Knowledge Of Application Domain Research Articles

A Model-Based Approach to Mobile Application Testing

Abstract Modeling the automated testing of mobile applications is a crucial aspect of mobile application automation testing. Due to the varied styles and complex interactions of mobile applications, automated modeling methods are urgently required, particularly in the context of their short development cycles, large numbers, and fast version iterations and updates. In this paper, we address the challenge of exploring mobile application behavior and state based on robotic testing environment without invading the application interior, and propose a method for automated exploration of GUI components and GUI events of applications combined with application domain knowledge to generate mobile application GUI semantic test models. Our results show that the proposed semantic model achieves 70.6% and 82.4% defect detection rate in the robot vision environment and simulation environment, respectively. Compared with the comparative testing method that can only find application crash defects, our method can explore both crash defects and functional anomalies with the application semantic understanding and domain knowledge, thereby extending the automated mobile application functional testing capability of mobile applications. In response to the limitations of mobile application automated testing modeling mentioned above, this paper introduces an automated testing method based on semantic models. It uses the proposed semantic testing model to guide the purposeful exploration of the tested application’s states. Subsequently, it generates positive and negative test cases based on the domain knowledge associated with the semantic model. This modeling approach leverages domain models in the mobile application field to conduct automated modeling tests imbued with functional significance, guided by domain knowledge. This optimization aims to address the shortcomings of current automated testing, particularly in terms of model reuse and test expansion.

ML-FaaS: Toward Exploiting the Serverless Paradigm to Facilitate Machine Learning Functions as a Service

Serverless computing has emerged as a revolutionary model that enables the deployment of applications and services by raising the level of abstraction from the underline resources. Its main functionality is enlightened by the notion of Function-asa-Service (FaaS) as the core means to realize efficient serverless offerings. Following the shift from traditional architectures to microservices -by attaining flexibility, productivity, portability, and performance in industrial-scale IT projects, the serverless model introduces even more fine-grained services, named “nanoservices”, which facilitate required scalability by abstracting the deployment and management of the infrastructure resources. On the application space, advances in big data analysis contribute towards extracting actionable knowledge in various application domains. In this context, approaches for big data analysis aim at exploiting the added value of serverless architectures. To this end, we are presenting an extendable and generalized approach for facilitating the provision of Machine Learning Functions-as-a-Service (MLFaaS). The proposed approach outstrips the classical atomic and standard isolated services by facilitating composite services, i.e., workflows/pipelines of ML tasks, thus enabling the realization of the complete data path functions as required by data scientists. We demonstrate the operation of the proposed approach by modeling a real-world analytics scenario as an ML workflow pipeline and evaluate its performance in terms of performance. Furthermore, we address the challenge of utilizing a function oriented service template recommendation system, by expanding the serverless functional boundaries towards a holistic Quality-of-Service (QoS)-aware service function selection approach based on Artificial Intelligence techniques. These techniques propose the optimal number of functions to be implemented in a pipeline by exploiting the importance of response time as the primary key of the application’s performance.

Rule-based semi-automated tools for mapping seabed morphology from bathymetry data

Seabed morphology maps and data are critical for knowledge-building and best practice management of marine environments. To facilitate objective and repeatable production of these maps, we have developed a number of semi-automated, rule-based GIS tools (Geoscience Australia’s Semi-automated Morphological Mapping Tools - GA-SaMMT) to operationalise the mapping of a common set of bathymetric high and bathymetric low seabed Morphological Features. The tools have a graphical user interface and were developed using Python scripts under the widely-adopted proprietary ArcGIS Pro platform. The utility of these tools was tested across nine case study areas that represent a diverse range of complex bathymetric and physiographic settings. Overall, the mapping results are found to be more consistent than manual mapping and allow for capture of greater detail across a range of spatial scales. The mapping results demonstrate a number of advantages of GA-SaMMT, including: 1) requirement of only a bathymetry grid as sole data input; 2) flexibility to apply domain knowledge to user-defined tool parameters, or to instead use the default parameter settings; 3) repeatability and consistency in the mapping outputs when using a consistent set of tool parameters (user defined or default); 4) high-degree of objectivity; and 5) efficiency in mapping a large number (thousands) of seabed morphology features in a single dataset. In addition, GA-SaMMT can comprehensively quantify the characteristics of individual seabed bathymetric high and low features, respectively generating 34 and 46 metrics for each type of feature. Our results indicate that attribute metrics are invaluable in the interpretation and modelling of mapped Morphology Features and provide insights into their formative processes and habitat potential for marine communities.

