Reviewerly/exHarmony huggingface dataset:
Condensed representations of changes in dynamic graphs through emerging subgraph mining

Change mining is one of the main subjects of analysis on time-evolving data. Regardless of the distribution of the changes over the data, often the algorithms return very large sets of results. In fact, one class of algorithms designed for change mining is based on pattern mining, which notoriously suffers from the problem of a huge number of returned patterns. Moreover, the complexity of some types of data, like dynamic graphs, could make the size of the final changes even larger, which makes interpretation difficult or even impossible. This paper represents the first attempt, to our knowledge, to build condensed representations of changes from dynamic graphs. We study changes captured with the pattern (subgraph) mining framework and focus on the discovery of subgraphs able to (i) represent evident changes and (ii) convey graph-based information that is not already expressed by other subgraphs. To do this, we revise an existing approach by introducing the notion of emerging subgraphs, used to remove uninteresting changes and the notions of closed and maximal subgraphs, used to remove redundant changes. Experiments performed on real-world dynamic graphs show that the condensed representations maintain the accuracy levels of the original approach and often offer a loss-less representation of the detected changes.
Efficiency of Local Learning Rules in Threshold-Linear Associative Networks

We derive the Gardner storage capacity for associative networks of threshold linear units, and show that with Hebbian learning they can operate closer to such Gardner bound than binary networks, and even surpass it. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like backpropagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.
Danger-Aware Adaptive Composition of DRL Agents for Self-Navigation

Self-navigation, referred as the capability of automatically reaching the goal while avoiding collisions with obstacles, is a fundamental skill required for mobile robots. Recently, deep reinforcement learning (DRL) has shown great potential in the development of robot navigation algorithms. However, it is still difficult to train the robot to learn goal-reaching and obstacle-avoidance skills simultaneously. On the other hand, although many DRL-based obstacle-avoidance algorithms are proposed, few of them are reused for more complex navigation tasks. In this paper, a novel danger-aware adaptive composition (DAAC) framework is proposed to combine two individually DRL-trained agents, obstacle-avoidance and goal-reaching, to construct a navigation agent without any redesigning and retraining. The key to this adaptive composition approach is that the value function outputted by the obstacle-avoidance agent serves as an indicator for evaluating the risk level of the current situation, which in turn determines the contribution of these two agents for the next move. Simulation and real-world testing results show that the composed Navigation network can control the robot to accomplish difficult navigation tasks, e.g. reaching a series of successive goals in an unknown and complex environment safely and quickly.
A Novel Multi-Agent Parallel-Critic Network Architecture for Cooperative-Competitive Reinforcement Learning

Multi-agent deep reinforcement learning (MDRL) is an emerging research hotspot and application direction in the field of machine learning and artificial intelligence.MDRL covers many algorithms, rules and frameworks, it is currently researched in swarm system, energy allocation optimization, stocking analysis, sequential social dilemma, and with extremely bright future.In this paper, a parallel-critic method based on classic MDRL algorithm MADDPG is proposed to alleviate the training instability problem in cooperative-competitive multi-agent environment.Furthermore, a policy smoothing technique is introduced to our proposed method to decrease the variance of learning policies.The suggested method is evaluated in three different scenarios of authoritative multi-agent particle environment (MPE).Multiple statistical data of experimental results show that our method significantly improves the training stability and performance compared to vanilla MADDPG.
Person re-identification for smart cities: State-of-the-art and the path ahead

One of the indispensable pillars of a smart city is its surveillance infrastructure, and it requires smart techniques to analyze the videos acquired from the surveillance cameras. Person re-identification (PRId) is one of the fundamental tasks in automated visual surveillance, and it has been an area of extensive research spanning the past decade. PRId aims at finding a person who has previously been seen or identified using some unique descriptor of the person. This survey comprises a broad spectrum of PRId methods spanning from traditional to deep-learning, being analyzed and compared. This survey also discusses various PRId frameworks based on machine learning and deep learning. This study emphasizes the challenges in building PRId systems for the benefits of smart cities and presents a critical overview of recent progress and the state-of-the-art approaches to solving some significant challenges of existing PRId systems.
Fewer people, more flames: How pre-existing beliefs and volume of negative comments impact online news readers’ verbal aggression

