title: Color Emotions for Image Classification and Retrieval

abstract: Many content-based image retrieval systems are not takinginto account high level semantic information. In this paperwe describe our attempts to include color-based emotion-relatedproperties of images in the search. We show that using coloremotion metrics in content-based image retrieval leads to interestingmethods for image retrieval and classification based onsemantic concepts. The color emotion metric used is derivedfrom psychophysical experiments and uses three scales: activity,weight and heat. It was originally designed for single-colorcombinations and later extended to include pairs of colors. Weshow that a modified approach for statistical analysis of coloremotions in images, involving transformations of ordinary RGBhistograms,provides a useful tool for image classification andretrieval. The methods used are both very fast in feature extraction,and descriptor vectors are very short. This is essential inour application where we intend to use it for searching huge imagedatabases containing millions or billions of images.


Creating a Raspberry Pi-Based Beowulf Cluster


title: Learning and Evaluating General Linguistic Intelligence

abstract: We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task. Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (e.g., for fine tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving general tasks (e.g., document question answering), our models are overfitting to the quirks of particular datasets (e.g., SQuAD). We discuss missing components and conjecture on how to make progress toward general linguistic intelligence.


STT-MRAM Operating at 0.38V Using Negative-Resistance Sense Amplifier


title: Relationship between generic strategies, competitive advantage and organizational performance: An empirical analysis

abstract: In recent years, there has been a growing intensity of competition in virtually all areas of business in both markets upstream for raw materials such as components, supplies, capital and technology and markets downstream for consumer goods and services. This paper examines the relationships among generic strategy, competitive advantage, and organizational performance. Firstly, the nature of generic strategies, competitive advantage, and organizational performance is examined. Secondly, the relationship between generic strategies and competitive advantage is analyzed. Finally, the implications of generic strategies, organizational performance, performance measures and competitive advantage are studied. This study focuses on: (i) the relationship of generic strategy and organisational performance in Australian manufacturing companies participating in the “Best Practice Program in Australia”, (ii) the relationship between generic strategies and competitive advantage, and (iii) the relationship among generic strategies, competitive advantage and organisational performance. © 1999 Elsevier Science Ltd. All rights reserved.


title: Financial Dependence and Growth

abstract: Does finance affect economic growth? A number of studies have identified a positive correlation between the level of development of a country's financial sector and the rate of growth of its per capita income. As has been noted elsewhere, the observed correlation does not necessarily imply a causal relationship. This paper examines whether financial development facilitates economic growth by scrutinizing one rationale for such a relationship; that financial development reduces the costs of external finance to firms. Specifically, we ask whether industrial sectors that are relatively more in need of external finance develop disproportionately faster in countries with more developed financial markets. We find this to be true in a large sample of countries over the 1980s. We show this result is unlikely to be driven by omitted variables, outliers, or reverse causality.


title: A LITERATURE STUDY ON TRAFFIC, TRAFFIC CONTROL DEVICES, ROUNDABOUTS, PEDESTRIAN ACCESS

abstract: Traffic is formally organized in many jurisdictions, with marked lanes, junctions, intersections, interchanges, traffic signals, or signs. Traffic is often classified by type: heavy motor vehicle (e.g., car, truck); other vehicle (e.g., moped, bicycle); and pedestrian. Different classes may share speed limits and easement, or may be segregated. Some jurisdictions may have very detailed and complex rules of the road while others rely more on drivers' common sense and willingness to cooperate. Organization typically produces a better combination of travel safety and efficiency. Events which disrupt the flow and may cause traffic to degenerate into a disorganized mess include: road construction, collisions and debris in the roadway. Signs evolved from local practice, cities and states copying neighbors, and inventing what they needed, and then later standardizing (first for rural and urban areas separately, and then jointly) after the value of coordination became apparent when automobile travelers crossed jurisdictional boundaries. From the first center line in Michigan in 1911, stop sign in 1915, a 1923 established the basis of the shapes used for road signs today. The Mississippi Valley Association of State Highway Departments proposed the following. Shapes which were most complex to make (circle, octagon) would indicate most danger and be used least often. This paper deals with literature study on Traffic, Traffic Control Devices, Roundabouts, Pedestrian Access.


title: Automatically Generating Discussion Questions

abstract: Automatic question generation can support instruction and learning. However, work to date has produced mostly “shallow” questions that fall short of supporting deep learning and discussion. We propose an extension to a state-of-the-art question generation system that allows it to produce deep, subjective questions suitable for group discussion. We evaluate the questions generated by this system against a panel of experienced judges, and find that our approach fares significantly better than the baseline system.


title: How to make Zuse's Z3 a universal computer

abstract: The computing machine Z3, built by Konrad Zuse between 1938 and 1941, could execute only fixed sequences of floating point arithmetical operations (addition, subtraction, multiplication, division, and square root) coded in a punched tape. An interesting question to ask, from the viewpoint of the history of computing, is whether or not these operations are sufficient for universal computation. The paper shows that, in fact, a single program loop containing these arithmetical instructions can simulate any Turing machine whose tape is of a given finite size. This is done by simulating conditional branching and indirect addressing by purely arithmetical means. Zuse's Z3 is therefore, at least in principle, as universal as today's computers that have a bounded addressing space. A side effect of this result is that the size of the program stored on punched tape increases enormously.


Microsoft CEP Server and Online Behavioral Targeting


title: TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.


title: A biometric security scheme for wireless body area networks

abstract: Abstract Wireless body area networks (WBANs) are receiving significant interest as the next generation of wireless networks and emerging technology in the field of health monitoring. One of the most important factors for the acceptance of WBANs is the provision of appropriate security and access control mechanisms. Due to its nature in transferring the patients' sensitive data, WBAN has both classical and specific security requirements. In this paper, we survey such requirements and propose a new security scheme for satisfying them in WBANs. The proposed scheme deals with the overall network architecture, including intra- and inter-WBAN tiers, and proposes two mutual authentication and key exchange protocols for diverse WBAN environments. In our scheme, we use biometrics as one part of the solution for authentication and key exchange, and the simple password three-party key exchange protocol as the other part of the WBAN security. Our scheme meets security requirements along with energy-constraint considerations. We verify our scheme through BAN Logic. Unlike the majority of the existing security protocols, our scheme proposes a solution for entire WBANs communications, from biosensors to the medical server as a trusted third party.


title: Semantic Web applications : a framework for industry and business exploitation – What is needed for the adoption of the Semantic Web from the market and industry

abstract: In recent years, Semantic Web (SW) research has resulted in significant outcomes. Various industries have adopted SW technologies, while the 'deep web' is still pursuing the critical transformation point, in which the majority of data found on the deep web will be exploited through SW value layers. In this article we analyse the SW applications from a 'market' perspective. We are setting the key requirements for real-world information systems that are SW-enabled and we discuss the major difficulties for the SW uptake that has been delayed. This article contributes to the literature of SW and knowledge management providing a context for discourse towards best practices on SW-based information systems.


title: Artwork 3D model database indexing and classification

abstract: This paper presents a framework for the indexing and retrieval of artwork 3D models, allowing global and partial model classification and retrieval. The first part of the paper deals with database classification based on global shape descriptors. A search engine ''RETIN-3D'', using a SVM classifier coupled with an active learning strategy allows to retrieve categories of similar objects. In a second part, the classification is improved thanks to a local description of the models. A new framework for 3D surface segmentation is proposed. Shape descriptors are adapted to surface regions and kernels on descriptor bags are used to perform the database classification. Our system is designed for classifying and retrieving in ancient artwork 3D databases, and results from this application domain are presented and commented along the paper.


title: Video-based face recognition using probabilistic appearance manifolds

abstract: This paper presents a method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space, the complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.