Feature on Teaching and Technology: Teaching MBA Students Business Report Writing Using Social Media Technologies

Data-driven decision making has now moved beyond its traditional domains—operations research, business economics, computer sciences, and business statistics—to “softer subjects,” such as human resource management, organization behavior, and business communication. In this context, teaching with technology encourages students to systematically apply domain knowledge to communicate across a wide variety of stakeholders. In the era of multimodal forms of communication and multiple data sources, management students must be analytical when writing compelling reports and giving persuasive presentations. They should be well versed in using both quantitative and qualitative techniques for report writing and presentation. Drawing on authentic user-generated comments on social media, this article presents two case studies on (a) crisis communication by 30 CEOs and (b) culture shock experienced by foreign tourists sojourning in India, China, and the United Arab Emirates, to demonstrate how master’s in business administration (MBA) students could derive insights from the online comments to make strategic decisions for organizational benefit and make reports based on those findings. The article asserts that this could help to cultivate a data-analytic mindset among the students by preparing them to communicate small (and big) data-driven analysis to relevant stakeholders. It attempts to suggest ways to develop MBA students’ ability to analyze their potential audiences as well as to generate meaningful insights from the available information on social media websites. Finally, it hopes to nudge business communication instructors to embrace multidisciplinary perspectives for planning a technology-based business communication assignment involving the social media landscape. Instructors can not only use the two case studies to illustrate ways to integrate technology with teaching but also create their own mini cases to improve the decision-making, report-writing, and business report presentation skills of their students.

Memetic Algorithms for Spatial Partitioning Problems

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called s warm-based s p atial meme ti c al gorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.

Deep reinforcement learning for transit signal priority in a connected environment

Transit signal priority (TSP) is an effective measure to reduce traffic congestion and improve bus efficiency in metropolises. The connected vehicle technology and reinforcement learning (RL) algorithms can respectively provide more detailed and accurate information and more robust algorithms to traffic signal control systems, to develop smarter TSP strategies. This paper proposes an extended Dueling Double Deep Q-learning with invalid action masking (eD3QNI) algorithm for TSP strategy in a connected environment. The algorithm considers multiple conflicting bus priority requests and the constraints on the traffic light and phase skipping rule, aiming to improve the person delay of buses. Its performance is evaluated by simulation for a single intersection with two traffic demands and random arrivals, schedule deviations, occupancies of buses. Results demonstrate that eD3QNI produces lower average person delay and schedule delay than fixed-time signal, active TSP strategies, and other common RL methods. It also shows that the invalid action masking (IAM) method is superior to the usual variable decision points (VDP) method in terms of high convergence speed, effective performance improvement, and application of domain knowledge on the RL algorithm. The penetration rates of connected buses do not affect the converging speed of the proposed method, and an environment with a higher penetration rate will show better performance. Moreover, under the proposed method, different specific reward functions can be incorporated as desired to realize different operational goals for the TSP strategies.

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved by the deep learning (DL) models. With this motivation, this paper presents an intelligent deep learning-based multi-retinal disease diagnosis (IDL-MRDD) framework using fundus images. The proposed model aims to classify the color fundus images into different classes namely AMD, DR, Glaucoma, Hypertensive Retinopathy, Normal, Others, and Pathological Myopia. Besides, the artificial flora algorithm with Shannon’s function (AFA-SF) based multi-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected. In addition, SqueezeNet based feature extractor is employed to generate a collection of feature vectors. Finally, the stacked sparse Autoencoder (SSAE) model is applied as a classifier to distinguish the input images into distinct retinal diseases. The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset, comprising data instances from different classes. The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

Constellation reconfiguration is a critical issue to recover from the satellite failure, maintain the regular operation, and enhance the overall performance. The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size. To solve such real-world problems that can be hardly solved by traditional algorithms, the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space. An adaptive innovationdriven multi-objective evolutionary algorithm (MOEA-AI) employing automated innovation (AI) and adaptive operator selection (AOS) is proposed to extract and apply domain knowledge. The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism. To prevent the overuse of knowledge-dependent operators, AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators. It evaluates and selects operators according to their actual performance, which helps to identify useful operators from the candidate set. The efficacy of the MOEA-AI framework is demonstrated by the simulation of emergency missions. It was verified that the proposed algorithm can discover a non-dominant solution set with better quality, more homogeneous distribution, and better adaptation to practical situations.

