Query: Using Social Network Analysis as a Strategy for E-Commerce Recommendation

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Hybrid Recommender Systems: Survey and Experiments <=SEP=> Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.

Recommender Systems Research: A Connection-Centric Survey <=SEP=> Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

Method to find community structures based on information centrality. <=SEP=> Community structures are an important feature of many social, biological, and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries [M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)]. We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n4) , is very effective especially when the communities are very mixed and hardly detectable by the other methods.

Collaborative Recommendation via Adaptive Association Rule Mining <=SEP=> Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are ine cient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm speci cally designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be speci ed in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a speci c target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is signi cantly better than that of traditional correlationbased approaches. Corresponding author. Present a liation: Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481 USA, e-mail: salvarez@wellesley.edu

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Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule <=SEP=> Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert's opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.

Face detection using SURF cascade <=SEP=> We present a novel boosting cascade based face detection framework using SURF features. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by two key contributions. First, the proposed framework deals with only several hundreds of multidimensional local SURF patches instead of hundreds of thousands of single dimensional haar features in the VJ framework. Second, it takes AUC as a single criterion for the convergence test of each cascade stage rather than the two conflicting criteria (false-positive-rate and detection-rate) in the VJ framework. These modifications yield much faster training convergence and much fewer stages in the final cascade. We made experiments on training face detector from large scale database. Results shows that the proposed method is able to train face detectors within one hour through scanning billions of negative samples on current personal computers. Furthermore, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed.

Emergence of 3D Printed Fashion: Navigating the Ambiguity of Materiality Through Collective Design <=SEP=> The emergence of 3D printing technology is being embraced by an increasing number of fashion designers. Due to the nascent and evolving nature of the technology, however, there is significant ambiguity around this technology’s implications for the practices of fashion design. Based on the theoretical perspective of sociomateriality and the concept of translation, and drawing on archives, interviews, and other forms of qualitative data, this study’s preliminary findings show that fashion designers navigate this ambiguity by pursuing a collective design process with diverse stakeholders to actively perceive and leverage the affordances and constraints of the technology. The ongoing interaction among a network of heterogeneous actors gives rise to innovative perceptions, practices, and products, which collectively shape the emergence of the field of 3D printed fashion.

Finding the face in the crowd: an anger superiority effect. <=SEP=> Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.

Addressing cold start in recommender systems: a semi-supervised co-training algorithm <=SEP=> Cold start is one of the most challenging problems in recommender systems. In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL. Specifically, we use a factorization model to capture fine-grained user-item context. Then, in order to build a model that is able to boost the recommendation performance by leveraging the context, we propose a semi-supervised ensemble learning algorithm. The algorithm constructs different (weak) prediction models using examples with different contexts and then employs the co-training strategy to allow each (weak) prediction model to learn from the other prediction models. The method has several distinguished advantages over the standard recommendation methods for addressing the cold-start problem. First, it defines a fine-grained context that is more accurate for modeling the user-item preference. Second, the method can naturally support supervised learning and semi-supervised learning, which provides a flexible way to incorporate the unlabeled data.
 The proposed algorithms are evaluated on two real-world datasets. The experimental results show that with our method the recommendation accuracy is significantly improved compared to the standard algorithms and the cold-start problem is largely alleviated.

Learning Extractors from Unlabeled Text using Relevant Databases <=SEP=> Supervised machine learning algorithms for information extraction generally require large amounts of training data. In many cases where labeling training data is burdensome, there may, however, already exist an incomplete database relevant to the task at hand. Records from this database can be used to label text strings that express the same information. For tasks where text strings do not follow the same format or layout, and additionally may contain extra information, labeling the strings completely may be problematic. This paper presents a method for training extractors which fill in missing labels of a text sequence that is partially labeled using simple high-precision heuristics. Furthermore, we improve the algorithm by utilizing labeled fields from the database. In experiments with BibTeX records and research paper citation strings, we show a significant improvement in extraction accuracy over a baseline that only relies on the database for training data.

Cloaker: Hardware Supported Rootkit Concealment <=SEP=> Rootkits are used by malicious attackers who desire to run software on a compromised machine without being detected. They have become stealthier over the years as a consequence of the ongoing struggle between attackers and system defenders. In order to explore the next step in rootkit evolution and to build strong defenses, we look at this issue from the point of view of an attacker. We construct Cloaker, a proof-of-concept rootkit for the ARM platform that is non- persistent and only relies on hardware state modifications for concealment and operation. A primary goal in the design of Cloaker is to not alter any part of the host operating system (OS) code or data, thereby achieving immunity to all existing rootkit detection techniques which perform integrity, behavior and signature checks of the host OS. Cloaker also demonstrates that a self-contained execution environment for malicious code can be provided without relying on the host OS for any services. Integrity checks of hardware state in each of the machine's devices are required in order to detect rootkits such as Cloaker. We present a framework for the Linux kernel that incorporates integrity checks of hardware state performed by device drivers in order to counter the threat posed by rootkits such as Cloaker.

