Query: Organizing Books and Authors by Multilayer SOM

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Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges <=SEP=> Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Introduction to Modern Information Retrieval <=SEP=> Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.

Probabilistic Latent Semantic Indexing <=SEP=> Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

Group recommendations with rank aggregation and collaborative filtering <=SEP=> The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members.
 This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.

Distributional Clustering of English Words <=SEP=> We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical “soft” clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.

of lexical appRoach and machine leaRning methods foR sentiment analysis <=SEP=> The machine learning approach is represented with two methods: the maximum entropy method and support vector machine. Text representation for the maximum entropy method includes the information about the proportion of positive and negative words and collocations, the quantity of interrogation and exclamation marks, emoticons, obscene language. For the support vector machine binary vectors with cosine normalization are built on texts.

IT worker turnover: an empirical examination of intrinsic motivation <=SEP=> This study examined intrinsic motivation's influence on information technology (IT) workers' attitudes and intentions. Drawing on Human Resource management research (Eby & Freeman, 1999), we model Intrinsic motivation as mediating the influence of motivators (i.e., intrinsic job characteristics) and hygiene factors (i.e., pay and supervisory satisfaction) on workplace attitudes (i.e., job satisfaction and affective organizational commitment). In turn, workplace attitudes mediate the influence of intrinsic motivation on turnover intention. The model was tested using data collected from public sector IT workers in the Southeastern United States. Although intrinsic motivation did not fully mediate the influence of motivators and hygiene factors, findings suggest that intrinsic motivation positively influences workplace attitudes and has a mediated influence on turnover intent. Implications for research and practice are offered.

Redemption: Real-Time Protection Against Ransomware at End-Hosts <=SEP=> Ransomware is a form of extortion-based attack that locks the victim’s digital resources and requests money to release them. The recent resurgence of high-profile ransomware attacks, particularly in critical sectors such as the health care industry, has highlighted the pressing need for effective defenses. While users are always advised to have a reliable backup strategy, the growing number of paying victims in recent years suggests that an endpoint defense that is able to stop and recover from ransomware’s destructive behavior is needed. In this paper, we introduce Redemption, a novel defense that makes the operating system more resilient to ransomware attacks. Our approach requires minimal modification of the operating system to maintain a transparent buffer for all storage I/O. At the same time, our system monitors the I/O request patterns of applications on a per-process basis for signs of ransomware-like behavior. If I/O request patterns are observed that indicate possible ransomware activity, the offending processes can be terminated and the data restored. Our evaluation demonstrates that Redemption can ensure zero data loss against current ransomware families without detracting from the user experience or inducing alarm fatigue. In addition, we show that Redemption incurs modest overhead, averaging 2.6% for realistic workloads.

Chemical Composition, Antibacterial and Antioxidant Activities of Six Essentials Oils from the Alliaceae Family <=SEP=> Six essential oils (EOs) from the Alliaceae family, namely garlic (Allium sativum), onion (Allium cepa), leek (Allium porrum), Chinese chive (Allium tuberosum), shallot (Allium ascalonicum) and chive (Allium schoenoprasum) were characterized by GC and GC-MS and evaluated for their functional food properties. Antibacterial properties were tested on five food-borne pathogens: Two Gram-positive Staphylococcus aureus (ATCC 25923), Listeria monocytogenes (ATCC 19115) and three Gram-negative Salmonella Typhimurium (ATCC 14028), Escherichia coli (ATCC 8739) and Campylobacter jejuni (ATCC 33291) bacteria. Antioxidant and radical-scavenging properties were tested by means of Folin-Ciocalteu and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. Garlic, Chinese chive and onion EOs had the highest antibacterial activity whereas shallot and leek EOs were the strongest antioxidants. Heating caused a decrease in the antioxidant activity of these Eos, as shown in the Total Polar Materials (TPM) test. Suggestions on relationships between chemical composition and biological activities are presented. Results show that the EOs could be of value in the food industry as alternatives to synthetic antioxidants.

Cross-Entropy Method for Reinforcement Learning <=SEP=> Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Tetris, which varies from game to game in field size and the chance of a single block occuring. In comparison to a non-parameterized policy we find that a parameterized policy is able to outperform the non-parameterized policy. The increased complexity of learning such a policy using the Cross-Entropy method however reaches a limit at which the policy is too complex to learn and performance drops below the non-parameterized policy.

Web-based 3D Reconstruction Service <=SEP=> The use of 3D information in the field of cultural heritage is increasing year by year. From this field comes a large demand for cheaper and more flexible ways of 3D reconstruction. This paper describes a web-based 3D reconstruction service, developed to relieve those needs of the cultural heritage field. This service consists of a pipeline that starts with the user uploading images of an object or scene(s) he wants to reconstruct in 3D. The automatic reconstruction process, running on a server connected to a cluster of computers, computes the camera calibration, as well as dense depth (or range-) maps for the images. This result can be downloaded from an ftp server and visualized with a specific tool running on the user’s PC.