Efficient Semantic Enrichment Process for Spatiotemporal Trajectories

The increasing availability of location-acquisition technologies has enabled collecting large-scale spatiotemporal trajectories, from which we can derive semantic information in urban environments, including location, time, direction, speed, and point of interest. Such semantic information can give us a semantic interpretation of movement behaviors of moving objects. However, existing semantic enrichment process approaches, which can produce semantic trajectories, are generally time-consuming. In this paper, we propose an efficient semantic enrichment process framework to annotate spatiotemporal trajectories by using geographic and application domain knowledge. The framework mainly includes preannotated semantic trajectory storage phase, spatiotemporal similarity measurement phase, and semantic information matching phase. Having observed the common trajectories in the same geospatial object scenes, we propose a semantic information matching algorithm to match semantic information in preannotated semantic trajectories to new spatiotemporal trajectories. In order to improve the efficiency of this approach, we build a spatial index to enhance the preannotated semantic trajectories. Finally, the experimental results based on a real dataset demonstrate the effectiveness and efficiency of our proposed approaches.

Weakly-Supervised Opinion Summarization by Leveraging External Information

Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previous works require additional human effort to identify relevant aspects, we instead apply domain knowledge from external sources to automatically achieve the same goal. This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge. This explicit memory can help obtain a better opinion representation and infer the aspect information more precisely. We evaluate this method on both aspect identification and opinion summarization tasks. Our experiments show that AspMem outperforms the state-of-the-art methods even though, unlike the baselines, it does not rely on human supervision which is carefully handcrafted for the given tasks.

TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.

Reducing uncertainty and obtaining superior performance by segmentation based on algebraic inequalities

The paper demonstrates for the first time uncertainty reduction and attaining superior performance through segmentation based on algebraic inequalities. Meaningful interpretation of algebraic inequalities has been used for generating new knowledge in unrelated application domains. Thus, the method of segmentation through an abstract inequality led to a new theorem related to electrical circuits. The power output from a source with particular voltage, on elements connected in series, is smaller than the total power output from the segmented sources applied to the individual elements. Segmentation attained through the same abstract inequality led to another new theorem related to electrical capacitors. The energy stored by a charge of given size on a single capacitor is smaller than the total energy stored in multiple capacitors with the same equivalent capacity, by segmenting the initial charge over the separate capacitors. Finally, inequalities based on sub-additive and super-additive functions have been introduced for reducing uncertainty and obtaining superior performance by a segmentation or aggregation of controlling factors. By a meaningful interpretation of sub-additive and super-additive inequalities, superior performance has been achieved for processes described by a power-law dependence.

Interoperability of disparate engineering domain ontologies using basic formal ontology

ABSTRACTAs engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasoning and semantic querying. Unfortunately, considerable barriers hinder wider deployment of ontologies in engineering. Key among these is lack of a shared top-level ontology to unify and organise disparate aspects of the field and coordinate co-development of orthogonal ontologies. As a result, many engineering ontologies are limited to their scope, and functionally difficult to extend or interoperate with other engineering ontologies. This paper demonstrates how the use of a top-level ontology, specifically the Basic Formal Ontology (BFO), greatly facilitates interoperability of multiple engineering-related ontologies. We constructed a system of formal linked ontologies by re-engineering legacy ontologies to be conformant with BFO and developing new BFO-conformant ontologies to capture knowledge in the engineering design, enterprise, human factors, manufacturing, and application domain of additive manufacturing. The resulting Integrated Framework for Additively Manufactured Products (IFAMP), including the body knowledge instantiated on its basis, serve as the basis for a proposed Design with Additive Manufacturing Method (DWAM), which we believe can support the design of innovative products with semantically enhanced ideation tools and enhanced access to application domain knowledge. The method and its facilitation through the ontological framework are demonstrated using a case study in medicine.

A hybrid approach for identifying the structure of a Bayesian network model

Bayesian network (BN) has been widely used in modeling expert knowledge and reasoning in many application domains, due to its power of representing probabilistic knowledge over a set of interacting variables under significant uncertain. However, the actual construction of a Bayesian network model for a domain remains to be a challenging task since it involves learning the causal structure and the conditional probability table of BN variables from experts, and/or observational data. In this paper, we explore the unique challenge of applying Bayesian network to model how public safety officials represent and reason about threats of evolving mass protest. Anticipating threats of mass protests is crucial for choosing proper intervention strategies, but it is also inherently difficult because a large number of interacting factors contribute to the dynamics of protests with high uncertainty and contingency. In constructing the BN model for this complex domain, we found that traditional methods of discovering BN structure either from data or experts are inadequate, due to the scarcity of data and the highly stochastic nature of mass protest events. Instead, we proposed a hybrid approach (called “ISM-K2”) which enhances the BN structure learning methods (K2 algorithm) by expert knowledge elicited using the ISM (interpretive structural model) method. We show that the BN model constructed using ISM-K2 approach is superior than three other base-line models (the logistic regression model, the BN constructed only by expert knowledge from ISM, and the BN constructed only by data learning). Finally, we show the potential of using our BN threat assessment model for supporting practical policing decisions.

