Confluence of aspects for sequence diagrams

The last decade has seen several aspect language proposals for UML 2 sequence diagrams. Aspects allow the modeler to define crosscutting concerns of sequence diagrams and to have these woven with the sequence diagrams of a so-called base model, in order to create a woven model. In a real-world scenario, there may be multiple aspects applicable to the same base model. This raises the need to analyse the set of aspects to identify possible aspect interactions (dependencies and conflicts) between applications of aspects. We call a set of aspects terminating if they may not be applied infinitely many times for any given base model. Furthermore, we call a set of terminating aspects confluent, if they, for any given base model, always yield the same final result regardless of the order in which they are applied. Since confluence must hold for any base model, this is a much stronger result than many of the current approaches that have addressed detection of aspect interactions limited to a specific base model. Our aspects are specified using standard sequence diagrams with some extensions. In this paper, we present a confluence theory specialized for our highly expressive aspect language. For the most expressive aspects, we prove that confluence is undecidable. For another class of aspects with considerable expressiveness, we prescribe an algorithm to check confluence. This algorithm is based on what we call an extended critical pair analysis. These results are useful both for modelers and researchers working with sequence diagram aspects and for researchers wanting to establish a confluence theory for other aspect-oriented modelling or model transformation approaches.     

SEL: A unified algorithm for salient entity linking

The entity linking task consists in automatically identifying and linking the entities mentioned in a text to their uniform resource identifiers in a given knowledge base. This task is very challenging due to its natural language ambiguity. However, not all the entities mentioned in the document have the same utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in the document, also known as salient entities (SE), is attracting increasing interest. In this paper, we propose salient entity linking, a novel supervised 2‐step algorithm comprehensively addressing both entity linking and saliency detection. The first step is aimed at identifying a set of candidate entities that are likely to be mentioned in the document. The second step, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on 2 different data sets show that the proposed algorithm outperforms state‐of‐the‐art competitors and is able to detect SE with high accuracy. Furthermore, we used salient entity linking for extractive text summarization. We found that entity saliency can be incorporated into text summarizers to extract salient sentences from text. The resulting summarizers outperform well‐known summarization systems, proving the importance of using the SE information.     

An Abstract Decision Procedure for Satisfiability in the Theory of Recursive Data Types

The theory of recursive data types is a valuable modeling tool for software verification. In the past, decision procedures have been proposed for both the full theory and its universal fragment. However, previous work has been limited in various ways. In this paper, we present a general algorithm for the universal fragment. The algorithm is presented declaratively as a set of abstract rules which are terminating, sound, and complete. We show how other algorithms can be realized as strategies within our general framework. Finally, we propose a new strategy and give experimental results showing that it performs well in practice.     

PROPOSITIONAL DYNAMIC LOGIC FOR REASONING ABOUT FIRST‐CLASS AGENT INTERACTION PROTOCOLS

For agents to fulfill their potential of being intelligent and adaptive, it is useful to model their interaction protocols as executable entities that can be referenced, inspected, composed, shared, and invoked between agents, all at runtime. We use the term first‐class protocol to refer to such protocols. Rather than having hard‐coded decision‐making mechanisms for choosing their next move, agents can inspect the protocol specification at runtime to do so, increasing their flexibility. In this article, we show that propositional dynamic logic (PDL) can be used to represent and reason about the outcomes of first‐class protocols. We define a proof system for PDL that permits reasoning about recursively defined protocols. The proof system is divided into two parts: one for reasoning about terminating protocols, and one for reasoning about nonterminating protocols. We prove that proofs about terminating protocols can be automated, while proofs about nonterminating protocols are unable to be automated in some cases. We prove that, for a restricted class of nonterminating protocols, proofs about them can be transformed to proofs about terminating protocols, making them automatable.     