5G uplink performance of filter bank multi-carrier <=SEP=> One of 5G communication typical scenarios is machine-to-machine communication with large number of devices, which proposes new requirements for uplink transmission. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) is one of the popular nonorthogonal waveform candidates for 5G transmission. In this paper, FBMC/OQAM and cyclic prefix OFDM with single antenna settings are comprehensively evaluated and compared in uplink transmission form four aspects: power spectral density, timing and frequency misalignments, guard bands between adjacent users and time efficiency. At last, our comparison results show that FBMC is more suitable for 5G uplink multi-user asynchronous transmissions than CP-OFDM.

An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing <=SEP=> Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

A new classification of lip zones to customize injectable lip augmentation. <=SEP=> OBJECTIVES
To present a new classification of 15 anatomical lip zones used to direct placement of injectable fillers during lip augmentation and to evaluate the new classification's ability to customize lip contour and size.


METHODS
Study participants were consecutive patients presenting to a facial plastic and reconstructive surgery practice for injectable lip augmentation with a nonanimal-sourced stabilized hyaluronic acid (Restylane; Medicis Aesthetic Inc, Scottsdale, Arizona). A nonrandomized, prospective case series.


RESULTS
A total of 137 treatments were performed on lips of 66 patients. The mean (SD) satisfaction score was 4.5 (0.6) on an integral scale of 1 (dissatisfied) to 5 (most satisfied). The mean (SD) persistence until lips returned to preoperative appearance based on patient subjective evaluation was 4.9 (1.5) months. Patients were free of adverse effects.


CONCLUSIONS
Using a new classification of lip anatomical zones to direct the injection of a nonanimal-sourced stabilized hyaluronic acid has increased my ability to better control lip shape and size in lip augmentation. This technique was met with high patient satisfaction and no adverse effects. Persistence of injected nonanimal-sourced stabilized hyaluronic acid was similar to that seen in other studies.

Detection of pneumonia clouds in chest X-ray using image processing approach <=SEP=> Finding ways to automate diagnostics from medical images, has continuously been one of the most interesting areas of software development. This article presents a novel approach for detecting the presence of pneumonia clouds in chest X-rays (CXR) by using only Image processing techniques. For this, we have worked on 40 analog chest CXRs pertaining to Normal and Pneumonia infected patients. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds we have used Otsu thresholding which will segregate the healthy part of lung from the pneumonia infected cloudy regions. We are proposing to compute the ratio of area of healthy lung region to total lung region to establish a result. The task has been performed using Python and OpenCV as they are free, opensource tools and can be used by all, without any legality issues or cost implication.

Rigid-Foldable Thick Origami <=SEP=> In this paper, a method is proposed for geometrically constructing thick panel structures that follow the kinetic behavior of rigid origami by using tapered or two-ply panels and hinges located at their edges. The proposed method can convert generalized pattern of rigid-foldable origami into thick panels structure with kinetic motion, which leads to novel designs of origami for various engineering purposes including architecture.

Probabilistic neural network and its application <=SEP=> Nuclear marine apparatus is a huge complicated system, most of which equipments are of nonlin-earity, time varying, coupling and inexactness. Neuralnet is widely applied in nuclear fault diagnosis for its approaching any kinds of nonlinearity mapping. At present, BP neural net is used more widely, but the layers of the net and the neurones on each layer can not be delimited easily; such net may fall into the minimum point in the course of training. In this essay, PNN proves effective in diagnosing faults on nuclearmarine apparatus.

Broadband and High-Gain SIW-Fed Antenna Array for 5G Applications <=SEP=> A broadband and high-gain substrate integrated waveguide (SIW)-fed antenna array is demonstrated for 5G applications. Aperture coupling is adopted as the interconnecting method in the two-layer SIW feeding network. Two pairs of metallic posts act as dipoles and are surrounded by an air-filled cavity. The height of the cavity is designed to be higher than that of the posts to broaden the bandwidth. The measured impedance bandwidth of the proposed <inline-formula> <tex-math notation="LaTeX">$8{\times} 8$ </tex-math></inline-formula> antenna array is 16.3% from 35.4 to 41.7 GHz for <inline-formula> <tex-math notation="LaTeX">$|S_{11}|\leqslant -10$ </tex-math></inline-formula> dB, and the measured peak gain is 26.7 dBi at 40 GHz. The measured radiation efficiency of the antenna array is 83.2% at 40 GHz. The proposed antenna array which is suitable for 5G applications at 37 and 39 GHz bands shows stable radiation patterns, high gain, and broad bandwidth.

Scalable and Secure Logistic Regression via Homomorphic Encryption <=SEP=> Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.

Cinnamon for diabetes mellitus. <=SEP=> BACKGROUND
Diabetes mellitus is a chronic metabolic disorder that is associated with an increased risk of cardiovascular disease, retinopathy, nephropathy, neuropathy, sexual dysfunction and periodontal disease. Improvements in glycaemic control may help to reduce the risk of these complications. Several animal studies show that cinnamon may be effective in improving glycaemic control. While these effects have been explored in humans also, findings from these studies have not yet been systematically reviewed.