In recent years, scholars have documented an increase in flaming behavior, which refers to a strong and hostile expression of emotions and feelings online. In order to identify the mechanism and specific circumstances of flaming, this study employed emotional contagion as the main theoretical framework and conducted a 2 (argument presented in a news article: support gun control vs. against gun control) × 2 (volume of negative comments: small vs. large) × 2 (news readers' pre-existing beliefs: support gun control vs. against gun control) between-subjects experiment. Participants were asked to read an online news article about a gun-related topic as well as negative reader comments before they wrote a comment of their own. The experimental results showed that participants were more likely to use swear words in their comments when their viewpoint was diametrically opposed to the argument presented in the news article and when the article was accompanied by a low volume of negative comments. This interaction effect was mediated by negative emotions that participants experienced when reading the article and its associated comments. These findings have theoretical implications for future research pertaining to the identification of online circumstances and causes that could help mitigate the occurrence of flaming.
Privacy-Preserving Big Data Publication: (K, L) Anonymity

The explosion in variety and volume of information in the public domains provides an enormous opportunity for analysis and business purposes. Availability of private information is of explicit interest in sanctionative highly tailored services tuned to individual desires. Though this is highly favorable to the individuals, the conventional anonymization techniques still possess threats to the privacy of individuals through reidentification attacks. The focus of this paper is to propose a privacy-preserving approach called (K, L) Anonymity that combines k-anonymity and Laplace differential privacy techniques. This coherent model guarantees privacy from linkage attacks as the risk is mitigated through experimental results. The proposed model also addressed the shortcomings of other traditional privacy-preserving mechanisms and validated with publicly available datasets.
Metric calibration of unfocused plenoptic cameras for three-dimensional shape measurement

A metric calibration method for light-field three-dimensional (3-D) reconstruction based on plenoptic disk features is presented. Each of these features that relates to a single 3-D point in free physical space could be directly extracted from the raw light field. By establishing the point-to-feature correspondence, the optical parameters of unfocused plenoptic camera would be estimated through nonlinear optimization, which could then be exploited to metrically generate the 3-D point cloud of an object. For verification, all detected plenoptic disk features are reconstructed to 3-D space and then light-field reprojection is performed. In addition, to demonstrate the performance of the proposed method, comparative experiments are carried out on a commercial unfocused plenoptic camera system. Then, the acquired datasets are processed using the published method based on plenoptic disk features and our proposed calibration method, respectively. Experimental results show that the proposed calibration algorithm is more reliable and faster than the existing published one, especially for small field-of-view applications.
MSPPIR: Multi-Source Privacy-Preserving Image Retrieval in cloud computing

Content-Based Image Retrieval (CBIR) techniques have been widely researched and in service with cloud computing like Google Images. However, the images always contain rich sensitive information. In this case, privacy protection becomes a big problem as the cloud always cannot be fully trusted. Many privacy-preserving image retrieval schemes have been proposed, in which the image owner can upload the encrypted images to the cloud, and the owner himself or the authorized user can execute the secure retrieval with the help of the cloud. Nevertheless, few existing researchers notice the multi-source scene that is more practical. In this paper, we analyze the difficulties in Multi-Source Privacy-Preserving Image Retrieval (MSPPIR). Then we use the image in JPEG-format as the example, to propose a scheme called JES-MSIR, namely a novel JPEG image Encryption Scheme which is made for Multi-Source content-based Image Retrieval. JES-MSIR can support the requirements of MSPPIR, including the constant-rounds secure retrieval from multiple sources and the union of multiple sources for better retrieval services. Experiment results and security analysis on the proposed scheme show its efficiency, security, and accuracy.
Device Feature Extraction Based on Parallel Neural Network Training for Replay Spoofing Detection

A device feature, which contains information from both the recording channel and playback channel, is of paramount importance in replay spoofing detection. Presently, no technical reports, pertaining to the usage of device information for spoofing detection to achieve speaker verification, have been published. In this article, we propose to build a replay device feature (RDF) extractor on the basis of the genuine-replay-pair parallel neural network training database. The target of the neural network for such an RDF extractor is constrained in a spectrum domain such as discrete Fourier transform or constant-Q transform. Different types of neural networks are investigated for the parallel training framework. Finally the RDF extractor is formed by applying discrete cosine transform to the output vector of neural network. The bidirectional long short term memory gives the best performance among the investigated types of neural networks. The experimental results on ASVspoof 2017 corpus version 2.0 indicate that the equal error rate (EER) of a replay detection system with the proposed RDF has a value of 15.08%. Furthermore, by combining the RDF with constant-Q cepstral coefficients plus the log energy in score level, the EER of the detection system can be further reduced to 8.99%. In addition, the experimental results also reveal that the RDF exhibits great complementarity with many handcrafted features.
Rich heterogeneous information preserving network representation learning

