title: CREDIT CARD FRAUD DETECTION BASED ON BEHAVIOR MINING

text: NIMISHA PHILIP, SHERLY K.K Department of Computer Science & Engineering, Toc H Institute of Science & Technology email:nimishavellapally@gmail.com Department of Information Technology, Toc H Institute of Science & Technology, Ernakulam, Kerala, India shrly_shilu@yahoo.com Abstract — Globalization and increased use of the Internet for online shopping has resulted in a considerable proliferation of credit card transactions throughout the world.Higher acceptability and convenience of credit cards for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by adversaries. Most of the well known algorithms for fraud detection are based on supervised training Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing FDS try to capture behavioral patterns as rules which are static .This becomes ineffective when cardholder develops new patterns of behavior Here, we propose a unsupervised method to dynamically profile behavior pattern of customer Then the incoming transactions are compared against the user profile to indicate the anomalies, based on which the corresponding warnings are outputted. A FP tree based pattern matching algorithm is used to evaluate how unusual the new transactions are.


title: The relationship between gamma frequency and running speed differs for slow and fast gamma rhythms in freely behaving rats.

text: In hippocampal area CA1 of rats, the frequency of gamma activity has been shown to increase with running speed (Ahmed and Mehta, 2012). This finding suggests that different gamma frequencies simply allow for different timings of transitions across cell assemblies at varying running speeds, rather than serving unique functions. However, accumulating evidence supports the conclusion that slow (∼25-55 Hz) and fast (∼60-100 Hz) gamma are distinct network states with different functions. If slow and fast gamma constitute distinct network states, then it is possible that slow and fast gamma frequencies are differentially affected by running speed. In this study, we tested this hypothesis and found that slow and fast gamma frequencies change differently as a function of running speed in hippocampal areas CA1 and CA3, and in the superficial layers of the medial entorhinal cortex (MEC). Fast gamma frequencies increased with increasing running speed in all three areas. Slow gamma frequencies changed significantly less across different speeds. Furthermore, at high running speeds, CA3 firing rates were low, and MEC firing rates were high, suggesting that CA1 transitions from CA3 inputs to MEC inputs as running speed increases. These results support the hypothesis that slow and fast gamma reflect functionally distinct states in the hippocampal network, with fast gamma driven by MEC at high running speeds and slow gamma driven by CA3 at low running speeds.


title: Design and Implementation of a TEM Stripline for EMC Testing

text: In this paper, a stripline for radiated field immunity testing of audio/video products is designed, constructed, calibrated, and verified. Since the canonical one is only suitable for testing equipments with height less than 70 cm, there is a need of a new device which is also in compliance with EN 55020 standard for testing new large sets, like 47'' thin-film transistor liquid crystal display (TFT LCD) television sets, in the frequency range of 150 kHz-150 MHz. Increasing the height and width of testing area causes important problems in terms of field uniformity regarding the higher order modes, characteristic impedance, and reflections in addition to the room resonances and field interference sources like corners at the edges of stripline. Comprehensive numerical study is performed to overcome these problems and obtain the optimum design before the construction. Measured data show that the new stripline is in very good agreement with the specifications determined by the standards and therefore it can be used for the electromagnetic compatibility (EMC) testing.


Giving a new makeover to STEAM: Establishing YouTube beauty gurus as digital literacy educators through messages and effects on viewers


title: De-anonymizing scale-free social networks by percolation graph matching

text: We address the problem of social network de-anonymization when relationships between people are described by scale-free graphs. In particular, we propose a rigorous, asymptotic mathematical analysis of the network de-anonymization problem while capturing the impact of power-law node degree distribution, which is a fundamental and quite ubiquitous feature of many complex systems such as social networks. By applying bootstrap percolation and a novel graph slicing technique, we prove that large inhomogeneities in the node degree lead to a dramatic reduction of the initial set of nodes that must be known a priori (the seeds) in order to successfully identify all other users. We characterize the size of this set when seeds are selected using different criteria, and we show that their number can be as small as n% for any small ε > 0. Our results are validated through simulation experiments on real social network graphs.


title: SpiderMAV: Perching and stabilizing micro aerial vehicles with bio-inspired tensile anchoring systems

text: Whilst Micro Aerial Vehicles (MAVs) possess a variety of promising capabilities, their high energy consumption severely limits applications where flight endurance is of high importance. Reducing energy usage is one of the main challenges in advancing aerial robot utility. To address this bottleneck in the development of unmanned aerial vehicle applications, this work proposes an bioinspired mechanical approach and develops an aerial robotic system for greater endurance enabled by low power station-keeping. The aerial robotic system consists of an multirotor MAV and anchoring modules capable of launching multiple tensile anchors to fixed structures in its operating envelope. The resulting tensile perch is capable of providing a mechanically stabilized mode for high accuracy operation in 3D workspace. We explore generalised geometric and static modelling of the stabilisation concept using screw theory. Following the analytical modelling of the integrated robotic system, the tensile anchoring modules employing high pressure gas actuation are designed, prototyped and then integrated to a quadrotor platform. The presented design is validated with experimental tests, demonstrating the stabilization capability even in a windy environment.


title: SUSAN—A New Approach to Low Level Image Processing

text: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.


title: An accident prediction approach based on XGBoost

text: As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.


title: Generating spiking time series with Generative Adversarial Networks : an application on banking transactions by Luca Simonetto 11413522 September 2018

text: The task of data generation using Generative Models has recently gained more and more attention from the scientific community, as the number of applications in which these models work surprisingly well is constantly increasing. Some examples are image and video generation, speech synthesis and style transfer, pose guided image generation, cross-domain transfer and super resolution. Contrarily to such tasks generating data coming from the banking domain poses a different challenge, due to its atypical structure when compared with traditional data and its limited availability due to privacy restrictions. In this work, we analyze the feasibility of generating spiking time series patterns appearing in the banking environment using Generative Adversarial Networks. We develop a novel end-to-end framework for training, testing and comparing different generative models using both quantitative and qualitative metrics. Finally, we propose a novel approach that combines Variational Autoencoders with Generative Adversarial Networks in order to learn a loss function for datasets in which good similarity metrics are difficult to define.


