Reviewerly/exHarmony huggingface dataset:
Removing background interference for crowd counting via de-background detail convolutional network

Crowd counting is a challenging vision task which aims to accurately estimate the crowd count from a single image. To this end, we propose a novel architecture called De-background Detail Convolutional Network (DDCN) to learn a mapping from the input image to the corresponding crowd density map. DDCN focuses on removing the interference of background from crowds and reducing the mapping range from input to output. Such design optimizes the learning process to a large extent. The proposed DDCN is composed of three components: a decomposer, a feature extraction CNN and a regression CNN. Specifically, the decomposer produces a detail layer by subtracting the background interference from the crowd image. Feature extraction CNN works for extracting high level features and regression CNN is used to estimate the density map. In addition, a weighted Euclidean loss is designed to calculate the Euclidean distances of the crowd and the background separately with different loss weights, which further improves the counting performance. Extensive experiments were conducted on three crowd counting datasets to validate the performance of DCNN. And experimental results demonstrate that DDCN achieves performance improvements compared with the state-of-the-art.
Energy-efficient, low-latency realization of neural networks through boolean logic minimization

Deep neural networks have been successfully deployed in a wide variety of applications including computer vision and speech recognition. To cope with computational and storage complexity of these models, this paper presents a training method that enables a radically different approach for realization of deep neural networks through Boolean logic minimization. The aforementioned realization completely removes the energy-hungry step of accessing memory for obtaining model parameters, consumes about two orders of magnitude fewer computing resources compared to realizations that use floating-point operations, and has a substantially lower latency.
Portrait Relief Modeling from a Single Image

We present a novel solution to enable portrait relief modeling from a single image. The main challenges are geometry reconstruction, facial details recovery and depth structure preservation. Previous image-based methods are developed for portrait bas-relief modeling in 2.5D form, but not adequate for 3D-like high relief modeling with undercut features. In this paper, we propose a template-based framework to generate portrait reliefs of various forms. Our method benefits from Shape-from-Shading (SFS). Specifically, we use bi-Laplacian mesh deformation to guide the relief modeling. Given a portrait image, we first use a template face to fit the portrait. We then apply bi-Laplacian mesh deformation to align the facial features. Afterwards, SFS-based reconstruction with a few user interactions is used to optimize the face depth, and create a relief with similar appearance to the input. Both depth structures and geometric details can be well constructed in the final relief. Experiments and comparisons to other methods demonstrate the effectiveness of the proposed method.
Effects of salinity on microbialite‐associated production in Great Salt Lake, Utah

Microbialites, organosedimentary carbonate structures, cover approximately 20% of the basin floor in the south arm of Great Salt Lake, which ranges from ~12 to 15% salinity. Photosynthetic microbial mats associated with these benthic mounds contribute biomass that supports secondary production in the ecosystem, including that of the brine shrimp, Artemia franciscana. However, the effects of predicted increases in the salinity of the lake on the productivity and composition of these mats and on A. franciscana fecundity is not well documented. In the present study, we applied molecular and microcosm-based approaches to investigate the effects of changing salinity on (1) the primary productivity, abundance, and composition of microbialite-associated mats of GSL, and (2) the fecundity and survivability of the secondary consumer, A. franciscana. When compared to microcosms incubated closest to the in situ measured salinity of 15.6%, the abundance of 16S rRNA gene templates increased in microcosms with lower salinities and decreased in those with higher salinities following a 7-week incubation period. The abundance of 16S rRNA gene sequences affiliated with dominant primary producers, including the cyanobacterium Euhalothece and the diatom Navicula, increased in microcosms incubated at decreased salinity, but decreased in microcosms incubated at increased salinity. Increased salinity also decreased the rate of primary production in microcosm assays containing mats incubated for 7 weeks and decreased the number of A. franciscana cysts that hatched and survived. These results indicate that an increase in the salinity of GSL is likely to have a negative impact on the productivity of microbialite communities and the fecundity and survivability of A. franciscana. These observations suggest that a sustained increase in the salinity of GSL and the effects this has on primary and secondary production could have an upward and negative cascading effect on higher-trophic-level ecological compartments that depend on A. franciscana as a food source, including a number of species of migratory birds.
Trust Relationship Prediction in Alibaba E-Commerce Platform