OBJECTIVES
To evaluate the effects of cinnamon in patients with diabetes mellitus.


SEARCH METHODS
Pertinent randomised controlled trials were identified through AARP Ageline, AMED, AMI, BioMed Central gateway, CAM on PubMed, CINAHL, Dissertations Abstracts International, EMBASE, Health Source Nursing/Academic edition, International Pharmaceutical Abstracts, MEDLINE, Natural medicines comprehensive database, The Cochrane Library and TRIP database. Clinical trial registers and the reference lists of included trials were searched also (all up to January 2012). Content experts and manufacturers of cinnamon extracts were also contacted.


SELECTION CRITERIA
All randomised controlled trials comparing the effects of orally administered monopreparations of cinnamon (Cinnamomum spp.) to placebo, active medication or no treatment in persons with either type 1 or type 2 diabetes mellitus.


DATA COLLECTION AND ANALYSIS
Two review authors independently selected trials, assessed risk of bias and trial quality, and extracted data. We contacted study authors for missing information.


MAIN RESULTS
Ten prospective, parallel-group design, randomised controlled trials, involving a total of 577 participants with type 1 and type 2 diabetes mellitus, were identified. Risk of bias was high or unclear in all but two trials, which were assessed as having moderate risk of bias. Risk of bias in some domains was high in 50% of trials. Oral monopreparations of cinnamon (predominantly Cinnamomum cassia) were administered at a mean dose of 2 g daily, for a period ranging from 4 to 16 weeks. The effect of cinnamon on fasting blood glucose level was inconclusive. No statistically significant difference in glycosylated haemoglobin A1c (HbA1c), serum insulin or postprandial glucose was found between cinnamon and control groups. There were insufficient data to pool results for insulin sensitivity. No trials reported health-related quality of life, morbidity, mortality or costs. Adverse reactions to oral cinnamon were infrequent and generally mild in nature.


AUTHORS' CONCLUSIONS
There is insufficient evidence to support the use of cinnamon for type 1 or type 2 diabetes mellitus. Further trials, which address the issues of allocation concealment and blinding, are now required. The inclusion of other important endpoints, such as health-related quality of life, diabetes complications and costs, is also needed.

A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection <=SEP=> Urban black hole, as a traffic anomaly, has caused lots of catastrophic accidents in many big cities nowadays. Traditional methods only depend on the single source data (e.g., taxi trajectories) to design blackhole detection algorithm from one point of view, which is rather incomplete to describe the regional crowd flow. In this paper, we model the urban black holes in each region of New York City (NYC) at different time intervals with a 3-dimensional tensor by fusing cross-domain data sources. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we leverage the knowledge from geographical features, 311 complaint features and human mobility features to recover the blackhole situation throughout NYC. The information can facilitate local residents and officials' decision making. We evaluate our model with five datasets related to NYC, diagnosing the urban black holes that cannot be identified (or earlier than those detected) by a single dataset. Experimental results demonstrate the advantages beyond four baseline methods.

Optimum auto exposure based on high-dynamic-range histogram <=SEP=> This paper introduces a new approach for optimum auto exposure (AE) based on the high-dynamic-range histogram (HDH) of a scene. The exposure control is optimal in terms of recorded information of the scene. The advantages over AE based on mean values of images will be shown as well as a proof of concept implementation and results. Futhermore possible extensions to this approach are discussed. The consideration of the HDH for auto exposure enables new possibilities for auto exposure control for mutliple-slope cameras. An AE method for multiple-slope cameras is proposed which controls exposure as well as the transition curve parameters in order to record a maximum of information from the scene. It considers controllable dynamic range as well as corse quantization in bright image regions. Key–Words: Optimum Auto Exposure, High Dynamic Range (HDR), multiple-slope camera January 26, 2007

3HAN: A Deep Neural Network for Fake News Detection <=SEP=> 

A Middleware for Fast and Flexible Sensor Network Deployment <=SEP=> A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface. The GSN implementation is available from http://globalsn.sourceforge.net/.

Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC <=SEP=> High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

Discovery of Online Shopping Patterns Across Websites <=SEP=> • In the online world, customers can easily navigate to different online stores to make purchases • Market basket analysis is often used to discover associations among products for brick-and-mortar stores, but rarely for online shops • Defined to online shopping patterns and developed 2 novel methods to perform market basket analysis across websites • The methods presented in this paper can be only not only to online shopping application, but to other dimensions as well. Uyanga, Do young suk

Towards Literate Artificial Intelligence <=SEP=> Speaker: Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document: http://www.cs.cmu.edu/~mrinmays/thesis_proposal.pdf Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.

Plasmodium vivax segmentation using modified fuzzy divergence <=SEP=> This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions.

A Survey on Security for Mobile Devices <=SEP=> Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

Fast Linear Model for Knowledge Graph Embeddings <=SEP=> This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText1.