title: Emotional stability, core self-evaluations, and job outcomes: A review of the evidence and an agenda for future research

text: In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.


title: Analysis of digital bang-bang clock and data recovery for multi-gigabit/s serial transceivers

text: A Harmonic Balance method for analyzing digital bang-bang clock and data recovery (CDR) is proposed in this paper. The jitter tolerance performance of the CDR is predicted by a function with variables that can be easily correlated to design parameters. A 6.25Gb/s serial transceiver was fabricated in 90nm CMOS technology. Measurements show that the jitter tolerance performance can be accurately predicted by the proposed method.


title: Compact Circularly-Polarized Patch Antenna Loaded With Metamaterial Structures

text: A metamaterial-inspired low-profile patch antenna is proposed and studied for circularly-polarized (CP) radiation. The present antenna, which has a single-fed configuration, is loaded with the composite right/left-handed (CRLH) mushroom-like structures and a reactive impedance surface (RIS) for miniaturization purpose. The CP radiation is realized by exciting two orthogonally-polarized modes simultaneously which are located in the left-handed (LH) region. The detailed antenna radiation characteristics are examined and illustrated with both simulated and experimental results. The CP performance can be controlled in several different ways. This antenna exhibits an overall size of 0.177λ0 × 0.181λ0 × 0.025λ0 at 2.58 GHz and a radiation efficiency around 72%. Finally, based on the proposed CP patch antenna, a compact dual-band dual linearly-polarized patch antenna has also been designed and fabricated. Promising experimental results are observed.


title: Low Profile Fully Planar Folded Dipole Antenna on a High Impedance Surface

text: A fully planar antenna design incorporating a high impedance surface (HIS) is presented. The HIS is composed by a periodic array of subwavelength dogbone-shaped conductors printed on top of a thin dielectric substrate and backed by a metallic ground plane. First, the characteristics of a dipole over PEC or PMC layers, a dielectric slab, and the HIS are compared and studied in detail, highlighting the advantages provided by the use of the HIS. Then, the design of a low profile folded dipole antenna working at 5.5 GHz on top of the HIS is described. The surface provides close to 6% antenna impedance bandwidth and increased gain up to 7 dBi, while shielding the lower half space from radiation. The antenna structure comprises three metal layers without any vias between them, and its overall thickness is 0.059λ0. The dipole is fed by a balanced twin lead line through a balun transformer integrated in the same antenna layer. A prototype has been built and measurements confirming simulation results are provided.


title: A Compact, Low-Profile Metasurface-Enabled Antenna for Wearable Medical Body-Area Network Devices

text: We propose a compact conformal wearable antenna that operates in the 2.36-2.4 GHz medical body-area network band. The antenna is enabled by placing a highly truncated metasurface, consisting of only a two by two array of I-shaped elements, underneath a planar monopole. In contrast to previously reported artificial magnetic conducting ground plane backed antenna designs, here the metasurface acts not only as a ground plane for isolation, but also as the main radiator. An antenna prototype was fabricated and tested, showing a strong agreement between simulation and measurement. Comparing to previously proposed wearable antennas, the demonstrated antenna has a compact form factor of 0.5 λ0 ×0.3 λ0 ×0.028 λ0, all while achieving a 5.5% impedance bandwidth, a gain of 6.2 dBi, and a front-to-back ratio higher than 23 dB. Further numerical and experimental investigations reveal that the performance of the antenna is extraordinarily robust to both structural deformation and human body loading, far superior to both planar monopoles and microstrip patch antennas. Additionally, the introduced metal backed metasurface enables a 95.3% reduction in the specific absorption rate, making such an antenna a prime candidate for incorporation into various wearable devices.


title: Mining Contextual Preference Rules for Building User Profiles

text: The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying some important properties (soundness and conciseness). We evaluate the efficacy of the proposed method in a series of experiments executed on a real-world database of user preferences about movies.


title: Wearable driver drowsiness detection using electrooculography signal

text: Every year, more than 100,000 automobile crashes are caused by driver drowsiness. Various technologies have been developed to address this issue, including vehicle-based measurements, behavior change detection, and physiological signal analysis. Both vehicle-based measurements and behavior change detection require bulky components. They also identify the driver's drowsiness too late for effective accident prevention. The physiological signal changes in an early stage and can be used to detect the on-set of driver drowsiness. In this paper, the development of a wearable drowsiness detection system is introduced. This system measures the electrooculography (EOG) signal; transmits the signal to a smartphone wirelessly; and could alarm the driver based on a prediction algorithm that can estimate 0.5-second-ahead EOG signal behavior. This system is compact, comfortable, and cost effective. The 0.5-second-ahead estimation capability provides the critical time for a driver to correct the behavior, and ultimately saves lives.


title: Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

text: The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods.


title: Design, Fabrication, and Characterization of a 3-D CMOS Fluxgate Magnetometer

text: A dual-core 3-D microfluxgate magnetometer fabricated by a simple and inexpensive fabrication process is described in this paper. The microfluxgate is able to operate along a nearly linear V-B relationship at the second harmonic frequency and features good characteristics of high sensitivity and low noise response. These characteristic results indicate a field-to-voltage transfer coefficient of 11 V/T measured at the second harmonic frequency, power consumption of 67.3 mW, and a field noise response less than 12 nT/√ Hz at 1 Hz. In brief, our proposed device not only enhances responsivity capability and linear V-B characteristics, but also is CMOS process compatible, which is considered both function-efficient and cost-effective.


title: Performance Analysis of Big Data Gathering in Wireless Sensor Network Using an EM Based Clustering Scheme

text: Big-data is a popular term in the field of information and communication technology. Wireless Sensor Networks (WSN) is one of the eminent contributors of big data. WSN contains numerous sensor nodes that cooperatively monitor an environment. Each network consists of sensor node, memory, and communication device. Data generated by single sensor node is small but data generated by distributed sensor network is significantly large and it is termed as big-data. The critical issue in WSN is energy consumption and data gathering. This paper mainly focus on Expectation Maximization (EM) based clustering scheme implementation and the performance analysis of WSN using single mobile sink to eight mobile sink. From the analysis we also derived a relationship between the number of mobile sinks required for a particular network with a given number of sensor nodes. Experimental results show that the number of mobile sinks is also an important parameter to efficiently gather information in WSN.


