Disparity between the Programmatic Views and the User Perceptions of Mobile Apps

User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty - B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives

Trust in Vehicle-to-Vehicle Communication

In traditional Pedestrian Automatic Emergency Braking (PAEB) system, vehicles equipped with onboard sensors such as radar, camera, and infrared detect pedestrians, alert the driver and/ or automatically take actions to prevent vehicle-pedestrian collision. In some situations, a vehicle may not be able to detect a pedestrian due to blind spots. Such a vehicle could benefit from the sensor data from neighboring vehicles in making such safety critical decisions. We propose a trust model for ensuring shared data are valid and trustworthy for use in making safety critical decisions. Simulation results of the proposed trust model show promise

Parallel Methods for Evidence and Trust based Selection and Recommendation of Software Apps from Online Marketplaces

With the popularity of various online software marketplaces, third-party vendors are creating many instances of software applications ('apps') for mobile and desktop devices targeting the same set of requirements. This abundance makes the task of selecting and recommending (S&R) apps, with a high degree of assurance, for a specific scenario a significant challenge. The S&R process is a precursor for composing any trusted system made out of such individually selected apps. In addition to feature-based information, about these apps, these marketplaces contain large volumes of user reviews. These reviews contain unstructured user sentiments about app features and the onus of using these reviews in the S&R process is put on the user. This approach is ad-hoc, laborious and typically leads to a superficial incorporation of the reviews in the S&R process by the users. However, due to the large volumes of such reviews and associated computing, these two techniques are not able to provide expected results in real-time or near real-time. Therefore, in this paper, we present two parallel versions (i.e., batch processing and stream processing) of these algorithms and empirically validate their performance using publically available datasets from the Amazon and Android marketplaces. The results of our study show that these parallel versions achieve near real-time performance, when measured as the end-to-end response time, while selecting and recommending apps for specific queries

A Holistic Ranking Scheme for Apps

App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view

Cyberbullying Detection System with Multiple Server Configurations

Due to the proliferation of online networking, friendships and relationships - social communications have reached a whole new level. As a result of this scenario, there is an increasing evidence that social applications are frequently used for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. To encounter this problem, we have designed a distributed cyberbullying detection system that will detect bullying messages and drop them before they are sent to the intended receiver. A prototype has been created using the principles of NLP, Machine Learning and Distributed Systems. Preliminary studies conducted with it, indicate a strong promise of our approach

Heuristic Based Sensor Ranking Algorithm for Indoor Tracking Applications

poster abstractLocation awareness in an indoor setup is an important function necessary in many application domains such as asset management, critical care, and augmented reality. Location awareness, or tracking, of an object within an indoor setting requires a high degree of accuracy, as room-to-room location may be very important. With the current proliferation of smart devices, with often a multitude of built-in sensors, and inexpensive sensors it is now possible to build a network of sensors, for the purpose of tracking, within an indoor environment without the high cost of installing the needed tracking infrastructure. In an effort to increase accuracy, as well as coverage area, various different sensors may be used in the tracking of an object. In this heterogeneous tracking situation, it is important for the tracking infrastructure to quickly and accurately decide which, all or a subset, of available sensors to use. Challenges related to heterogeneous data fusion and clock synchronization, must be addressed in order to provide accurate location estimates. We have proposed a heuristic based ranking algorithm to address these challenges. In this algorithm, the individual sensors are ranked based upon their quality of service (QoS) attributes and the resulting ranking is used by a filtering service during the sensor selection process. This information is provided to the filtering service when a sensor joins the tracking infrastructure and is subsequently only updated during idle periods, thereby, there avoiding additional overhead. We have implemented this algorithm into the existing prototypical Enhanced Distributed Object Tracking System or e-DOTS. e-DOTS has been extensively experimented with and the results of these experimentation validate the hypothesis that accurate indoor tracking can be achieved using a heterogeneous ensemble of cheap and mobile sensors. Our current investigation involves the incorporation of trust associated with sensors and deploying e-DOTS in a typical healthcare setup

