Query: Analyzing inter-application communication in Android

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A First Step Towards Automated Detection of Buffer Overrun Vulnerabilities <=SEP=> We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotely-exploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed

On lightweight mobile phone application certification <=SEP=> Users have begun downloading an increasingly large number of mobile phone applications in response to advancements in handsets and wireless networks. The increased number of applications results in a greater chance of installing Trojans and similar malware. In this paper, we propose the Kirin security service for Android, which performs lightweight certification of applications to mitigate malware at install time. Kirin certification uses security rules, which are templates designed to conservatively match undesirable properties in security configuration bundled with applications. We use a variant of security requirements engineering techniques to perform an in-depth security analysis of Android to produce a set of rules that match malware characteristics. In a sample of 311 of the most popular applications downloaded from the official Android Market, Kirin and our rules found 5 applications that implement dangerous functionality and therefore should be installed with extreme caution. Upon close inspection, another five applications asserted dangerous rights, but were within the scope of reasonable functional needs. These results indicate that security configuration bundled with Android applications provides practical means of detecting malware.

Understanding Android Security <=SEP=> Google's Android platform is a widely anticipated open source operating system for mobile phones. This article describes Android's security model and attempts to unmask the complexity of secure application development. The authors conclude by identifying lessons and opportunities for future enhancements.

Protecting privacy using the decentralized label model <=SEP=> Stronger protection is needed for the confidentiality and integrity of data, because programs containing untrusted code are the rule rather than the exception. Information flow control allows the enforcement of end-to-end security policies, but has been difficult to put into practice. This article describes the decentralized label model, a new label model for control of information flow in systems with mutual distrust and decentralized authority. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-grained data sharing. It supports static program analysis of information flow, so that programs can be certified to permit only acceptable information flows, while largely avoiding the overhead of run-time checking. The article introduces the language Jif, an extension to Java that provides static checking of information flow using the decentralized label model.

Firewalls and internet security: Repelling the wily hacker, 2nd ed. [Book Review] <=SEP=> General Interest How the Internet Works, 7th ed., Preston Gralla (Que, 2004, 368 pp., ISBN 0-78972973-3, $29.99; www.quepublishing.com) Preston Gralla is a well-known technology columnist and author. In this large-format book lavishly illustrated by Michael Troller, Gralla explains all of the concepts that most people are likely to encounter in connecting to the Internet. The explanations are not deep, but they systematically cover the basics. Most people, even experts, will probably find at least some of the explanations helpful. For example, I feel that I know quite a bit about the Internet, but Gralla’s explanation of proxy servers clarified the subject for me. The illustrations are colorful and attractive, but in many cases are merely decorative. Visual learners may find them helpful, though I doubt that anyone could get much from the book just by looking at the pictures. The text, on the other hand, can probably stand alone. If you know people who are just starting to use the Internet and electronic mail, consider giving them this book. It may save you from having to answer some difficult questions.

Event Tactic Analysis Based on Broadcast Sports Video <=SEP=> Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal–spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal–spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study <=SEP=> BACKGROUND
Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention.


OBJECTIVE
The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media.


METHODS
A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors.


RESULTS
A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC.


CONCLUSIONS
SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.

Architecture decisions: demystifying architecture <=SEP=> We believe that a key to demystifying architecture products lies in the architecture decisions concept. We can make the architecture more transparent and clarify its rationale for all stakeholders by explicitly documenting major architecture decisions.

Elderly people at home: technological help in everyday activities <=SEP=> The aim of this paper is to understand to what extent elderly people are likely to accept a technological aid in performing everyday activities. In this perspective, the present research focused on elderly people's strategies in performing everyday activities at home, in order to understand in what domestic domains technology can be considered an acceptable help. We administered a questionnaire focusing on preferred strategies in carrying out common domestic tasks, and on attitudes towards new technologies and home modification to a sample of 123 elderly people living in Rome. Results show that the adoption of a strategy, including the introduction of technological devices, is highly problem-specific, while personal factors are relevant only in particular situations. With increasing age, people are more inclined to give up, and higher educational levels correspond to more frequent technological solutions.

k-Shape: Efficient and Accurate Clustering of Time Series <=SEP=> The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we describe k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. An extensive experimental evaluation against partitional, hierarchical, and spectral clustering methods, with the most competitive distance measures, showed the robustness of k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and efficient clustering approach for time series with broad applications.