title: All-dielectric subwavelength metasurface focusing lens.

text: We have proposed, designed, manufactured and tested low loss dielectric micro-lenses for infrared (IR) radiation based on a dielectric metamaterial layer. This metamaterial layer was created by patterning a dielectric surface and etching to sub-micron depths. For a proof-of-concept lens demonstration, we have chosen a fine patterned array of nano-pillars with variable diameters. Gradient index (GRIN) properties were achieved by engineering the nano-pattern characteristics across the lens, so that the effective optical density of the dielectric metamaterial layer peaks around the lens center, and gradually drops at the lens periphery. A set of lens designs with reduced reflection and tailorable phase gradients have been developed and tested, demonstrating focal distances of a few hundred microns, beam area contraction ratio up to three, and insertion losses as low as 11%.


title: Ontology-Based Information Extraction for Knowledge Enrichment and Validation

text: Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.


title: SecMANO: Towards Network Functions Virtualization (NFV) Based Security MANagement and Orchestration

text: Network Functions Virtualization (NFV) has recently emerged as one of the major technological driving forces that significantly accelerate the evolution of today's computer and communication networks. Despite the advantages of NFV, e.g., saving investment cost, optimizing resource consumption, improving operational efficiency, simplifying network service lifecycle management, lots of novel security threats and vulnerabilities will be introduced, thereby impeding its further development and deployment in practice. In this paper, we briefly report our threat analysis in the context of NFV, and identify the corresponding security requirements. The purpose is to establish a comprehensive threat taxonomy and provide a guideline to develop effective security countermeasures. Furthermore, a conceptual design framework for NFV based security management and service orchestration is presented, with an objective to dynamically and adaptively deploy and manage security functions on the demands of users and customers. A use case about NFV based access control is also developed, illustrating the feasibility and advantages of implementing NFV based security management and orchestration.


title: Information security management needs more holistic approach: A literature review

text: Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers’ attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management’s roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT nformation architecture infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area. © 2015 Elsevier Ltd. All rights reserved.


title: Incorporating Unsupervised Learning in Activity Recognition

text: Users are constantly involved in a multitude of activities in ever-changing context. Analyzing activities in contextrich environments has become a great challenge in contextawareness research. Traditional methods for activity recognition, such as classification, cannot cope with the variety and dynamicity of context and activities. In this paper, we propose an activity recognition approach that incorporates unsupervised learning. We analyze the feasibility of applying subspace clustering—a specific type of unsupervised learning— to high-dimensional, heterogeneous sensory input. Then we present the correspondence between clustering output and classification input. This approach has the potential to discover implicit, evolving activities, and can provide valuable assistance to traditional classification based methods. As sensors become prevalent means in context detection and information channels proliferate to make context sharing easier, it is increasingly challenging to interpret context and analyze its effects on the activities (Lim and Dey 2010). We argue that applying traditional approaches to activity recognition may become more and more difficult to apply in context and activity-rich environments. In the literature, context attributes used for learning activities are chosen by either empirical assumption or dimension reduction to render a small set of features (Krause, Smailagic, and Siewiorek 2006). These approaches are infeasible in face of a broad spectrum of context information. The most significant drawback is that they fail to acknowledge the large variety of features needed to describe different activities. For activity recognition, most previous works applied supervised learning approaches that aimed at predicting activities among a set of known classes(Ferscha et al. 2004). These approaches, however, are also challenged when coping with new and fast evolving activities. Unsupervised learning, particularly clustering, has been highly successful for revealing implicit relationships and regularities in large data sets. Intuitively, we can envisage an activity recognition approach that applies clustering to context history. The clusters, representing frequent context patterns, can suggest activities and their contextual conditions. The results can be used independently for analyzing and interpreting activities. Furthermore, clusters can reveal the scopes and conditions Copyright c © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. of activities and interactions. This information is valuable when determining the scopes of information sharing in pervasive environments. Although clustering is a promising approach in discovering associations within context, the feasibility of traditional clustering is questionable in dealing with high dimensionality and heterogeneity of context data. In this paper, we will first conduct a detailed analysis about the challenges to apply clustering for activity recognition. Afterwards we introduce two recent subspace clustering methods that can address these challenges. Lastly, based on the analysis of unsupervised activity recognition, we will propose an activity recognition framework that incorporates clustering in conventional classification. We will show that the two directions are complementary to each other, and developing a hybrid approach will greatly benefit activity context awareness. Unsupervised activity learning


title: Active Learning with Feedback on Both Features and Instances

text: We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorization tasks indicate that there is significant potential in improving classifier performance by feature re-weighting, beyond that achieved via membership queries alone (traditional active learning) if we have access to an oracle that can point to the important (most predictive) features. Our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion of the most relevant features (over 50% in our experiments). We find that on average, labeling a feature takes much less time than labeling a document. We devise an algorithm that interleaves labeling features and documents which significantly accelerates standard active learning in our simulation experiments. Feature feedback can complement traditional active learning in applications such as news filtering, e-mail classification, and personalization, where the human teacher can have significant knowledge on the relevance of features.


title: Fast image stitching and editing for panorama painting on mobile phones

text: We propose a fast image stitching and editing approach for panorama painting and have implemented it on a mobile phone. A seam search using dynamic programming finds a good boundary along which to merge the source images, and a transition smoothing process using instant image cloning removes merging artifacts, yielding highquality panoramic images. A sequential image stitching procedure requires only keeping the current source image in memory during image stitching, which saves memory and is more suitable to mobile phone implementations.