Detecting Driver Drowsiness Based on Sensors: A Review <=SEP=> In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

New approach for clustering of big data: DisK-means <=SEP=> The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.

Comparative study of different methods of social network analysis and visualization <=SEP=> A Social Network is a social structure made of individuals or organizations that are linked by one or more specific types of interdependency, such as friends, kinship, terrorist relation, conflict, financial exchange, disease transmission (epidemiology), airline routes etc. Social Network Analysis is an approach to the study of human or organization social interactions. It can be used to investigate kinship patterns, community structure or the organization of other formal and informal social networks. Recently, email networks have been popular source of data for both analysis and visualization of social network of a person or organization. In this paper, three types of visualization technique to analyze social networks have been considered - Force directed layout, Spherical layout and Clustered layout. Each method was evaluated with various data sets from an organization. Force directed layout is used to view the total network structure (Overview). Spherical layout is a 3D method to reveal communication patterns and relationships between different groups. Clustered graph layout visualizes a large amount of data in an efficient and compact form. It gives a hierarchical view of the network. Among the three methods, Clustered layout is the best to handle large network and group relationship. Finally, the comparative study of these three methods has been given.

A Hybrid Feature Selection Algorithm For Classification Unbalanced Data Processsing <=SEP=> The performance and accuracy of classifier are affected by the result of feature selection directly. Based on the one-class F-Score feature selection and the improved F-Score feature selection and genetic algorithm, combined with machine learning methods like the K nearest neighbor, support vector machine, random forest, naive Bayes, a hybrid feature selection algorithm is proposed to process the two classification unbalanced data problem and multi classification problem. Compared with the traditional machine learning algorithm, it can search in wider feature space and promote classifier to deal with the characteristics of unbalanced data sets according to heuristic rules, which can handle the problem of unbalanced classification better. The experiment results show that the area under receiver operating characteristic curve for two classifications and the accuracy rate for multi classification problem have been improved compared with other models

A Systematic Analysis of XSS Sanitization in Web Application Frameworks <=SEP=> While most research on XSS defense has focused on techniques for securing existing applications and re-architecting browser mechanisms, sanitization remains the industry-standard defense mechanism. By streamlining and automating XSS sanitization, web application frameworks stand in a good position to stop XSS but have received little research attention. In order to drive research on web frameworks, we systematically study the security of the XSS sanitization abstractions frameworks provide. We develop a novel model of the web browserions frameworks provide. We develop a novel model of the web browser and characterize the challenges of XSS sanitization. Based on the model, we systematically evaluate the XSS abstractions in 14 major commercially-used web frameworks. We find that frameworks often do not address critical parts of the XSS conundrum. We perform an empirical analysis of 8 large web applications to extract the requirements of sanitization primitives from the perspective of realworld applications. Our study shows that there is a wide gap between the abstractions provided by frameworks and the requirements of applications.

An integrated approach of diet and exercise recommendations for diabetes patients <=SEP=> Diabetes is among one of the fastest growing disease all over the world. Controlled diet and proper exercise are considered as a treatment to control diabetes. However, food and exercise suggestions in existing solutions do not consider integrated knowledge from personal profile, preferences, current vital signs, diabetes domain, food domain and exercise domain. Furthermore, there is a strong correlation of diet and exercise. We have implemented an ontology based integrated approach to combine knowledge from various domains to generate diet and exercise suggestions for diabetics. The solution is developed as a Semantic Healthcare Assistant for Diet and Exercise (SHADE). For each domain (person, diabetes, food and exercise) we have defined separate ontology along with rules and then an integrated ontology combines these individual ontologies. Finally, diet recommendations are presented in the form of various alternative menus such that each menu is a healthy and balanced diet.

Low-profile dual-band circularly polarized microstrip antenna for GNSS applications <=SEP=> This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.