South Asian Stability-Instability Paradox : Another Perspective


title: Combating VR sickness through subtle dynamic field-of-view modification

abstract: Virtual Reality (VR) sickness can cause intense discomfort, shorten the duration of a VR experience, and create an aversion to further use of VR. High-quality tracking systems can minimize the mismatch between a user's visual perception of the virtual environment (VE) and the response of their vestibular system, diminishing VR sickness for moving users. However, this does not help users who do not or cannot move physically the way they move virtually, because of preference or physical limitations such as a disability. It has been noted that decreasing field of view (FOV) tends to decrease VR sickness, though at the expense of sense of presence. To address this tradeoff, we explore the effect of dynamically, yet subtly, changing a physically stationary person's FOV in response to visually perceived motion as they virtually traverse a VE. We report the results of a two-session, multi-day study with 30 participants. Each participant was seated in a stationary chair, wearing a stereoscopic head-worn display, and used control and FOV-modifying conditions in the same VE. Our data suggests that by strategically and automatically manipulating FOV during a VR session, we can reduce the degree of VR sickness perceived by participants and help them adapt to VR, without decreasing their subjective level of presence, and minimizing their awareness of the intervention.


title: CHALLENGES IN PARALLEL GRAPH PROCESSING

abstract: Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications are not necessarily effective for large-scale graph problems. In this paper we present the inter-relationships between graph problems, software, and parallel hardware in the current state of the art and discuss how those issues present inherent challenges in solving large-scale graph problems. The range of these challenges suggests a research agenda for the development of scalable high-performance software for graph problems.


title: World of Empowered IoT Users

abstract: In a world deploying an Internet of Things, sensors and actuators are owned, accessed, and activated by a plethora of individuals and organizations. Access to the data produced by this world can both be beneficial and have drawbacks to society. This data potentially represents the activities of millions of individuals and their possessions collected by billions of "things'. Aggregations of this data can be analyzed through the Internet and Clouds. This raises possible privacy, security, moral and ethical challenges whose solutions will require flexible protection mechanisms. How do we "acquire" and "distribute" data at the IoT world scale while retaining the rights of individuals and organizations to protect, use, and share their data? Clearly a well-defined mechanism and control needs to regulate access to the data and its aggregations. Our paper describes a user-centric multi-level multiple granularity mechanism to share the data from these devices to people and organizations. Revisiting the fundamental mechanisms in security for providing protection, our solution uses capabilities, access lists, and access rights following well-understood formal notions for reasoning about access. Our contribution is to describe an auditable, transparent, distributed, decentralized, publication-subscription based, robust mechanism and automation of these ideas in the IoT realm that is well-matched to the current generation of clouds. It is based on well-tested principles and practices used in crypto currencies exploiting block chains of transactions. The scheme puts users (including organizational entities) in the center of control over the access to their collections of sensory data. In our paper, we describe a deployment of these ideas for health care, smart cities, and autonomous cars.


title: Stitching the gadgets: on the ineffectiveness of coarse-grained control-flow integrity protection

abstract: Return-oriented programming (ROP) offers a robust attack technique that has, not surprisingly, been extensively used to exploit bugs in modern software programs (e.g., web browsers and PDF readers). ROP attacks require no code injection, and have already been shown to be powerful enough to bypass fine-grained memory randomization (ASLR) defenses. To counter this ingenious attack strategy, several proposals for enforcement of (coarse-grained) control-flow integrity (CFI) have emerged. The key argument put forth by these works is that coarse-grained CFI policies are sufficient to prevent ROP attacks. As this reasoning has gained traction, ideas put forth in these proposals have even been incorporated into coarse-grained CFI defenses in widely adopted tools (e.g., Microsoft's EMET framework). ::: ::: In this paper, we provide the first comprehensive security analysis of various CFI solutions (covering kBouncer, ROPecker, CFI for COTS binaries, ROP-Guard, and Microsoft EMET 4.1). A key contribution is in demonstrating that these techniques can be effectively undermined, even under weak adversarial assumptions. More specifically, we show that with bare minimum assumptions, turing-complete and real-world ROP attacks can still be launched even when the strictest of enforcement policies is in use. To do so, we introduce several new ROP attack primitives, and demonstrate the practicality of our approach by transforming existing real-world exploits into more stealthy attacks that bypass coarse-grained CFI defenses.


title: Direct and gestural interaction with relief: a 2.5D shape display

abstract: Actuated shape output provides novel opportunities for experiencing, creating and manipulating 3D content in the physical world. While various shape displays have been proposed, a common approach utilizes an array of linear actuators to form 2.5D surfaces. Through identifying a set of common interactions for viewing and manipulating content on shape displays, we argue why input modalities beyond direct touch are required. The combination of freehand gestures and direct touch provides additional degrees of freedom and resolves input ambiguities, while keeping the locus of interaction on the shape output. To demonstrate the proposed combination of input modalities and explore applications for 2.5D shape displays, two example scenarios are implemented on a prototype system.


title: Impact of IT Infrastructure on Customer Service Performance: The Role of Micro-IT Capabilities and Online Customer Engagement.

abstract: This research argues that information technology (IT) capabilities can be classified in macroand micro-IT capabilities. We propose a conceptual model in which IT infrastructure capability (a macroIT capability) enables the development of social media and e-business technology capabilities (two micro-IT capabilities) to online engage customers and improve the firm’s customer service performance. We test the proposed model by using the variance-based structural equation modelling technique and the method of estimation of partial least squares employing an innovative secondary dataset on a sample of 100 small firms included in the 2013 Forbes America’s Best Small Companies ranking. The empirical analysis suggests that IT infrastructure capability positively affects customer service performance through two micro-IT capabilities (social media and e-business technology) and social and conventional online customer engagement.


title: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

abstract: Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse, delayed feedback: (1) a complex discrete stochastic decision process, and (2) the classic ATARI game `Montezuma's Revenge'.


title: A field guide to boxology: preliminary classification of architectural styles for software systems

abstract: Software architects use a number of commonly-recognized "styles" to guide their design of system structures. Each of these is appropriate for some classes of problems, but none is suitable for all problems. How, then, does a software designer choose an architecture suitable for the problem at hand? Two kinds of information are required: (1) careful discrimination among the candidate architectures and (2) design guidance on how to make appropriate choices. In this paper, we support careful discrimination with a preliminary classification of styles. We use a 2D classification strategy with control and data issues as the dominant organizing axes. We position the major styles within this space and use finer-grained discriminations to elaborate variations on the styles. This provides a framework for organizing design guidance, which we partially flesh out with rules of thumb.


title: Control of Distributed Uninterruptible Power Supply Systems

abstract: In the last years, the use of distributed uninterruptible power supply (UPS) systems has been growing into the market, becoming an alternative to large conventional UPS systems. In addition, with the increasing interest in renewable energy integration and distributed generation, distributed UPS systems can be a suitable solution for storage energy in micro grids. This paper depicts the most important control schemes for the parallel operation of UPS systems. Active load-sharing techniques and droop control approaches are described. The recent improvements and variants of these control techniques are presented.


title: DKPro WSD: A Generalized UIMA-based Framework for Word Sense Disambiguation

abstract: Implementations of word sense disambiguation (WSD) algorithms tend to be tied to a particular test corpus format and sense inventory. This makes it difficult to test their performance on new data sets, or to compare them against past algorithms implemented for different data sets. In this paper we present DKPro WSD, a freely licensed, general-purpose framework for WSD which is both modular and extensible. DKPro WSD abstracts the WSD process in such a way that test corpora, sense inventories, and algorithms can be freely swapped. Its UIMA-based architecture makes it easy to add support for new resources and algorithms. Related tasks such as word sense induction and entity linking are also supported.


title: Determining The Sentiment Of Opinions

abstract: Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.


