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Gateways in the Internet of Things (IoT) play a decisive role in routing the preprocessed filtered data to cloud platforms. While investigating gateways, the need for a comprehensive systematic literature review that can elaborate the working and functionalities of IoT gateways was felt. This paper presents a systematic literature review of IoT gateways in general and smart gateways in particular. It considered papers published over the last 10 years, i.e., from 2011 till July 2021. A methodical literature analysis technique is used in this review; out of 2347 papers, 67 articles are selected for complete analysis based on well-defined criteria. The survey starts with the broad categorization of gateways in IoT as basic and smart gateways. Further, smart gateways are sub-divided into three categories as passive gateways, semi-automated and fully-automated gateways. The survey is performed based on well-defined criteria, which include: the type of gateways, their requirements, tools/platforms, the approach adopted, evaluation and their application domain. This paper demonstrates further bifurcation of functional requirements of IoT gateways. Research gaps and open issues have been identified in the area. Future prospects have accordingly being suggested to help academicians embark on advanced research.

Estimating the cost and schedule of agile software projects is critical at an early phase to establish baseline budgets and schedules for the selection of competitive bidders. The challenge is that common agile sizing measures such as story points and user stories are not practical for early estimation as these are often reported after contract award in DoD. This study provides a set of effort and schedule estimation models for agile projects using a sizing measure that is available before proposal evaluation based on data from 36 DoD agile projects. The results suggest that initial software requirements, defined as the sum of functions and external interfaces, is an effective sizing measure for early estimation of effort and schedule of agile projects. The models accuracy improves when application domain groups and peak staff are added as inputs.

One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, Complex Event Processing (CEP), used in conjunction with an Enterprise Service Bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using Flow-Based Programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this paper, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.

Refactoring detection is crucial for a variety of applications and tasks: (i) empirical studies about code evolution, (ii) tools for library API migration, (iii) code reviews and change comprehension. However, recent research has questioned the accuracy of the state-of-the-art refactoring mining tools, which poses threats to the reliability of the detected refactorings. Moreover, the majority of refactoring mining tools depend on code similarity thresholds. Finding universal threshold values that can work well for all projects, regardless of their architectural style, application domain, and development practices is extremely challenging. Therefore, in a previous work [1], we introduced the first refactoring mining tool that does not require any code similarity thresholds to operate. In this work, we extend our tool to support low-level refactorings that take place within the body of methods. To evaluate our tool, we created one of the most accurate, complete, and representative refactoring oracles to date, including 7,223 true instances for 40 different refactoring types detected by one (minimum) up to six (maximum) different tools, and validated by one up to four refactoring experts. Our evaluation showed that our approach achieves the highest average precision (99.6%) and recall (94%) among all competitive tools, and on median is 2.6 times faster than the second faster competitive tool.

Friction stir welding (FSW) is a new solid-state welding technique used to join similar and dissimilar materials, especially aluminium alloys. Friction stir (FS) welding proposes numerous benefits compared to conventional fusion welding techniques and can be successfully applied in shipbuilding, aerospace, automobile, and railway industries. This paper gives a comprehensive review of the FSW process and its technical aspects. The effects of tool pin profiles, feed rate, tool tilt angle and welding speed, etc., and mechanical and metallurgical behavior of FS welded joints have been reviewed in this article. In addition, this paper covers the literature review of friction stir welding of similar and dissimilar aluminum alloys (AA), optimization techniques, welding defects, and fatigue behavior of FS welded joints. Furthermore, a summarized report of work done by many researchers in friction stir welding is also presented. Finally, it also identifies the future scope, applications, and research gap.

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. The case for Android apps is not an exception. Therefore, in this paper we describe the process we followed to create (i) a taxonomy of mutation operations and, (ii) two tools, MDroid+ and MutAPK for mutant generation of Android apps. To this end, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 software artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented them in two tools, the first enabling mutant generation at the source code level, and the second designed to perform mutations at APK level. The rationale for having a dual-approach is based on the fact that source code is not always available when conducting mutation testing. Thus, mutation testing for APKs enables new scenarios in which researchers/practitioners only have access to APK files. The taxonomy, proposed operators, and tools have been evaluated in terms of the number of non-compilable, trivial, equivalent, and duplicate mutants generated and their capacity to represent real faults in Android apps as compared to other well-known mutation tools.

Cyber-physical systems (CPSs) are part of most critical infrastructures such as industrial automation and transportation systems. Thus, security incidents targeting CPSs can have disruptive consequences to assets and people. As prior incidents tend to re-occur, sharing knowledge about these incidents can help organizations be more prepared to prevent, mitigate or investigate future incidents. This paper proposes a novel approach to enable representation and sharing of knowledge about CPS incidents across different organizations. To support sharing, we represent incident knowledge (incident patterns) capturing incident characteristics that can manifest again, such as incident activities or vulnerabilities exploited by offenders. Incident patterns are a more abstract representation of specific incident instances and, thus, are general enough to be applicable to various systems - different than the one in which the incident occurred. They can also avoid disclosing potentially sensitive information about an organization's assets and resources. We provide an automated technique to extract an incident pattern from a specific incident instance. To understand how an incident pattern can manifest again in other cyber-physical systems, we also provide an automated technique to instantiate incident patterns to specific systems. We demonstrate the feasibility of our approach in the application domain of smart buildings. We evaluate correctness, scalability, and performance using two substantive scenarios inspired by real-world systems and incidents.

Most existing formation control approaches for Unmanned Aerial Vehicle (UAV) swarm assume that global position and global coordinate frame are directly available for each agent. To extend the application domain, this paper proposes a distributed bearing-based formation control scheme, without any reliance on global position or global coordinate frame. The interactions among UAVs are described by a directed topology with two-leader structure. To address the issue of unavailable global coordinate frame, we first present a distributed orientation estimation law for each UAV to determine its orientation under the coordinate frame of the first leader. Based on the orientation estimation, we then design a bearing-based formation control law to globally asymptotically track target moving formations. Finally, simulation results are provided to validate the proposed method, which show that the translation, scale and orientation of the formation can be flexibly controlled via two leaders.

A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes.The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. The ML based techniques do not follow the conventional method based on identification of acoustic properties. Rather, ML techniques build their own trained model (algorithm) using readily available data. They do so by finding out the hidden patterns in speech signals, and acquire predictive intelligence through learning. Therefore, ML techniques can be said to provide a more generalized model for phoneme classification. In this paper, we present a comprehensive survey of ML tools to build phoneme recognizers. We also highlight some applications of speech (especially phoneme) recognition which illustrate the current scope as well as future prospects of APR.

The concept of atmospheric cues has been developed in environmental psychology to denote physical attractions from the natural habitat. This research adopts the idea with an application in the travel application domain. Guided by the stimulus–organism–response (S–O–R) paradigm and media richness theory, we synthesize an integrated model to assess the effect of a travel app's atmospheric cues on a sequence of tourist emotional and behavioral responses. Empirical evidence presented in this research illuminates an important serial mediating process of flow experience, perceived usefulness, perceived enjoyment, and attitude on both app reusage and site revisit intentions. This research concludes with theoretical and practical implications to point to the critical role of advanced atmospheric capabilities of travel apps in inducing favorable emotional and behavioral responses.

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis.

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy.

The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodology, This work was partially supported by the Spanish Government [grant number RTI2018-095255-B-I00]; and the Madrid Research Council [Grant Number P2018/TCS-4314]

Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.

Proceedings of the IEEE 109(9), 1463-1493 (2021). doi:10.1109/JPROC.2020.3043399, Published by Institute of Electrical and Electronics Engineers, New York, NY [u.a.]