title: Fast and Robust Hand Tracking Using Detection-Guided Optimization

abstract: Markerless tracking of hands and fingers is a promising enabler for human-computer interaction. However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a fast method for accurately tracking rapid and complex articulations of the hand using a single depth camera. Our algorithm uses a novel detectionguided optimization strategy that increases the robustness and speed of pose estimation. In the detection step, a randomized decision forest classifies pixels into parts of the hand. In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth. Our approach needs comparably less computational resources which makes it extremely fast (50 fps without GPU support). The approach also supports varying static, or moving, camera-to-scene arrangements. We show the benefits of our method by evaluating on public datasets and comparing against previous work.


title: Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions

abstract: Computers are incredibly fast, accurate, and stupid. ::: ::: Human beings are incredibly slow, inaccurate, and brilliant. ::: ::: Together they are powerful beyond imagination ::: ::: (Einstein never said that [1]).


title: Effective face frontalization in unconstrained images

abstract: “Frontalization” is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses. Previous frontalization methods did this by attempting to approximate 3D facial shapes for each query image. We observe that 3D face shape estimation from unconstrained photos may be a harder problem than frontalization and can potentially introduce facial misalignments. Instead, we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.


title: An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists

abstract: Topological Data Analysis (tda) is a recent and fast growing eld providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of tda for non experts. 1 Introduction and motivation Topological Data Analysis (tda) is a recent eld that emerged from various works in applied (algebraic) topology and computational geometry during the rst decade of the century. Although one can trace back geometric approaches for data analysis quite far in the past, tda really started as a eld with the pioneering works of Edelsbrunner et al. (2002) and Zomorodian and Carlsson (2005) in persistent homology and was popularized in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the idea that topology and geometry provide a powerful approach to infer robust qualitative, and sometimes quantitative, information about the structure of data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical, statistical and algorithmic methods to infer, analyze and exploit the complex topological and geometric structures underlying data that are often represented as point clouds in Euclidean or more general metric spaces. During the last few years, a considerable eort has been made to provide robust and ecient data structures and algorithms for tda that are now implemented and available and easy to use through standard libraries such as the Gudhi library (C++ and Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a). Although it is still rapidly evolving, tda now provides a set of mature and ecient tools that can be used in combination or complementary to other data sciences tools. The tdapipeline. tda has recently known developments in various directions and application elds. There now exist a large variety of methods inspired by topological and geometric approaches. Providing a complete overview of all these existing approaches is beyond the scope of this introductory survey. However, most of them rely on the following basic and standard pipeline that will serve as the backbone of this paper: 1. The input is assumed to be a nite set of points coming with a notion of distance-or similarity between them. This distance can be induced by the metric in the ambient space (e.g. the Euclidean metric when the data are embedded in R d) or come as an intrinsic metric dened by a pairwise distance matrix. The denition of the metric on the data is usually given as an input or guided by the application. It is however important to notice that the choice of the metric may be critical to reveal interesting topological and geometric features of the data.


title: Fine-Grained Image Search

abstract: Large-scale image search has been attracting lots of attention from both academic and commercial fields. The conventional bag-of-visual-words (BoVW) model with inverted index is verified efficient at retrieving near-duplicate images, but it is less capable of discovering fine-grained concepts in the query and returning semantically matched search results. In this paper, we suggest that instance search should return not only near- duplicate images, but also fine-grained results, which is usually the actual intention of a user. We propose a new and interesting problem named fine-grained image search, which means that we prefer those images containing the same fine-grained concept with the query. We formulate the problem by constructing a hierarchical database and defining an evaluation method. We thereafter introduce a baseline system using fine-grained classification scores to represent and co-index images so that the semantic attributes are better incorporated in the online querying stage. Large-scale experiments reveal that promising search results are achieved with reasonable time and memory consumption. We hope this paper will be the foundation for future work on image search. We also expect more follow-up efforts along this research topic and look forward to commercial fine-grained image search engines.


Modeling and analysis of an extrinsic Fabry-Perot interferometer performance using MATLAB


title: An efficient routing algorithm based on segment routing in software-defined networking

abstract: Software-defined networking (SDN) is an emerging architecture that offers advantages over traditional network architecture. Segment routing (SR) defines the path of information through the network via an ordered list of multi-protocol label switching (MPLS) mechanisms on the packet headers at the ingress device, and this system makes SDN routing management more simple and efficient. SR can also solve some scalability issues in SDN. In this paper, we propose a routing algorithm for SDN with SR that can meet the bandwidth requirements of routing requests. Our algorithm considers the balance of traffic load and reduces the extra cost of packet header size in a network. Simulation results show that the performance of our algorithm is better than that of previously developed algorithms in terms of the average network throughput and the average rejection rate of routing requests.


title: A free-head, simple calibration, gaze tracking system that enables gaze-based interaction

abstract: Human eye gaze is a strong candidate to create a new application area based on human-computer interaction. To implement a really practical gaze-based interaction system, gaze detection must be realized without placing any restriction on the user's behavior or comfort. This paper describes a gaze tracking system that offers freehead, simple personal calibration. It does not require the user wear anything on her head, and she can move her head freely. Personal calibration takes only a very short time; the user is asked to look at two markers on the screen. An experiment shows that the accuracy of the implemented system is about 1.0 degrees (view angle).


