title: An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)

text: The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.


title: IccTA: Detecting Inter-Component Privacy Leaks in Android Apps

text:
Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components.
 Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter-component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.


title: Water Quality Measurement and Control from Remote Station for Pisiculture Using NI myRIO Beena

text: A Wireless sensor network is designed and implemented to automatically and remotely monitor and control the water quality parameters such as temperature, turbidity, pH and dissolved oxygen required for pisiculture. The measurements of the system which is at base station are communicated to the remote farmer through built-in Wi-Fi of NI-myRIO and displayed on user’s computer screen from where he can monitor and change the settings of the system. An audio output is also produced from speakers of the computer. SMS alert is sent to farmer’s mobile for variations in the monitored parameters of the system at the base station via GSM modem. The programming of the entire system is done using LabVIEW software and NI myRIO which has inbuilt processor, FPGA, Wi-Fi and web servicing capabilities. Web publishing tool of LabVIEW allows internet access to the front panel of the system to any remote user.


title: Lessons Learned from AlphaGo

text: The game of Go is known to be one of the most complicated board games. Competing in Go against a professional human player has been a long-standing challenge for AI. In this paper we shed light on the AlphaGo program that could beat a Go world champion, which was previously considered non-achievable for the state of the art AI.


title: Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge

text: Rubik’s Revenge, a 4× 4× 4 variant of the Rubik’s puzzles, remains to date as an unsolved puzzle. That is to say, we do not have a method or successful categorization to optimally solve every one of its approximately 7.401×10 possible configurations. Rubik’s Cube, Rubik’s Revenge’s predecessor (3×3×3), with its approximately 4.33 × 10 possible configurations, has only recently been completely solved by Rokicki et. al, further finding that any configuration requires no more than 20 moves. [8] With the sheer dimension of Rubik’s Revenge and its total configuration space, a brute-force method of finding all optimal solutions would be in vain. Similar to the methods used by Rokicki et. al on Rubik’s Cube, in this paper we develop a method for solving arbitrary configurations of Rubik’s Revenge in phases, using a combination of a powerful algorithm known as IDA* and a useful definition of distance in the cube space. While time-series results were not successfully gathered, it will be shown that this method far outweighs current human-solving methods and can be used to determine loose upper bounds for the cube space. Discussion will suggest that this method can also be applied to other puzzles with the proper transformations.


title: Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

text: Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.


title: Detection of non-flat ground surfaces using V-Disparity images

text: Ground plane detection plays an important role in stereo vision based obstacle detection methods. Recently, V-Disparity image has been widely used for ground plane detection. The existing approach based on V-Disparity image can detect flat ground successfully but have difficulty in detecting non-flat ground. In this paper, we discuss the representation of non-flat ground in V-Disparity image, based on which we propose a method to detect non-flat ground using V-Disparity image.


title: Across the Digital Divide: A Cross-Country Multi-Technology Analysis of the Determinants of IT Penetration

text: This paper studies the country-level digital divide across successive generations of IT, providing detailed insights into the magnitude and changing nature of the divide. We examine a panel of 40 countries from 1985-2001, based on data from three distinct generations of IT: mainframes, personal computers, and the Internet. Using two measures of IT penetration, we conduct an empirical investigation of socio-economic factors driving the digital divide. We find that IT penetration is positively associated with national income for all three technology generations, and the association between penetration and income is stronger for countries with higher levels of IT penetration. We also examine other demographic and economic factors, going beyond income, and find significant differences in the nature of their effects across countries at different stages of IT adoption. Importantly, factors that previously may have been expanding the divide with earlier technologies are narrowing the gap as the Internet becomes the defining technology of the Information Age.


title: A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer

text: Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.


title: NGMN View on 5G Architecture

text: The fifth generation of mobile technology (5G) will address the socio-economic demands and business contexts of 2020 and beyond. Leading operators from around the globe have defined their vision for 5G, considering customer and business contexts as well as potential technologies and migration issues in an initiative set up by the NGMN Alliance. While their concrete vision is described in the NGMN 5G whitepaper, this paper describes the key points, such as 5G design principles, 5G components, network slicing, 5G radio access technology and 5G interfacing options that have implications to the 5G architecture design.


title: Highly deformable 3-D printed soft robot generating inching and crawling locomotions with variable friction legs

text: Soft and continuum robots have the useful capability of adopting intricate postures and conforming to complex shapes. Furthermore, structures built from soft materials propagate mechanical energy from one part of the body to another, depending on its body shape, boundary condition, stiffness distribution, and so on. This makes the robots capable of producing a large number of force profiles to achieve useful behaviors and functionalities, even using a small number of actuators. Exploiting the soft mechanical property also enables to generate functional frictional forces, which is a key issue in controlling robot locomotion. In this paper, a highly deformable 3-D printed soft robot (PS robot) is presented, which is capable of generating complex, robust gaits on different inclines using a novel variable friction leg design. This design changes the frictional force depending on the robot posture and shape to facilitate robot locomotion. Shape memory alloy (SMA) coils are embedded into the robot in such a way that they act both as structural elements and actuators. This is the first soft robot platform produced by 3-D printing making fabrication simple and fast.