title: Studying Evolution with Self-Replicating Computer Programs

text: A critical discussion is presented on the use of self-replicating program systems as tools for the formulation of generalised theories of evolution. Results generated by such systems must be treated with caution, but, if used properly, they can ooer us unprecedented opportunities for empirical , comparative studies. A new system called Cosmos is introduced, which is based upon Ray's Tierra 15]. The major diierence between Cosmos and previous systems is that individual self-replicating programs in Cosmos are modelled (in a very simpliied fashion) on cellular organisms. Previous systems have generally used simpler self-replicators. The hope is that Cosmos may be better able to address questions concerning the sudden emergence of complex multicellular biological organisms during the Cambrian explosion. Results of initial exploratory runs are presented, which are somewhat diierent to those of similar runs on Tierra. These diierences were expected , and indicate the sensitivity of such systems to the precise details of the language in which the self-replicating programs are written. With the strengths and weaknesses of the methodology in mind, some directions for future research with Cosmos are discussed. Within the last decade, computers have become powerful and aaordable enough to enable a number of research groups to study the evolution of life in a new way. Rather than following the traditional approach of trying to capture properties of whole populations in mathematical models, the new approach models a large number of individual self-replicating entities which are competing against each other for resources required for replication. This is achieved by creating a computer which can run a large number of self-replicating programs in parallel 1. 1 In practice, a virtual computer is created (i.e. implemented in software), with parallelism simulated by time-slicing between Tom Ray pioneered this approach with his Tierra system 15, 16]. Since then, a number of other systems have also been developed, including Avida, developed by Chris Adami and Titus Brown 2], Computer Zoo, written by Jakob Skipper 18], and John Koza's system of self-replicating LISP-like programs 8]. Using such a methodology to study evolutionary systems is attractive for a number of reasons. For example, as the self-replicators are being modelled individually rather than as populations, the simulation respects the fact that a gene does not work in isolation. Rather, it is part of a large ensemble of genes which must all work together with some degree of cooperation in order for the individual organism …


title: CPLR: Collaborative pairwise learning to rank for personalized recommendation

text: Compared with explicit feedback data, implicit feedback data is easier to be collected and more widespread. However, implicit feedback data is also more difficult to be analyzed due to the lack of negative examples. Bayesian personalized ranking (BPR) is a well-known algorithm for personalized recommendation on implicit feedback data due to its high performance. However, it (1) treats all the unobserved feedback the same as negative examples which may be just caused by unseen, (2) treats all the observed feedback the same as positive examples which may be just caused by noisy action, and (3) assumes the preferences of users are independent which is difficult to achieve in reality. To solve all these problems, we propose a novel personalized recommendation algorithm called collaborative pairwise learning to rank (CPLR), which considers the influence between users on the preferences for both items with observed feedback and items without. To take these information into consideration, we try to optimize a generalized AUC instead of the standard AUC used in BPR. CPLR can be seen as a generalized BPR. Besides BPR, several extension algorithms of BPR, like social BPR (SBPR) and group preference based BPR (GBPR), are special cases of CPLR. Extensive experiments demonstrate the promise of our approach in comparison with traditional collaborative filtering methods and state-of-the-art pairwise learning to rank algorithms. Compared with the performance of baseline algorithms on five real-world data sets, the improvements of CPLR are over 17%, 23% and 22% for Pre@5, MAP and NDCG, respectively. © 2018 Published by Elsevier B.V.


title: Bangla license plate reader for metropolitan cities of Bangladesh using template matching

text: A simple algorithm for license plate reader for metropolitan cities in Bangladesh has been proposed in this paper. We have made use of a feature of Bangla script called “Matra” which joins the characters of a word together. Since the various words of a standard Bangladeshi license plate also have Matra, we have proposed a method where these words are segmented as single connected components and later recognized through template matching of words. There are also numbers and single letters in the number plate which are segmented and recognized as single characters. We have demonstrated that if we exploit the feature of Matra this way, the complexity of the algorithm is decreased, and the proposed system works smoothly for images taken under various conditions.


title: A Functional MRI Study of Happy and Sad Emotions in Music with and without Lyrics

text: Musical emotions, such as happiness and sadness, have been investigated using instrumental music devoid of linguistic content. However, pop and rock, the most common musical genres, utilize lyrics for conveying emotions. Using participants' self-selected musical excerpts, we studied their behavior and brain responses to elucidate how lyrics interact with musical emotion processing, as reflected by emotion recognition and activation of limbic areas involved in affective experience. We extracted samples from subjects' selections of sad and happy pieces and sorted them according to the presence of lyrics. Acoustic feature analysis showed that music with lyrics differed from music without lyrics in spectral centroid, a feature related to perceptual brightness, whereas sad music with lyrics did not diverge from happy music without lyrics, indicating the role of other factors in emotion classification. Behavioral ratings revealed that happy music without lyrics induced stronger positive emotions than happy music with lyrics. We also acquired functional magnetic resonance imaging data while subjects performed affective tasks regarding the music. First, using ecological and acoustically variable stimuli, we broadened previous findings about the brain processing of musical emotions and of songs versus instrumental music. Additionally, contrasts between sad music with versus without lyrics recruited the parahippocampal gyrus, the amygdala, the claustrum, the putamen, the precentral gyrus, the medial and inferior frontal gyri (including Broca's area), and the auditory cortex, while the reverse contrast produced no activations. Happy music without lyrics activated structures of the limbic system and the right pars opercularis of the inferior frontal gyrus, whereas auditory regions alone responded to happy music with lyrics. These findings point to the role of acoustic cues for the experience of happiness in music and to the importance of lyrics for sad musical emotions.


title: An Approach for Schema Extraction of JSON and Extended JSON Document Collections

text: JSON documents are raising as a common format for data representation due to the increasing popularity of NoSQL document-oriented databases. One of the reasons for such a popularity is their ability to handle large volumes of data at the absence of an explicit data schema. However, schema information is sometimes essential for applications during data retrieval, integration and analysis tasks, for example. Given this context, this paper presents an approach that extracts a schema from a JSON or Extended JSON document collection stored in a NoSQL document-oriented database or other document repository. Aggregation operations are considered in order to obtain a schema for each distinct structure in the collection, and a hierarchical data structure is proposed to group these schemas in order to generate a global schema in JSON Schema format. Experiments conducted on actual datasets, like DBPedia and Foursquare, demonstrate that the accuracy of the generated schemas is equivalent or even superior than related work.


title: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

text: After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.


