Our analysis demonstrates both how WeChat as a technology and Chinese as a language, afford novel and creative ways to construct messages and interactions: users may write messages with Chinese characters in standard or nonstandard forms, including vernacular Cantonese; they may purposefully select non-standard characters to key and accent messages. This study unpacks new forms of online data and demonstrates new methods of social action.
A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data

The k-means algorithm is a widely used method that starts with an initial partitioning of the data and then iteratively converges towards the local solution by reducing the Sum of Squared Errors (SSE). It is known to suffer from the cluster center initialization problem and the iterative step simply (re-)labels the data points based on the initial partition. Most improvements to k-means proposed in the literature focus on the initialization step alone but make no attempt to guide the iterative convergence by exploiting statistical information from the data. Using higher order statistics (such as paths from random walks in a graph) and the duality in the data (as in co-clustering), for instance, are known ways to improve the clustering results. What is unique and significant in our proposed approach is that we embed these concepts into the k-means algorithm rather than just using them as an external distance measure and present a unified framework called the k-means based co-clustering (kCC) Algorithm. The initialization step has been modified to include multiple points to represent each cluster center such that points within a cluster are close together but are far from points representing other clusters. Moreover, neighborhood walk statistics is proposed as a semantic similarity technique for both cluster assignment and center re-estimation in the iterative process. The effectiveness of the combined approach is evaluated on several standard data sets. Our results show that kCC performs better as compared to the baseline k-means and other state-of-the-art improvements.
Singular value decomposition based recommendation using imputed data

Among widely used recommendation methods, singular value decomposition (SVD) based approaches are the most successful ones. Although SVD-based methods are effective, they suffer from the problem of data sparsity, which could lead to poor recommendation quality. This paper proposes a novel imputation-based recommendation method, called the imputation-based SVD (ISVD), to solve the problem of data sparsity in SVD-based methods. Firstly, we propose a neighbor selection algorithm based on a similarity measure for users and items. In this algorithm, we set two thresholds to select effective neighbors for each user and item. Secondly, we generate the imputed data according to the neighbors' ratings. Finally, we imputed these data into the SVD framework. By using imputed training data in SVD, our method can learn the prediction model accurately. We have tested our method on the MovieLens 100k, MovieLens 1M, Netflix and Filmtrust datasets. Experiment results show that our method outperforms the state-of-the-art ones. This study not only offers new insights into generating imputed data but also provides a guide to the alleviation of data sparsity in SVD-based methods.
Achieving Differentially Private Location Privacy in Edge-Assistant Connected Vehicles

Connected vehicles can provide safer and more satisfying services for drivers by using information sensing and sharing. However, current network architecture cannot support massive and real-time data transmissions due to the poor-quality wireless links. To provide real-time data processing and improve drivers' security, edge computing is regarded as a promising method to offer more efficient services by placing computing and storage resources at the network edge. In this paper, we will introduce the concept of edge-assistant connected vehicles and propose some promising applications to reduce the network traffic and provide real-time services with the help of massive edge nodes. Unlike in the traditional cloud-based connected vehicles, edge nodes are introduced to enable vehicles to obtain real-time and distributed processing services. Furthermore, considering the location privacy issue in the new architecture, we propose a novel differentially privacy-preserving location-based service usage framework deployed on the edge node, designed to provide an adjustable privacy protection solution to balance the utility and privacy. Finally, we conduct extensive experiments to verify the proposed framework.
Applications of trace element chemistry of pyrite and chalcopyrite in glacial sediments to mineral exploration targeting: Example from the Churchill Province, northern Quebec, Canada

Bedrock in arctic and subarctic regions is covered by glacial deposits making the discovery of new mineral deposits difficult. Indicator mineral methods using glacial sediment have thus been developed for mineral exploration in such drift-covered areas. However, sulfide indicator minerals have been under utilized because it was thought that they would not survive glacial transport and post-depositional oxidation during soil formation. In this contribution we show that the 0.25–1 mm non-ferromagnetic heavy mineral concentrates of Quaternary till and esker samples from the Churchill province in northern Quebec, Canada, contain thousands of pyrite and chalcopyrite grains and a few sulfarsenide grains. Accordingly, sulfide minerals do survive glacial and glaciofluvial transport, even in the relatively oxidizing environment of the eskers, and their presence indicates the potential presence of mineralized bedrock up ice. The study area is therefore ideal to test the use of sulfide mineral chemistry for mineral assessment and vectoring. The composition of the pyrite and chalcopyrite grains recovered from the glacial deposits have been determined by LA-ICP-MS and compared with known values for sulfides in magmatic and hydrothermal deposits. Although some elements (e.g. Ag, Cu, Zn, Pb, W, Ba, La, and Yb) are enriched in narrow rims on some sulfide grains, indicating their limited mobility during oxidation, most elements have not been mobilized and reflect initial sulfide compositions in bedrock sources. The binary diagram Co/Sb versus Se/As shows that most of the pyrite grains in surficial sediments are of magmatic origin although some are from hydrothermal sources. The hydrothermal pyrites are enriched in hydrothermal pathfinders (Au, Hg, Ag, Tl, Pb, Zn, Cu, and Mo). The ternary diagram Se-Cd-Ni shows that chalcopyrites from both magmatic and hydrothermal deposits are present in glacial sediments. The high Cd/Zn ratios of the hydrothermal chalcopyrites are indicative of a high crystallization temperature, typical of metamorphosed VMS or SEDEX deposits. Integrated maps combining bedrock geology, glacial transport directions, sample locations, and sulfide grain compositions and populations can be used to delineate target sectors for mineral exploration. Here sulfides have been transported over ~100 km roughly towards north, from sources in the Rachel-Laporte Zone and the Labrador Trough, where metasedimentary/metavolcanic rocks and mafic/ultramafic intrusive rocks are favorable hosts for hydrothermal and magmatic mineralization, respectively.
A Location-Based Factorization Machine Model for Web Service QoS Prediction

