Hazem M. Hajj

A Framework for Emotion Mining from Text in Online Social Networks

Online Social Networks are so popular nowadays that they are a major component of an individual’s social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances. The goal is to extract the emotional content of texts in online social networks. The interest is in whether the text is an expression of the writer’s emotions or not. For this purpose, text mining techniques are performed on comments retrieved from a social network. The framework includes a model for data collection, database schemas, data processing and data mining steps. The informal language of online social networks is a main point to consider before performing any text mining techniques. This is why the framework includes the development of special lexicons. In general, the paper presents a new perspective for studying friendship relations and emotions’ expression in online social networks where it deals with the nature of these sites and the nature of the language used. It considers Lebanese Face book users as a case study. The technique adopted is unsupervised, it mainly uses the k-means clustering algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting friendship.

A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.

Slow port scanning detection

Port scanning is the most popular reconnaissance technique attackers use to discover services they can break into. Port scanning detection has received a lot of attention by researchers. However a slow port scan attack can deceive most of the existing Intrusion Detection Systems (IDS). In this paper, we present a new, simple, and efficient method for detecting slow port scans. Our proposed method is mainly composed of two phases: (1) a feature collection phase that analyzes network traffic and extracts the features needed to classify a certain IP as malicious or not. (2) A classification phase that divides the IPs, based on the collected features, into three groups: normal IPs, suspicious IPs and scanner IPs. The IPs our approach classify as suspicious are kept for the next (K) time windows for further examination to decide whether they represent scanners or legitimate users. Hence, this approach is different than the traditional approach used by IDSs that classifies IPs as either legitimate or scanners, and thus producing a high number of false positives and false negatives. A small Local Area Network was put together to test our proposed method. The experiments show the effectiveness of our proposed method in correctly identifying malicious scanners when both normal and slow port scan were performed using the three most common TCP port scanning techniques. Moreover, our method detects malicious scanners that are otherwise not detected using well known IDSs such as Snort.

Emotion Recognition from Text Based on Automatically Generated Rules

With the growth of the Internet community, textual data has proven to be the main tool of communication in human-machine and human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. For example, by providing insights to users for personal preferences and automated recommendations based on their emotional state. In this work, we propose a framework for emotion classification in English sentences where emotions are treated as generalized concepts extracted from the sentences. We start by generating an intermediate emotional data representation of a given input sentence based on its syntactic and semantic structure. We then generalize this representation using various ontologies such as Word Net and Concept Net, which results in an emotion seed that we call an emotion recognition rule (ERR). Finally, we use a suite of classifiers to compare the generated ERR with a set of reference ERRs extracted from a training set in a similar fashion. The used classifiers are k-nearest neighbors (KNN) with handcrafted similarity measure, Point Mutual Information (PMI), and PMI with Information Retrieval (PMI-IR). When applied on different datasets, the proposed approach significantly outperformed the existing state-of-the art machine learning and rule-based classifiers with an average F-Score of 84%.

On the Design of Energy-Aware 3G/WiFi Heterogeneous Networks under Realistic Conditions

In this paper, we consider the design of 3G/WiFi heterogeneous networks under realistic operational conditions. The aim is reduce the energy consumed from batteries on mobile devices by utilizing the multiple available wireless interfaces and dynamically switching between 3G and WiFi. We conduct a set of experimental measurements in various network scenarios in order to identify the main components that impact energy consumption in mobile devices while connected to 3G and WiFi networks. The measurement results are then used to derive a generic analytical energy model as a function of the download data size and the effective download bit rate. A basic algorithm to switch dynamically between 3G and WiFi is designed based on the derived analytical energy model. An Android-based mobile application is developed to test the performance of the switching algorithm in real scenarios. Experimental results demonstrate notable energy reduction gains and, thus, highlight the potential benefits of intelligent switching in heterogeneous networks.

A Distributed Reconfigurable Active SSD Platform for Data Intensive Applications

In this paper, we propose to combine active solid state drives and reconfigurable FPGAs into a storage-compute node to use as a building block in a distributed, high performance computation platform for data intensive applications. We propose a complete framework for middleware functionality through an API abstraction layer that hides the complexity of accessing and processing data stored on these distributed nodes, thus allowing programmers to focus on the application, and not the underlying specialized architecture. The application in turn is re-architected to maximize its performance by delegating selected computations down to the storage-compute node. We present preliminary results measured on a real hardware prototype of a single-node. These results show that our proposed architecture provides more than a 2x improvement in performance over non-reconfigurable active-disk architectures that use electromechanical disks for storage and a 6x improvement in performance over a platform that performs the computation on the middle server.