Enhancing decentralized service discovery in open service-oriented multi-agent systems

Service-oriented multi-agent systems are dynamic systems that are populated by heterogeneous agents. These agents model their functionality as services in order to allow heterogeneous agents or other entities to interact with each other in a standardized way. Furthermore, due to the large-scale and adaptative needs of the system, traditional directory facilitators or middle-agents are not suitable for the management of agent services. This article proposes the introduction of homophily in service-oriented multi-agent systems to create efficient decentralized and self-organized structures where agents have a greater probability of establishing links with similar agents than with dissimilar ones. This similarity is based on two social dimensions: the set of services that an agent provides and the organizational roles that it plays. A second contribution is an algorithm for service discovery that it is carried out taking into account the local information that is related to the homophily between agents. The experiments compare our proposal with other proposals in distributed environments. The results show that the proposed structure and algorithm offer desirable features for service discovery in decentralized environments. Specifically, these features provide short paths and a high success rate in the service discovery process and resilience under deliberate failures.     

Trust Evolution Policies for Security in Collaborative Ad Hoc Applications

The vision of pervasive computing has introduced the notion of a vast, networked infrastructure of heterogeneous entities interact through collaborative applications, e.g., playing a multi-player online game on the way to work. This will require interactions between users who may be marginally known or completely unknown to each other, or in situations where complete information is unavailable. This introduces the problem of assigning access rights to such marginally known or unknown entities.Explicit trust management has emerged as a solution to the problem of dealing with partial information about other users and the context in which the interaction takes place. We have implemented an access control mechanism based on the human notion of trust, where recommendations or initial participation in low risk interactions will allow entities to slowly build trust in each other. As the trust between two entities grows, interactions that entail a higher degree of risk may be allowed to proceed. We have used this mechanism in a simple role-based access control mechanism that uses trust to assign roles to users in a distributed blackjack card game application. This application allows us to experiment with different policies for trust-based admission control and trust evolution. In this paper we present an evaluation of policies specifying trust dynamics, which shows that our prototype reacts appropriately to the behaviour of other users and that the system updates trust values and implements admission policies in a manner similar to what would be expected from human trust assessment. This indicates that trust evolution policies can replace explicit human intervention in application scenarios that are similar to the evaluated prototype.     

Annealed embeddings of communication patterns in an interconnectioncached network

The communication needs of many parallel applications exhibit what we call switching locality. In such applications, each computation entity (process, thread, etc.) tends to restrict its communication to a small set of other entities. The physical location or proximity of these entities can be arbitrary, as long as the communication degree is small. The Interconnection Cached Network (ICN) is a reconfigurable network ideally suited for exploiting such locality. The use of fast small crossbar switches (Interconnection Caches) with a larger, but slower, reconfigurable network (optimized for connectivity) lets the ICN adapt to the communication requirements of individual applications, potentially achieving higher performance. Embedding communication patterns efficiently in an ICN, requires finding a bounded l-contraction of the underlying communication graph. The problem of identifying whether a graph has a bounded and l-contraction for a given integer l is known to be NP-complete for l>2. We describe a heuristic algorithm based on simulated annealing for this problem. We test the effectiveness of our approach by using it to embed graphs, representing regular communication patterns, for which the best solutions are deterministically known. The algorithm does not rely on any structural information of the communication pattern and is therefore applicable to irregular patterns as well. The results of applying our heuristics to embed such irregular graphs are also presented     

Feature‐Preserving Reconstruction of Singular Surfaces

Reconstructing a surface mesh from a set of discrete point samples is a fundamental problem in geometric modeling. It becomes challenging in presence of ‘singularities’ such as boundaries, sharp features, and non‐manifolds. A few of the current research in reconstruction have addressed handling some of these singularities, but a unified approach to handle them all is missing. In this paper we allow the presence of various singularities by requiring that the sampled object is a collection of smooth surface patches with boundaries that can meet or intersect. Our algorithm first identifies and reconstructs the features where singularities occur. Next, it reconstructs the surface patches containing these feature curves. The identification and reconstruction of feature curves are achieved by a novel combination of the Gaussian weighted graph Laplacian and the Reeb graphs. The global reconstruction is achieved by a method akin to the well known Cocone reconstruction, but with weighted Delaunay triangulation that allows protecting the feature samples with balls. We provide various experimental results to demonstrate the effectiveness of our feature‐preserving singular surface reconstruction algorithm.     