A pr 2 01 8 Performance Loss of Hybrid Beamforming with Imperfect Phase Shifters in Millimeter Wave Systems


title: Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation

abstract: Deep Convolutional Neural Networks (DCNN) have been proven to be effective for various computer vision problems. In this work, we demonstrate its effectiveness on a continuous object orientation estimation task, which requires prediction of 0 to 360 degrees orientation of the objects. We do so by proposing and comparing three continuous orientation prediction approaches designed for the DCNNs. The first two approaches work by representing an orientation as a point on a unit circle and minimizing either L2 loss or angular difference loss. The third method works by first converting the continuous orientation estimation task into a set of discrete orientation estimation tasks and then converting the discrete orientation outputs back to the continuous orientation using a mean-shift algorithm. By evaluating on a vehicle orientation estimation task and a pedestrian orientation estimation task, we demonstrate that the discretization-based approach not only works better than the other two approaches but also achieves state-of-the-art performance. We also demonstrate that finding an appropriate feature representation is critical to achieve a good performance when adapting a DCNN trained for an image recognition task.


title: Data privacy protection using multiple cloud storages

abstract: With the rapid development of cloud computing and storage technologies, an increasing number of businesses and individuals are using cloud to store or backup data. However, when users upload private data to the cloud, they lose absolute control over the data. With the continued development of cloud storage, the protection of users' privacy has become a pertinent issue to be addressed. Currently, most users make use of a mainstream encryption algorithm to encrypt data before uploading and to decrypt the data after downloading and before use. To solve this problem, in this paper, we propose a new cloud storage privacy protection method that involves bit split (BS) and bit combination (BC). BS is performed on data and reassembled to form numerous part-files before these are uploaded to the different cloud storage servers. After downloading, the part-files from multiple cloud servers are combined to re-create the original file. Experiments show that this method is effective in protecting the privacy of the users' data, with a performance improvement of ~20 times compared with traditional encryption and decryption methods.


The generalized correlation methods for estimation of time delay with application to electromyography


title: An Overview and Empirical Comparison of Distance Metric Learning Methods

abstract: In this paper, we first offer an overview of advances in the field of distance metric learning. Then, we empirically compare selected methods using a common experimental protocol. The number of distance metric learning algorithms proposed keeps growing due to their effectiveness and wide application. However, existing surveys are either outdated or they focus only on a few methods. As a result, there is an increasing need to summarize the obtained knowledge in a concise, yet informative manner. Moreover, existing surveys do not conduct comprehensive experimental comparisons. On the other hand, individual distance metric learning papers compare the performance of the proposed approach with only a few related methods and under different settings. This highlights the need for an experimental evaluation using a common and challenging protocol. To this end, we conduct face verification experiments, as this task poses significant challenges due to varying conditions during data acquisition. In addition, face verification is a natural application for distance metric learning because the encountered challenge is to define a distance function that: 1) accurately expresses the notion of similarity for verification; 2) is robust to noisy data; 3) generalizes well to unseen subjects; and 4) scales well with the dimensionality and number of training samples. In particular, we utilize well-tested features to assess the performance of selected methods following the experimental protocol of the state-of-the-art database labeled faces in the wild. A summary of the results is presented along with a discussion of the insights obtained and lessons learned by employing the corresponding algorithms.


title: Word-based and Character-based Word Segmentation Models: Comparison and Combination

abstract: We present a theoretical and empirical comparative analysis of the two dominant categories of approaches in Chinese word segmentation: word-based models and character-based models. We show that, in spite of similar performance overall, the two models produce different distribution of segmentation errors, in a way that can be explained by theoretical properties of the two models. The analysis is further exploited to improve segmentation accuracy by integrating a word-based segmenter and a character-based segmenter. A Bootstrap Aggregating model is proposed. By letting multiple segmenters vote, our model improves segmentation consistently on the four different data sets from the second SIGHAN bakeoff.


title: Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting

abstract: Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations.


title: Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation

abstract: We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL (Minimum Description Length) criterion using the variational principle. We show that existing techniques in early vision such as, snake/balloon models, region growing, and Bayes/MDL are addressing different aspects of the same problem and they can be unified within a common statistical framework which combines their advantages. We analyze how to optimize the precision of the resulting boundary location by studying the statistical properties of the region competition algorithm and discuss what are good initial conditions for the algorithm. Our method is generalized to color and texture segmentation and is demonstrated on grey level images, color images and texture images.<<ETX>>


title: Attribute extraction and scoring: A probabilistic approach

abstract: Knowledge bases, which consist of concepts, entities, attributes and relations, are increasingly important in a wide range of applications. We argue that knowledge about attributes (of concepts or entities) plays a critical role in inferencing. In this paper, we propose methods to derive attributes for millions of concepts and we quantify the typicality of the attributes with regard to their corresponding concepts. We employ multiple data sources such as web documents, search logs, and existing knowledge bases, and we derive typicality scores for attributes by aggregating different distributions derived from different sources using different methods. To the best of our knowledge, ours is the first approach to integrate concept- and instance-based patterns into probabilistic typicality scores that scale to broad concept space. We have conducted extensive experiments to show the effectiveness of our approach.


title: Mathematical modeling for extraction of essential oil from Aquilaria crassna by hydrodistillation and quality of agarwood oil

abstract: Agarwood oil is widely used in medicine, perfume and cosmetic industries due to its unique properties and high medicinal value. However, knowledge on distillation process and standard for quality of the oil are far from sufficient. In this work, agarwood oil was hydrodistilled at various temperatures in order to study the distillation kinetics and quality of the oil. Higher oil yield and better quality of the oil were obtained at higher operating temperature. The mechanism of oil hydrodistillation consisted of two stages: washing stage and diffusion stage. The diffusion stage was the controlling step of oil hydrodistillation. Both mass transfer kinetic coefficients increased with increasing distillation temperatures. The oil gain at a higher temperature had a darker color and seemed to stick longer to the human skin. The different physical properties of the oil were a result of different chemical constituents exhibited in GC-MS profiles.


Inferential Role Semantics for Natural Language


title: Spectra of some self-exciting and mutually exciting point processes

abstract: SUMMARY In recent years methods of data analysis for point processes have received some attention, for example, by Cox & Lewis (1966) and Lewis (1964). In particular Bartlett (1963a,b) has introduced methods of analysis based on the point spectrum. Theoretical models are relatively sparse. In this paper the theoretical properties of a class of processes with particular reference to the point spectrum or corresponding covariance density functions are discussed. A particular result is a self-exciting process with the same second-order properties as a certain doubly stochastic process. These are not distinguishable by methods of data analysis based on these properties.


title: Introduction to Cryptocurrencies

abstract: We provide a research-oriented introduction to the cryptographic currencies. We start with a description of Bitcoin and its main design principles. We then discuss some of its weaknesses, and show some ideas for dealing with them. We also talk about the mechanics of the mining pools and ideas for discouraging the mining pool creation. We provide an introduction to the smart contracts, and give some examples of them, including the multiparty lotteries. We then present alternative currencies that were designed to remedy some of the problems of Bitcoin. In particular, we talk about the Litecoin, the Primecoin, the Permacoin, the Zerocoin, the Proofs of Stake and the Proofs of Space. We also discuss the most important research challenges in this area.


title: Real-time Facial Expression Recognition Based on Adaptive Canny Operator Edge Detection

abstract: In this paper, proposed a method of real-time facial expression recognition based on adaptive Canny operator edge detection. In this method, first used face location based on an adaptive skin color and structure model. Then, used facial expression feature extraction method based on adaptive Canny operator edge detection and AAM (Active Appearance Model) algorithm combined, which reduced the computational complexity and improved the accuracy of feature point location. During the using of Canny operator edge detection, the entire image was divided into multiple sub-images. And according to the edge gradient information of the sub-images, dynamic threshold was generated self-adaptively combined with the characteristics information of global edge gradient, which improved the edge detection results. Finally, used least -squares method to classify and identify the characteristics information. Experiments showed the effectiveness of this method in facial expression recognition and that it can meet the requirements in real-time systems.


title: The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective

abstract: One of the most influential ideas in the past 30 years is the efficient markets hypothesis, the idea that market prices incorporate all information rationally and instantaneously. The emerging discipline of behavioral economics and finance has challenged the EMH, arguing that markets are not rational, but rather driven by fear and greed. Research in the cognitive neurosciences suggests these two perspectives are opposite sides of the same coin. An adaptive markets hypothesis that reconciles market efficiency with behavioral alternatives applies the principles of evolution?competition, adaptation, and natural selection?to financial interactions. Extending Simon9s notion of ?satisficing? with evolutionary dynamics, the author argues that much of what behaviorists cite as counter-examples to economic rationality?loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases?is in fact consistent with an evolutionary model of individual adaptation to a changing environment via simple heuristics. The adaptive markets hypothesis offers a number of surprisingly concrete implications for portfolio management.