title: NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

text: This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and allows stability to be enforced so that blocks can be unrolled adaptively to a pattern-dependent processing depth. NAIS-Net induces non-trivial, Lipschitz input-output maps, even for an infinite unroll length. We prove that the network is globally asymptotically stable so that for every initial condition there is exactly one input-dependent equilibrium assuming tanh units, and multiple stable equilibria for ReL units. An efficient implementation that enforces the stability under derived conditions for both fully-connected and convolutional layers is also presented. Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to ResNets.


title: Digital dentistry: an overview of recent developments for CAD/CAM generated restorations

text: As in many other industries, production stages are increasingly becoming automated in dental technology. As the price of dental laboratory work has become a major factor in treatment planning and therapy, automation could enable more competitive production in high-wage areas like Western Europe and the USA. Advances in computer technology now enable cost-effective production of individual pieces. Dental restorations produced with computer assistance have become more common in recent years. Most dental companies have access to CAD/CAM procedures, either in the dental practice, the dental laboratory or in the form of production centres. The many benefits associated with CAD/CAM generated dental restorations include: the access to new, almost defect-free, industrially prefabricated and controlled materials; an increase in quality and reproducibility and also data storage commensurate with a standardised chain of production; an improvement in precision and planning, as well as an increase in efficiency. As a result of continual developments in computer hardware and software, new methods of production and new treatment concepts are to be expected, which will enable an additional reduction in costs. Dentists, who will be confronted with these techniques in the future, require certain basic knowledge if they are to benefit from these new procedures. This article gives an overview of CAD/CAM-technologies and systems available for dentistry today.


title: Cognitive perspectives on SLA The Associative-Cognitive CREED *

text: This paper outlines current cognitive perspectives on second language acquisition (SLA). The Associative-Cognitive CREED holds that SLA is governed by the same principles of associative and cognitive learning that underpin the rest of human knowledge. The major principles of the framework are that SLA is Construction-based, Rational, Exemplar-driven, Emergent, and Dialectic. Language learning involves the acquisition of constructions that map linguistic form and function. Competence and performance both emerge from the dynamic system that is the frequency-tuned conspiracy of memorized exemplars of use of these constructions, with competence being the integrated sum of prior usage and performance being its dynamic contextualized activation. The system is rational in that it optimally reflects prior first language (L1) usage. The L1 tunes the ways in which learners attend to language. Learned-attention transfers to L2 and it is this L1 entrenchment that limits the endstate of usage-based SLA. But these limitations can be overcome by recruiting learner consciousness, putting them into a dialectic tension between the conflicting forces of their current stable states of interlanguage and the evidence of explicit form-focused feedback, either linguistic, pragmatic, or metalinguistic, that allows socially scaffolded development. The paper directs the reader to recent review articles in these key areas and weighs the implications of this framework.


title: Improving Text Classification by Shrinkage in a Hierarchy of Classes

text: When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the accuracy of a naive Bayes text classi er can be signi cantly improved by taking advantage of a hierarchy of classes. We adopt an established statistical technique called shrinkage that smoothes parameter estimates of a data-sparse child with its parent in order to obtain more robust parameter estimates. The approach is also employed in deleted interpolation, a technique for smoothing n-grams in language modeling for speech recognition. Our method scales well to large data sets, with numerous categories in large hierarchies. Experimental results on three real-world data sets from UseNet, Yahoo, and corporate web pages show improved performance, with a reduction in error up to 29% over the traditional at classi er.


title: Clinicopathological Findings of Chronic Rejection in a Face Grafted Patient.

text:
BACKGROUND
Skin chronic rejection (CR) in vascularized composite allotransplantation has not been included in the Banff classification yet. We report a face-transplant patient who developed cutaneous clinicopathologic changes suggestive of CR.

METHODS
The recipient was a 27-year-old man with severe disfigurement of the lower face due to a pyrotechnic explosion. He received a facial allograft, including mandible, cheeks, lips, and chin, in November 2009. Immunosuppression included antithymocyte globulins and bone-marrow infusion then steroids, tacrolimus, and mycophenolate mofetil.

RESULTS
During the first posttransplant year the acute rejection episodes were characterized by reversible oedema and erythema of the graft. Subsequently, the patient developed primary asymptomatic Epstein-Barr virus (EBV) infection, followed by EBV+ B-cell lymphoma and hepatic EBV-associated posttransplant smooth muscle tumors; therefore, the immunosuppressive treatment was greatly reduced. Since the second posttransplant year, the allografted facial skin became progressively sclerotic and presented pigmented macules on a background of hypopigmentation and teleangiectasias, resulting in a poikilodermatous aspect. Skin biopsies showed epidermal atrophy, basal cell vacuolization, and diffuse dermal sclerosis in the absence of significant dermal cell infiltration. The dermal capillaries showed thickened walls and narrowed lumina, whereas the large vessels did not show significant alterations. Neither donor-specific antibodies nor vascular Cd4 deposits were detected.A dysfunction of the graft functions occurred. It was evidenced by a decrease in mouth opening and modification of some phonemes although lip closure was still possible allowing food intake.