title: A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories

text: Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images ( ). It is based on incorporating “generic” knowledge which may be obtained from previously learnt models of unrelated categories. We operate in a variational Bayesian framework: object categories are represented by probabilistic models, and “prior” knowledge is represented as a probability density function on the parameters of these models. The “posterior” model for an object category is obtained by updating the prior in the light of one or more observations. Our ideas are demonstrated on four diverse categories (human faces, airplanes, motorcycles, spotted cats). Initially three categories are learnt from hundreds of training examples, and a “prior” is estimated from these. Then the model of the fourth category is learnt from 1 to 5 training examples, and is used for detecting new exemplars a set of test images.


title: Quotidian resilience: Exploring mechanisms that drive resilience from a perspective of everyday stress and coping

text: Resilience is often associated with extreme trauma or overcoming extraordinary odds. This way of thinking about resilience leaves most of the ontogenetic picture a mystery. In the following review we put forth the Everyday Stress Resilience Hypothesis where resilience is analyzed from a systems perspective and seen as a process of regulating everyday life stressors. Successful regulation accumulates into regulatory resilience which emerges during early development from successful coping with the inherent stress in typical interactions. These quotidian stressful events lead to activation of behavioral and physiologic systems. Stress that is effectively resolved in the short run and with reiteration over the long-term increases children's as well as adults' capacity to cope with more intense stressors. Infants, however, lack the regulatory capacities to take on this task by themselves. Therefore, through communicative and regulatory processes during infant-adult interactions, we demonstrate that the roots of regulatory resilience originate in infants' relationship with their caregiver and that maternal sensitivity can help or hinder the growth of resilience.


title: Histological assessment of the palatal mucosa and greater palatine artery with reference to subepithelial connective tissue grafting

text: This study aimed to measure the thickness of the epithelium and lamina propria of the palatal mucosa and to elucidate the location of the greater palatine artery to provide the anatomical basis for subepithelial connective tissue grafting. Thirty-two maxillary specimens, taken from the canine distal area to the first molar distal area, were embedded in paraffin and stained with hematoxylin-eosin. The thickness of the epithelium and lamina propria of the palatal mucosa was measured at three positions on these specimens, starting from 3 mm below the alveolar crest and in 3-mm intervals. The location of the greater palatine artery was evaluated by using image-processing software. The mean epithelial thickness decreased significantly in the posterior teeth; it was 0.41, 0.36, 0.32, and 0.30 mm in the canine, first premolar, second premolar, and first molar distal areas, respectively. The lamina propria was significantly thicker in the canine distal; it was 1.36, 1.08, 1.09, and 1.05 mm, respectively. The mean length from the alveolar crest to the greater palatine artery increased toward the posterior molar; it was 7.76, 9.21, 10.93, and 11.28 mm, respectively. The mean depth from the surface of the palatal mucosa to the greater palatine artery decreased from the canine distal to the first premolar distal but increased again toward the posterior molar; it was 3.97, 3.09, 3.58, and 5.50 mm, respectively. Detailed histological assessments of the lamina propria of the palatal mucosa and the greater palatine artery are expected to provide useful anatomical guidelines for subepithelial connective tissue grafting.


title: Pose-Invariant Face Alignment with a Single CNN

text: Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, which can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare across multiple CNN architectures, all with the visualization layer, to further demonstrate the advantage of its utilization.


title: Persian Text Normalization using Classification Tree and Support Vector Machine

text: Text normalization is one of the most important tasks in text processing and text to speech conversion. In this paper, we propose a machine learning method to determine the type of Farsi language non-standard words (NSWs) by only using the structure of these words. Two methods including support vector machines (SVM) and classification and regression trees (CART) were used and evaluated on different training and test sets for NSW type classification in Farsi. The experimental results show that, NSW type classification in Farsi can be efficiently done by using only the structural form of Farsi non-standard words. In addition, the results is compared with a previous work done on normalization using multi-layer perceptron (MLP) neural network and shows that SVM outperforms MLP in both number of efforts and total performance


title: Fbufs: A High-Bandwidth Cross-Domain Transfer Facility

text: We have designed and implemented a new operating system facility for I/O buffer management and data transferacross protection domain boundaries on shared memory machines. This facility, called fast buffers (fbufs), combines virtual page remapping with shared virtual memory, and exploits locality in I/O traffic to achieve high throughput without compromising protection, security, or modularity. goal is to help deliver the high bandwidth afforded by emerging high-speed networks to user-level processes, both in monolithic and microkernel-based operating systems.This paper outlines the requirements for a cross-domain transfer facility, describes the design of the fbuf mechanism that meets these requirements, and experimentally quantifies the impact of fbufs on network performance.


title: Conditional High-Order Boltzmann Machines for Supervised Relation Learning

text: Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.


title: Rectifying the Bound Document Image Captured by the Camera: A Model Based Approach

text: A model based approach for rectifying the camera image of the bound document has been developed, i.e., the surface of the document is represented by a general cylindrical surface. The principle of using the model to unwrap the image is discussed. Practically, the skeleton of each horizontal text line is extracted to help estimate the parameter of the model, and rectify the images. To use the model, only a few priori is required, and no more auxiliary device is necessary. Experiment results are given to demonstrate the feasibility and the stability of the method.


title: The SAGE project: a storage centric approach for exascale computing: invited paper

text:
SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a storage centric approach as it is capable of storing and processing large data volumes at the Exascale regime.
 SAGE addresses the convergence of Big Data Analysis and HPC in an era of next-generation data centric computing. This convergence is driven by the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors where data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. A first prototype of the SAGE system has been been implemented and installed at the Jülich Supercomputing Center. The SAGE storage system consists of multiple types of storage device technologies in a multi-tier I/O hierarchy, including flash, disk, and non-volatile memory technologies. The main SAGE software component is the Seagate Mero Object Storage that is accessible via the Clovis API and higher level interfaces. The SAGE project also includes scientific applications for the validation of the SAGE concepts.
 The objective of this paper is to present the SAGE project concepts, the prototype of the SAGE platform and discuss the software architecture of the SAGE system.


title: Outlier Detection with Kernel Density Functions

text: Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).


title: Episodic Training for Domain Generalization

text: Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domain with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. As a demonstration, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training it for robustness to novel problems. This provides the largest-scale demonstration of heterogeneous DG to date.


title: Personality Based E-Recruitment System

text: Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.


title: Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder

text: The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.