title: The Endoscopic Surgical Skill Qualification System in Urological Laparoscopy: A Novel System in Japan

abstract: Purpose: The Japanese Urological Association, and Japanese Society of Endourology and ESWL have established the Endoscopic Surgical Skill Qualification System in urological laparoscopy. The system consists of a urological section as well as gynecology and general surgery sections. We present details of the Endoscopic Surgical Skill Qualification System and year 1 results.Materials and Methods: Endoscopic Surgical Skill Qualification System requirements test the ability to complete common laparoscopic surgeries in each field. In urology applicants are required to complete adrenalectomies or nephrectomies appropriately and safely. Applicants should have 2 years of experience with laparoscopic practice, in addition to having completed a 6-year formal urological training program. Also, each surgeon must have performed more than 20 laparoscopic surgeries. According to assessment guidelines applicant skills are assessed by 2 referees who view unedited videotapes showing the entire laparoscopic procedure. To est...


title: Deep Reinforcement Learning in Large Discrete Action Spaces

abstract: Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many real-world tasks involving large numbers of discrete actions for which current methods are difficult or even often impossible to apply. An ability to generalize over the set of actions as well as sub-linear complexity relative to the size of the set are both necessary to handle such tasks. Current approaches are not able to provide both of these, which motivates the work in this paper. Our proposed approach leverages prior information about the actions to embed them in a continuous space upon which it can generalize. Additionally, approximate nearest-neighbor methods allow for logarithmic-time lookup complexity relative to the number of actions, which is necessary for time-wise tractable training. This combined approach allows reinforcement learning methods to be applied to large-scale learning problems previously intractable with current methods. We demonstrate our algorithm’s abilities on a series of tasks having up to one million actions.


Jointly Embedding Knowledge Graphs and Logical Rules


title: Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models

abstract: We describe two probabilistic models for unsupervised word-sense disambiguation using parallel corpora. The first model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense inventory for the target language, and recasts their approach in a probabilistic framework. The second model, which we call the Concept model, is a hierarchical model that uses a concept latent variable to relate different language specific sense labels. We show that both models improve performance on the word sense disambiguation task over previous unsupervised approaches, with the Concept model showing the largest improvement. Furthermore, in learning the Concept model, as a by-product, we learn a sense inventory for the parallel language.


title: SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

abstract: Symmetric positive semidefinite (SPSD) matrix approximation is an important problem with applications in kernel methods. However, existing SPSD matrix approximation methods such as the Nystr\"om method only have weak error bounds. In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds. We call it the prototype model for it has more efficient and effective extensions, and some of its extensions have high scalability. Though the prototype model itself is not suitable for large-scale data, it is still useful to study its properties, on which the analysis of its extensions relies. ::: This paper offers novel theoretical analysis, efficient algorithms, and a highly accurate extension. First, we establish a lower error bound for the prototype model and improve the error bound of an existing column selection algorithm to match the lower bound. In this way, we obtain the first optimal column selection algorithm for the prototype model. We also prove that the prototype model is exact under certain conditions. Second, we develop a simple column selection algorithm with a provable error bound. Third, we propose a so-called spectral shifting model to make the approximation more accurate when the eigenvalues of the matrix decay slowly, and the improvement is theoretically quantified. The spectral shifting method can also be applied to improve other SPSD matrix approximation models.


Natural Language Processing with Python


title: Implementation of a real-time computer for space applications

abstract: This thesis is a pre-study presenting the challenges involved in constructing an embedded instrument control unit (ICU) and an introduction of the technologies involved. The ICU is to be constructed using an FPGA implementing a GRLIB/Leon 3 soft processor and running a real-time operating system with communication through an ESA Spacewire IP core. The starting point was a Xilinx Virtex-5 Ml507 development card, the GRLIB development library and a choice between various RTOS. ::: A Spacewire core has been attached to the AHB bus of a Leon 3 processor design, tested on-board the Virtex-5 FPGA and verified using Grmon and testing software. From the available RTOSs Rtems was chosen as the best candidate and an Ubuntu host development environment was installed featuring the necessary software and drivers. Software was then compiled for this architecture in order to test, verify and benchmark the complete system. ::: The complete system is working except for a small error in the error handling within the Spacewire core and communication over Spacewire has been established with a loopback cable. The FPGA has plenty of space left after implementation of the Leon 3 design and is a good candidate when this project goes live.


title: Eye movement control during reading: Effects of word frequency and orthographic familiarity

abstract: Word frequency and orthographic familiarity were independently manipulated as readers’ eye movements were recorded. Word frequency influenced fixation durations and the probability of word skipping when orthographic familiarity was controlled. These results indicate that lexical processing of words can influence saccade programming (as shown by fixation durations and which words are fixated). Orthographic familiarity, but not word frequency, influenced the duration of prior fixations. These results provide evidence for orthographic, but not lexical, parafoveal-on-foveal effects. Overall, the findings have a crucial implication for models of eye movement control in reading: There must be sufficient time for lexical factors to influence saccade programming before saccade metrics and timing are finalized. The conclusions are critical for the fundamental architecture of models of eye movement control in reading— namely, how to reconcile long saccade programming times and complex linguistic influences on saccades during reading.