title: Task scheduling in the Cloud Environments based on an Artificial Bee Colony Algorithm

abstract: Nowadays, Cloud computing has gained vast attention due to its technological advancement, cost reduction and availability. In the Cloud environments, the suitable scheduling of the received tasks over service provides has become an important and vital problem. The scheduling problem in Cloud environments is an NP- hard problem. Therefore, many heuristics have been proposed to solve this problem up to now. In this paper, we propose a new bee colony algorithm to schedule the tasks on service providers in the Cloud environments. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks.


title: Toward an Instructionally Oriented Theory of Example-Based Learning.

abstract: Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (b) phases of skill acquisition, and (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible.


title: Series-Connected Grounding of Common-Mode EMI Filter

abstract: Due to the effects of parasitic inductance, the common groundline (CGL) of capacitors in common-mode (CM) electromagnetic interference (EMI) filter is influential to filtration performance of the filter. In this paper, the effect of a single-point grounding method of CM EMI filter is first analyzed, and then, in order to eliminate the negative effect of CGL, a series-connected grounding method is introduced and analyzed. The effectiveness of the series-connected grounding method on improving the performance of CM EMI filter with CGL is verified through experiment.


title: Adversarial support vector machine learning

abstract: Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.


Comparative Analysis of Hand Gesture Recognition Techniques


title: A 39.5-dB SNR, 300-Hz Frame-Rate, 56 × 70-Channel Read-Out IC for Electromagnetic Resonance Touch Panels

abstract: In this paper, a pen detection analog system for an electromagnetic resonance (EMR) touch panel is presented. An adaptive charge pump is proposed in the transmitter to drive a high current to the EMR panel and achieve a high signal-to-noise ratio (SNR) in the receiver. To compensate for the mismatches between the channels due to fabrication variations of the EMR panels and process variations of the analog front-ends (AFEs), a transmitter driver with an adaptive controller is proposed, and an AFE calibration is implemented in the transmitter and the receiver, respectively. Also, a 9-bit, 2-MS/s time-interleaved flash successive-approximation-register (SAR) analog-to-digital converter (ADC) with 3-bit flash and 6-bit SAR ADCs is proposed to increase the frame rate with small area and power consumption. External noises, such as display and charger noises, can be rejected by the differential processing, analog low-pass filter, and digital filters in the digital signal processor (DSP). This read-out integrated circuit is implemented in a 0.18-μm Bipolar-CMOS-DMOS (BCD), and the die area is ${\text{40 mm}}^{\text{2}}$ including DSP and ADCs. The power consumptions are 110 and 45 mW in the transmitter and receiver modes, respectively. The SNR is about 39.5 dB and the frame rate is about 300 Hz in the 56 × 70 touch panel.


title: Leadership in Teams: A Functional Approach to Understanding Leadership Structures and Processes

abstract: As the use of teams has increased in organizations, research has begun to focus on the role of leadership in fostering team success. This review sought to summarize this literature and advance research and theory by focusing on leadership processes within a team and describing how team leadership can arise from four distinct sources inside and outside a team. Then, drawing from this inclusive, team-centric view of leadership, the authors describe 15 team leadership functions that help teams satisfy their critical needs and regulate their behavior in the service of goal accomplishment. This integrative view of team leadership enables the summarization of past research and identification of promising areas of future research.


title: It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates

abstract: Automatically detecting human social intentions from spoken conversation is an important task for dialogue understanding. Since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended social meaning. We investigate this difference between intention and perception by using a spoken corpus of speed-dates in which both the speaker and the listener rated the speaker on flirtatiousness. Our flirtation-detection system uses prosodic, dialogue, and lexical features to detect a speaker's intent to flirt with up to 71.5% accuracy, significantly outperforming the baseline, but also outperforming the human inter-locuters. Our system addresses lexical feature sparsity given the small amount of training data by using an autoencoder network to map sparse lexical feature vectors into 30 compressed features. Our analysis shows that humans are very poor perceivers of intended flirtatiousness, instead often projecting their own intended behavior onto their interlocutors.


title: Origami: A Convolutional Network Accelerator

abstract: Today advanced computer vision (CV) systems of ever increasing complexity are being deployed in a growing number of application scenarios with strong real-time and power constraints. Current trends in CV clearly show a rise of neural network-based algorithms, which have recently broken many object detection and localization records. These approaches are very flexible and can be used to tackle many different challenges by only changing their parameters. In this paper, we present the first convolutional network accelerator which is scalable to network sizes that are currently only handled by workstation GPUs, but remains within the power envelope of embedded systems. The architecture has been implemented on 3.09 mm2 core area in UMC 65 nm technology, capable of a throughput of 274 GOp/s at 369 GOp/s/W with an external memory bandwidth of just 525 MB/s full-duplex " a decrease of more than 90% from previous work.


title: Mindfulness: Top–down or bottom–up emotion regulation strategy?

abstract: Abstract The beneficial clinical effects of mindfulness practices are receiving increasing support from empirical studies. However, the functional neural mechanisms underlying these benefits have not been thoroughly investigated. Some authors suggest that mindfulness should be described as a ‘top–down’ emotion regulation strategy, while others suggest that mindfulness should be described as a ‘bottom–up’ emotion regulation strategy. Current discrepancies might derive from the many different descriptions and applications of mindfulness. The present review aims to discuss current descriptions of mindfulness and the relationship existing between mindfulness practice and most commonly investigated emotion regulation strategies. Recent results from functional neuro-imaging studies investigating mindfulness training within the context of emotion regulation are presented. We suggest that mindfulness training is associated with ‘top–down’ emotion regulation in short-term practitioners and with ‘bottom–up’ emotion regulation in long-term practitioners. Limitations of current evidence and suggestions for future research on this topic are discussed.


title: Continuous N-gram Representations for Authorship Attribution

abstract: This paper presents work on using continuous ::: representations for authorship attribution. ::: In contrast to previous work, ::: which uses discrete feature representations, ::: our model learns continuous representations ::: for n-gram features via a neural ::: network jointly with the classification ::: layer. Experimental results demonstrate ::: that the proposed model outperforms the ::: state-of-the-art on two datasets, while producing ::: comparable results on the remaining ::: two.


title: Dynamic transport scheduling under multiple resource constraints

abstract: This paper presents a heuristic for the dynamic vehicle scheduling problem with multiple resource capacity constraints. In the envisaged application, an automated transport system using Automated Guided Vehicles, bottleneck resources are (1) vehicles, (2) docks for loading/unloading, (3) vehicle parking places, and (4) load storage space. This problem is hard, because interrelated activities (loading, transportation, unloading) at several geographical locations have to be scheduled under multiple resource constraints, where the bottleneck resource varies over time. Besides, the method should be suitable for real-time planning. We developed a dedicated serial scheduling method and analyzed its dynamic behavior using discrete event simulation. We found that our method is very well able to find good vehicle schedules satisfying all resource constraints. For comparison, we used a simple approach where we left out the resource constraints and extended the processing times by statistically estimated waiting times to account for finite capacities. We found that our newly designed method finds better schedules in terms of service levels.


Place of sanctuary: an appreciative inquiry approach to discovering how communities support breastfeeding and parenting


Reputation Management in Online Social Networks - A New Clustering-based Approach.


title: Enhancing the Network Embedding Quality with Structural Similarity

abstract: Neural network techniques are widely used in network embedding, boosting the result of node classification, link prediction, visualization and other tasks in both aspects of efficiency and quality. All the state of art algorithms put effort on the neighborhood information and try to make full use of it. However, it is hard to recognize core periphery structures simply based on neighborhood. In this paper, we first discuss the influence brought by random-walk based sampling strategies to the embedding results. Theoretical and experimental evidences show that random-walk based sampling strategies fail to fully capture structural equivalence. We present a new method, SNS, that performs network embeddings using structural information (namely graphlets) to enhance its quality. SNS effectively utilizes both neighbor information and local-subgraphs similarity to learn node embeddings. This is the first framework that combines these two aspects as far as we know, positively merging two important areas in graph mining and machine learning. Moreover, we investigate what kinds of local-subgraph features matter the most on the node classification task, which enables us to further improve the embedding quality. Experiments show that our algorithm outperforms other unsupervised and semi-supervised neural network embedding algorithms on several real-world datasets.