CONCLUSIONS
This is the first report suggestive of CR in a face allotransplantation after immunosuppression minimization.


title: Multiparty Computation from Somewhat Homomorphic Encryption

text: We propose a general multiparty computation protocol secure against an active adversary corrupting up to n−1 of the n players. The protocol may be used to compute securely arithmetic circuits over any finite field Fpk . Our protocol consists of a preprocessing phase that is both independent of the function to be computed and of the inputs, and a much more efficient online phase where the actual computation takes place. The online phase is unconditionally secure and has total computational (and communication) complexity linear in n, the number of players, where earlier work was quadratic in n. Moreover, the work done by each player is only a small constant factor larger than what one would need to compute the circuit in the clear. We show this is optimal for computation in large fields. In practice, for 3 players, a secure 64-bit multiplication can be done in 0.05 ms. Our preprocessing is based on a somewhat homomorphic cryptosystem. We extend a scheme by Brakerski et al., so that we can perform distributed decryption and handle many values in parallel in one ciphertext. The computational complexity of our preprocessing phase is dominated by the public-key operations, we need O(n/s) operations per secure multiplication where s is a parameter that increases with the security parameter of the cryptosystem. Earlier work in this model needed Ω(n) operations. In practice, the preprocessing prepares a secure 64-bit multiplication for 3 players in about 13 ms.


title: How To Exchange Secrets with Oblivious Transfer

text: As this paper puts forward the notion of “Oblivious Transfers” and is a well-known and frequently cited paper, I felt I should typeset the manuscript, and here is the result. While typesetting, I tried to stick to the original manuscript as much as possible. However, there were some cases, such as a few typos or punctuation marks, which were changed. As in many papers on cryptography, Alice and Bob play the role of participants of the given cryptographic protocols. For the sake of readability, Alice’s and Bob’s messages were printed in red and blue ink, respectively. This work was carefully proofread by my colleague Y. Sobhdel (sobhdel@ce.sharif.edu). Thanks also to H. M. Moghaddam for mentioning a minor mistake in an earlier version. That said, I will be thankful if you inform me of any possible mistakes.


title: Proving the TLS Handshake Secure (As It Is)

text: The TLS Internet Standard features a mixed bag of cryptographic algorithms and constructions, letting clients and servers negotiate their use for each run of the handshake. Although many ciphersuites are now well-understood in isolation, their composition remains problematic, and yet it is critical to obtain practical security guarantees for TLS. We experimentally confirm that all mainstream implementations of TLS share key materials between different algorithms, some of them of dubious strength. We outline attacks in their handling of resumption and renegotiation, stressing the need to model multiple related instances of the handshake. We study the provable security of the TLS handshake, as it is implemented and deployed. To capture the details of the standard and its main extensions, we rely on miTLS, a verified reference implementation of the protocol. miTLS inter-operates with mainstream browsers and servers for many protocol versions, configurations, and ciphersuites; and it provides application-level, provable security for some. We propose new agile security definitions and assumptions for the signatures, key encapsulation mechanisms (KEM), and key derivation algorithms used by the TLS handshake. By necessity, our definitions are stronger than those expected with simple modern protocols. To validate our model of key encapsulation, we prove that both RSA and Diffie-Hellman ciphersuites satisfy our definition for the KEM. In particular, we formalize the use of PKCS#1v1.5 encryption in TLS, including recommended countermeasures against Bleichenbacher attacks, and build a 3,000-line EasyCrypt proof of the security of the resulting master secret KEM against replayable chosen-ciphertext attacks under the assumption that ciphertexts are hard to re-randomize. Based on our new agile definitions, we construct a modular proof of security for the miTLS reference implementation of the handshake, including ciphersuite negotiation, key exchange, renegotiation, and resumption, treated as a detailed 3,600-line executable model. We present our main definitions, constructions, and proofs for an abstract model of the protocol, featuring series of related runs of the handshake with different ciphersuites. We also describe its refinement to account for the whole reference implementation, based on automated verification tools.


title: Founding Cryptography on Oblivious Transfer - Efficiently

text: We present a simple and efficient compiler for transforming secure multi-party computation (MPC) protocols that enjoy security only with an honest majority into MPC protocols that guarantee security with no honest majority, in the oblivious-transfer (OT) hybrid model. Our technique works by combining a secure protocol in the honest majority setting with a protocol achieving only security against semi-honest parties in the setting of no honest majority. Applying our compiler to variants of protocols from the literature, we get several applications for secure two-party computation and for MPC with no honest majority. These include: • Constant-rate two-party computation in the OT-hybrid model. We obtain a statistically UC-secure two-party protocol in the OT-hybrid model that can evaluate a general circuit C of size s and depth d with a total communication complexity of O(s)+ poly(k, d, log s) and O(d) rounds. The above result generalizes to a constant number of parties. • Extending OTs in the malicious model. We obtain a computationally efficient protocol for generating many string OTs from few string OTs with only a constant amortized communication overhead compared to the total length of the string OTs. • Black-box constructions for constant-round MPC with no honest majority. We obtain general computationally UC-secure MPC protocols in the OT-hybrid model that use only a constant number of rounds, and only make a black-box access to a pseudorandom generator. This gives the first constant-round protocols for three or more parties that only make a black-box use of cryptographic primitives (and avoid expensive zero-knowledge proofs).