title: Foveal TFCC Tear Classification and Treatment

abstract: Disorders of the triangular fibrocartilage complex (TFCC) represent one of the most frequent causes of ulnar-sided pain and disability in the wrist. TFCC lesions are currently categorized according to the classification system proposed by Andrew K. Palmer 1 in 1989, which considers two main classes: Class 1, Traumatic lesions and Class 2, Degenerative lesions. These classes are further divided into different types, depending on the location of the tear and the presence or absence of associated chondromalacic changes. Class 1 traumatic lesions are organized according to the tear’s location. Type 1-B injuries consist of traumatic tears involving the ulnar periphery of the TFCC and may be associated with an ulnar styloid fracture. Histologic and functional anatomy show that the ulnar side of the TFCC is arranged in a complex tridimensional structure and consists of 3 components: the proximal triangular ligament, the distal hammock structure, and the ulnar collateral ligament (UCL). 2 According to its structure and function, the UCL is assimilated to the distal hammock structure to make up the distal component of the TFCC (dcTFCC), which supports and suspends the ulnar carpus. 2 The proximal component of the TFCC


title: Ferrite loaded tunable SIW slot antenna

abstract: This work proposes a design of novel magnetically tunable substrate integrated waveguide (SIW) slot antenna with one ferrite slab loaded in the antenna. The operation frequency of the antenna can be tuned more than 380 MHz in the X-band from 9.32 to 9.70 GHz when the bias magnetic field changes from 0 to 0.12 tesla. Within the tunable frequency range, antenna gain is more than 6 dBi. The radiation patterns and the gain are almost not affected by the changing of applied magnetic field, showing a good high gain antenna with frequency tunable feature.


Ontology Engineering for Collaborative Embedded Systems - Requirements and Initial Approach


title: Handover Key Management in an LTE-based Unmanned Aerial Vehicle Control Network

abstract: Recently, UAV (Unmanned Aerial Vehicle) related technologies have significantly developed and its prospective applications include precision agriculture, disaster relief operation, flying crowd surveillance, flying intelligent transportation service, and flying information gathering and dissemination. To operate those promising UAVs in each country's general airspace, we need a reliable and secure link that can well command and control the UAVs. Besides, a mobile telecommunication network such as LTE (Long Term Evolution) can be employed to connect multiple UAVs with their pilots through the network. In this paper, we propose an LTE-based communication architecture to support the control of UAVs. Then, we propose a handover key management scheme for the LTE-based UAV control network. We analyze security of the handover key management scheme and compare it with other schemes. Security analyses show that our scheme keeps a communication key of a serving GRS (Ground Relay Station) safe even though neighboring GRSes are compromised. We plan to do some simulations in order to prove performance and security benefits of our scheme.


title: Hypovitaminosis D in developing countries—prevalence, risk factors and outcomes

abstract: Clinical manifestations of hypovitaminosis D, a highly prevalent disorder worldwide, include musculoskeletal disorders as well as nonclassical manifestations, such as increased risk of respiratory infections, diabetes mellitus and possibly cardiovascular diseases. This Review discusses the prevalence of hypovitaminosis D across the lifecycle, and risk factors and health outcomes associated with it in developing countries.


title: RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines

abstract: Automatic Machine Learning is a growing area of machine learning that has a similar objective to the area of hyper-heuristics: to automatically recommend optimized pipelines, algorithms or appropriate parameters to specific tasks without much dependency on user knowledge. The background knowledge required to solve the task at hand is actually embedded into a search mechanism that builds personalized solutions to the task. Following this idea, this paper proposes RECIPE (REsilient ClassifIcation Pipeline Evolution), a framework based on grammar-based genetic programming that builds customized classification pipelines. The framework is flexible enough to receive different grammars and can be easily extended to other machine learning tasks. RECIPE overcomes the drawbacks of previous evolutionary-based frameworks, such as generating invalid individuals, and organizes a high number of possible suitable data pre-processing and classification methods into a grammar. Results of f-measure obtained by RECIPE are compared to those two state-of-the-art methods, and shown to be as good as or better than those previously reported in the literature. RECIPE represents a first step towards a complete framework for dealing with different machine learning tasks with the minimum required human intervention.


title: Accurate Computation of a Streamflow Drought Index

abstract: In this study, the authors investigated an approach to calculate the standardized streamflow index (SSI), which allows accurate spatial and temporal comparison of the hydrological conditions of a stream or set of streams. For this purpose, the capability of six three-parameter distributions (lognormal, Pearson Type III, log-logistic, general extreme value, generalized Pareto, and Weibull) and two different approaches to select the most suitable distribution the best monthly fit (BMF) and the minimum orthogonal distance (MD), were tested by using a monthly streamflow data set for the Ebro Basin (Spain). This large Mediterranean basin is characterized by high variability in the magnitude of streamflows and in seasonal regimes. The results show that the most commonly used probability distributions for flow frequency analysis provided good fits to the streamflow series. Thus, the visual inspection of the L-moment diagrams and the results of the Kolmogorov-Smirnov test did not enable the selection of a single ...


title: Is uncertainty over Brexit damaging the UK and European equities

abstract: The possibility the UK might leave the European Union –also known as Brexit– is a major source of concern. This article seeks to assess the costs of uncertainty over Brexit by delving into the impacts of the attention given to this event (via Google Trends and Twitter) on UK, German and French equities, while controlling for the effects of global financial and economic factors. We use different econometric tools enable to measure the strength of Brexit’ effect as alternative to tail distributions (quantile regression approach) and spectral components (frequency domain causality test). Despite a heaviest awareness that it is difficult to properly quantify the costs of uncertainty over Brexit, this study provides evidence that the severity of Brexit’ impact was not uniform across the investigated equities. Germany suffered most if the British exit from Europe happens, followed by France and UK. These results are fairly robust among the different methods and the internet proxies used.


title: A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

abstract: Online social data is at the core of many popular web platforms, driving the agenda of researchers in both academia and industry. The promises of social data are many, including understanding "what the world thinks" about social issues, brands, or other entities, and enabling better decision-making in a variety of fields like public policy and healthcare. Yet, many academics and practitioners are increasingly warning against the naive usage of social data. They highlight that there are biases and inaccuracies occurring at the source of the data, and also introduced during the data processing. There are methodological limitations, as well as ethical boundaries and unexpected consequences.


