OpenRaiser/Intern-Atlas huggingface dataset:
Performance Tuning and Scaling Enterprise Blockchain Applications
Venue: arXiv cs.DC 2019
Year: 2019

Blockchain scalability can be complicated and costly. As enterprises begin to adopt blockchain technology to solve business problems, there are valid concerns if blockchain applications can support the transactional demands of production systems. In fact, the multiple distributed components and protocols that underlie blockchain applications makes performance optimization a non-trivial task. Blockchain performance optimization and scalability require a methodology to reduce complexity and cost. Furthermore, existing performance results often lack the requirements, load, and infrastructure of a production application. In this paper, we first develop a methodical approach to performance tuning enterprise blockchain applications to increase performance and transaction capacity. The methodology is applied to an enterprise blockchain-based application (leveraging Hyperledger Fabric) for performance tuning and optimization with the goal of bridging the gap between laboratory and production deployed system performance. We then present extensive results and analysis of our performance testing for on-premise and cloud deployments, in which we were able to scale the application from 30 to 3000 TPS without forking the Hyperledger Fabric source code and maintaining a reasonable infrastructure footprint. We also provide blockchain application and platform recommendations for performance improvement.
PILS: Exploring high-order neighborhoods by pattern mining and injection
Venue: arXiv cs.AI 2019
Year: 2019

We introduce pattern injection local search (PILS), an optimization strategy that uses pattern mining to explore high-order local-search neighborhoods, and illustrate its application on the vehicle routing problem. PILS operates by storing a limited number of frequent patterns from elite solutions. During the local search, each pattern is used to define one move in which 1) incompatible edges are disconnected, 2) the edges defined by the pattern are reconnected, and 3) the remaining solution fragments are optimally reconnected. Each such move is accepted only in case of solution improvement. As visible in our experiments, this strategy results in a new paradigm of local search, which complements and enhances classical search approaches in a controllable amount of computational time. We demonstrate that PILS identifies useful high-order moves (e.g., 9-opt and 10-opt) which would otherwise not be found by enumeration, and that it significantly improves the performance of state-of-the-art population-based and neighborhood-centered metaheuristics.
Self-adaption grey DBSCAN clustering
Venue: arXiv cs.LG 2019
Year: 2019

Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to obtain the grey local density indicator, and then this indicator is applied to make self-adapting noise identification for obtaining a dense subset of clustering dataset, finally, the DBSCAN which automatically selects parameters is utilized to cluster the dense subset. Several frequently-used datasets were used to demonstrate the performance and effectiveness of the proposed clustering algorithm and to compare the results with those of other state-of-the-art algorithms. The comprehensive comparisons indicate that our method has advantages over other compared methods.
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints. However, these approaches usually ignore the spatial-temporal global context as well as the local relation between inter-frame and intra-frame. In this paper, we propose a focusing and diffusion mechanism to enhance graph convolutional networks by paying attention to the kinematic dependence of articulated human pose in a frame and their implicit dependencies over frames. In the focusing process, we introduce an attention module to learn a latent node over the intra-frame joints to convey spatial contextual information. In this way, the sparse connections between joints in a frame can be well captured, while the global context over the entire sequence is further captured by these hidden nodes with a bidirectional LSTM. In the diffusing process, the learned spatial-temporal contextual information is passed back to the spatial joints, leading to a bidirectional attentive graph convolutional network (BAGCN) that can facilitate skeleton-based action recognition. Extensive experiments on the challenging NTU RGB+D and Skeleton-Kinetics benchmarks demonstrate the efficacy of our approach.
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
Venue: arXiv cs.IR 2019
Year: 2019

In this paper, we analyze a large dataset of user-generated music listening events from Last.fm, focusing on users aged 6 to 18 years. Our contribution is two-fold. First, we study the music genre preferences of this young user group and analyze these preferences for homogeneity within more fine-grained age groups and with respect to gender and countries. Second, we investigate the performance of a collaborative filtering recommender when tailoring music recommendations to different age groups. We find that doing so improves performance for all user groups up to 18 years, but decreases performance for adult users aged 19 years and older.
A Logical Model for Supporting Social Commonsense Knowledge Acquisition
Venue: arXiv cs.AI 2019
Year: 2019