Energy-throughput tradeoffs in cellular/WiFi heterogeneous networks with traffic splitting

Heterogeneous networks are expected to play a major role towards meeting the exploding traffic demand over cellular systems. Particularly, existing WiFi hotspots will be dynamically utilized to offload the traffic of cellular mobile subscribers. This will be further facilitated by forthcoming advances in mobile device capabilities that will include the ability to operate multiple wireless interfaces simultaneously. To this end, we focus in this work on cellular/WiFi heterogeneous networks with traffic splitting where a mobile device can utilize existing cellular and WiFi links simultaneously to achieve various performance gains. We propose a multi-objective approach for traffic splitting that captures the tradeoffs between throughput maximization on one hand and battery energy minimization on the other hand. We evaluate the proposed approach using parameters determined via experimental measurements using Samsung Galaxy SIII mobile devices. Results are presented for various scenarios in order to quantify and analyze the throughput-energy tradeoffs of traffic splitting in cellular/WiFi heterogeneous networks.

An Algorithm-Centric Energy Aware Design Methodology

The goal of this brief is to present a unique top-down design methodology for developing energy-aware algorithms based on energy profiling. The key idea revolves around identifying and measuring components of code with high energy consumption. There are two major contributions of this brief: 1) a method for identifying components with high energy consumption in compute-intensive applications. To this end, we target operations called kernels, which are frequently used operations in the algorithm; 2) a method for estimating software energy for the identified software components, in particular for kernels and load/store operations. The energy evaluation method involves isolated code with assembly injection. Furthermore, to ensure reliable results, we use physical energy measurements conducted on specially instrumented circuit boards to provide actual and not just simulated measurements. To evaluate the proposed methods, we conducted two case studies using data mining algorithms: K-nearest neighbors and linear regression. The results highlight the contributions of kernels and memory energy to total energy.

A novel mobile wireless sensing system for realtime monitoring of posture and spine stress

Poor posture or extra stress on the spine has been shown to lead to a variety of spinal disorders including chronic back pain, and to incur numerous health costs to society. For this reason, workplace ergonomics is rapidly becoming indispensable in all major corporations. Making the individual continuously aware of poor posture may reduce out-of-posture tendencies and encourage healthy spinal habits. We have developed a novel wireless mobile sensing system which monitors spine stress in real-time by detecting poor back posture and strain on the back due to prolonged sitting or standing. The system provides a new method of measuring spine stress at both the back and the feet by integrating posture sensors with strain sensors. Posture and strain data is collected by means of a posture sensor at the neck and weight sensors at the feet. Data is transmitted wirelessly to a central processing station and real-time feedback is provided to the users mobile device when sustained bad posture is detected. Moreover, the position of the patient (sitting, standing, or walking) can be determined by analysis of the weight sensor data and is visualized in real-time, along with back posture, at the central station by means of a graphical animation. Finally, data from all sensors is stored in a database to enable post processing and data analysis, and a summary report of daily posture and physical activity is sent to the users email. The use of centralized processing allows for high performance data analysis and storage at the central station which enables tracking of the individuals progress. We demonstrate effectiveness of our system in simultaneously monitoring posture and position by testing in numerous situations.

A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets

Most advanced mobile applications require server-based and communication. This often causes additional energy consumption on the already energy-limited mobile devices. In this work, we provide to address these limitations on the mobile for Opinion Mining in Arabic. Instead of relying on compute-intensive NLP processing, the method uses an Arabic lexical resource stored on the device. Text is stemmed, and the words are then matched to our own developed ArSenL. ArSenL is the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) developed using a combination of English SentiWordnet (ESWN), Arabic WordNet, and the Arabic Morphological Analyzer (AraMorph). The scores from the matched stems are then processed through a classifier for determining the polarity. The method was tested on a published set of Arabic tweets, and an average accuracy of 67% was achieved. The developed mobile application is also made publicly available. The application takes as input a topic of interest and retrieves the latest Arabic tweets related to this topic. It then displays the tweets superimposed with colors representing sentiment labels as positive, negative or neutral. The application also provides visual summaries of searched topics and a history showing how the sentiments for a certain topic have been evolving.