title: Disambiguation of patent inventors and assignees using high-resolution geolocation data

abstract: Patent data represent a significant source of information on innovation and the evolution of technology through networks of citations, co-invention and co-assignment of new patents. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in the creation of a technology. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventor and assignees on more than 3.6 million patents found in the European Patent Office (EPO), under the Patent Cooperation treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show that our algorithm has both high precision and recall in comparison to a manual disambiguation of EPO assignee names in Boston and Paris, and show it performs well for a benchmark of USPTO inventor names that can be linked to a high-resolution address (but poorly for inventors that never provided a high quality address). The most significant benefit of this work is the high quality assignee disambiguation with worldwide coverage coupled with an inventor disambiguation that is competitive with other state of the art approaches. To our knowledge this is the broadest and most accurate simultaneous disambiguation and cross-linking of the inventor and assignee names for a significant fraction of patents in these three major patent collections.


title: A comprehensive study of Convergent and Commutative Replicated Data Types

abstract: Eventual consistency aims to ensure that replicas of some mutable shared object converge without foreground synchronisation. Previous approaches to eventual consistency are ad-hoc and error-prone. We study a principled approach: to base the design of shared data types on some simple formal conditions that are sufficient to guarantee eventual consistency. We call these types Convergent or Commutative Replicated Data Types (CRDTs). This paper formalises asynchronous object replication, either state based or operation based, and provides a sufficient condition appropriate for each case. It describes several useful CRDTs, including container data types supporting both \add and \remove operations with clean semantics, and more complex types such as graphs, montonic DAGs, and sequences. It discusses some properties needed to implement non-trivial CRDTs.


Biomechanical Stability of Dental Implants in Augmented Maxillary Sites: Results of a Randomized Clinical Study with Four Different Biomaterials and PRF and a Biological View on Guided Bone Regeneration


CD40/CD40 LIGAND INTERACTIONS IN IMMUNE RESPONSES AND PULMONARY IMMUNITY


title: Distributed Deep Learning for Answer Selection

abstract: This paper is an empirical study of the distributed deep learning for a question answering subtask: answer selection. Comparison studies of SGD, MSGD, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the message passing interface based distributed framework can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training: with 120 workers, an 83x speedup is achievable and running time is decreased from 107.9 hours to 1.3 hours, which will benefit the productivity significantly.


title: GraspIt! - Robotics & Automation Magazine, IEEE

abstract: A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.


title: E-government evaluation: a framework and case study

abstract: Abstract The importance of measuring the performance of e-government cannot be overemphasized. In this paper, a flexible framework is suggested to choose an appropriate strategy to measure the tangible and intangible benefits of e-government. An Indian case study of NDMC (New Delhi Municipal Corporation) has been taken up for analysis and placement into the framework. The results obtained suggest that to have a proper evaluation of tangible and intangible benefits of e-government, the projects should be in a mature stage with proper information systems in place. All of the e-government projects in India are still in a nascent stage; hence, proper information flow for calculating ‘return on e-government’ considering tangible and intangible benefits cannot be fully ascertained.


title: Log-DenseNet: How to Sparsify a DenseNet

abstract: Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. This work first experimentally shows that one key advantage of skip connections is to have short distances among feature layers during backpropagation. Specifically, using a fixed number of skip connections, the connection patterns with shorter backpropagation distance among layers have more accurate predictions. Following this insight, we propose a connection template, Log-DenseNet, which, in comparison to DenseNet, only slightly increases the backpropagation distances among layers from 1 to ($1 + \log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$. Hence, \logdenses are easier to scale than DenseNets, and no longer require careful GPU memory management. We demonstrate the effectiveness of our design principle by showing better performance than DenseNets on tabula rasa semantic segmentation, and competitive results on visual recognition.


title: Towards Quantum Chemistry on a Quantum Computer

abstract: Exact first-principles calculations of molecular properties are currently intractable because their computational cost grows exponentially with both the number of atoms and basis set size. A solution is to move to a radically different model of computing by building a quantum computer, which is a device that uses quantum systems themselves to store and process data. Here we report the application of the latest photonic quantum computer technology to calculate properties of the smallest molecular system: the hydrogen molecule in a minimal basis. We calculate the complete energy spectrum to 20 bits of precision and discuss how the technique can be expanded to solve large-scale chemical problems that lie beyond the reach of modern supercomputers. These results represent an early practical step toward a powerful tool with a broad range of quantum-chemical applications. ne of the most important and difficult problems in science is the accurate calculation of material properties from first principles. Indeed, this can be viewed as the central challenge for theorists from an enormous range of fields, including chemistry, condensed matter physics, materials science, biophysics and biochemistry. The challenge of first-principles calculations arises because the computational resources required to obtain exact (that is, full configuration interaction) solutions of the


title: Mining Summaries for Knowledge Graph Search

abstract: Mining and searching heterogeneous and large knowledge graphs is challenging under real-world resource constraints such as response time. This paper studies a framework that discover to facilitate knowledge graph search. 1) We introduce a class of summaries characterized by graph patterns. In contrast to conventional summaries defined by frequent subgraphs, the summaries are capable of adaptively summarize entities with similar neighbors up to a bounded hop. 2) We formulate the computation of graph summarization as a bi-criteria pattern mining problem. Given a knowledge graph G, the problem is to discover k diversified summaries that maximizes the informativeness measure. Although this problem is NP-hard, we show that it is 2-approximable. We also introduce an online mining algorithm that trade-off speed and accuracy, under given resource constraints. 3) We develop query evaluation algorithms that make use of the summaries as views. These algorithms efficiently compute (approximate) answers with high accuracy, and only refer to a small number of summaries. Our experimental study verifies that online mining over large knowledge graphs is feasible, and can suggest bounded search in knowledge graphs.