title: Harnessing the power of FPGAs using altera's OpenCL compiler

text:
In recent years, Field-Programmable Gate Arrays have become extremely powerful computational platforms that can efficiently solve many complex problems. The most modern FPGAs comprise effectively millions of programmable elements, signal processing elements and high-speed interfaces, all of which are necessary to deliver a complete solution. The power of FPGAs is unlocked via low-level programming languages such as VHDL and Verilog, which allow designers to explicitly specify the behavior of each programmable element. While these languages provide a means to create highly efficient logic circuits, they are akin to "assembly language" programming for modern processors. This is a serious limiting factor for both productivity and the adoption of FPGAs on a wider scale. In this talk, we use the OpenCL language to explore techniques that allow us to program FPGAs at a level of abstraction closer to traditional software-centric approaches. OpenCL is an industry standard parallel language based on 'C' that offers numerous advantages that enable designers to take full advantage of the capabilities offered by FPGAs, while providing a high-level design entry language that is familiar to a wide range of programmers.
 To demonstrate the advantages a high-level programming language can offer, we demonstrate how to use Altera's OpenCL Compiler on a set of case studies. The first application is single-precision general-element matrix multiplication (SGEMM). It is an example of a highly-parallel algorithm for which an efficient circuit structures are well known. We show how this application can be implemented in OpenCL and how the high-level description can be optimized to generate the most efficient circuit in hardware. The second application is a Fast Fourier Transform (FFT), which is a classical FPGA benchmark that is known to have a good implementation on FPGAs. We show how we can implement the FFT algorithm, while exploring the many different possible architectural choices that lead to an optimized implementation for a given FPGA. Finally, we discuss a Monte-Carlo Black-Scholes simulation, which demonstrates the computational power of FPGAs. We describe how a random number generator in conjunction with computationally intensive operations can be harnessed on an FPGA to generate a high-speed benchmark, which also consumes far less power than the same benchmark running on a comparable GPU. We conclude the tutorial with a set of live demonstrations.
 Through this tutorial we show the benefits high-level languages offer for system-level design and productivity. In particular, Altera's OpenCL compiler is shown to enable high-performance application design that fully utilizes capabilities of modern FPGAs.


title: A Portable SDR Non-Orthogonal Multiple Access Testbed for 5G Networks

text: Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.


title: Lower Bounds for Finding Stationary Points of Non-Convex , Smooth High-Dimensional Functions ∗

text: We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex, high-dimensional functions. For functions with Lipschitz continuous pth derivative, we show that all algorithms—even randomized algorithms observing arbitrarily high-order derivatives—have worst-case iteration count Ω( −(p+1)/p). Our results imply that the O( −2) convergence rate of gradient descent is unimprovable without additional assumptions (e.g. Lipschitz Hessian), and that cubic regularization of Newton’s method and pth order regularization in general are similarly optimal. Additionally, we prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates better than O( −8/5), which is within −1/15 of the recently established Õ( −5/3) rate for accelerated gradient descent.


title: Bayesian Neural Word Embedding

text: Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the SkipGram objective and a detailed step by step description is provided. We present experimental results that demonstrate the performance of the proposed algorithm for word analogy and similarity tasks on six different datasets and show it is competitive with the original Skip-Gram method.


title: Exploring the factors affecting MOOC retention: A survey study

text: Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).


title: Corporate Sustainability : First Evidence on Materiality

text: An increasing number of companies make sustainability investments, and an increasing number of investors integrate sustainability performance data in their capital allocation decisions. To date however, the prior academic literature has not distinguished between investments in material versus immaterial sustainability issues. We develop a novel dataset by hand-mapping data on sustainability investments classified as material for each industry into firm-specific performance data on a variety of sustainability investments. This allows us to present new evidence on the value implications of sustainability investments. Using calendar-time portfolio stock return regressions we find that firms with good performance on material sustainability issues significantly outperform firms with poor performance on these issues, suggesting that investments in sustainability issues are shareholder-value enhancing. Further, firms with good performance on sustainability issues not classified as material do not underperform firms with poor performance on these same issues, suggesting investments in sustainability issues are at a minimum not value-destroying. Finally, firms with good performance on material issues and concurrently poor performance on immaterial issues perform the best. These results speak to the efficiency of firms’ sustainability investments, and also have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.  Mozaffar Khan is a visiting Associate Professor at Harvard Business School. George Serafeim is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. Aaron Yoon is a doctoral student at Harvard Business School. We are grateful for comments from seminar participants at National University of Singapore and Harvard Business School. We are grateful for financial support from the Division of Faculty Research and Development at Harvard Business School. George Serafeim has served on the Standards Council of SASB. Contact emails: mkhan@hbs.edu; gserafeim@hbs.edu; ayoon@hbs.edu.


title: VBaaS: VNF Benchmark-as-a-Service

text: When rolling out Network Function Virtualization (NFV) services, resource monitoring becomes a critical task subject to different cost-accuracy tradeoffs depending on whether continuous monitoring or more static infrastructure resource views are taken. In this context, we propose Virtualized Network Functions (VNF) Benchmark-as-a-Service (VBaaS) to enable not only run-time resource evaluation but also test-before-deploy opportunities for VNFs and NFV Infrastructures. We describe the motivation behind VBaaS and its main value proposition for a number of use cases around the orchestration tasks of VNF Forwarding Graphs We present the main components of VBaaS along their system interactions and interfaces, discussing the main benefits of adopting VBaaS and open research issues. Addressing the identified challenges and finalizing our proof of concept VBaaS are our main ongoing work activities.


title: AN ACCEPTANCE MODEL FOR USEFUL AND FUN INFORMATION SYSTEMS

text: Investigating the factors associated with user acceptance of new software systems has been an important research stream in the field of information systems for many years. The technology acceptance model has long been used to examine the acceptance of utilitarian systems. Recently, it has been used to examine recreational or pleasure-oriented systems. Many examples exist of software that, depending on the context of use, can be used for productive and pleasurable interaction. This paper examines the determinants of use of one such “dual” system. A survey of users of a dual system was conducted. Results show that perceived usefulness is more important in determining intention to use than perceived enjoyment, and that perceived ease of use has no direct impact on intention, but still has a strong indirect effect.


title: Interactive Neural Translation Assistance for Human Translators

text: We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.