Multilingual Cyberbullying Detection System

As the use of social media has evolved in recent times, so has the ability to cyberbully victims using it. The last decade has witnessed a surge of cyberbullying-this bullying is not only limited to English but also happens in other languages. A large number of mobile device users are in Asian countries such as India. Such a large audience is a fertile ground for cyberbullies -hence, it is very important to detect cyberbullying in multiple languages. Most of the current approaches to identify cyberbullying are focused on English text, and a very few approaches are venturing into other languages. This paper proposes a Multilingual Cyberbullying Detection System for detection of cyberbullying in two Indian languages- Hindi and Marathi. We have developed a prototype that operates across data sets created for these two languages. Using this prototype, we have carried out experiments to detect cyberbullying in these two languages. The results of our experiments show an accuracy up-to 97% and Fl-score up-to 96% on many datasets for both the languages

Using Machine Learning Techniques to Classify and Predict Static Code Analysis Tool Warnings

This paper discusses our work on using software engineering metrics (i.e., source code metrics) to classify an error message generated by a Static Code Analysis (SCA) tool as a true-positive, false-positive, or false-negative. Specifically, we compare the performance of Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forests, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) over eight datasets. The performance of the techniques is assessed by computing the F-measure metric, which is defined as the weighted harmonic mean of the precision and recall of the predicted model. The overall results of the study show that the F-measure value of the predicted model, which is generated using Random Forests technique, ranges from 83% to 98%. Additionally, the Random Forests technique outperforms the other techniques. Lastly, our results indicate that the complexity and coupling metrics have the most impact on whether a SCA tool with generate a false-positive warning or not

A Unified Approach for the Integration of Distributed Heterogeneous Software Components

Proceedings of the 2001 Monterey Workshop (Sponsored by DARPA, ONR, ARO and AFOSR), pp: 109-119, Monterey, CA, 2001Distributed systems are omnipresent these days. Creating efficient and robust software for such systems is a highly complex task. One possible approach to developing distributed software is based on the integration of heterogeneous sofwtare components that are scattered across many machines. In this paper, a comprehensive framework that will allow a seamless integration of distributed heterogeneous software components is proposed. This framework involves: a) a metamodel for components and associated hierarchical setup for indicating the contracts and constraints of the components. b) an automatic generation of glues and wrappers, based on a designer's specifications, for achieving interoperability, c) a formal mechanism for precisely describing the meta-model, and d) a formalization of quality of service (QoS) offered by each component and ensemble of components. A case study from the domain of distributed information filtering is described in the context of this framework.This material is based upon work supported by, or in part by, the U.S. Office of Naval Research under award number N00014-01-1-0746. This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number 40473-MA

TRUSTED SERVICE REPRESENTATION AND SELECTION FOR GENERATING DISTRIBUTED REAL-TIME AND EMBEDDED SYSTEMS

poster abstractToday’s enterprise distributed real-time and embedded (DRE) systems are created from reusable software components and services. This approach is promising because of its economic advantages (e.g., maximizing reuse of existing business-logic). It, however, is plagued by the challenge of selecting a subset of software components and services from those that are readily available because the selection process can be both costly and time-consuming, and the description of available services is often ambiguous and easy to misinterpret. Moreover, there is always the chance that a selected service does not adhere to its promises. This implies that trust, which we de-fine as the degree of confidence that a software component or service ad-heres to its specification, plays an important role in this selection process. We call this process trusted selection. Current state-of-the-art methods use multi-level contracts made up of four levels (i.e., syntax, semantics, synchronization and Quality of Service (QoS)) to facilitate service and component selection. This method, however, does not take trust into account thereby making it hard to support trusted selection. Our research therefore improves upon state-of-the-art in multi-level specification by incorporating trust contract into it. We incorporate trust into the multi-level specification by representing trust using subjective logic, which evaluates trust using a tuple of belief, disbelief, and uncertainty. Our current results show our trust-enabled multi-level specification reduces mis-interpretation, mismatch, and misuse of selected services