This paper introduces how to infer trust relationships from billion-scale networked data to benefit Alibaba E-Commerce business. To effectively leverage the network correlations between labeled and unlabeled relationships to predict trust relationships, we formalize trust into multiple types and propose a graphical model to incorporate type-based dyadic and triadic correlations, namely eTrust. We also present a fast learning algorithm in order to handle billion-scale networks. Systematically, we evaluate the proposed methods on four different genres of datasets with labeled trust relationships: Alibaba, Epinions, Ciao, and Advogato. Experimental results show that the proposed methods achieve significantly better performance than several comparison methods (+1.7-32.3% by accuracy; p <; <; 0:01, with t-test). Most importantly, when handling the real large networked data with over 1,200,000,000 edges (Ali-large), our method achieves 2,000× speedup to infer trust relationships, comparing with the traditional graph learning algorithms. Finally, we have applied the inferred trust relationships to Alibaba E-commerce platform: Taobao, and achieved 2.75 percent improvement on gross merchandise volume (GMV).
Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features

Copy-move forgery in images is a popular tampering method, in which a portion of an image is copied and pasted on some other location of the same image. This paper proposes an enhancement of block based copy-move forgery detection using hybrid local features extraction. In this system, the image is divided into non-overlapping blocks and SURF features are computed from each block. SURF features are matched using 2NN procedure and large blocks are formed by considering the eight neighboring blocks of each SURF features match block. Maximally Stable Extremal Regions are detected from each large region and the extracted SURF descriptors from these regions are compared for matching. Finally, affine transform is applied to remove the outliers. The proposed system is experimented using MICC-F220, MICC-F2000 and MICC-F600 benchmark datasets and it yields better performance in comparison with state of the art techniques.
Learning motion primitives for planning swift maneuvers of quadrotor

This work proposes a novel, learning-based method to leverage navigation time performance of unmanned aerial vehicles in dense environments by planning swift maneuvers using motion primitives. In the proposed planning framework, desirable motion primitives are explored by reinforcement learning. Two-stage training composed of learning in simulations and real flights is conducted to build up a swift motion primitive library. The library is then referred in real-time and the primitives are utilized by an intelligent control authority switch mechanism when swift maneuvers are needed for particular portions of a trajectory. Since the library is constructed upon realistic Gazebo simulations and real flights together, probable modeling uncertainties which can degrade planning performance are minimal. Moreover, since the library is in the form of motion primitives, it is computationally inexpensive to be retained and used for planning as compared to solving optimal motion planning problem algebraically. Overall, the proposed method allows for exceptional, swift maneuvers and enhances navigation time performance in dense environments up to 20% as being demonstrated by real flights with Diatone FPV250 quadrotor equipped with PX4 FMU.
Unsupervised human activity analysis for intelligent mobile robots

The success of intelligent mobile robots operating and collaborating with humans in daily living environments depends on their ability to generalise and learn human movements, and obtain a shared understanding of an observed scene. In this paper we aim to understand human activities being performed in real-world environments from long-term observation from an autonomous mobile robot. For our purposes, a human activity is defined to be a changing spatial configuration of a person's body interacting with key objects that provide some functionality within an environment. To alleviate the perceptual limitations of a mobile robot, restricted by its obscured and incomplete sensory modalities, potentially noisy visual observations are mapped into an abstract qualitative space in order to generalise patterns invariant to exact quantitative positions within the real world. A number of qualitative spatial-temporal representations are used to capture different aspects of the relations between the human subject and their environment. Analogously to information retrieval on text corpora, a generative probabilistic technique is used to recover latent, semantically-meaningful concepts in the encoded observations in an unsupervised manner. The small number of concepts discovered are considered as human activity classes, granting the robot a low-dimensional understanding of visually observed complex scenes. Finally, variational inference is used to facilitate incremental and continuous updating of such concepts that allows the mobile robot to efficiently learn and update its models of human activity over time resulting in efficient life-long learning.
An efficient privacy protection scheme for data security in video surveillance