ProXray: Protocol Model Learning and Guided Firmware Analysis

The number of Internet of Things (IoT) has reached 7 billion globally in early 2018 and are nearly ubiquitous in daily life. Knowing whether or not these devices are safe and secure to use is becoming critical. IoT devices usually implement communication protocols such as USB and Bluetooth within firmware to allow a wide range of functionality. Thus analyzing firmware using domain knowledge from these protocols is vital to understand device behavior, detect implementation bugs, and identify malicious components. Unfortunately, due to the complexity of these protocols, there is usually no formal specification available that can help automate the firmware analysis; as a result significant manual effort is currently required to study these protocols and to reverse engineer the device firmware. In this paper, we propose a new firmware analysis methodology using symbolic execution called ProXray, which can learn a protocol model from known firmware, and apply the model to recognize the protocol relevant fields and detect functionality within unknown firmware automatically. After the training phase, ProXray can fully automate the firmware analysis process while supporting user’s queries in the form of protocol relevant constraints. We have applied ProXray to the USB and the Bluetooth protocols by learning protocol constraint models from firmware that implement these protocols. We are then able to map protocol fields and identify USB functionality automatically within all 6 unknown USB firmware while achieving more than an order of magnitude speedup in reaching protocol relevant targets in unknown Bluetooth firmware. Our model achieved high coverage of the USB and Bluetooth specifications for several important protocol fields. ProXray provides a new method to apply domain knowledge to firmware analysis automatically.

Supplement to the Article: The Role of Application Domain Knowledge in Understanding Supply Chain Process Models

This document provides additional materials and data analysis that complement the laboratory experiment described in the following article: Leukel, J.; Hubl, M. 2018. “The Role of Application Domain Knowledge in Understanding Supply Chain Process Models”. This supplement consists of the following parts: 1. Participants background, 2. Modeling knowledge, 3. Comprehension tasks, 4. Schema-based problem-solving tasks, 5. Modeling grammar. The purpose of the supplement is to enable researchers to fully replicate our experiment.

A parallel computing architecture for high-performance OWL reasoning

Abstract The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.

MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks

Multimedia Sensor Networks (MSNs) have gained much attention in recent years from the emerging trends of Internet of Things (IoT). They can be found in different scenarios in our everyday life (e.g., smart homes, smart buildings). Sensors in MSNs can have different capacities, produce multiple kinds of outputs, and have different output encoding formats. Thus, detecting complex events, which requires the aggregation of several sensor readings, can be difficult due to the lack of a generic model that can describe: (i) sensor networks infrastructure, (ii) individual sensor specificities, as well as (iii) multimedia data, while allowing the alignment with the application domain knowledge. In this study, we propose Multimedia Semantic Sensor Network Ontology (MSSN-Onto) to ensure MSNs modeling and provide both syntactic and semantic data interoperability for defining and detecting events in various domains. To show the readiness of MSSN-Onto, we used it as the core ontology of a dedicated framework (briefly defined here). We also adopted MSSN-Onto in HIT2GAP European Project. A prototype has been implemented to conduct a set of tests. Experimental results show that MSSN-Onto can be used to: (i) effectively model MSNs and multimedia data; (ii) define complex events; and (iii) allow to build an efficient event querying engine for MSNs.

Understanding the complexity of axiom pinpointing in lightweight description logics

Lightweight description logics are knowledge representation formalisms characterised by the low complexity of their standard reasoning tasks. They have been successfully employed for constructing large ontologies that model domain knowledge in several different practical applications. In order to maintain these ontologies, it is often necessary to detect the axioms that cause a given consequence. This task is commonly known as axiom pinpointing.In this paper, we provide a thorough analysis of the complexity of several decision, counting, and enumeration problems associated to axiom pinpointing in lightweight description logics.

An efficient semi-supervised community detection framework in social networks.

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.