title: Better Key Sizes (and Attacks) for LWE-Based Encryption

text: We analyze the concrete security and key sizes of theoretically sound lattice-based encryption schemes based on the “learning with errors” (LWE) problem. Our main contributions are: (1) a new lattice attack on LWE that combines basis reduction with an enumeration algorithm admitting a time/success tradeoff, which performs better than the simple distinguishing attack considered in prior analyses; (2) concrete parameters and security estimates for an LWE-based cryptosystem that is more compact and efficient than the well-known schemes from the literature. Our new key sizes are up to 10 times smaller than prior examples, while providing even stronger concrete security levels.


title: Time-series detection of perspiration as a liveness test in fingerprint devices

text: Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.


title: Applying WinWin to Quality Requirements: A Case Study

text: This paper describes the application of the WinWin paradigm to identify and resolve conflicts in a series of real-client, student-developer digital library projects. The paper is based on a case study of the statistical analysis of 15 projects and an in-depth analysis of one representative project. These analyses focus on the conflict resolution process, stakeholders' roles and their relationships to quality artifacts, and tool effectiveness. We show that stakeholders tend to accept satisfactory rather than optimal resolutions. Users and customers are more proactive in stating win conditions, whereas developers are more active in working toward resolutions. Further, we suggest that knowledge-based automated aids have potential to significantly enhance process effectiveness and efficiency. Finally, we conclude that such processes and tools have theoretical and practical implications in the quest for better software requirements elicitation.


title: Value Sensitive Design and Information Systems

text: Value Sensitive Design is a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process. It employs an integrative and iterative tripartite methodology, consisting of conceptual, empirical, and technical investigations. We explicate Value Sensitive Design by drawing on three case studies. The first study concerns information and control of web browser cookies, implicating the value of informed consent. The second study concerns using high-definition plasma displays in an office environment to provide a window to the outside world, implicating the values of physical and psychological well-being and privacy in public spaces. The third study concerns an integrated land use, transportation, and environmental simulation system to support public deliberation and debate on major land use and transportation decisions, implicating the values of fairness, accountability, and support for the democratic process, as well as a highly diverse range of values that might be held by different stakeholders, such as environmental sustainability, opportunities for business expansion, or walkable neighborhoods. We conclude with direct and practical suggestions for how to engage in Value Sensitive Design.


title: Linear dimensionality reduction: survey, insights, and generalizations

text: Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher’s linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.


title: The complex dielectric constant of pure and sea water from microwave satellite observations

text: We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.


Unmanned surface vehicles: An overview of developments and challenges


title: Concept-Based Information Retrieval Using Explicit Semantic Analysis

text: Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.


title: Measuring and Analyzing Write Amplification Characteristics of Solid State Disks

text: Write amplification brings endurance challenges to NAND Flash-based solid state disks (SSDs) such as impacts upon their write endurance and lifetime. A large write amplification degrades program/erase cycles (P/Es) of NAND Flashes and reduces the endurance and performance of SSDs. The write amplification problem is mainly triggered by garbage collections, wear-leveling, metadata updates, and mapping table updates. Write amplification is defined as the ratio of data volume written by an SSD controller to data volume written by a host. In this paper, we propose a four-level model of write amplification for SSDs. The four levels considered in our model include the channel level, chip level, die level, and plane level. In light of this model, we design a method of analyzing write amplification of SSDs to trace SSD endurance and performance by incorporating the Ready/Busy (R/B) signal of NAND Flash. Our practical approach aims to measure the value of write amplification for an entire SSD rather than NAND Flashes. To validate our measurement technique and model, we implement a verified SSD (vSSD) system and perform a cross-comparison on a set of SSDs, which are stressed by micro-benchmarks and I/O traces. A new method for SSDs is adopted in our measurements to study the R/B signals of NAND Flashes in an SSD. Experimental results show that our model is accurate and the measurement technique is generally applicable to any SSDs.


title: Dietary phenolics: chemistry, bioavailability and effects on health.