Raptor 2 : Design of fast biped robot for 3 D environments using active tail stabilization


title: Implication and evaluation techniques for proving fault equivalence

abstract: Efficient identification of fault equivalence is essential for the completeness and efficiency of diagnostic test pattern generation. In this paper, we present new techniques to prove diagnostic fault equivalence. The techniques are based on implication of the faulty values, and functional evaluation at the dominator gate of the fault sites. The experimental results for all ISCAS85 circuits and full scan versions of ISCAS89 circuits show significant improvement compared to previously proposed techniques in both the number of equivalent pairs identified and the time to prove equivalence.


title: Neutral Theory and Relative Species Abundance in Ecology

abstract: The theory of island biogeography1 asserts that an island or a local community approaches an equilibrium species richness as a result of the interplay between the immigration of species from the much larger metacommunity source area and local extinction of species on the island (local community). Hubbell2 generalized this neutral theory to explore the expected steady-state distribution of relative species abundance (RSA) in the local community under restricted immigration. Here we present a theoretical framework for the unified neutral theory of biodiversity2 and an analytical solution for the distribution of the RSA both in the metacommunity (Fisher's log series) and in the local community, where there are fewer rare species. Rare species are more extinction-prone, and once they go locally extinct, they take longer to re-immigrate than do common species. Contrary to recent assertions3, we show that the analytical solution provides a better fit, with fewer free parameters, to the RSA distribution of tree species on Barro Colorado Island, Panama4, than the lognormal distribution5,6.


title: An experiment on DES statistical cryptanalysis

abstract: Linear cryptanalysis and differential cryptanalysis are the most important methods of attack against block ciphers. Their efficiency have been demonstrated against several ciphers, including the Data Encryption Standard. We prove that both of them can be considered, improved and joined in a more general statistical framework. We also show that the very same results as those obtained in the case of DES can be found without any linear analysis and we slightly improve them into an attack with theoretical complexity 242.9 We can apply another statistical attack-the ?2 cryptanalysis-on the same characteristics without a definite idea of what happens in the encryption process. It appears to be roughly as efficient as both differential and linear cryptanalysis. We propose a new heuristic method to find good characteristics. It has found an attack against DES absolutely equivalent to M. Matsui's (1994) one by following a distinct path.


A New Ensemble Model based Support Vector Machine for Credit Assessing


KENKEN PUZZLE – BASED IMAGE ENCRYPTION ALGORITHM


title: Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

abstract: This paper studied automatic identification of malaria infected cells using deep learning methods. We used whole slide images of thin blood stains to compile an dataset of malaria-infected red blood cells and non-infected cells, as labeled by a group of four pathologists. We evaluated three types of well-known convolutional neural networks, including the LeNet, AlexNet and GoogLeNet. Simulation results showed that all these deep convolution neural networks achieved classification accuracies of over 95%, higher than the accuracy of about 92% attainable by using the support vector machine method. Moreover, the deep learning methods have the advantage of being able to automatically learn the features from the input data, thereby requiring minimal inputs from human experts for automated malaria diagnosis.


title: CCR - A Content-Collaborative Reciprocal Recommender for Online Dating

abstract: We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.


title: A Study of Interpulse Instability in Gallium Nitride Power Amplifiers in Multifunction Radars

abstract: The incorporation of gallium nitride (GaN) power amplifiers (PAs) into future high-power aperture radar systems is certain; however, the introduction of this technology into multifunction radar systems will present new challenges to radar engineers. This paper describes a broad investigation into amplitude and phase transients produced by GaN PAs when they are excited with multifunction radar waveforms. These transients are the result of self-heating electrothermal memory effects and are manifested as interpulse instabilities that can negatively impact the coherent processing of multiple pulses. A behavioral model based on a Foster network topology has been developed to replicate the measured amplitude and phase transients accurately. This model has been used to develop a digital predistortion technique that successfully mitigates the impact of the transients. The moving target indicator improvement factor and the root mean square pulse-to-pulse stability are used as a metric to assess the impact of transients on radar system performance and to test the effectiveness of a novel digital predistortion concept.


title: An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining

abstract: Traditional pattern growth-based approaches for sequential pattern mining derive length-(k+1) patterns based on the projected databases of length-k patterns recursively. At each level of recursion, they unidirectionally grow the length of detected patterns by one along the suffix of detected patterns, which needs k levels of recursion to find a length-k pattern. In this paper, a novel data structure, UpDown Directed Acyclic Graph (UDDAG), is invented for efficient sequential pattern mining. UDDAG allows bidirectional pattern growth along both ends of detected patterns. Thus, a length-k pattern can be detected in [log2k + 1] levels of recursion at best, which results in fewer levels of recursion and faster pattern growth. When minSup is large such that the average pattern length is close to 1, UDDAG and PrefixSpan have similar performance because the problem degrades into frequent item counting problem. However, UDDAG scales up much better. It often outperforms PrefixSpan by almost one order of magnitude in scalability tests. UDDAG is also considerably faster than Spade and LapinSpam. Except for extreme cases, UDDAG uses comparable memory to that of PrefixSpan and less memory than Spade and LapinSpam. Additionally, the special feature of UDDAG enables its extension toward applications involving searching in large spaces.


title: Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations

abstract: Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.


title: A Robust Level-Set Algorithm for Centerline Extraction

abstract: We present a robust method for extracting 3D centerlines from volumetric datasets. We start from a 2D skeletonization method to locate voxels centered with respect to three orthogonal slicing directions. Next, we introduce a new detection criterion to extract the centerline voxels from the above skeletons, followed by a thinning, reconnection, and a ranking step. Overall, the proposed method produces centerlines that are object-centered, connected, one voxel thick, robust with respect to object noisiness, handles arbitrary object topologies, comes with a simple pruning threshold, and is fast to compute. We compare our results with two other methods on a variety of real-world datasets.


title: ’ s repository of research publications and other research outputs “ Fairy rings ” of participation : the invisible network influencing participation in online communities

abstract: Individuals participate in many different ways in online communities. There is an extensive body of research describing participation as a key metaphor in communities of practice and stressing that participatory mobility is influenced by underground multidirectional activities, directed away from the notion of periphery to the centre practices and taking the shape of expansive swarming and multidirectional pulsations. This article describes an ongoing observational study proposing a model that attempts to determine how users participate in online communities and what influences them to alter the way in which they participate. We performed daily observations on user participatory behaviour in 50 online communities using public domain – anonymous data available in the communities. The specific communities were selected because they are related to learning and support learning activities within their networks. The data observations collected were analysed using Compendium, a hypermedia knowledge mapping and sense-making tool, to represent and structure the data, make complex cross data queries, test hypotheses and build representation of real examples to support our claims. Initial findings indicate that users connect, participate, contribute and collaborate on a shared objective, transferring information and pooling knowledge within and between communities in four different modes. During their online journey, users switched between modes of participation or even remained in one specific mode, implying that the way in which users participate in an online community is not just related to the mode of participation and the level of engagement with the community but it is also due to hidden reasons or motivations, an invisible network of interactions of elements that affect the willingness of the user to participate. This layer is not immediately evident in the user actions but can be inferred by analysing user reactions. It is argued that user participation in online communities occurs in two layers; the “visible” layer of participation with the different modes; and the “invisible” layer of element interactions, similar to formations observed in nature when a radically spreading underground network of fungi activity results in a ring or arc formation of mushrooms, also known as a “fairy ring”. These underground multidirectional activities influence participation and participatory mobility. Following an open scientific inquiry approach and an open research paradigm we plan to share these observations with a wider audience of practitioners, researchers and theorists for all to test or contest our arguments, and to enrich, question, or support our model.