With the prevalence of web services, a large number of similar web services are provided by different providers. To select the optimal service among these service candidates, Quality of Service (QoS), representing the non-functional characteristics, plays an important role. To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. Collaborative web service QoS prediction is one of the most popular approaches. Based on the historical QoS data, collaborative QoS prediction methods employ memory-based collaborative filtering (CF), model-based CF, or their hybrids to predict QoS values. However, these methods usually only consider the QoS information of similar users and services, neglecting the correlation between them. To enhance the prediction accuracy, we propose a novel method to predict QoS values based on factorization machine, which leverages not only QoS information of users and services but also the user and service neighbor’s information. To evaluate our approach, we conduct experiments on a large-scale real-world dataset with 1,974,675 web service invocations. The experiment results show that our approach achieves higher prediction accuracy than other QoS prediction methods.
Multi-User Verifiable Searchable Symmetric Encryption for Cloud Storage

In a cloud data storage system, symmetric key encryption is usually used to encrypt files due to its high efficiency. In order allow the untrusted/semi-trusted cloud storage server to perform searching over encrypted data while maintaining data confidentiality, searchable symmetric encryption (SSE) has been proposed. In a typical SSE scheme, a users stores encrypted files on a cloud storage server and later can retrieve the encrypted files containing specific keywords. The basic security requirement of SSE is that the cloud server learns no information about the files or the keywords during the searching process. Some SSE schemes also offer additional functionalities such as detecting cheating behavior of a malicious server (i.e., verifiability) and allowing update (e.g., modifying, deleting and adding) of documents on the server. However, the previous (verifiable) SSE schemes were designed for single users, which means the searching can only be done by the data owner, whereas in reality people often use cloud storage to share files with other users. In this paper we present a multi-user verifiable searchable symmetric encryption (MVSSE) scheme that achieves all the desirable features of a verifiable SSE and allows multiple users to perform searching. We then define an ideal functionality for MVSSE under the Universally Composable (UC-) security framework and prove that our ideal functionality implies the security requirements of a secure MVSSE, and our multi-user verifiable SSE scheme is UC-secure. We also implement our scheme to verify its high performance based on some real dataset.
Personalized recommendation by matrix co-factorization with tags and time information

Personalized recommendation systems have solved the information overload problem caused by large volumes of Web data effectively. However, most existing recommendation algorithms are weak in handling the problem of rating data sparsity that characterizes most recommender systems and results in deteriorated recommendation accuracy. The results in the KDDCUP and Netflix competition have proven that the matrix factorization algorithm achieves better performance than other recommendation algorithms when the rating data is scarce. However, the highly sparse rating matrix will cause the overfitting problem in matrix factorization. Although regularization can relieve the issue of overfitting to some extent, it is still a significant challenge to train an effective model for recommender systems when the data is highly sparse. Therefore, this paper proposes a co-SVD model to enrich the single data source and mitigate the overfitting problem in matrix factorization. The user preferences are enriched not only by rating data but also the tag data; subsequently, the relevance between tags and item features are explored. Furthermore, according to the assumption that user preferences will change with time, we optimize the preference and relevance by adding the temporal influence. Based on the MovieLens benchmark datasets, the experimental results indicate that the proposed co-SVD method is more effective than other baselines. Matrix co-factorization provides an effective method to the solve data sparsity problem with additional information. The method can be used to address this problem in various expert and intelligent systems such as recommendation advertisements, e-commerce sites, and social media platforms, all of which require a relatively large amount of input data from users.
Deep Inductive Graph Representation Learning

This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks.In particular, DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order.Contrary to previous work, DeepGL learns relational functions (each representing a feature) that naturally generalize across-networks and are therefore useful for graphbased transfer learning tasks.Moreover, DeepGL naturally supports attributed graphs, learns interpretable inductive graph representations, and is space-efficient (by learning sparse feature vectors).In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of OðjEjÞ, and scalable for large networks via an efficient parallel implementation.Compared with recent methods, DeepGL is (1) effective for across-network transfer learning tasks and large (attributed) graphs, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 106x speedup in runtime performance, and (4) accurate with an average improvement in AUC of 20 percent or more on many learning tasks and across a wide variety of networks.