The advancement in video surveillance has raised significant concerns about privacy protection. The existing methods focus on identifying the sensitive region and preserving the behavior of the target, however, they ignore the recoverability of private content. In this paper, we propose a novel and efficient privacy protection scheme for data security in video surveillance, which jointly addresses several key challenges, including de-identification, behavior preservation, recoverability, and compressibility in one unified system. Our method constructs a public stream and a private residual error stream by blurring the private sensitive region. With our scheme, ordinary users could recognize the behaviors in the public identity-protected video stream for surveillance purpose, while authorized users are able to access the recovered private content (e.g., for law investigations). Moreover, the compressed privacy protected region and residual error could be able to save the costs associated with transmission and storage. The extensive experiments on two standard surveillance datasets and a user study demonstrate the effectiveness of our privacy protection system.
A Novel Algorithm of Constructing Highly Nonlinear S-p-boxes

The role of substitution boxes is very important in block ciphers. Substitution boxes are utilized to create confusion in the cryptosystem. However, to create both confusion and diffusion in any cryptosystem p-boxes and chaos base substitution boxes are designed. In this work, a simple method is presented that serves both ways. This method is based on composition of the action of symmetric group on Galois field and inversion map. This construction method provides a large number of highly non-linear substitution permutation boxes having the property of confusion as well as diffusion. These substitution permutation boxes have all the cryptography properties. Their utilization in the image encryption application is measured by majority logic criterion. We named these newly designed substitution boxes (S-boxes) as substitution permutation boxes (S-p-boxes), because they serve as both substitution boxes (S-boxes) as well as permutation boxes (p-boxes).
Bots Acting Like Humans: Understanding and Preventing Harm

Bots are algorithmically driven entities that act like humans in conversations via Twitter, on Facebook, in chats or Q&A sites.This article studies how they may affect on-line conversations, provides a taxonomy of harms that may be caused, and discusses how to prevent harm by studying when abuses occur.Many technologists consider chatbots one of the hottest technologies in recent times (https://bit.ly/2od0Tdw),an opinion fueled for example by Facebook's release of its Messenger API in 2016.In April 2017, Facebook reported 100,000 monthly active bots on the Messenger platform.In March 2017 Varol et al. 1 estimated that between 9% and 15% of active Twitter accounts are bots (29-49 million accounts out of 328 millions, https://bit.ly/2v3AT6O).Gartner estimates that by 2020 85% of customer requests will be handled by bots, while Inbenta estimates 1.8 billion unique customer chatbot users by 2021. 2 The technological advancements of chatbots undoubtedly produced a hype on its own, yet bots today are by far not limited to instant messaging only.Bots permeate all kinds of on-line conversations in Twitter, Facebook, Instagram, Q&A sites, on-line newspapers, emails, and the like.They are everywhere where there are humans conversing with each other via the Internet, legitimately or illegitimately.For example, Messenger explicitly allows bots in its chats, while WhatsApp states that it blocks phone numbers generating bot traffic (https://bit.ly/2HhW9wG).Inspired by Bessi and Ferrara, 3 we thus understand bots (sometimes also called sybils 4 ) generically as algorithmically driven entities that on the surface act like legitimate human users in online conversations.Even though on-line bots are multiplying their presence in public and private communications, most organizations and users still do not have the knowledge, skills, or understanding to craft a successful strategy to keep up with the possible unintended consequences of this presence.If we
Combining multilevel feature extraction and multi-loss learning for person re-identification

The goal of person re-identification (re-id) is to match images of the same person captured by multiple cameras with non-overlapping views. It is a challenging task due to the large spatial displacement and human pose change of person images across different views. Recently, the deep Convolutional Neural Network (CNN) has significantly improved the performance of person re-id. In this paper, we present a hybrid deep model that combines multilevel feature extraction and multi-loss learning for more robust pedestrian descriptors. The multi-loss function jointly optimizes the verification task that aims to verify if two images belong to same person, and the recognition task that aims to predict the identity of each image. Specifically, given two person images, we first apply a deep learning network, called Feature Aggregation Network (FAN), to extract their multilevel CNN features by fusing the information of different layers. For the verification task, a Recurrent Comparative Network (RCN) is presented to learn joint representation of paired CNN features. RCN determines whether two images depict the same person through focusing on discriminative regions and alternatively comparing their appearance. It is an algorithmic imitation of human decision-making process, in which a person repeatedly compares two objects before making decision about their similarity. For the recognition task, a parameter-free operation termed Global Average Pooling (GAP) is followed after each CNN feature to extract identity-related features. Extensive experiments are conducted on four datasets, including CUHK03, CUHK01, Market1501 and DukeMTMC, and the experimental results demonstrate the effectiveness of our presented method.
Beyond Outlier Detection: LookOut for Pictorial Explanation