Discrimination between Similar Languages, Varieties and Dialects using CNN- and LSTM-based Deep Neural Networks.


title: VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text

abstract: The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.


title: SIMPLIcity: a region-based retrieval system for picture libraries and biomedical image databases

abstract: In this demonstration, we present SIMPLIcity, an image retrieval system for picture libraries and biomedical image databases. The system uses a wavelet-based approach for feature extraction, real-time region segmentation, the Integration Region Matching (IRM) metric, and image classification methods. Tested on large-scale picture libraries and a database of pathology images, the system has demonstrated accurate and fast retrieval. It also exceptionally robust to image alterations.


title: Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle

abstract: We extend the arc-hybrid transition system for dependency parsing with a SWAP transition that enables reordering of the words and construction of non-projective trees. Although this extension poten ...


title: WSDM Cup 2018: Music Recommendation and Churn Prediction

abstract: Excellent recommendation system facilitates users retrieving contents they like and, what»s much more important - the contents they might like but they are not aware of yet. It will further increase the satisfaction of users and increase the retention rate and conversion rate indirectly. While the public's now listening to all kinds of music, recommendation algorithms still struggle in key areas. Without enough historical data, how would an algorithm know if listeners will like a new song or a new artist? And, how would it know what songs to recommend brand new users? In WSDM Cup 2018, the first task is to solve the abovementioned challenges to build a better music recommendation system. The 2nd task in the Cup focuses on churn prediction. For a subscription business, accurately predicting churn is critical to long-term success. Even slight variations in churn can drastically affect profits. In this task, participants are asked to build an algorithm that predicts whether a user will churn after their subscription expires. The competition data and award are provided by KKBOX, a leading music streaming service in Taiwan.


title: Tunable Balun Low-Noise Amplifier in 65 nm CMOS Technology

abstract: The presented paper includes the design and im- plementation of a 65 nm CMOS low-noise amplifier (LNA) based on inductive source degeneration. The amplifier is realized with an active balun enabling a single-ended in- put which is an important requirement for low-cost system on chip implementations. The LNA has a tunable band- pass characteristic from 4.7 GHz up to 5.6 GHz and a con- tinuously tunable gain from 20 dB down to 2 dB, which en- ables the required flexibility for multi-standard, multi-band receiver architectures. The gain and band tuning is realized with an optimized tunable active resistor in parallel to a tun- able L-C tank amplifier load. The amplifier achieves an IIP3 linearity of -8 dBm and a noise figure of 2.7 dB at the highest gain and frequency setting with a low power consumption of 10 mW. The high flexibility of the proposed LNA structure to- gether with the overall good performance make it well suited for future multi-standard low-cost receiver front-ends. Modern wireless communication systems including multimedia capabilities are developing at high pace, with an increasing number of wireless standards for mobile cellu- lar communication (eg. GSM/EDGE, UMTS, LTE), broad- cast applications like DVB-SH, and short-range data com- munication standards like the IEEE 802.11 WLAN or Blue- tooth. To take advantage of the services provided by these mobile communication standards, the integration of multi- standard, multi-band terminals in a single radio architecture with a minimized number of external RF components is nec- essary. This requires novel RF architectures in advanced low-cost CMOS technologies, based on software defined ra- dio and cognitive radio concepts. The wide spread of fre- quency bands from 0.1 GHz up to about 6 GHz, in combina- tion with the high number of concurrent applications, makes the classical integration approach of independent parallel receiver-frontends more and more inefficient and technically demanding. Reusability and tunability of RF key-building blocks like low-noise amplifiers (LNA) will be a manda- tory requirement for future RF transceivers. To reduce the RF performance limitations of modern CMOS technologies like low-gain, low supply voltage and high technology vari- ations, new digitally assisted RF radio solutions can be used to leverage the signal-processing and integration capabilities of CMOS technologies.


title: Target detection based on L- and C-band PolSAR data

abstract: For SAR signals with different frequency, the scattering characteristic of objects is generally different. Hence, more information can be obtained with multi-frequency SAR. It is easier to detect different targets by using multi-frequency PolSAR data, and even for the man-made targets in forest area. To effectively detect natural and man-made targets, the scattering components of different scattering mechanisms obtained from L- and C-band PolSAR data by using polarimetric target decomposition could be combined. The experiment result indicates that different targets can be detected by using multi-frequency PolSAR data.


title: Friending, IMing, and Hanging Out Face-to-Face: Overlap in Adolescents' Online and Offline Social Networks

abstract: Many new and important developmental issues are encountered during adolescence, which is also a time when Internet use becomes increasingly popular. Studies have shown that adolescents are using these online spaces to address developmental issues, especially needs for intimacy and connection to others. Online communication with its potential for interacting with unknown others, may put teens at increased risk. Two hundred and fifty-one high school students completed an in-person survey, and 126 of these completed an additional online questionnaire about how and why they use the Internet, their activities on social networking sites (e.g., Facebook, MySpace) and their reasons for participation, and how they perceive these online spaces to impact their friendships. To examine the extent of overlap between online and offline friends, participants were asked to list the names of their top interaction partners offline and online (Facebook and instant messaging). Results reveal that adolescents mainly use social networking sites to connect with others, in particular with people known from offline contexts. While adolescents report little monitoring by their parents, there was no evidence that teens are putting themselves at risk by interacting with unknown others. Instead, adolescents seem to use the Internet, especially social networking sites, to connect with known others. While the study found moderate overlap between teens’ closest online and offline friends, the patterns suggest that adolescents use online contexts to strengthen offline relationships.


title: A 0.45-V, 14.6-nW CMOS Subthreshold Voltage Reference With No Resistors and No BJTs

abstract: We present a low-voltage low-power CMOS subthreshold voltage reference with no resistors and no bipolar junction transistors in a wide temperature range. The temperature stability is improved by second-order compensation. By employing a bulk-driven technique and the MOS transistors working in the subthreshold region, the supply voltage and the power dissipation are reduced. Moreover, a trimming circuit is adopted to compensate for the process-related reference voltage variation. The proposed voltage reference has been fabricated with the 0.18- $\mu\text{m} $ 1.8-V CMOS process. The measurement results show that the minimum power supply voltage is 0.45 V, the power consumption is 14.6 nW, the average temperature coefficient measured from $-40\ ^{\circ}\mbox{C} $ to 125 °C is 63.6 ppm/°C, and the line regulation is 1.2 mV/V in the power supply voltage ranging from 0.45 to 1.8 V. In addition, the chip area is 0.012 mm2.


title: Combining response surface methodology with numerical models for optimization of class-based queueing systems

abstract: In general, decision support is one of the main purposes of model-based analysis of systems. Response surface methodology (RSM) is an optimization technique that has been applied frequently in practice, but few automated variants are currently available. In this paper, we propose the combination of RSM with numerical analysis methods to solve continuous time Markov chain models of class-based queueing systems (CBQ). We consider first- and second-order models in RSM to identify an optimal parameter configuration for CBQ as part of the differentiated service architecture. Among the many known numerical solution methods for large Markov chains, we consider a Gauss-Seidel solver with relaxation that relies on a hierarchical Kronecker representation as implemented in the APNN Toolbox. To effectively apply the proposed optimization methodology we determine a suitable configuration of RSM and compare the results with previous results for optimizing CBQ.


title: Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept

abstract: During the last decade markerless motion capture techniques have gained an increasing interest in the biomechanics community. In the clinical field, however, the application of markerless techniques is still debated. This is mainly due to a limited number of papers dedicated to the comparison with the state of the art of marker based motion capture, in term of repeatability of the three dimensional joints' kinematics. In the present work the application of markerless technique to data acquired with a marker-based system was investigated. All videos and external data were recorded with the same motion capture system and included the possibility to use markerless and marker-based methods simultaneously. Three dimensional markerless joint kinematics was estimated and compared with the one determined with traditional marker based systems, through the evaluation of root mean square distance between joint rotations. In order to compare the performance of markerless and marker-based systems in terms of clinically relevant joint angles estimation, the same anatomical frames of reference were defined for both systems. Differences in calibration and synchronization of the cameras were excluded by applying the same wand calibration and lens distortion correction to both techniques. Best results were achieved for knee flexion-extension angle, with an average root mean square distance of 11.75 deg, corresponding to 18.35% of the range of motion. Sagittal plane kinematics was estimated better than on the other planes also for hip and ankle (root mean square distance of 17.62 deg e.g. 44.66%, and 7.17 deg e.g. 33.12%), meanwhile estimates for hip joint were the most incorrect. This technique enables users of markerless technology to compare differences with marker-based in order to define the degree of applicability of markerless technique.