A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs) <=SEP=> 

CDnet 2014: An Expanded Change Detection Benchmark Dataset <=SEP=> Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.

Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter <=SEP=> In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors.

Autonomous collision avoidance for unmanned surface ships using onboard monocular vision <=SEP=> This study presents the development of vision-based techniques for autonomous collision avoidance by an unmanned surface ship using an onboard monocular camera. In order to determine the initiation of an evasive maneuver, the range and bearing measurements of each target traffic ship with respect to the observer (e.g., own ship) need to be provided for trajectory estimation. A tracking estimator is used to estimate the target ship trajectory in the framework of bearings-only tracking based on the extended Kalman filter (EKF) algorithm with a Continuous White Noise Acceleration (CWNA) model. To enhance the observability of the tracking filter, the vertical pixel distance from the water horizon to each target ship is used as a range measurement. When the estimated separation distance between the target and own ships is less than a predefined minimum separation, the own ship alters its heading angle to avoid an imminent collision following the standard marine traffic rules. Field experiment results are presented and discussed to demonstrate the feasibility and validity of the proposed approach.

Combining Models from Multiple Sources for RGB-D Scene Recognition <=SEP=> Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at highlevels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities.

Enhancing the Effectiveness of Work Groups and Teams. <=SEP=> Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern. Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research-literally thousands of studies-focused on understanding and influencing the processes that underlie team effectiveness. Our goal in this monograph is to sift through this voluminous literature to identify what we know, what we think we know, and what we need to know to improve the effectiveness of work groups and teams. We begin by defining team effectiveness and establishing the conceptual underpinnings of our approach to understanding it. We then turn to our review, which concentrates primarily on topics that have well-developed theoretical and empirical foundations, to ensure that our conclusions and recommendations are on firm footing. Our review begins by focusing on cognitive, motivational/affective, and behavioral team processes-processes that enable team members to combine their resources to resolve task demands and, in so doing, be effective. We then turn our attention to identifying interventions, or "levers," that can shape or align team processes and thereby provide tools and applications that can improve team effectiveness. Topic-specific conclusions and recommendations are given throughout the review. There is a solid foundation for concluding that there is an emerging science of team effectiveness and that findings from this research foundation provide several means to improve team effectiveness. In the concluding section, we summarize our primary findings to highlight specific research, application, and policy recommendations for enhancing the effectiveness of work groups and teams.

Using audio and visual information for single channel speaker separation <=SEP=> This work proposes a method to exploit both audio and visual speech information to extract a target speaker from a mixture of competing speakers. The work begins by taking an effective audio-only method of speaker separation, namely the soft mask method, and modifying its operation to allow visual speech information to improve the separation process. The audio input is taken from a single channel and includes the mixture of speakers, where as a separate set of visual features are extracted from each speaker. This allows modification of the separation process to include not only the audio speech but also visual speech from each speaker in the mixture. Experimental results are presented that compare the proposed audio-visual speaker separation with audio-only and visual-only methods using both speech quality and speech intelligibility metrics.

Data-Driven Modeling for Chinese Ancient Architecture <=SEP=> The existing 3D modeling studies of Chinese ancient architecture are mostly procedure driven and rely on fixed construction rules. Therefore, these methods have limited applications in virtual reality (VR) engineering. We propose a data-driven approach to synthesize 3D models from existing 3D data that provides more flexibility and fills the gap between academic studies and VR engineering. First, 3D architecture models were preprocessed and decomposed into components, and the components were clustered by their geometric features. Second, a Bayesian network was generated by learning from the dataset to represent the internal relationships between the architectural components. Third, the inference results of the trained network were utilized to generate a reasonable relationship matching to support the synthesis of the structural components. The proposed method can be used in 3D content creation for VR development and directly supports VR applications in practice.