Self-organizing feature maps for the vehicle routing problem with backhauls

In the Vehicle Routing Problem with Backhauls (VRPB), a central depot, a fleet of homogeneous vehicles, and a set of customers are given. The set of customers is divided into two subsets. The first (second) set of linehauls (backhauls) consists of customers with known quantity of goods to be delivered from (collected to) the depot. The VRPB objective is to design a set of minimum cost routes; originating and terminating at the central depot to service the set of customers. In this paper, we develop a self-organizing feature maps algorithm, which uses unsupervised competitive neural network concepts. The definition of the architecture of the neural network and its learning rule are the main contribution. The architecture consists of two types of chains: linehaul and backhaul chains. Linehaul chains interact exclusively with linehaul customers. Similarly, backhaul chains interact exclusively with backhaul customers. Additonal types of interactions are introduced in order to form feasible VRPB solution when the algorithm converges. The generated routes are then improved using the well-known 2-opt procedure. The implemented algorithm is compared with other approaches in the literature. The computational results are reported for standard benchmark test problems. They show that the proposed approach is competitive with the most efficient metaheuristics.     

Highly private blockchain-based management system for digital COVID-19 certificates

As a result of the declaration of the COVID-19 pandemic, several proposals of blockchain-based solutions for digital COVID-19 certificates have been presented. Considering that health data have high privacy requirements, a health data management system must fulfil several strict privacy and security requirements. On the one hand, confidentiality of the medical data must be assured, being the data owner (the patient) the actor that maintain control over the privacy of their certificates. On the other hand, the entities involved in the generation and validation of certificates must be supervised by a regulatory authority. This set of requirements are generally not achieved together in previous proposals. Moreover, it is required that a digital COVID-19 certificate management protocol provides an easy verification process and also strongly avoid the risk of forgery. In this paper we present the design and implementation of a protocol to manage digital COVID-19 certificates where individual users decide how to share their private data in a hierarchical system. In order to achieve this, we put together two different technologies: the use of a proxy re-encryption (PRE) service in conjunction with a blockchain-based protocol. Additionally, our protocol introduces an authority to control and regulate the centers that can generate digital COVID-19 certificates and offers two kinds of validation of certificates for registered and non-registered verification entities. Therefore, the paper achieves all the requirements, that is, data sovereignty, high privacy, forgery avoidance, regulation of entities, security and easy verification.

     

Fast Image‐Based Modeling of Astronomical Nebulae

Astronomical nebulae are among the most complex and visually appealing phenomena known outside the bounds of the Solar System. However, our fixed vantage point on Earth limits us to a single known view of these objects, and their intricate volumetric structure cannot be recovered directly. Recent approaches to reconstructing a volumetric 3D model use the approximate symmetry inherent to many nebulae, but require several hours of computation time on large multi‐GPU clusters. We present a novel reconstruction algorithm based on group sparsity that reaches or even exceeds the quality of prior results while taking only a fraction of the time on a conventional desktop PC, thereby enabling end users in planetariums or educational facilities to produce high‐quality content without expensive hardware or manual modeling. In principle, our approach can be generalized to other transparent phenomena with arbitrary types of user‐specified symmetries.     

Parallel quantum-inspired evolutionary algorithms for community detection in social networks

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers.In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization.In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs.The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.     