PPPs and Infrastructure Investment


title: Learning image representations from the pixel level via hierarchical sparse coding

abstract: We present a method for learning image representations using a two-layer sparse coding scheme at the pixel level. The first layer encodes local patches of an image. After pooling within local regions, the first layer codes are then passed to the second layer, which jointly encodes signals from the region. Unlike traditional sparse coding methods that encode local patches independently, this approach accounts for high-order dependency among patterns in a local image neighborhood. We develop algorithms for data encoding and codebook learning, and show in experiments that the method leads to more invariant and discriminative image representations. The algorithm gives excellent results for hand-written digit recognition on MNIST and object recognition on the Caltech101 benchmark. This marks the first time that such accuracies have been achieved using automatically learned features from the pixel level, rather than using hand-designed descriptors.


title: Reaction Wheels Desaturation Using Magnetorquers and Static Input Allocation

abstract: Considering the most widely spread configuration of actuators for low orbit satellites, namely a set of reaction wheels and set of magnetorquers, we revisit the classical cross-product control law solution for achieving attitude stabilization and momentum dumping. We show how the classical solution has a quasi-cascade structure that, under a suitable input-to-state (ISS) assumption, can be stabilized by high gain, thereby making the actuators more inclined to saturate. Motivated by this, we propose a revisited version of this control law that transforms the quasi-cascade into a real cascade. Then, we show that both strategies are such that the attitude control is affected by the momentum dumping, and that they both require a suitable ISS property. To overcome these drawbacks, we propose a new allocation-based controller, which makes the attitude dynamics completely independent of the momentum dumping and induces global asymptotic stability without any ISS requirement. Several formal statements and simulation results support our discussions and highlight the pros and cons of the different control strategies.


title: Tracking ship traffic with Space-Based AIS: Experience gained in first months of operations

abstract: The Automatic Identification System (AIS) is based on VHF radio transmissions of ships' identity, position, speed and heading, in addition to other key parameters. In 2004, the Norwegian Defence Research Establishment (FFI) undertook studies to evaluate if the AIS signals could be detected in low Earth orbit. Since then, the interest in Space-Based AIS reception has grown significantly, and both public and private sector organizations have established programs to study the issue, and demonstrate such a capability in orbit. FFI is conducting two such programs. The objective of the first program was to launch a nano-satellite equipped with an AIS receiver into a near polar orbit, to demonstrate Space-Based AIS reception at high latitudes. The satellite was launched from India 12th July 2010. Even though the satellite has not finished commissioning, the receiver is operated with real-time transmission of received AIS data to the Norwegian Coastal Administration. The second program is an ESA-funded project to operate an AIS receiver on the European Columbus module of the International Space Station. Mounting of the equipment, the NORAIS receiver, was completed in April 2010. Currently, the AIS receiver has operated for more than three months, picking up several million AIS messages from more than 60 000 ship identities. In this paper, we will present experience gained with the space-based AIS systems, highlight aspects of tracking ships throughout their voyage, and comment on possible contributions to port security.


title: Sciatica of nondisc origin and piriformis syndrome: diagnosis by magnetic resonance neurography and interventional magnetic resonance imaging with outcome study of resulting treatment.

abstract: OBJECT ::: Because lumbar magnetic resonance (MR) imaging fails to identify a treatable cause of chronic sciatica in nearly 1 million patients annually, the authors conducted MR neurography and interventional MR imaging in 239 consecutive patients with sciatica in whom standard diagnosis and treatment failed to effect improvement. ::: ::: ::: METHODS ::: After performing MR neurography and interventional MR imaging, the final rediagnoses included the following: piriformis syndrome (67.8%), distal foraminal nerve root entrapment (6%), ischial tunnel syndrome (4.7%), discogenic pain with referred leg pain (3.4%), pudendal nerve entrapment with referred pain (3%), distal sciatic entrapment (2.1%), sciatic tumor (1.7%), lumbosacral plexus entrapment (1.3%), unappreciated lateral disc herniation (1.3%), nerve root injury due to spinal surgery (1.3%), inadequate spinal nerve root decompression (0.8%), lumbar stenosis (0.8%), sacroiliac joint inflammation (0.8%), lumbosacral plexus tumor (0.4%), sacral fracture (0.4%), and no diagnosis (4.2%). Open MR-guided Marcaine injection into the piriformis muscle produced the following results: no response (15.7%), relief of greater than 8 months (14.9%), relief lasting 2 to 4 months with continuing relief after second injection (7.5%), relief for 2 to 4 months with subsequent recurrence (36.6%), and relief for 1 to 14 days with full recurrence (25.4%). Piriformis surgery (62 operations; 3-cm incision, transgluteal approach, 55% outpatient; 40% with local or epidural anesthesia) resulted in excellent outcome in 58.5%, good outcome in 22.6%, limited benefit in 13.2%, no benefit in 3.8%, and worsened symptoms in 1.9%. ::: ::: ::: CONCLUSIONS ::: This Class A quality evaluation of MR neurography's diagnostic efficacy revealed that piriformis muscle asymmetry and sciatic nerve hyperintensity at the sciatic notch exhibited a 93% specificity and 64% sensitivity in distinguishing patients with piriformis syndrome from those without who had similar symptoms (p < 0.01). Evaluation of the nerve beyond the proximal foramen provided eight additional diagnostic categories affecting 96% of these patients. More than 80% of the population good or excellent functional outcome was achieved.


title: Registration Protocol for Health IoT Platform to Share the Use of Medical Devices

abstract: This paper proposes an open healthcare platform structure design and suggests an authenticated registration process that links to a mobile device to enable convenient registration and sharing of diverse types of medical devices and services. Furthermore, in order to verify the effectiveness of the proposed registration method, the paper introduces and implements a health IoT-based mobile application. This research was completed with the intent that the results will contribute to user authentication and platform development for IoT-based medical devices in the future


A RISK ASSESSMENT STUDY OF CHILDREN PRIVACY OF THE MOBILE APPs


title: A broadband substrate integrated waveguide (SIW) filter

abstract: An eleven-stage broadband SIW filter has been designed, fabricated and measured. The measured results show that the SIW filter can be operated from 9.25 GHz to 11.25 GHz with good selectivity performance. The filter has a small size, low weight, and can be integrated with other SIW components without any transitions. It's suitable for the design of microwave and millimeter wave integrated circuits


title: Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds

abstract: In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space and the learned feature space. Neighborhoods are important as they allow to compute local or global point features depending on the spatial extend of the neighborhood. Additionally, we incorporate dedicated loss functions to further structure the learned point feature space: the pairwise distance loss and the centroid loss. We show how to apply these mechanisms to the task of 3D semantic segmentation of point clouds and report state-of-the-art performance on indoor and outdoor datasets.


title: Worm-Like Robotic Locomotion with a Compliant Modular Mesh

abstract: In order to mimic and better understand the way an earthworm uses its many segments to navigate diverse terrain, this paper describes the design, performance, and sensing capabilities of a new modular soft robotic worm. The robot, Compliant Modular Mesh Worm CMMWorm, utilizes a compliant mesh actuated at modular segments to create waveforms along its body. These waveforms can generate peristaltic motion of the body similar to that of an earthworm. The modular mesh is constructed from 3-D printed and commercially available parts allowing for the testing of a variety of components that can be easily interchanged. In addition to having independently controlled segments and interchangeable mesh properties, CMMWorm also has greater range of contraction 52% of maximum diameter than our previous robot Softworm 73% of maximum diameter. The six-segment robot can traverse flat ground and pipes. We show that a segment is able to detect the wall of a pipe and return to its initial position using actuator-based load-sensing. A simple kinematic model predicts the outer diameter of the worm robot's mesh as a function of encoder position.


title: Super-Regenerative Receiver

abstract: This paper describes the super-regenerative receiver (SRR); a device widely used in short distance telemetry and remote control applications. The difficulty in modeling the regenerative behavior served as the basis for "cut and try" methodology based on results obtained from the simplified model using conventional approach. This work presents the SRR behavior to practical implementations by dynamically controlling the injection-mode levels of the super-regenerative oscillator (SRO) for optimum sensitivity and dynamic ranges.