The Anisotropic Noise in Stochastic Gradient Descent : Its Behavior of Escaping from Minima and Regularization Effects <=SEP=> Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through investigating this general optimization dynamics, we analyze the behavior of SGD on escaping from minima and its regularization effects. A novel indicator is derived to characterize the efficiency of escaping from minima through measuring the alignment of noise covariance and the curvature of loss function. Based on this indicator, two conditions are established to show which type of noise structure is superior to isotropic noise in term of escaping efficiency. We further show that the anisotropic noise in SGD satisfies the two conditions, and thus helps to escape from sharp and poor minima effectively, towards more stable and flat minima that typically generalize well. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.

STRADS: a distributed framework for scheduled model parallel machine learning <=SEP=> Machine learning (ML) algorithms are commonly applied to big data, using distributed systems that partition the data across machines and allow each machine to read and update all ML model parameters --- a strategy known as data parallelism. An alternative and complimentary strategy, model parallelism, partitions the model parameters for non-shared parallel access and updates, and may periodically repartition the parameters to facilitate communication. Model parallelism is motivated by two challenges that data-parallelism does not usually address: (1) parameters may be dependent, thus naive concurrent updates can introduce errors that slow convergence or even cause algorithm failure; (2) model parameters converge at different rates, thus a small subset of parameters can bottleneck ML algorithm completion. We propose scheduled model parallelism (SchMP), a programming approach that improves ML algorithm convergence speed by efficiently scheduling parameter updates, taking into account parameter dependencies and uneven convergence. To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling, matrix factorization, sparse least-squares (Lasso) regression and sparse logistic regression. By improving ML progress per iteration through SchMP programming whilst improving iteration throughput through STRADS we show that SchMP programs running on STRADS outperform non-model-parallel ML implementations: for example, SchMP LDA and SchMP Lasso respectively achieve 10x and 5x faster convergence than recent, well-established baselines.

Ka-Band Linear to Circular Polarization Converter Based on Multilayer Slab With Broadband Performance <=SEP=> In this paper, a Ka-band polarization converter is presented, which is based on multilayer slab. In order to improve impedance matching, metallic circular traces are printed periodically on each dielectric multilayer slab. Simulated results of the polarizer show that it can transform linearly polarized (LP) to circularly polarized (CP) fields over a frequency band from 23 to 35GHz (42%) with an insertion loss less than 0.5 dB. The transmitted CP wave by the polarizer is approximately robust under oblique illuminations. The polarizer is fabricated and measured by a wideband horn antenna satisfying the simulated results. Next, in order to design a high-gain CP structure around 30 GHz, an 8-element LP array antenna with Chebyshev tapered distribution is designed and integrated with the polarizer. Obviously, the antenna limits the overall bandwidth (nearly 28 to 31.5 GHz) due to the narrowband nature of the LP antenna array. When the polarizer is illuminated by an incident LP wave, the two linear components of the transmitted wave with approximately equal amplitudes and 90° phase difference on the frequency band of interest are produced. Experimental results of the proposed structure show a pure CP with a gain of 13 dBi at 30 GHz, which can be suitable for millimeter wave communication.

Sequence-to-Dependency Neural Machine Translation <=SEP=> Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and JapaneseEnglish translation tasks.