text: There is much epidemiological evidence that diets rich in fruit and vegetables can reduce the incidence of non-communicable diseases such as cardiovascular diseases, diabetes, cancer and stroke. These protective effects are attributed, in part, to phenolic secondary metabolites. This review summarizes the chemistry, biosynthesis and occurrence of the compounds involved, namely the C6-C3-C6 flavonoids-anthocyanins, dihydrochalcones, flavan-3-ols, flavanones, flavones, flavonols and isoflavones. It also includes tannins, phenolic acids, hydroxycinnamates and stilbenes and the transformation of plant phenols associated with food processing (for example, production of black tea, roasted coffee and matured wines), these latter often being the major dietary sources. Events that occur following ingestion are discussed, in particular, the deglycosylation, glucuronidation, sulfation and methylation steps that occur at various points during passage through the wall of the small intestine into the circulatory system and subsequent transport to the liver in the portal vein.We also summarise the fate of compounds that are not absorbed in the small intestine, but which pass into the large intestine where they are degraded by the colonic microflora to phenolic acids, which can be absorbed into the circulatory system and subjected to phase II metabolism prior to excretion. Initially, the protective effect of dietary phenolics was thought to be due to their antioxidant properties which resulted in a lowering of the levels of free radicals within the body.However, there is now emerging evidence that themetabolites of dietary phenolics,which appear in the circulatory systemin nmol/L to low mmol/L concentrations, exertmodulatory effects in cells through selective actions on different components of the intracellular signalling cascades vital for cellular functions such as growth, proliferation and apoptosis. In addition, the intracellular concentrations required to affect cell signalling pathways are considerably lower than those required to impact on antioxidant capacity. The mechanisms underlying these processes are discussed.


title: Visual Event Recognition in Videos by Learning from Web Data

text: We propose a visual event recognition framework for consumer videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). Observing that consumer videos generally contain large intraclass variations within the same type of events, we first propose a new method, called Aligned Space-Time Pyramid Matching (ASTPM), to measure the distance between any two video clips. Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). For each pyramid level and each type of local features, we first train a set of SVM classifiers based on the combined training set from two domains by using multiple base kernels from different kernel types and parameters, which are then fused with equal weights to obtain a prelearned average classifier. In A-MKL, for each event class we learn an adapted target classifier based on multiple base kernels and the prelearned average classifiers from this event class or all the event classes by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data. We also conduct an in-depth investigation on various aspects of the proposed method A-MKL, such as the analysis on the combination coefficients on the prelearned classifiers, the convergence of the learning algorithm, and the performance variation by using different proportions of labeled consumer videos. Moreover, we show that A-MKL using the prelearned classifiers from all the event classes leads to better performance when compared with A-MKL using the prelearned classifiers only from each individual event class.


title: Averaged Collapsed Variational Bayes Inference

text: This paper presents the Averaged CVB (ACVB) inference and offers convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. CVB inferences yield more precise inferences of Bayesian probabilistic models than Variational Bayes (VB) inferences. However, their convergence aspect is fairly unknown and has not been scrutinized. To make CVB more useful, we study their convergence behaviors in a empirical and practical approach. We develop a convergence-guaranteed algorithm for any CVB-based inference called ACVB, which enables automatic convergence detection and frees non-expert practitioners from the difficult and costly manual monitoring of inference processes. In experiments, ACVB inferences are comparable to or better than those of existing inference methods and deterministic, fast, and provide easier convergence detection. These features are especially convenient for practitioners who want precise Bayesian inference with assured convergence.


title: Measuring Machine Intelligence Through Visual Question Answering

text: As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.


title: Issues in User Authentication 1

text: CHI 2007 Workshop: Security user studies: methodol gies and best practices, April 2007. ABSTRACT In this brief paper we outline our recent work on u sability studies of user authentication systems. In particul ar, we have conducted studies on password managers and graphical password systems, using in-lab experiment s, interviews, and broader field studies. We then disc us some of the important issues arising, including mental m odels of security, the role of persuasion, and the nature of identify theft.


title: Network Traffic Management

text: The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.


title: Internal Uniplanar Antenna for LTE/GSM/UMTS Operation in a Tablet Computer

text: With the use of an internal printed loop matching circuit, a small-size uniplanar antenna with eight-band LTE/GSM/UMTS operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 262/1610 MHz for the LTE/GSM/UMTS bands, respectively. Only with the antenna size of $40\times 15\times 0.8\ {\rm mm}^{3}$, the proposed planar antenna has the compact operation with more than 30% antenna size reduction. Furthermore, the measured peak gains and antenna efficiencies are about 2.81/4.97 dBi and 79/87% for the LTE/GSM/UMTS bands, respectively.


title: Novice Programmers and the Problem Description Effect

text:
It is often debated whether a problem presented in a straightforward minimalist fashion is better, or worse, for learning than the same problem presented with a "real-life" or "concrete" context. The presentation, contextualization, or "problem description" has been well studied over several decades in disciplines such as mathematics education and psychology; however, little has been published in the field of computing education. In psychology it has been found that not only the presence of context, but the type of context can have dramatic results on problem success. In mathematics education it has been demonstrated that there are non-mathematical factors in problem presentation that can affect success in solving the problem and learning. The contextual background of a problem can also impact cognitive load, which should be considered when evaluating the effects of context. Further, it has been found that regarding cognitive load, computer science has unique characteristics compared to other disciplines, with the consequence that results from other disciplines may not apply to computer science, thus requiring investigation within computer science.
 This paper presents a multi-national, multi-institutional study of the effects of problem contextualization on novice programmer success in a typical CS1 exercise.


