scholarly journals On Transparency and Accountability of Smart Assistants in Smart Cities

2019 ◽  
Vol 9 (24) ◽  
pp. 5344 ◽  
Author(s):  
Haroon Elahi ◽  
Guojun Wang ◽  
Tao Peng ◽  
Jianer Chen
Keyword(s):  
Smart City ◽  
Smart Home ◽  
State Of The Art ◽  
Smart Cities ◽  
Application Programming Interface ◽  
Separation Of Concerns ◽  
Privacy Requirements ◽  
Application Programming ◽  
Programming Interface ◽  
Transparency And Accountability

Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

Recommending Relevant Tutorial Fragments for API-Related Natural Language Questions

2021 ◽  
Vol 31 (09) ◽  
pp. 1251-1275
Author(s):  
Di Wu ◽  
Xiao-Yuan Jing ◽  
Haowen Chen ◽  
Xiaohui Kong ◽  
Jifeng Xuan
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Metric Learning ◽  
Application Programming Interface ◽  
Manual Annotation ◽  
Candidate List ◽  
Novel Approach ◽  
Application Programming ◽  
Programming Interface ◽  
Reciprocal Rank

Application Programming Interface (API) tutorial is an important API learning resource. To help developers learn APIs, an API tutorial is often split into a number of consecutive units that describe the same topic (i.e. tutorial fragment). We regard a tutorial fragment explaining an API as a relevant fragment of the API. Automatically recommending relevant tutorial fragments can help developers learn how to use an API. However, existing approaches often employ supervised or unsupervised manner to recommend relevant fragments, which suffers from much manual annotation effort or inaccurate recommended results. Furthermore, these approaches only support developers to input exact API names. In practice, developers often do not know which APIs to use so that they are more likely to use natural language to describe API-related questions. In this paper, we propose a novel approach, called Tutorial Fragment Recommendation (TuFraRec), to effectively recommend relevant tutorial fragments for API-related natural language questions, without much manual annotation effort. For an API tutorial, we split it into fragments and extract APIs from each fragment to build API-fragment pairs. Given a question, TuFraRec first generates several clarification APIs that are related to the question. We use clarification APIs and API-fragment pairs to construct candidate API-fragment pairs. Then, we design a semi-supervised metric learning (SML)-based model to find relevant API-fragment pairs from the candidate list, which can work well with a few labeled API-fragment pairs and a large number of unlabeled API-fragment pairs. In this way, the manual effort for labeling the relevance of API-fragment pairs can be reduced. Finally, we sort and recommend relevant API-fragment pairs based on the recommended strategy. We evaluate TuFraRec on 200 API-related natural language questions and two public tutorial datasets (Java and Android). The results demonstrate that on average TuFraRec improves NDCG@5 by 0.06 and 0.09, and improves Mean Reciprocal Rank (MRR) by 0.07 and 0.09 on two tutorial datasets as compared with the state-of-the-art approach.

scholarly journals Ransomware Detection System for Android Applications

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 868 ◽  
Author(s):  
Samah Alsoghyer ◽  
Iman Almomani
Keyword(s):  
State Of The Art ◽  
Detection System ◽  
Virus Detection ◽  
Application Programming Interface ◽  
Research Community ◽  
Classification Model ◽  
Application Programming ◽  
Android Applications ◽  
Programming Interface ◽  
Unique Dataset

Android ransomware is one of the most threatening attacks nowadays. Ransomware in general encrypts or locks the files on the victim’s device and requests a payment in order to recover them. The available technologies are not enough as new ransomwares employ a combination of techniques to evade anti-virus detection. Moreover, the literature counts only a few studies that have proposed static and/or dynamic approaches to detect Android ransomware in particular. Additionally, there are plenty of open-source malware datasets; however, the research community is still lacking ransomware datasets. In this paper, the state-of-the-art of Android ransomware detection approaches were investigated. A deep comparative analysis was conducted which shed the key differences among the existing solutions. An application programming interface (API)-based ransomware detection system (API-RDS) was proposed to provide a static analysis paradigm for detecting Android ransomware apps. API-RDS focuses on examining API packages’ calls as leading indicator of ransomware activity to discriminate ransomware with high accuracy before it harms the user’s device. API packages’ calls of both benign and ransomware apps were thoroughly analyzed and compared. Significant API packages with corresponding methods were identified. The experimental results show that API-RDS outperformed other recent related approaches. API-RDS achieved 97% accuracy while reducing the complexity of the classification model by 26% due to features reduction. Moreover, this research designed a proactive mechanism based on a high quality unique ransomware dataset without duplicated samples. 2959 ransomware samples were collected, tested and reduced by almost 83% due to samples duplication. This research also contributes to constructing an up-to-date, unique dataset that covers the majority of existing Android ransomware families and recent clean apps that could be used as a labeled reference for research community.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

scholarly journals Advanced soft robot modeling in ChainQueen

Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik
Keyword(s):  
Application Programming Interface ◽  
Material Point ◽  
Soft Robotics ◽  
Soft Robot ◽  
Inference Control ◽  
Simulation And Optimization ◽  
Efficient Simulation ◽  
Application Programming ◽  
Robot Modeling ◽  
Programming Interface

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

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