Network representation learning has attracted increasing attention recently due to its applicability in network analysis. However, most existing network representation learning models only focus on preserving fragmentary aspects of network information, either node proximities or fixed semantic information. In this paper, we propose a novel algorithm named Rich Heterogeneous Information Preserving Network Representation Learning (HIRL), which integrates the high-order proximity among nodes and semantic information into a generic framework by exploiting a flexible autoencoder network. Based on the proposed algorithm, we can explore the hidden information in heterogeneous information networks through any custom form of path schema, and represents different types of nodes in a continuous and common vector space. Moreover, the proposed HIRL is applicable to homogeneous information networks. Extensive experimental results demonstrate that our approach can effectively preserve the information in networks under various path schemas, and performs better on real-world applications such as network reconstruction, link prediction, and node classification compared with the state-of-the-art methods.
Hacksaw

The currently deployed web authentication model, involving only entry-point authentication of users, does not do anything to protect against account takeover attacks. Once the attacker has compromised the entry-point authentication method, such as by learning a user's password or even two-factor authentication credentials via widely exploited mechanisms such as phishing and password database breaches, or has hijacked a login session, he can fully access and abuse the user's account and associated services. To respond to this critical vulnerability, we introduce the notion of non-stop post-entry authentication, to be integrated with any entry-point authentication method, using which the web service can proactively authenticate the user throughout the login session invisibly in the background without explicit user involvement and without the need for storing user-specific templates (like in biometric systems) thereby preserving user privacy.
Global and Adaptive Thresholding Technique for White Blood Cell Image Segmentation

Abstract Global and local thresholding are two thresholding approaches for white blood cell (WBC) image segmentation. Global thresholding determines the threshold value based on the histogram of the overall pixel intensity distribution of the image. In contrast, adaptive thresholding computes the threshold value for each fractional region of the image, so that each fractional region has a different threshold value. In this work, we are assessing both of these approaches for two threshold values. We extended the Otsu’s equation to calculate more than one threshold as it originally designed to find only a single threshold value. Adaptive thresholding first divides an image into fractional-image by considering an imaginary bounding box that surrounds the location of WBC, which involves the Gram-Schmidt orthogonalization method. For segmentation performance evaluation, we compare 35 blood smear test images which segmented by our proposed method, with their corresponding ground truth image to representing them in Zijdenbos Similarity Index (ZSI), precision, and recall measurement. Experimental results show that adaptive thresholding achieves average ZSI, precision and recall, 92.5%, 91.79%, 94.03%, while global thresholding achieves 30.72%, 23.38%, and 99.39% respectively.
Built-in Security Computer: Deploying Security-First Architecture Using Active Security Processor

Continually disclosed vulnerabilities reveal that traditional computer architecture lacks the consideration of security. This article proposes a security-first architecture, with an Active Security Processor (ASP) integrated to conventional computer architectures. To reduce the attack surface of ASP and improve the security of the whole system, the ASP is physically isolated from Computation Processor Units (CPU) with an asymmetric address space, which enables both ASP and CPU to run their operating system and applications independently in their own memory space. Furthermore, the ASP, which has the highest privilege (Super Root) of the whole system, possesses two advantageous features. First, the ASP can efficiently access all CPU resources and collect multi-dimensional information to monitor malicious behaviors, meanwhile, the CPU cannot access the ASP's private resources in any way. Second, instead of being scheduled by CPUs, the ASP can actively manage the security mechanisms employed in either CPUs or the ASP. Based on the security-first architecture, we introduce several typical security tasks running on ASP. With different considerations in terms of system overhead, complexity and performance, we also explore four typical system-level implementations for integrating the ASP to the security-first architecture. The first-generation ASP was designed and implemented based on the 40nm technology, and a security computer system was implemented based on it. Evaluations on this real hardware platform demonstrate that the security-first architecture can protect the system effectively with minor performance impacts on computing workloads.
Feynman checkers: towards algorithmic quantum theory