Overdose Fatalities Patterns of Abuse Among Unintentional Pharmaceutical <=SEP=> Supplementary material http://jama.ama-assn.org/cgi/content/full/300/22/2613/DC1 Report Video JAMA Correction Contact me if this article is corrected. Citations Contact me when this article is cited. This article has been cited 1 time. Topic collections Contact me when new articles are published in these topic areas. Drug Therapy, Other Pain; Public Health; Substance Abuse/ Alcoholism; Drug Therapy; Adverse Effects;

A Fiber Tracking Method for Building Patient Specific Dynamic Musculoskeletal Models from Diffusion Tensor Data <=SEP=> A new musculoskeltal simulation primitive, the strand, can provide an efficient way of simulating complicated muscle fiber architectures [1]. In this paper we present a new fiber tracking algorithm based on an energy minimizing active curve that is well suited for building these strand type models from diffusion tensor imaging data. We present fiber tracking results for the Brachioradialis muscle in the left forearm of a male subject. The algorithm produces a space filling arrangement of fibers that are uniformly distributed throughout the muscle and aligned with the underlying muscle fiber direction.

Design Optimal PID Controller for Quad Rotor System <=SEP=> Quad rotor aerial vehicles are one of the most flexible and adaptable platforms for undertaking aerial research. Quad rotor in simplicity is rotorcraft that has four lift-generation propellers (four rotors), two rotors rotate in clockwise and the other two rotate anticlockwise, the speed and direction of Quad rotor can be controlled by varying the speed of these rotors. This paper describes the PID controller has been used for controlling attitude, Roll, Pitch and Yaw direction. In addition, optimal PID controller has been achieving by using particle swarm optimization (PSO), Bacterial Foraging optimization (BFO) and the BF-PSO optimization. Finally, the Quad rotor model has been simulating for several scenarios of testing.

Brief screening instrument for post-traumatic stress disorder. <=SEP=> BACKGROUND
Brief screening instruments appear to be a viable way of detecting post-traumatic stress disorder (PTSD) but none has yet been adequately validated.


AIMS
To test and cross-validate a brief instrument that is simple to administer and score.


METHOD
Forty-one survivors of a rail crash were administered a questionnaire, followed by a structured clinical interview 1 week later.


RESULTS
Excellent prediction of a PTSD diagnosis was provided by respondents endorsing at least six re-experiencing or arousal symptoms, in any combination. The findings were replicated on data from a previous study of 157 crime victims.


CONCLUSIONS
Performance of the new measure was equivalent to agreement achieved between two full clinical interviews.

Image biomarker standardisation initiative version 1 . 4 <=SEP=> 

Birds of a ‘ ‘ bad ’ ’ feather flock together : The Dark Triad and mate choice <=SEP=> Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm. 2015 Elsevier Ltd. All rights reserved.

Treatment with the Self-Discovery Camp (SDiC) improves Internet gaming disorder. <=SEP=> INTRODUCTION
Internet gaming disorder (IGD) is a novel behavioral addiction that influences the physical, mental, and social aspects of health due to excessive Internet gaming. One type of intensive treatment for IGD is the therapeutic residential camp (TRC), which comprises many types of therapies, including psychotherapy, psychoeducational therapy, and cognitive behavioral therapy. The TRC was developed in South Korea and has been administered to many patients with IGD; however, its efficacy in other countries remains unknown. We investigated the efficacy of the Self-Discovery Camp (SDiC), a Japanese version of a TRC, and the correlations between individual characteristics and outcome measures.


METHODS
We recruited 10 patients with IGD (all male, mean age=16.2years, diagnosed using the DSM-5) to spend 8 nights and 9days at the SDiC. We measured gaming time as well as self-efficacy (using the Stages of Change Readiness and Treatment Eagerness Scale, a measure of therapeutic motivation and problem recognition).


RESULTS
Total gaming time was significantly lower 3months after the SDiC. Problem recognition and self-efficacy towards positive change also improved. Furthermore, there was a correlation between age of onset and problem recognition score.


CONCLUSIONS
Our results demonstrate the effectiveness of the SDiC for IGD, especially regarding gaming time and self-efficacy. Additionally, age of onset may be a useful predictor of IGD prognosis. Further studies with larger sample sizes and control groups, and that target long-term outcomes, are needed to extend our understanding of SDiC efficacy.

Incorporating Tweet Relationships into Topic Derivation <=SEP=> With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweettopic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information. Keywords-Topic Derivation; Twitter; Tweets Relationship;