title: A PVT-insensitive CMOS output driver with constant slew rate

text: In this paper, we propose an output driver which has constant slew rate over PVT variation. To make output driver's slew rate to be constant, rising and failing time of pre-driving node of output driver is kept constant by predriver, which has constant pull-up and pull-down path resistance. To make output voltage swing level to be constant, another replica bias circuit was used with CMOS push-pull output driver. HSPICE simulations show that the slew rate is 3.21 V/nS on fast condition and 3.12V/nS on slow condition with 1.4K pre-driver's path resistance. This circuit was implemented with 0.18 /spl mu/m CMOS process.


title: Sensorless vector control of asynchronous machine based on reduced order Kalman filter

text: The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.


title: Rapid solution of problems by nuclear-magnetic resonance quantum computation

text: We offer an improved method for using a nuclear-magnetic-resonance quantum computer (NMRQC) to solve the Deutsch-Jozsa problem. Two known obstacles to the application of the NMRQC are exponential diminishment of density-matrix elements with the number of bits, threatening weak signal levels, and the high cost of preparing a suitable starting state. A third obstacle is a heretofore unnoticed restriction on measurement operators available for use by an NMRQC. Variations on the function classes of the Deutsch-Jozsa problem are introduced, both to extend the range of problems advantageous for quantum computation and to escape all three obstacles to use of an NMRQC. By adapting it to one such function class, the Deutsch-Jozsa problem is made solvable without exponential loss of signal. The method involves an extra work bit and a polynomially more involved Oracle; it uses the thermal-equilibrium density matrix systematically for an arbitrary number of spins, thereby avoiding both the preparation of a pseudopure state and temporal averaging.


title: 3D Face Recognition using Kinect

text: 2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.


title: Metaheuristics in combinatorial optimization: Overview and conceptual comparison

text: The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.


title: AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels

text: The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.


title: MODEL DIFFERENCES IN THE ECLIPSE MODELING FRAMEWORK

text: Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining a fundamental importance and cannot be neglected in software modeling and development. In this respect, an interesting and useful operation between the designs of subsequent system versions is the difference management consisting in calculation, representation, and visualization. This work presents EMF Compare, an approach to model difference calculation and representation for the Eclipse modeling framework. Apart from leveraging the rank of model differences to that of first-class artifacts according to the “everything is a model” principle, the approach presents several properties which are discussed according to a conceptual framework.


title: A Blended Deep Learning Approach for Predicting User Intended Actions

text: User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.


title: OINT C LOUDS

text: Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.


title: Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

text: Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.


title: Enterprise systems implementation and accounting benefits

text: Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption. Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices. Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules. Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.


title: Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

text: In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.


title: Secure Logging as a Service—Delegating Log Management to the Cloud

text: Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.


title: Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70

text: Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.


title: Severely suppressed bone turnover: a potential complication of alendronate therapy.

text: Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.


title: Risk factors for shoulder and elbow injuries in adolescent baseball pitchers.

text:
BACKGROUND
There is little evidence supporting current safety recommendations for adolescent pitchers.

HYPOTHESIS
Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.

STUDY DESIGN
Case control study; Level of evidence, 3.

METHODS
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.

RESULTS
The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.

CONCLUSION
Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.

CLINICAL RELEVANCE
New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.


title: Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations

text: The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.


title: Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?

text: We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.


title: The Polar Parallel Coordinates Method for Time-Series Data Visualization

text: Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.


title: Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites

text: The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.


title: Ranking scientific publications: the effect of nonlinearity

text: Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.


title: GradNets: Dynamic Interpolation Between Neural Architectures

text: In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.


title: A Survey on NoSQL Stores

text: Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.


title: Distributed Manufacturing Scheduling Using Intelligent Agents

text: reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's opti-mality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm , and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations , each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule—that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m. The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, …


title: A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances

text: This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.


title: Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder.

text:
PURPOSE OF THE RESEARCH
This study aimed to determine the prevalence of addiction to social networking sites/platforms (SNS) and its comorbidity with other behavioral addiction and affective disorder among college students in Singapore. 1110 college students (age: M=21.46, SD=1.80) in Singapore completed measures assessing online social networking, unhealthy food intake and shopping addiction as well as depression, anxiety and mania. Descriptive analyses were conducted to investigate the prevalence and comorbidity of behavioral addiction and affective disorder. Chi-square tests were used to examine gender differences.

THE PRINCIPAL RESULTS
The prevalence rates of SNS, food and shopping addiction were 29.5%, 4.7% and 9.3% respectively for the total sample. SNS addiction was found to co-occur with food addiction (3%), shopping addiction (5%), and both food and shopping addiction (1%). The comorbidity rates of SNS addiction and affective disorder were 21% for depression, 27.7% for anxiety, and 26.1% for mania. Compared with the total sample, students with SNS addiction reported higher comorbidity rates with other behavioral addiction and affective disorder. In general, females as compared to males reported higher comorbidity rates of SNS addiction and affective disorder.