title: The recent excitement about neural networks

abstract: The remarkable properties of some recent computer algorithms for neural networks seemed to promise a fresh approach to understanding the computational properties of the brain. Unfortunately most of these neural nets are unrealistic in important respects.


title: Automatic tag generation and ranking for sensor-rich outdoor videos

abstract: Video tag annotations have become a useful and powerful feature to facilitate video search in many social media and web applications. The majority of tags assigned to videos are supplied by users - a task which is time consuming and may result in annotations that are subjective and lack precision. A number of studies have utilized content-based extraction techniques to automate tag generation. However, these methods are compute-intensive and challenging to apply across domains. Here, we describe a complementary approach for generating tags based on the geographic properties of videos. With today's sensor-equipped smartphones, the location and orientation of a camera can be continuously acquired in conjunction with the captured video stream. Our novel technique utilizes these sensor meta-data to automatically tag outdoor videos in a two step process. First, we model the viewable scenes of the video as geometric shapes by means of its accompanied sensor data and determine the geographic objects that are visible in the video by querying geo-information databases through the viewable scene descriptions. Subsequently we extract textual information about the visible objects to serve as tags. Second, we define six criteria to score the tag relevance and rank the obtained tags based on these scores. Then we associate the tags with the video and the accurately delimited segments of the video. To evaluate the proposed technique we implemented a prototype tag generator and conducted a user study. The results demonstrate significant benefits of our method in terms of automation and tag utility.


title: Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval

abstract: Replay is the sequential reactivation of hippocampal place cells that represent previously experienced behavioral trajectories. Although first studied during sleep, recent work suggests that replay occurs frequently in the awake state and could be a potential substrate for memory consolidation and retrieval.


Targeting the FXR Nuclear Receptor to Treat Liver Disease


title: Spam comments detection with self-extensible dictionary and text-based features

abstract: The new social media have become popular for information spreading, allowing online users to publish latest events and personal opinions. However, massive spam comments seriously decrease users' reading experience. To detect spam comments in Chinese social media, we employ semantic analysis to build the self-extensible dictionary which updates and extends itself with new cyber words automatically. The Semantic analysis brings extra semantic features which helps in text classification. Based on the statistical analysis of microblogging comments, we select four text-based features, which basically represent characteristics of Chinese spam comments. We use spam dictionary and text-based features to construct classifiers for detecting spam comments. Finally, we achieve an average detection accuracy of 93.6%, which is preferable to existing spam comments detection methods. Experimental results demonstrate that our method can effectively detect spam comments in Chinese microblogging field.


title: Face Recognition Based GLOH Descriptor and Integration of Local Features

abstract: In order to reduce the computational complexity of high-dimensional feature descriptor and improve the accuracy of recognition algorithm, the paper proposes a face recognition algorithm based on GLOH descriptor, with scale and geometric invariant. In the paper, face image is divided into four separate sub-regions and clusters the feature points extracted from the every region. In order to describe the feature operator and feature matching more effectively, the different region is to give different weight values according to the distinctiveness. The method of the whole combining with local clustering sub-region is employed for face recognition. The effectiveness of the algorithm is verified by experiments on the ORL face image database, which demonstrates good stability and robustness especially under the conditions of some confounding factors such as different facial expressions, postures and so on.


title: Automated generation of layout and control for quantum circuits

abstract: We present a computer-aided design flow for quantum circuits, complete with automatic layout and control logic extraction. To motivate automated layout for quantum circuits, we investigate grid-based layouts and show a performance variance of four times as we vary grid structure and initial qubit placement. We then propose two polynomial-time design heuristics: a greedy algorithm suitable for small, congestion-free quantum circuits and a dataflow-based analysis approach to placement and routing with implicit initial placement of qubits. Finally, we show that our dataflow-based heuristic generates better layouts than the state-of-the-art automated grid-based layout and scheduling mechanism in terms of latency and potential pipelinability, but at the cost of some area.


title: Kinematics Modeling of a Wheel-Based Pole Climbing Robot (UT-PCR)

abstract: This paper is concerned with the derivation of the kinematics model of the University of Tehran-Pole Climbing Robot (UT-PCR). As the first step, an appropriate set of coordinates is selected and used to describe the state of the robot. Nonholonomic constraints imposed by the wheels are then expressed as a set of differential equations. By describing these equations in terms of the state of the robot an underactuated driftless nonlinear control system with affine inputs that governs the motion of the robot is derived. A set of experimental results are also given to show the capability of the UT-PCR in climbing a stepped pole.