To make machine exhibit human-like abilities in the domains like robotics and conversation, social commonsense knowledge (SCK), i.e., common sense about social contexts and social roles, is absolutely necessarily. Therefor, our ultimate goal is to acquire large-scale SCK to support much more intelligent applications. Before that, we need to know clearly what is SCK and how to represent it, since automatic information processing requires data and knowledge are organized in structured and semantically related ways. For this reason, in this paper, we identify and formalize three basic types of SCK based on first-order theory. Firstly, we identify and formalize the interrelationships, such as having-role and having-social_relation, among social contexts, roles and players from the perspective of considering both contexts and roles as first-order citizens and not generating role instances. Secondly, we provide a four level structure to identify and formalize the intrinsic information, such as events and desires, of social contexts, roles and players, and illustrate the way of harvesting the intrinsic information of social contexts and roles from the exhibition of players in concrete contexts. And thirdly, enlightened by some observations of actual contexts, we further introduce and formalize the embedding of social contexts, and depict the way of excavating the intrinsic information of social contexts and roles from the embedded smaller and simpler contexts. The results of this paper lay the foundation not only for formalizing much more complex SCK but also for acquiring these three basic types of SCK.
N-gram Statistical Stemmer for Bangla Corpus
Venue: arXiv cs.CL 2019
Year: 2019

Stemming is a process that can be utilized to trim inflected words to stem or root form. It is useful for enhancing the retrieval effectiveness, especially for text search in order to solve the mismatch problems. Previous research on Bangla stemming mostly relied on eliminating multiple suffixes from a solitary word through a recursive rule based procedure to recover progressively applicable relative root. Our proposed system has enhanced the aforementioned exploration by actualizing one of the stemming algorithms called N-gram stemming. By utilizing an affiliation measure called dice coefficient, related sets of words are clustered depending on their character structure. The smallest word in one cluster may be considered as the stem. We additionally analyzed Affinity Propagation clustering algorithms with coefficient similarity as well as with median similarity. Our result indicates N-gram stemming techniques to be effective in general which gave us around 87% accurate clusters.
Utility-Driven Mining of Trend Information for Intelligent System
Venue: arXiv cs.DB 2019
Year: 2019

Useful knowledge, embedded in a database, is likely to change over time. Identifying recent changes in temporal databases can provide valuable up-to-date information to decision-makers. Nevertheless, techniques for mining high-utility patterns (HUPs) seldom consider recency as a criterion to discover patterns. Thus, the traditional utility mining framework is inadequate for obtaining up-to-date insights about real world data. In this paper, we address this issue by introducing a novel framework, named utility-driven mining of Recent/trend high-Utility Patterns (RUP) in temporal databases for intelligent systems, based on user-specified minimum recency and minimum utility thresholds. The utility-driven RUP algorithm is based on novel global and conditional downward closure properties, and a recency-utility tree. Moreover, it adopts a vertical compact recency-utility list structure to store the information required by the mining process. The developed RUP algorithm recursively discovers recent HUPs. It is also fast and consumes a small amount of memory due to its pattern discovery approach that does not generate candidates. Two improved versions of the algorithm with additional pruning strategies are also designed to speed up the discovery of patterns by reducing the search space. Results of a substantial experimental evaluation show that the proposed algorithm can efficiently identify all recent high-utility patterns in large-scale databases, and that the improved algorithm performs best.
Extended Models of Finite Automata
Venue: arXiv cs.FL 2019
Year: 2019

Many of the numerous automaton models proposed in the literature can be regarded as a finite automaton equipped with an additional storage mechanism. In this thesis, we focus on two such models, namely the finite automata over groups and the homing vector automata. A finite automaton over a group $ G $ is a nondeterministic finite automaton equipped with a register that holds an element of the group $ G $. The register is initialized to the identity element of the group and a computation is successful if the register is equal to the identity element at the end of the computation after being multiplied with a group element at every step. We investigate the language recognition power of finite automata over integer and rational matrix groups and reveal new relationships between the language classes corresponding to these models. We examine the effect of various parameters on the language recognition power. We establish a link between the decision problems of matrix semigroups and the corresponding automata. We present some new results about valence pushdown automata and context-free valence grammars. We also propose the new homing vector automaton model, which is a finite automaton equipped with a vector that can be multiplied with a matrix at each step. The vector can be checked for equivalence to the initial vector and the acceptance criterion is ending up in an accept state with the value of the vector being equal to the initial vector. We examine the effect of various restrictions on the model by confining the matrices to a particular set and allowing the equivalence test only at the end of the computation. We define the different variants of the model and compare their language recognition power with that of the classical models.
Memory Augmented Graph Neural Networks for Sequential Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.
Multi-Label Graph Convolutional Network Representation Learning
Venue: arXiv cs.LG 2019
Year: 2019

Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels, e.g., a user may belong to diverse interest groups of a social network, resulting in multi-label networks for many applications. The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning. In this paper, we propose a novel multi-label graph convolutional network (ML-GCN) for learning node representation for multi-label networks. To fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and they are seamlessly integrated under one objective function. The learned label representations can effectively preserve the inner-label interaction and node label properties, and are then aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach.
Multidimensional Variational Line Spectra Estimation
Venue: arXiv cs.IT 2019
Year: 2019