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

This paper aims at providing a fast near‐optimum solution to the multi‐mode resource‐constrained project scheduling problems (MRCPSPs), for projects with activities that have known deterministic renewable and nonrenewable resource requirements. The MRCPSP is known to be nondeterministic polynomial‐time hard and has been solved using various exact, heuristic, and meta‐heuristic procedures. In this paper, a modified variable neighborhood search heuristic algorithm is used as an advanced optimization technique that suits scheduling problems. For the experimental study, we have considered a standard set of 3929 multi‐mode benchmark instances from the project scheduling library with a range of projects comprising 10–30 activities. Moreover, for a better comparison, this research also considers a standard set of 4320 newly developed multi‐mode instances from MMLIB50, MMLIB100, and MMLIB+ datasets. With the limit of 50,000 schedules on these datasets, our proposed algorithm provides better makespan for 106, 34, and 1601 instances, respectively, which justifies the efficiency of the proposed algorithm, particularly for projects with a larger number of activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve.     

基于属性关系图的同名实体区分算法

同名问题在大规模的数据库或者数字化图书馆中普遍存在,且困扰着许多研究课题。本文首先提出一种新的图结构——属性关系图(ARG)形象地刻画实体特征及实体间的联系,并给出一种基于属性关系图框架的同名区分算法ARG-Resolution,对共享同一名字的作者进行分析,根据他们之间的相似度将其聚类,最终得到对应真正实体的各个结果聚类。实验证明挖掘作者间的潜在连接进一步提高了同名区分的质量,成功解决了同名问题。     

An innovative hybrid approach for extracting named entities from unstructured text data

Named entity recognition (NER) is the core part of information extraction that facilitates the automatic detection and classification of entities in natural language text into predefined categories, such as the names of persons, organizations, locations, and so on. The output of the NER task is crucial for many applications, including relation extraction, textual entailment, machine translation, information retrieval, etc. Literature shows that machine learning and deep learning approaches are the most widely used techniques for NER. However, for entity extraction, the abovementioned approaches demand the availability of a domain‐specific annotated data set. Our goal is to develop a hybrid NER system composed of rule‐based deep learning as well as clustering‐based approaches, which facilitates the extraction of generic entities (such as person, location, and organization) out of natural language texts of domains that lack generic named entities labeled domain data sets. The proposed approach takes the advantages of both deep learning and clustering approaches but separately, in combination with a knowledge‐based approach by using a postprocessing module. We evaluated the proposed methodology on court cases (judgments) as a use case since it contains generic named entities of different forms that are poorly or not present in open‐source NER data sets. We also evaluated our hybrid models on two benchmark data sets, namely, Computational Natural Language Learning (CoNLL) 2003 and Open Knowledge Extraction (OKE) 2016. The experimental results obtained from benchmark data sets show that our hybrid models achieved substantially better performance in terms of the F‐score in comparison to other competitive systems.     

A learn‐and‐construct framework for general mixed‐integer programming problems

In this paper, we propose a new framework for finding an initial feasible solution from a mixed‐integer programming (MIP) model. We call it learn‐and‐construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub‐MIPs that are solved by using a black box solver. Computational results on three well‐known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state‐of‐the‐art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.     

Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video

This paper presents a novel trajectory-based detection and tracking algorithm for locating the ball in broadcast soccer video (BSV). The problem of ball detection and tracking in BSV is well known to be very challenging because of the wide variation in the appearance of the ball over frames. Direct detection algorithms do not work well because the image of the ball may be distorted due to the high speed of the ball, occlusion, or merging with other objects in the frame. To overcome these challenges, we propose a two-phase trajectory-based algorithm in which we first generate a set of ball-candidates for each frame, and then use them to compute the set of ball trajectories. Informally, the two key ideas behind our strategy are 1) while it is very challenging to achieve high accuracy in locating the precise location of the ball, it is relatively easy to achieve very high accuracy in locating the ball among a set of ball-like candidates and 2) it is much better to study the trajectory information of the ball since the ball is the "most active" object in the BSV. Once the ball trajectories are computed, the ball locations can be reliably recovered from them. One important advantage of our algorithm is that it is able to reliably detect partially occluded or merged balls in the sequence. Two videos from the 2002 FIFA World Cup were used to evaluate our algorithm. It achieves a high accuracy of about 81% for ball location     

Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization

In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.