China’s Approaches to the Western-dominated International Law: A Historical Perspective from the Opium War to the South China Sea Arbitration Case


title: Attentional Parallel RNNs for Generating Punctuation in Transcribed Speech

abstract: Until very recently, the generation of punctuation marks for automatic speech recognition (ASR) output has been mostly done by looking at the syntactic structure of the recognized utterances. Prosodic cues such as breaks, speech rate, pitch intonation that influence placing of punctuation marks on speech transcripts have been seldom used. We propose a method that uses recurrent neural networks, taking prosodic and lexical information into account in order to predict punctuation marks for raw ASR output. Our experiments show that an attention mechanism over parallel sequences of prosodic cues aligned with transcribed speech improves accuracy of punctuation generation.


title: Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs

abstract: This paper presents an approach to organizing folktales based on a data structure called a plot graph, which captures the narrative flow of events in a folktale. The similarity between two folktales can be computed as the structural similarity between their corresponding plot graphs. This is performed using the well-known Needleman-Wunsch algorithm. To test the efficacy of this approach, experiments are carried out using a small collection of 24 folktales grouped into 5 categories based on the Aarne-Thompson index. The best result is obtained by combining the proposed structural-based similarity measure with a more conventional bag of words vector space model, where 19 out of the 24 folktales (79.16%) yield higher average similarity with folktales within their respective categories as opposed to across categories.


title: Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI

abstract: Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.


title: Synthesis and antiinflammatory activity of hexahydrothiopyrano[4,3-c]pyrazoles and related analogues.

abstract: A series of novel hexahydrothiopyrano[4,3-c]pyrazoles and related analogues were prepared and tested for antiinflammatory activity by using the mouse active Arthus reaction and the delayed hypersensitivity skin reaction in guinea pigs as primary screens. The compounds of most interest, 18 and 28, were further tested in a model of adjuvant-induced arthritis; in this system, both compounds were active when dosed intraperitoneally but failed to produce significant activity when dosed orally at subtoxic doses.


title: Finite-difference time-domain analysis of a dual resonance and shielded cellular antenna

abstract: This paper presents a dual-resonance shielded antenna suitable for cellular phone applications. The antenna is shielded with a metallic wall to reduce possible health hazards towards user. Unlike other cellular antennas, new antenna's operating bandwidth spans between the first two resonances of the antenna and an almost 50 ohms flat input resistance can be obtained within a bandwidth of 824-896 MHz, using proper antenna dimensions. A proto-type of the dual resonance shielded antenna has been built and input impedance, near and far field measurements have been performed. Near field measurements showed that excessive electromagnetic radiation towards user is blocked by 90% without significantly sacrificing the omnidirectional characteristics required for cellular phone devices. Input impedance measurements are compared with the numerical results and a good agreement has been achieved. The new antenna uses all copper wires and shield which makes it easy and inexpensive to build.


title: YawDD: a yawning detection dataset

abstract: In this paper, we present two video datasets of drivers with various facial characteristics, to be used for designing and testing algorithms and models for yawning detection. For collecting these videos, male and female candidates were asked to sit in the driver's seat of a car. The videos are taken in real and varying illumination conditions. In the first dataset, the camera is installed under the front mirror of the car. Each participant has three or four videos and each video contains different mouth conditions such as normal, talking/singing, and yawning. In the second dataset, the camera is installed on the dash in front of the driver, and each participant has one video with the above-mentioned different mouth conditions all in the same video. The car was parked for both datasets to keep the environment safe for the participants. As a benchmark, we also present the results of our own yawning detection method, and show that we can achieve a much higher accuracy in the scenario with the camera installed on the dash in front of the driver.


title: A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study

abstract: Massive amount of electronic medical records accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to extract knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. In this paper, we propose a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures for feature representation in higher-level abstraction to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. First, we apply our method on a small dataset related to a specific precision medicine problem, which focuses on prediction of left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk in a vulnerable demographic subgroup (African-Americans). Then we use two large datasets from eICU collaborative research database to predict the length of stay in Cardiac-ICU and Neuro-ICU based on high dimensional features. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others.


title: Brain tumor segmentation with Deep Neural Networks

abstract: Abstract In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we’ve found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.


title: Reversibly monitoring oxidation and reduction events in living biological systems: Recent development of redox-responsive reversible NIR biosensors and their applications in in vitro/in vivo fluorescence imaging

abstract: Reactive oxygen species (ROS) and changes in their redox cycles have great therapeutic potential for treating serious redox-related human diseases such as acute and chronic inflammation, diabetes, cancer and neurodegenerative disorders. This article presents a survey of the recently (2011–2016) developed NIR small-molecule biosensors for reversibly monitoring oxidation and reduction events in living cells and small animals through in vitro/in vivo fluorescence imaging. Emission and absorption profile, design strategy and fluorescence sensing mechanism, ROS selectivity and sensitivity, reversibility, ability of subcellular location and cytotoxicity are discussed for the NIR small-molecule biosensors capable of quantitatively, continuously and reversibly detecting transient ROS burst and redox changes at cellular level.


title: Making queueing theory more palatable to SDN/OpenFlow-based network practitioners

abstract: Software-Defined Networking (SDN) is an emerging networking technology that has attracted intense interest from both the industry and research communities. Thus far, it is primarily applied to datacenters and research network environments. Despite meticulous effort in planning and equipment selection prior to deployment, there remain unknowns that can affect the network's performance after equipment has been deployed and is fully operational. Network administrators and planners would benefit from a tool that is able to monitor the load on various network entities and visualize this in real-time and, even better, predict likely performance changes arising from traffic variation; this allows them to make prompt decisions to prevent seemingly small hotspots from becoming major bottlenecks. In this paper, we present a network visualization and performance prediction tool that enables network planners to examine how their networks' performance will be affected as the traffic loads and network utilization changes. This is a first of its kind where performance prediction is based on queueing analytic models of the network configuration coupled with real-time measurements taken from the network devices.