title: Accurate and robust localization of duplicated region in copy–move image forgery

text: Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.


title: Using Information Content to Evaluate Semantic Similarity in a Taxonomy

text: 1. Please explain how this manuscript advances this field of research and/or contributes something new to the literature. They tackle the problem of semantic similarity based on the information content the concepts share. They combine the taxonomic structure with the empirical problem estimates which provides better way of adapting knowledge to multiple context concepts. They also try to find a solution to the multiple inheritance problems.


title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

text: This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.


title: Automatic Word Sense Discrimination

text: This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.


title: Using WordNet to Disambiguate Word Senses for Text Retrieval

text: This paper describes an automatic indexing procedure that uses the “IS-A” relations contained within WordNet and the set of nouns contained in a text to select a sense for each plysemous noun in the text. The result of the indexing procedure is a vector in which some of the terms represent word senses instead of word stems. Retrieval experiments comparing the effectivenss of these sense-based vectors vs. stem-based vectors show the stem-based vectors to be superior overall, although the sense-based vectors do improve the performance of some queries. The overall degradation is due in large part to the difficulty of disambiguating senses in short query statements. An analysis of these results suggests two conclusions: the IS-A links define a generalization/specialization hierarchy that is not sufficient to reliably select the correct sense of a noun from the set of fine sense distinctions in WordNet; and missing correct matches because of incorrect sense resolution has a much more deleterious effect on retrieval performance than does making spurious matches.


title: Are Slice-Based Cohesion Metrics Actually Useful in Effort-Aware Post-Release Fault-Proneness Prediction? An Empirical Study

text: Background. Slice-based cohesion metrics leverage program slices with respect to the output variables of a module to quantify the strength of functional relatedness of the elements within the module. Although slice-based cohesion metrics have been proposed for many years, few empirical studies have been conducted to examine their actual usefulness in predicting fault-proneness. Objective. We aim to provide an in-depth understanding of the ability of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction, i.e. their effectiveness in helping practitioners find post-release faults when taking into account the effort needed to test or inspect the code. Method. We use the most commonly used code and process metrics, including size, structural complexity, Halstead's software science, and code churn metrics, as the baseline metrics. First, we employ principal component analysis to analyze the relationships between slice-based cohesion metrics and the baseline metrics. Then, we use univariate prediction models to investigate the correlations between slice-based cohesion metrics and post-release fault-proneness. Finally, we build multivariate prediction models to examine the effectiveness of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction when used alone or used together with the baseline code and process metrics. Results. Based on open-source software systems, our results show that: 1) slice-based cohesion metrics are not redundant with respect to the baseline code and process metrics; 2) most slice-based cohesion metrics are significantly negatively related to post-release fault-proneness; 3) slice-based cohesion metrics in general do not outperform the baseline metrics when predicting post-release fault-proneness; and 4) when used with the baseline metrics together, however, slice-based cohesion metrics can produce a statistically significant and practically important improvement of the effectiveness in effort-aware post-release fault-proneness prediction. Conclusion. Slice-based cohesion metrics are complementary to the most commonly used code and process metrics and are of practical value in the context of effort-aware post-release fault-proneness prediction.


title: Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

text: Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resourcescarce and do not have such parallel data due to low volume of queries and nonexistence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resourcescarce languages. We propose a sequenceto-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.


title: Spatial-Aware Object-Level Saliency Prediction by Learning Graphlet Hierarchies

text: To fill the semantic gap between the predictive power of computational saliency models and human behavior, this paper proposes to predict where people look at using spatial-aware object-level cues. While object-level saliency has been recently suggested by psychophysics experiments and shown effective with a few computational models, the spatial relationship between the objects has not yet been explored in this context. We in this work for the first time explicitly model such spatial relationship, as well as leveraging semantic information of an image to enhance object-level saliency modeling. The core computational module is a graphlet-based (i.e., graphlets are moderate-sized connected subgraphs) deep architecture, which hierarchically learns a saliency map from raw image pixels to object-level graphlets (oGLs) and further to spatial-level graphlets (sGLs). Eye tracking data are also used to leverage human experience in saliency prediction. Experimental results demonstrate that the proposed oGLs and sGLs well capture object-level and spatial-level cues relating to saliency, and the resulting saliency model performs competitively compared with the state-of-the-art.


title: An interleaving and load sharing method for multiphase LLC converters

text: Interleaving frequency-controlled LLC resonant converters will encounter load-sharing problem due to the tolerance of the resonant components. In this paper, full-wave and half-wave switch-controlled capacitors (SCCs) are used in LLC stages to solve this problem. By using resonant capacitance as a control variable, the output current can be modulated even when all the LLC stages are synchronized at the same switching frequency. A design procedure is developed. A 600W prototype is built to verify the feasibility.


title: Transforming Models with ATL 1

text: This paper presents ATL (ATLAS Transformation Language): a hybrid model transformation language that allows both declarative and imperative constructs to be used in transformation definitions. The paper describes the language syntax by using examples. Language semantics is described in pseudo-code and various optimizations of transformation executions are discussed. ATL is supported by a set of development tools such as an editor, a compiler, a virtual machine, and a debugger. A case study shows the applicability of the language constructs. Alternative ways for implementing the case study are outlined. ATL language features are classified according to a model that captures the commonalities and variabilities of the model transformations domain.


title: Assessing It/Business Alignment

text: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.