MAJOR CONCLUSIONS
SNS addiction has a high prevalence rate among college students in Singapore. Students with SNS addiction were vulnerable to experience other behavior addiction as well as affective disorder, especially among females.


title: Context Aware Computing for The Internet of Things: A Survey

text: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.


title: Cerberus: a context-aware security scheme for smart spaces

text: Ubiquitous computing has fueled the idea of constructing sentient, information-rich "smart spaces" that extend the boundaries of traditional computing to encompass physical spaces, embedded devices, sensors, and other machinery. To achieve this, smart spaces need to capture situational information so that they can detect changes in context and adapt themselves accordingly. However, without considering basic security issues ubiquitous computing environments could be rife with vulnerabilities. Ubiquitous computing environments impose new requirements on security. Security services, like authentication and access control, have to be non-intrusive, intelligent, and able to adapt to the rapidly changing contexts of the spaces. We present a ubiquitous security mechanism that integrates context-awareness with automated reasoning to perform authentication and access control in ubiquitous computing environments.


title: Managing Context Information for Adaptive Security in IoT Environments

text: Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.


title: A Context-Aware Security Architecture for Emerging Applications

text: We describe an approach to building security services for context-aware environments. Specifically, we focus on the design of security services that incorporate the use of security-relevant “context” to provide flexible access control and policy enforcement. We previously presented a generalized access control model that makes significant use of contextual information in policy definition. This document provides a concrete realization of such a model by presenting a system-level service architecture, as well as early implementation experience with the framework. Through our context-aware security services, our system architecture offers enhanced authentication services, more flexible access control and a security subsystem that can adapt itself based on current conditions in the environment. We discuss our architecture and implementation and show how it can be used to secure several sample applications.


title: Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

text: Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.


title: Message-Locked Encryption for Lock-Dependent Messages

text: Motivated by the problem of avoiding duplication in storage systems, Bellare, Keelveedhi, and Ristenpart have recently put forward the notion of Message-Locked Encryption (MLE) schemes which subsumes convergent encryption and its variants. Such schemes do not rely on permanent secret keys, but rather encrypt messages using keys derived from the messages themselves. We strengthen the notions of security proposed by Bellare et al. by considering plaintext distributions that may depend on the public parameters of the schemes. We refer to such inputs as lock-dependent messages. We construct two schemes that satisfy our new notions of security for message-locked encryption with lock-dependent messages. Our main construction deviates from the approach of Bellare et al. by avoiding the use of ciphertext components derived deterministically from the messages. We design a fully randomized scheme that supports an equality-testing algorithm defined on the ciphertexts. Our second construction has a deterministic ciphertext component that enables more efficient equality testing. Security for lock-dependent messages still holds under computational assumptions on the message distributions produced by the attacker. In both of our schemes the overhead in the length of the ciphertext is only additive and independent of the message length.


title: Miller Decoding Algorithm Using Sample Recovery and Correlation Method for UHF Gen2 RFID System

text: In this paper, by analyzing the characteristics of the received signal in both practical and theory environment, a Miller decoding algorithm basing on a sample recovery method and correlation concept is designed. The sample recovery method is designed to apply on the samples of the received signal which often include some degree of distortions caused by noise or timing jitter. Basing on the concept of matched filter, which is obtained by correlating the received signal with a known reference signal to detect the presence of the data symbol in the received signal, a Miller decoding algorithm utilizing correlation is designed. Together with the signal recovery method, the Miller decoding algorithm using correlation shows a good decoding performance under condition of timing jitter and high signal corruption rate caused by noise.


title: Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models

text: Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.


title: A Generative Entity-Mention Model for Linking Entities with Knowledge Base

text: Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entitymention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods.


title: Toward understanding characteristics of dedicated short range communications (DSRC) from a perspective of vehicular network engineers

text: IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. Here, using a large set of empirical measurement data taken in a rich variety of realistic driving environments, we attempt to characterize communication properties of DSRC as well as to analyze the causes of communication loss. Specifically, from a perspective of vehicular network engineers, the fundamental characteristic of DSRC communications is Packet Delivery Ratio (PDR). We investigate the impact of both uncontrollable environmental factors and controllable radio parameters on DSRC characteristics. Moreover, we also examine temporal correlation, spatial correlation and symmetric correlation of DSRC characteristics under realistic vehicular environments.


title: Learning Common Representation from RGB and Depth Images

text: We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.


title: Brain Activity during Episodic Retrieval of Autobiographical and Laboratory Events: An fMRI Study using a Novel Photo Paradigm

text: Functional neuroimaging studies of episodic memory retrieval generally measure brain activity while participants remember items encountered in the laboratory (controlled laboratory condition) or events from their own life (open autobiographical condition). Differences in activation between these conditions may reflect differences in retrieval processes, memory remoteness, emotional content, retrieval success, self-referential processing, visual/spatial memory, and recollection. To clarify the nature of these differences, a functional MRI study was conducted using a novel photo paradigm, which allows greater control over the autobiographical condition, including a measure of retrieval accuracy. Undergraduate students took photos in specified campus locations (controlled autobiographical condition), viewed in the laboratory similar photos taken by other participants (controlled laboratory condition), and were then scanned while recognizing the two kinds of photos. Both conditions activated a common episodic memory network that included medial temporal and prefrontal regions. Compared with the controlled laboratory condition, the controlled autobiographical condition elicited greater activity in regions associated with self-referential processing (medial prefrontal cortex), visual/ spatial memory (visual and parahippocampal regions), and recollection (hippocampus). The photo paradigm provides a way of investigating the functional neuroanatomy of real-life episodic memory under rigorous experimental control.