We survey and develop the most elementary model of electron motion introduced by Feynman. In this game, a checker moves on a checkerboard by simple rules, and we count the turns. Feynman checkers are also known as a one-dimensional quantum walk or an Ising model at imaginary temperature. We solve mathematically a Feynman problem from 1965, which was to prove that the discrete model (for large time, small average velocity, and small lattice step) is consistent with the continuum one. We study asymptotic properties of the model (for small lattice step and large time) improving the results due to Narlikar from 1972 and to Sunada and Tate from 2012. For the first time we observe and prove concentration of measure in the small-lattice-step limit. We perform the second quantization of the model. We also present a survey of known results on Feynman checkers. Bibliography: 53 titles.
Robust Subspace Clustering via Latent Smooth Representation Clustering

Subspace clustering aims to group high-dimensional data samples into several subspaces which they were generated. Among the existing subspace clustering methods, spectral clustering-based algorithms have attracted considerable attentions because of their predominant performances shown in many subspace clustering applications. In this paper, we proposed to apply smooth representation clustering (SMR) to the reconstruction coefficient vectors which were obtained by sparse subspace clustering (SSC). Because the reconstruction coefficient vectors could be regarded as a kind of good representations of original data samples, the proposed method could be considered as a SMR performed in a latent subspace found by SSC and hoped to achieve better performances. For solving the proposed latent smooth representation algorithm (LSMR), we presented an optimization method and also discussed the relationships between LSMR with some related algorithms. Finally, experiments conducted on several famous databases demonstrate that the proposed algorithm dominates the related algorithms.
A Dynamic Approach for Consistency Service in Cloud and Fog Environment

Users of data-intensive cloud and fog computing service require reliable and continuous access to very large amount of data from cloud and fog service providers. The size of data in such applications is massive. A cloud and fog use replication as a database management technique to improve availability, fault tolerance, and one of the major aims of the cloud and fog is to reduce job response time. To support such continuous data access, this paper proposes a dynamic replica consistency approach, Cloud Fog Dynamic Consistency (CFDC), which combines the advantages of the optimistic and pessimistic approaches to achieve and ensure local and global consistency service within cloud and fog systems. The proposed CFDC approach provides a suitable solution for the scale up of the cloud and fog environment to maximize the performance and quality of services of those systems.
Rooted Spanning Superpixels

Abstract This paper proposes a new approach for superpixel segmentation. It is formulated as finding a rooted spanning forest of a graph with respect to some roots and a path-cost function. The underlying graph represents an image, the roots serve as seeds for segmentation, each pixel is connected to one seed via a path, the path-cost function measures both the color similarity and spatial closeness between two pixels via a path, and each tree in the spanning forest represents one superpixel. Originating from the evenly distributed seeds, the superpixels are guided by a path-cost function to grow uniformly and adaptively, the pixel-by-pixel growing continues until they cover the whole image. The number of superpixels is controlled by the number of seeds. The connectivity is maintained by region growing. Good performances are assured by connecting each pixel to the similar seed, which are dominated by the path-cost function. It is evaluated by both the superpixel benchmark and supervoxel benchmark. Its performance is ranked as the second among top performing state-of-the-art methods. Moreover, it is much faster than the other superpixel and supervoxel methods.
Narrowband Angle of Arrival Estimation Exploiting Graph Topology and Graph Signals

Based on recent results of applying graph signal processing (GSP) to narrowband angle of arrival estimation for uniform linear arrays, we generalise the analysis to the case of arrays with elements placed arbitrarily in three dimensional space. We comment on the selection of the adjacency matrix, analyse how this new approach compares to the multiple signal classification (MUSIC) algorithm, and provide an efficient implementation. We demonstrate that the GSP approach can perform as well as the MUSIC algorithm in terms of accuracy and computational cost. Simulations indicate that the proposed GSP approach avoids the severe performance degradation with which MUSIC is associated at low signal to noise ratios.
Target Tracking Initiation for Multi-Static Multi-Frequency PCL System

In multiple target tracking, one of the crucial techniques is how to estimate unknown initiation points of tracks by incorporating a set of measurements under noisy and cluttered environment. Due to unknown data association and false alarms, all possible track initiation points need to be evaluated, which suffers from computational complexity. In this paper, we propose a computationally efficient track initiation method for multi-static multi-frequency passive coherent localization systems, where bistatic measurements from different illuminators are incorporated at a receiver to find the most probable track initiation points. By the proposed likelihood utility function, our method resolves unknown data association between measurements and targets to reduce computational complexity. In addition, the proposed method can be extended to process a set of multi-scan measurements in association-free manner, which results in improved estimation accuracy. Simulation results are provided to validate the performance of our proposed method.