Why is a given point in a dataset marked as an outlier by an off-the-shelf detection algorithm? Which feature(s) explain it the best? What is the best way to convince a human analyst that the point is indeed an outlier? We provide succinct, interpretable, and simple pictorial explanations of outlying behavior in multi-dimensional real-valued datasets while respecting the limited attention of human analysts. Specifically, we propose to output a few focus-plots, i.e., pairwise feature plots, from a few, carefully chosen feature sub-spaces. The proposed LookOut makes four contributions: (a) problem formulation: we introduce an “analyst-centered” problem formulation for explaining outliers via focus-plots, (b) explanation algorithm: we propose a plot-selection objective and the LookOut algorithm to approximate it with optimality guarantees, (c) generality: our explanation algorithm is both domain- and detector-agnostic, and (d) scalability: LookOut scales linearly with the size of input outliers to explain and the explanation budget. Our experiments show that LookOut performs near-ideally in terms of maximizing explanation objective on several real datasets, while producing visually interpretable and intuitive results in explaining groundtruth outliers. Code related to this paper is available at: https://github.com/NikhilGupta1997/Lookout .
Neural Variational Correlated Topic Modeling

With the rapid development of the Internet, millions of documents, such as news and web pages, are generated everyday. Mining the topics and knowledge on them has attracted a lot of interest on both academic and industrial areas. As one of the prevalent unsupervised data mining tools, topic models are usually explored as probabilistic generative models for large collections of texts. Traditional probabilistic topic models tend to find a closed form solution of model parameters and approach the intractable posteriors via approximation methods, which usually lead to the inaccurate inference of parameters and low efficiency when it comes to a quite large volume of data. Recently, an emerging trend of neural variational inference can overcome the above issues, which offers a scalable and powerful deep generative framework for modeling latent topics via neural networks. Interestingly, a common assumption for the most neural variational topic models is that topics are independent and irrelevant to each other. However, this assumption is unreasonable in many practical scenarios. In this paper, we propose a novel Centralized Transformation Flow to capture the correlations among topics by reshaping topic distributions. Furthermore, we present the Transformation Flow Lower Bound to improve the performance of the proposed model. Extensive experiments on two standard benchmark datasets have well-validated the effectiveness of the proposed approach.
Remote Sensing Image Matching Based Improved ORB in NSCT Domain

Aiming at the problem that the ORB algorithm has no scale invariance and low matching accuracy in image matching, an improved ORB algorithm is proposed on the basis of SURF algorithm. Based on the flexibility of NSCT in image decomposition and the effectiveness of the improved ORB algorithm in remote sensing image matching, an improved ORB algorithm based on NSCT domain is proposed for remote sensing image matching. The image to be matched and the reference image are decomposed by NSCT. Two corresponding low-frequency images are obtained. Then, to reduce the influence of high-frequency noise on matching results, two low-frequency images are inputted to the improved ORB algorithm to obtain initial match results. The RANSAC algorithm is adopted to eliminate the mismatching points and complete the image matching. The experimental results show that the algorithm can make up the problem that the ORB algorithm has no scale invariance, and effectively improve the matching speed and accuracy of scale and rotation changes between two images. Meanwhile, the algorithm is more robust than classical methods in many complex situations such as image blur, field of view change, and noise interference.
Dynamic Resource Management to Defend Against Advanced Persistent Threats in Fog Computing: A Game Theoretic Approach

Fog computing has gained tremendous popularity due to its capability of addressing the surging demand on high-quality ubiquitous mobile services. Nevertheless, the highly virtualized environment in fog computing leads to vulnerability to cyber attacks such as advanced persistent threats. In this paper, we propose a novel game approach of cyber risk management for the fog computing platform. We adopt the cyber-insurance concept to transfer cyber risks from fog computing platform to a third party. The system model under consideration consists of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform which is composed of multiple fog nodes. Meanwhile, the attacker dynamically adjusts the allocation of its attack computing resources to increase the probability of successful attack. Additionally, to prevent from the potential loss due to the attacks, the provider also makes a dynamic decision on the subscription of cyber-insurance for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. To model this dynamic interactive decision making problem, we formulate a dynamic Stackelberg game. In the lower-level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper-level, the cyber-insurer optimizes its premium strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable, and we investigate the Stackelberg equilibrium by capitalizing on tools from the optimal control theory. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.