title: A Single-Stage Integrated Boost-LLC AC–DC Converter With Quasi-Constant Bus Voltage for Multichannel LED Street-Lighting Applications

abstract: Single-stage boost-LLC ac–dc converters are popularly utilized as the front-end stage of multichannel LED street-lighting drivers. Pulse frequency modulation (PFM) strategy with a duty cycle of 0.5 is commonly utilized in these ac–dc converters. However, PFM has the following drawbacks: 1) high bus voltage limits the ac input voltage range, which makes it only suitable for the input voltage range of 85~135 V and 2) the maximum bus voltage can reach up to three times of the minimum bus voltage, which increases the difficulty of the LLC design. This paper proposed a hybrid modulation strategy, which consists of a bus voltage control loop using asymmetric pulsewidth modulation and an output voltage control loop using PFM. The stable output voltage and the quasi-constant bus voltage can be simultaneously achieved. Furthermore, by increasing bus voltage ripple and employing the twin-bus configuration, the short-lifetime electrolytic capacitors are eliminated, and the efficiency of the total LED driver can be greatly improved. Therefore, universal-input operation, high efficiency, low cost, and long lifetime are the key innovations of the proposed solution. A universal-input 100-W prototype is built to demonstrate the feature of the proposed solution. The experimental results show that the converter achieves a peak efficiency of 92.5% at the input voltage of 130 V and a high power factor value of above 0.95 over the whole input voltage range. With the proposed modulation strategy, the maximum bus voltage is just 500 V under universal input voltage condition, which is greatly lower than that of the traditional PFM controlled boost-LLC converters.


title: In Defense of MinHash Over SimHash

abstract: MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH) algorithms for large-scale data processing applications. Deciding which LSH to use for a particular problem at hand is an important question, which has no clear answer in the existing literature. In this study, we provide a theoretical answer (validated by experiments) that MinHash virtually always outperforms SimHash when the data are binary, as common in practice such as search. The collision probability of MinHash is a function of resemblance similarity (R), while the collision probability of SimHash is a function of cosine similarity (S). To provide a common basis for comparison, we evaluate retrieval results in terms of S for both MinHash and SimHash. This evaluation is valid as we can prove that MinHash is a valid LSH with respect to S, by using a general inequality S 2


title: Learning Shape Abstractions by Assembling Volumetric Primitives

abstract: We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically interpretable explanations of 3D objects, our framework also allows us to automatically discover and exploit consistent structure in the data. We demonstrate that using our method allows predicting shape representations which can be leveraged for obtaining a consistent parsing across the instances of a shape collection and constructing an interpretable shape similarity measure. We also examine applications for image-based prediction as well as shape manipulation.


title: Lithe: Lightweight Secure CoAP for the Internet of Things

abstract: The Internet of Things (IoT) enables a wide range of application scenarios with potentially critical actuating and sensing tasks, e.g., in the e-health domain. For communication at the application layer, resource-constrained devices are expected to employ the constrained application protocol (CoAP) that is currently being standardized at the Internet Engineering Task Force. To protect the transmission of sensitive information, secure CoAP mandates the use of datagram transport layer security (DTLS) as the underlying security protocol for authenticated and confidential communication. DTLS, however, was originally designed for comparably powerful devices that are interconnected via reliable, high-bandwidth links. In this paper, we present Lithe-an integration of DTLS and CoAP for the IoT. With Lithe, we additionally propose a novel DTLS header compression scheme that aims to significantly reduce the energy consumption by leveraging the 6LoWPAN standard. Most importantly, our proposed DTLS header compression scheme does not compromise the end-to-end security properties provided by DTLS. Simultaneously, it considerably reduces the number of transmitted bytes while maintaining DTLS standard compliance. We evaluate our approach based on a DTLS implementation for the Contiki operating system. Our evaluation results show significant gains in terms of packet size, energy consumption, processing time, and network-wide response times when compressed DTLS is enabled.


title: Not Just Depressed: Bipolar Disorder Prediction on Reddit

abstract: Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users' self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.


title: Identifying Image Tags from Instagram Hashtags Using the HITS Algorithm

abstract: Many automatic image annotation methods are based on the learning by example paradigm. Image tagging, through manual image inspection, is the first step towards this end. However, manual image annotation, even for creating the training sets, is time-consuming, complicated and contains human subjectivity errors. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are crucial. As we showed in one of our previous studies, tags accompanying photos in social media and especially the Instagram hashtags can be used for image annotation. However, it turned out that only a 20% of the Instagram hashtags are actually relevant to the content of the image they accompany. Identifying those hashtags through crowdsourcing is a plausible solution. In this work, we investigate the effectiveness of the HITS algorithm for identifying the right tags in a crowdsourced image tagging scenario. For this purpose, we create a bipartite graph in which the first type of nodes corresponds to the annotators and the second type to the tags they select, among the hashtags, to annotate a particular Instagram image. From the results, we conclude that the authority value of the HITS algorithm provides an accurate estimation of the appropriateness of each Instagram hashtag to be used as a tag for the image it accompanies while the hub value can be used to filter out the dishonest annotators.