title: Ambient RF Energy-Harvesting Technologies for Self-Sustainable Standalone Wireless Sensor Platforms

text: In this paper, various ambient energy-harvesting technologies (solar, thermal, wireless, and piezoelectric) are reviewed in detail and their applicability in the development of self-sustaining wireless platforms is discussed. Specifically, far-field low-power-density energy-harvesting technology is thoroughly investigated and a benchmarking prototype of an embedded microcontroller-enabled sensor platform has been successfully powered by an ambient ultrahigh-frequency (UHF) digital TV signal (512-566 MHz) where a broadcasting antenna is 6.3 km away from the proposed wireless energy-harvesting device. A high-efficiency dual-band ambient energy harvester at 915 MHz and 2.45 GHz and an energy harvester for on-body application at 460 MHz are also presented to verify the capabilities of ambient UHF/RF energy harvesting as an enabling technology for Internet of Things and smart skins applications.


title: Advanced Voltage Support and Active Power Flow Control in Grid-Connected Converters Under Unbalanced Conditions

text: Supporting the grid and improving its reliability have recently become major requirements for large distributed generation units. Under most grid faults, the accuracy of the traditional voltage support schemes (VSSs) is dramatically affected due to the existence of the zero-sequence voltage. Also, the traditional VSSs have been used only in the STATCOM applications, where the active power is zero. This paper proposes an advanced VSS in the converter-interfaced units, called zero-sequence compensated voltage support (ZCVS), to accurately regulate the three-phase voltages of the connection point within the pre-set safety limits. The proposed scheme not only compensates the zero-sequence component but also considers the active power injection. Unlike the traditional methods, the proposed VSS is adapted even in resistive distribution systems. The contribution of this paper is, however, ternate. As the second contribution, the limited active power oscillation (LAPO) is proposed to be augmented to the ZCVS. This feature limits the oscillation to a specified value which provides an adjustable dc-link voltage oscillation setting while simultaneously supporting the ac host grid, even under severe unbalanced faults. Third, the maximum active power delivery (MAPD) to the ac grid is also formulated for the ZCVS. The successful results of the proposed support scheme and complementary strategies are verified using selected simulation and experimental test cases.


title: A critical examination of 3D stackable NAND Flash memory architectures by simulation study of the scaling capability

text: Various 3D NAND Flash array architectures including P-BiCS, TCAT, VSAT, and VG are critically examined in this work by extensive 3D TCAD simulations. All structures have X,Y lateral scaling limitation since the minimal ONO thickness (∼20 nm) and poly channel thickness (∼10nm) can not be scaled further. Among them VG may have the best X-direction scalability to F∼2X nm node, and no penalty of increasing Z layer number since the channel current flows horizontally. We propose a buried-channel junction-free NAND to improve the read current for all 3D NAND arrays and our simulation results well support this structure. For the first time, “Z-interference” in 3D NAND Flash is examined and it indicates a new Z-direction scaling limitation. The present work is of crucial importance in understanding various 3D NAND Flash approaches.


title: ICIET ’ 14 Image Steganography Method Using Integer Wavelet Transform

text: Digital Steganography explains the art and science of writing hidden messages in such a way that, apart from the sender and intended recipient, no one suspects the existence of the message, a form of security through the state of being unknown. The main two famous schemes used for image steganography are spatial domain embedding and transform domain embedding. The main aim of Wavelet Transform is used to transform original image (cover image) from spatial domain to frequency domain. But here in our proposed work, Integer Wavelet Transform is performed on a gray level cover image and in turn embeds the message bitstream into the LSB's of the integer wavelet coefficients of a the image . The main purpose of the proposed work is to focus on improving embedding capacity and bring down the distortion occurring to the stego image. The refinement of the algorithm plays an important role for accomplishing higher embedding capacity and low distortion rate. The experimental results prove that the assessment metric such as PSNR is improved in a high manner. The experimental results show that the algorithm has a high capacity and a good invisibility.


Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates


title: Business ecosystem as the new approach to complex adaptive business environments

text: This paper discusses the concept of business ecosystem. Business ecosystem is a relatively new concept in the field of business research, and there is still a lot of work to be done to establish it. First the subject is approached by examining a biological ecosystem, especially how biological ecosystems are defined, how they evolve and how they are classified and structured. Second, different analogies of biological ecosystem are reviewed, including industrial ecosystem, economy as an ecosystem, digital business ecosystem and social ecosystem. Third, business ecosystem concept is outlined by discussing views of main contributors and then bringing authors’ own definition out. Fourth, the emerging research field of complexity in social sciences is brought out due to authors’ attitude to consider ecosystems and business ecosystems as complex, adaptive systems. The focal complexity aspects appearing in business ecosystems are presented; they are self-organization, emergence, co-evolution and adaptation. By connecting business ecosystem concept to complexity research, it is possible to bring new insights to changing business environments.