SERIES 2006-01-1426 Security Needs for the Future Intelligent Vehicles


title: On Dual Co-Polarized SAR Measurements of the Ocean Surface

abstract: An effective methodology using satellite high-resolution polarized information to interpret and quantitatively assess various surface ocean phenomena is suggested. Using a sample RADARSAT-2 quad-polarization ocean synthetic aperture radar (SAR) scene, the dual co-polarization (VV and HH) radar data are combined into polarization difference, polarization ratio, and nonpolarized components. As demonstrated, these field quantities provide means to distinguish Bragg scattering mechanism and radar returns from breaking waves. As shown, quantitative characteristics of the surface manifestation of ocean currents, slicks, and wind field features in these dual co-polarization properties are very different and may be effectively used in the development of new SAR detection and discrimination algorithms.


title: Novel single-phase five-level VIENNA-type rectifier with model predictive current control

abstract: A novel single-phase five-level active rectifier based on the VIENNA-type rectifier with model predictive current control is presented. The proposed topology operates in unidirectional mode, imposing a sinusoidal grid-side current with unitary power factor. A unidirectional electric vehicle battery charger is the target application in which the proposed rectifier is used; however, it can also be used as an active rectifier for other purposes aiming to improve the efficiency of ac-to-dc rectification. The model predictive current control is used to select the active rectifier state during each sampling period, trying to minimize the grid current error and obtain low total harmonic distortion. The suitability and performance of the proposed topology of active rectifier, as well as the principle of operation and the digital control algorithm, are evaluated through simulation and experimental results.


title: Crisis intervention: a review.

abstract: Critical incidents are sudden, unexpected, often life-threatening time-limited events that may overwhelm an individual’s capacity to respond adaptively. Frequently, extreme critical incident stressors may result in personal crises, traumatic stress, and even Posttraumatic Stress Disorder. This paper presents a concise, fully-referenced, state-of-the-art review of crisis intervention procedures within the context of Critical Incident Stress Management (CISM; Everly & Mitchell, 1999; Flannery, 1999) [International Journal of Emergency Mental Health, 2000, 2(2), 119-125].


title: Ontology-Based Personalized Course Recommendation Framework

abstract: Choosing a higher education course at university is not an easy task for students. A wide range of courses are offered by the individual universities whose delivery mode and entry requirements differ. A personalized recommendation system can be an effective way of suggesting the relevant courses to the prospective students. This paper introduces a novel approach that personalizes course recommendations that will match the individual needs of users. The proposed approach developed a framework of an ontology-based hybrid-filtering system called the ontology-based personalized course recommendation (OPCR). This approach aims to integrate the information from multiple sources based on the hierarchical ontology similarity with a view to enhancing the efficiency and the user satisfaction and to provide students with appropriate recommendations. The OPCR combines collaborative-based filtering with content-based filtering. It also considers familiar related concepts that are evident in the profiles of both the student and the course, determining the similarity between them. Furthermore, OPCR uses an ontology mapping technique, recommending jobs that will be available following the completion of each course. This method can enable students to gain a comprehensive knowledge of courses based on their relevance, using dynamic ontology mapping to link the course profiles and student profiles with job profiles. Results show that a filtering algorithm that uses hierarchically related concepts produces better outcomes compared to a filtering method that considers only keyword similarity. In addition, the quality of the recommendations is improved when the ontology similarity between the items’ and the users’ profiles were utilized. This approach, using a dynamic ontology mapping, is flexible and can be adapted to different domains. The proposed framework can be used to filter the items for both postgraduate courses and items from other domains.


title: Development of Photo Forensics Algorithm by Detecting Photoshop Manipulation using Error Level Analysis

abstract: Nowadays, image manipulation is common due to the availability of image processing software, such as Adobe Photoshop or GIMP. The original image captured by digital camera or smartphone normally is saved in the JPEG format due to its popularity. JPEG algorithm works on image grids, compressed independently, having size of 8x8 pixels. For unmodified image, all 8x8 grids should have a similar error level. For resaving operation, each block should degrade at approximately the same rate due to the introduction of similar amount of errors across the entire image. For modified image, the altered blocks should have higher error potential compred to the remaining part of the image. The objective of this paper is to develop a photo forensics algorithm which can detect any photo manipulation. The error level analysis (ELA) was further enhanced using vertical and horizontal histograms of ELA image to pinpoint the exact location of modification. Results showed that our proposed algorithm could identify successfully the modified image as well as showing the exact location of modifications.


title: Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns

abstract: Context-aware computing often involves tracking peoples' location. Many studies and applications highlight the importance of keeping people's location information private. We discuss two types of location- based services; location-tracking services that are based on other parties tracking the user's location and position-aware services that rely on the device's knowledge of its own location. We present an experimental case study that examines people's concern for location privacy and compare this to the use of location-based services. We find that even though the perceived usefulness of the two different types of services is the same, location- tracking services generate more concern for privacy than position-aware services. We conclude that development emphasis should be given to position-aware services but that location-tracking services have a potential for success if users are given a simple option for turning the location-tracking off.


title: An Ontology Design Pattern for Cooking Recipes - Classroom Created

abstract: We present a description and result of an ontology modeling process taken to the classroom. The application domain considered was cooking recipes. The modeling goal was to bridge heterogeneity across representational choices by developing a content ontology design pattern which is general enough to allow for the integration of information from different web sites. We will discuss the pattern developed, and report on corresponding insights and lessons learned.


title: Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis

abstract: Finding the weakness of the products from the customers' feedback can help manufacturers improve their product quality and competitive strength. In recent years, more and more people express their opinions about products online, and both the feedback of manufacturers' products or their competitors' products could be easily collected. However, it's impossible for manufacturers to read every review to analyze the weakness of their products. Therefore, finding product weakness from online reviews becomes a meaningful work. In this paper, we introduce such an expert system, Weakness Finder, which can help manufacturers find their product weakness from Chinese reviews by using aspects based sentiment analysis. An aspect is an attribute or component of a product, such as price, degerm, moisturizing are the aspects of the body wash products. Weakness Finder extracts the features and groups explicit features by using morpheme based method and Hownet based similarity measure, and identify and group the implicit features with collocation selection method for each aspect. Then utilize sentence based sentiment analysis method to determine the polarity of each aspect in sentences. The weakness of product could be found because the weakness is probably the most unsatisfied aspect in customers' reviews, or the aspect which is more unsatisfied when compared with their competitor's product reviews. Weakness Finder has been used to help a body wash manufacturer find their product weakness, and our experimental results demonstrate the good performance of the Weakness Finder.


title: GESPAR: Efficient Phase Retrieval of Sparse Signals

abstract: We consider the problem of phase retrieval, namely, recovery of a signal from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of the Fourier phase information, this problem is ill-posed. Therefore, prior information on the signal is needed in order to enable its recovery. In this work we consider the case in which the signal is known to be sparse, i.e., it consists of a small number of nonzero elements in an appropriate basis. We propose a fast local search method for recovering a sparse signal from measurements of its Fourier transform (or other linear transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse Retrieval. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that GESPAR is fast and more accurate than existing techniques in a variety of settings.