The fundamental multidimensional line spectral estimation problem is addressed utilizing the Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) algorithm, multidimensional VALSE (MDVALSE) is developed. MDVALSE inherits the advantages of VALSE such as automatically estimating the model order, noise variance and providing uncertain degrees of frequency estimates. Compared to VALSE, the multidimensional frequencies of a single component is treated as a whole, and the probability density function is projected as independent univariate von Mises distribution to perform tractable inference. Besides, for the initialization, efficient fast Fourier transform (FFT) is adopted to significantly reduce the computation complexity of MDVALSE. Numerical results demonstrate the effectiveness of the MDVALSE, compared to state-of-art methods.
Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition
Venue: arXiv cs.RO 2019
Year: 2019

Elevator button recognition is considered an indispensable function for enabling the autonomous elevator operation of mobile robots. However, due to unfavorable image conditions and various image distortions, the recognition accuracy remains to be improved. In this paper, we present a novel algorithm that can autonomously correct perspective distortions of elevator panel images. The algorithm first leverages the Gaussian Mixture Model (GMM) to conduct a grid fitting process based on button recognition results, then utilizes the estimated grid centers as reference features to estimate camera motions for correcting perspective distortions. The algorithm performs on a single image autonomously and does not need explicit feature detection or feature matching procedure, which is much more robust to noises and outliers than traditional feature-based geometric approaches. To verify the effectiveness of the algorithm, we collect an elevator panel dataset of 50 images captured from different angles of view. Experimental results show that the proposed algorithm can accurately estimate camera motions and effectively remove perspective distortions.
Temperature-Aware Virtual Data Center Embedding to Avoid Hot Spots in Data Centers
Venue: arXiv cs.NI 2019
Year: 2019

Virtual Data Center (VDC) embedding has drawn significant attention recently because of growing need for efficient and flexible means of Data Center (DC) resource allocation. Existing studies on VDC embedding mainly focus on improving DCs' resource utilization. However, an important problem that has not been considered in VDC embedding solutions is the creation of hot spots by excessive heat dissipation and hot air generation from racks in DCs, which have significant adverse effect on energy consumption of the cooling system and IT equipment lifespan. To address this issue, we propose a temperature-aware VDC embedding scheme to avoid hot spots by minimizing the maximum temperature of hot air emitted from each rack. Meanwhile, we also aim to reduce the total power consumption of IT equipment in this scheme. A Mixed Integer Linear Programming (MILP) model and a heuristic algorithm are developed to implement the proposed VDC embedding scheme. Numerical results show that the proposed temperature-aware embedding scheme can significantly outperforms a load-balanced embedding scheme in terms of maximum rack temperature, total power consumption of IT equipment, and VDC rejection ratio.
An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP
Venue: arXiv cs.CV 2019
Year: 2019

Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP (DASH). Due to the frequent fluctuations of the network bandwidth and complex variations of video content, it is difficult to deal with the varying network conditions and video content perfectly by using a single rate adaptation method. In this paper, we propose an ensemble rate adaptation framework for DASH, which aims to leverage the advantages of multiple methods involved in the framework to improve the quality of experience (QoE) of users. The proposed framework is simple yet very effective. Specifically, the proposed framework is composed of two modules, i.e., the method pool and method controller. In the method pool, several rate adap tation methods are integrated. At each decision time, only the method that can achieve the best QoE is chosen to determine the bitrate of the requested video segment. Besides, we also propose two strategies for switching methods, i.e., InstAnt Method Switching, and InterMittent Method Switching, for the method controller to determine which method can provide the best QoEs. Simulation results demonstrate that, the proposed framework always achieves the highest QoE for the change of channel environment and video complexity, compared with state-of-the-art rate adaptation methods.
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Present
Venue: arXiv cs.SE 2019
Year: 2019

Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation.
URSA: Precise Capacity Planning and Contention-aware Scheduling for Public Clouds
Venue: arXiv cs.DC 2019
Year: 2019

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the "just-enough" resource specification that fulfills the performance requirement of every database workload is profitable. To this end, we propose URSA, a capacity planning and workload scheduling system for dbPaaS Clouds. USRA is comprised by an online capacity planner, a performance interference estimator, and a contention-aware scheduling engine. The capacity planner identifies the most cost-efficient resource specification for a database workload to achieve the required performance online. The interference estimator quantifies the pressure on the shared resource and the sensitivity to the shared resource contention of each database workload. The scheduling engine schedules the workloads across Cloud nodes carefully to eliminate unfair performance interference between the co-located workloads. Our real system experimental results show that URSA reduces 27.5% of CPU usage and 53.4% of memory usage for database workloads while satisfying their performance requirements. Meanwhile, URSA reduces the performance unfairness between the co-located workloads by 42.8% compared with the Kubernetes.
Evolutionary Clustering via Message Passing
Venue: arXiv cs.LG 2019
Year: 2019

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.