Object-Oriented Design Process Model <=SEP=> ion x x x x Relationship x x x x x

The PLEX Cards and its techniques as sources of inspiration when designing for playfulness <=SEP=> Playfulness can be observed in all areas of human activity. It is an attitude of making activities more enjoyable. Designing for playfulness involves creating objects that elicit a playful approach and provide enjoyable experiences. In this paper, we introduce the design and evaluation of the PLEX Cards and its two related idea generation techniques. The cards were created to communicate the 22 categories of a playful experiences framework to designers and other stakeholders who wish to design for playfulness. We have evaluated the helpfulness of both the cards and their associated techniques in two studies. The results show that the PLEX Cards and its associated techniques are valuable sources of inspiration when designing for playfulness.

Cloud Federation: Effects of Federated Compute Resources on Quality of Service and Cost* <=SEP=> Cloud Federation is one concept to confront challenges that still persist in Cloud Computing, such as vendor lock-in or compliance requirements. The lack of a standardized meaning for the term Cloud Federation has led to multiple conflicting definitions and an unclear prospect of its possible benefits. Taking a client-side perspective on federated compute services, we analyse how choosing a certain federation strategy affects Quality of Service and cost of the resulting service or application. Based on a use case, we experimentally prove our analysis to be correct and describe the different trade-offs that exist within each of the strategies.

The nucleotide sequence of a HMW glutenin subunit gene located on chromosome 1A of wheat (Triticum aestivum L.). <=SEP=> A cloned 8.2 kb EcoRI fragment has been isolated from a genomic library of DNA derived from Triticum aestivum L. cv. Cheyenne. This fragment contains sequences related to the high molecular weight (HMW) subunits of glutenin, proteins considered to be important in determining the elastic properties of gluten. The cloned HMW subunit gene appears to be derived from chromosome 1A. The nucleotide sequence of this gene has provided new information on the structure and evolution of the HMW subunits. However, hybrid-selection translation experiments suggest that this gene is silent.

dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning <=SEP=> Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need of capturing dynamics for prediction on a toy data set created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real world data sets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.

Operational capabilities development in mediated offshore software services models <=SEP=> The paper expands theoretical and empirical understanding of capabilities development in the mediated offshore outsourcing model whereby a small or a medium-sized firm delivers offshore software services to a larger information technology firm that in turn contracts and interfaces with the actual end-client onshore firms. Such a mediated model has received little prior research attention, although it is common particularly among Chinese firms exporting services to Japan, the largest export market for Chinese software services. We conducted case studies in four China-based software companies to understand the mechanisms used to develop their operational capabilities. We focused on client-specific, process, and human resources capabilities that have been previously associated with vendor success. We found a range of learning mechanisms to build the capabilities in offshore firms. Results show that the development of human resources capabilities was most challenging in the mediated model; yet foundational for the development of the other capabilities. This paper contributes to the information systems literature by improving our understanding of the development of operational capabilities in smalland medium-sized Chinese firms that deploy the mediated model of offshore

Unsupervised detection of surface defects: A two-step approach <=SEP=> In this paper, we focus on the problem of finding anomalies in surface images. Despite enormous research efforts and advances, it still remains a big challenge to be solved. This paper proposes a unified approach for defect detection. Our proposed method consists of two phases: (1) global estimation and (2) local refinement. First, we roughly estimate defects by applying a spectral-based approach in a global manner. We then locally refine the estimated region based on the distributions of pixel intensities derived from defect and defect-free regions. Experimental results show that the proposed method outperforms the previous defect detection methods and gives robust results even in noisy surface defect images.

The Ideology of Interactivity (or Video Games and Taylorization of Leisure) <=SEP=> Interactivity is one of the key conceptual apparatuses through which video games have been theorized thus far. As many writers have noted, video games are distinct from other forms of media because player actions seem to have direct, immediate consequences in the world depicted onscreen. But in many ways, this “interactive” feature of video games tends to manifest itself as a relentless series of demands, or a way of disciplining player behavior. In this sense, it seems more accurate to describe the human-machine interface made possible by gaming as an aggressive form of “interpellation” or hailing. Drawing primarily upon the work of Louis Althusser, I argue that traditional theories of interactivity fail to acknowledge the work of video games—in other words, the extent to which video games define and reconstitute players as subjects of ideology.

Generalized Methods for the Design of Quasi-Ideal Symmetric and Asymmetric Coupled-Line Sections and Directional Couplers <=SEP=> Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.

Bullying, cyberbullying, and mental health in young people. <=SEP=> OBJECTIVE
To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls.


METHODS
Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%).


RESULTS
The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls.


CONCLUSIONS
Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.

Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations <=SEP=> Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subjects appearance (i.e.,personalization), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.