title: Development of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection

text: of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection J. U. Knickerbocker P. S. Andry L. P. Buchwalter A. Deutsch R. R. Horton K. A. Jenkins Y. H. Kwark G. McVicker C. S. Patel R. J. Polastre C. Schuster A. Sharma S. M. Sri-Jayantha C. W. Surovic C. K. Tsang B. C. Webb S. L. Wright S. R. McKnight E. J. Sprogis B. Dang System-on-Package (SOP) technology based on silicon carriers has the potential to provide modular design flexibility and highperformance integration of heterogeneous chip technologies and to support robust chip manufacturing with high-yield/low-cost chips for a wide range of twoand three-dimensional product applications. Key technology enablers include silicon through-vias, high-density wiring, high-I/O chip interconnection, and supporting test and assembly technologies. The silicon through-vias are a key feature permitting efficient area array signal, power, and ground interconnection through these thinned silicon packages. Highdensity wiring and high-density chip I/O interconnection can enable tight integration of heterogeneous chip technologies which approximate the performance of an integrated system-on-chip with a ‘‘virtual chip’’ using the silicon package for integration. Silicon carrier fabrication leverages existing manufacturing capability and mid-UV lithography to provide very dense package wiring following CMOS back-end-of-line design rules. Further, the thermal expansion of the silicon carrier package matches the chip, which helps maintain reliability even as the high-density chip microbump interconnections scale to smaller size. In addition to heterogeneous chip integration, SOP products may leverage the integration of passive components, active devices, and electrooptic structures to enhance system-level performance while also maintaining functional test capability and known good chips when needed. This paper describes the technical challenges and recent progress made in the development of silicon carrier technology for potential new applications.


title: Optical Character Recognition from Text Image

text: Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact character using feature matching between the extracted character and the template of all characters as a measure of similarity.


title: Pomegranate: fast and flexible probabilistic modeling in python

text: We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate


title: Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework

text: Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user’s explicit awareness. For example, measures of the user’s cognitive workload might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI’s principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user’s state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user’s state to evaluate the digital flow of information to the user’s brain, tweaking this output in a feedback loop to the user’s benefit.


title: 3D face recognition using local binary patterns

text: It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition. & 2012 Elsevier B.V. All rights reserved.


title: Server Siblings: Identifying Shared IPv4/IPv6 Infrastructure Via Active Fingerprinting

text: We present, validate, and apply an active measurement technique that ascertains whether candidate IPv4 and IPv6 server addresses are “siblings,” i.e., assigned to the same physical machine. In contrast to prior efforts limited to passive monitoring, opportunistic measurements, or end-client populations, we propose an active methodology that generalizes to all TCP-reachable devices, including servers. Our method extends prior device fingerprinting techniques to improve their feasibility in modern environments, and uses them to support measurement-based detection of sibling interfaces. We validate our technique against a diverse set of 65 web servers with known sibling addresses and find it to be over 97% accurate with 99% precision. Finally, we apply the technique to characterize the top ∼6,400 Alexa IPv6-capable web domains, and discover that a DNS name in common does not imply that the corresponding IPv4 and IPv6 addresses are on the same machine, network, or even autonomous system. Understanding sibling and non-sibling relationships gives insight not only into IPv6 deployment and evolution, but also helps characterize the potential for correlated failures and susceptibility to certain attacks.


title: Syntax Parsing: Implementation Using Grammar-Rules for English Language

text: Syntactic parsing deals with syntactic structure of a sentence. The word 'syntax' refers to the grammatical arrangement of words in a sentence and their relationship with each other. The objective of syntactic analysis is to find syntactic structure of a sentence which is usually depicted as a tree. Identifying the syntactic structure is useful in determining the meaning of a sentence. Natural language processing is an arena of computer science and linguistics, concerned with the dealings amongst computers and human languages. It processes the data through lexical analysis, Syntax analysis, Semantic analysis, Discourse processing, Pragmatic analysis. This paper gives various parsing methods. The algorithm in this paper splits the English sentences into parts using POS tagger, It identifies the type of sentence (Facts, active, passive etc.) and then parses these sentences using grammar rules of Natural language. The algorithm has been tested on real sentences of English and it accomplished an accuracy of 81%.


title: English Language Learners and Academic Achievement : Revisiting the Threshold Hypothesis

text: This nonexperimental study explored the predictive strength of English proficiency levels on academic achievement of middle school students in a sample of 17,470 native English-speaking (NES) students, 558 English language learners (current ELLs), and 500 redesignated fluent English proficient students (former ELLs). Results of multilevel analyses indicated that after controlling for relevant studentand school-level characteristics, former ELLs significantly outperformed current ELL and NES students in reading (effect sizes: 1.07 and 0.52) and mathematics (effect sizes: 0.86 and 0.42). The results support Cummins’s (1979, 2000) lower level threshold hypothesis predicting that upon reaching adequate proficiency in the language of schooling and testing, ELLs would no longer experience academic disadvantages. Refinements for the theory and directions for future research are discussed.


title: LSCitter: building social machines by augmenting existing social networks with interaction models

text: We present LSCitter, an implemented framework for supporting human interaction on social networks with formal models of interaction, designed as a generic tool for creating social machines on existing infrastructure. Interaction models can be used to choreograph distributed systems, providing points of coordination and communication between multiple interacting actors. While existing social networks specify how interactions happen---who messages go to and when, the effects of carrying out actions---these are typically implicit, opaque and non user-editable. Treating interaction models as first class objects allows the creation of electronic institutions, on which users can then choose the kinds of interaction they wish to engage in, with protocols which are explicit, visible and modifiable. However, there is typically a cost to users to engage with these institutions. In this paper we introduce the notion of "shadow institutions", where actions on existing social networks are mapped onto formal interaction protocols, allowing participants access to computational intelligence in a seamless, zero-cost manner to carry out computation and store information.


title: PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks

text: Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.


title: The See-Through System: From implementation to test-drive

text: Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.


title: MediaPaaS: A Cloud-Based Media Processing Platform for Elastic Live Broadcasting

text: Mobility is changing the way of how people consume live media content. By staying always connected with the Internet from various mobile devices, people expect to have enhanced TV viewing experience from anywhere on any device. Therefore, live broadcasting needs to be widely accessible and customizable, instead of being passive content only on TV. In this paper we present a cloud-based media processing platform, called MediaPaaS, for enabling elastic live broadcasting in the cloud. As an ecosystem-oriented solution for content providers, we outsource complex media processing from both content providers and terminal devices to the cloud. A distributed media processing model is proposed to enable dynamic pipeline composition and cross-pipeline task sharing in the cloud for flexible live content processing. Also, a prediction-based task scheduling algorithm is presented to minimize cloud resource usage without affecting quality of streams. The MediaPaaS platform allows third-party application developers to extend its capability to enable certain customization for running live channels. To our knowledge, this paper is the first work to openly discuss the detailed design issues of a cloud-based platform for elastic live broadcasting.


title: The UJI librarian robot

text: This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.