title: Compact Design of a Hydraulic Driving Robot for Intraoperative MRI-Guided Bilateral Stereotactic Neurosurgery

abstract: In this letter, we present an intraoperative magnetic resonance imaging (MRI)-guided robot for bilateral stereotactic procedures. Its compact design enables robot's operation within the constrained space of standard imaging head coil. magnetic resonance (MR) safe and high-performance hydraulic transmissions are incorporated. A maximum stiffness coefficient of 24.35 N/mm can be achieved with transmission fluid preloaded at 2 bar. Sufficient targeting accuracy (average within ≤1.73 mm) has been demonstrated in a simulated needle insertion task of deep brain stimulation. A novel MR-based wireless tracking technique is adopted. It is capable of offering real-time and continuous (30-40 Hz) three-dimensional (3-D) localization of robotic instrument under the proper MR tracking sequence. It outperforms the conventional method of using low-contrast passive fiducials that can only be revealed in the MR image domain. Two wireless tracking units/markers are integrated with the robot, which are two miniaturized coil circuits in size of 1.5 × 5 × 0.2 mm ::: 3 ::: , fabricated on flexible thin films. A navigation test was performed under the standard MRI settings in order to visualize the 3-D localization of the robotic instrument. MRI compatibility test was also carried out to prove the minimal interference to MR images of the presented hydraulic robotic platform.


title: A combined two-method MPPT control scheme for grid-connected photovoltaic systems

abstract: In order to increase the output efficiency of a grid-connected photovoltaic (PV) system it is important to have an efficient maximum power point tracker (MPPT). In the case of low irradiation, the perturb and observe (PO) and incremental conductance (IC) methods have a poor efficiency, because of the poor resolution in the acquired signals, when a fixed point implementation is done. A cost-effective two-method MPPT control scheme is proposed in this paper to track the maximum power point (MPP) at both low and high irradiation, by combining a constant voltage (CV) method and a modified PO algorithm. Simulation and experimental results are presented to verify the performance of the proposed method


title: From Information Bottleneck To Activation Norm Penalty

abstract: Many regularization methods have been proposed to prevent overfitting in neural networks. Recently, a regularization method has been proposed to optimize the variational lower bound of the Information Bottleneck Lagrangian. However, this method cannot be generalized to regular neural network architectures. We present the activation norm penalty that is derived from the information bottleneck principle and is theoretically grounded in a variation dropout framework. Unlike in previous literature, it can be applied to any general neural network. We demonstrate that this penalty can give consistent improvements to different state of the art architectures both in language modeling and image classification. We present analyses on the properties of this penalty and compare it to other methods that also reduce mutual information.


title: A KNOWLEDGE REPRESENTATION LANGUAGE FOR NATURAL LANGUAGE PROCESSING, SIMULATION AND REASONING

abstract: OntoAgent is an environment that supports the cognitive modeling of societies of intelligent agents that emulate human beings. Like traditional intelligent agents, OntoAgent agents execute the core functionalities of perception, reasoning and action. Unlike most traditional agents, they engage in extensive "translation" functions in order to render perceived inputs into the unambiguous, ontologically-grounded knowledge representation language (KRL) that is used to model their knowledge, memory and reasoning. This paper describes the KRL of OntoAgent with a special focus on the many runtime functions used to translate between perceived inputs and the KRL, as well as to manipulate KRL structures for reasoning and simulation.


title: Cyber-Physical Attacks on UAS Networks- Challenges and Open Research Problems

abstract: Assignment of critical missions to unmanned aerial vehicles (UAV) is bound to widen the grounds for adversarial intentions in the cyber domain, potentially ranging from disruption of command and control links to capture and use of airborne nodes for kinetic attacks. Ensuring the security of electronic and communications in multi-UAV systems is of paramount importance for their safe and reliable integration with military and civilian airspaces. Over the past decade, this active field of research has produced many notable studies and novel proposals for attacks and mitigation techniques in UAV networks. Yet, the generic modeling of such networks as typical MANETs and isolated systems has left various vulnerabilities out of the investigative focus of the research community. This paper aims to emphasize on some of the critical challenges in securing UAV networks against attacks targeting vulnerabilities specific to such systems and their cyber-physical aspects.


title: Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation

abstract: The main disadvantage of the k-means algorithm is that the number of clusters, K, must be supplied as a parameter. In this paper we present a simple validity measure based on the intra-cluster and inter-cluster distance measures which allows the number of clusters to be determined automatically. The basic procedure involves producing all the segmented images for 2 clusters up to Kmax clusters, where Kmax represents an upper limit on the number of clusters. Then our validity measure is calculated to determine which is the best clustering by finding the minimum value for our measure. The validity measure is tested for synthetic images for which the number of clusters in known, and is also implemented for natural images.


title: Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content

abstract: Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text's stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.


title: Learning bicycle stunts

abstract: We present a general approach for simulating and controlling a human character that is riding a bicycle. The two main components of our system are offline learning and online simulation. We simulate the bicycle and the rider as an articulated rigid body system. The rider is controlled by a policy that is optimized through offline learning. We apply policy search to learn the optimal policies, which are parameterized with splines or neural networks for different bicycle maneuvers. We use Neuroevolution of Augmenting Topology (NEAT) to optimize both the parametrization and the parameters of our policies. The learned controllers are robust enough to withstand large perturbations and allow interactive user control. The rider not only learns to steer and to balance in normal riding situations, but also learns to perform a wide variety of stunts, including wheelie, endo, bunny hop, front wheel pivot and back hop.


title: Texture optimization for example-based synthesis

abstract: We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization.


title: Preventing SQL Injection attack using pattern matching algorithm

abstract: SQL injection attacks, a class of injection flaw in which specially crafted input strings leads to illegal queries to databases, are one of the topmost threats to web applications. A Number of research prototypes and commercial products that maintain the queries structure in web applications have been developed. But these techniques either fail to address the full scope of the problem or have limitations. Based on our observation that the injected string in a SQL injection attack is interpreted differently on different databases.Injection attack is a method that can inject any kind of malicious string or anomaly string on the original string. Pattern matching is a technique that can be used to identify or detect any anomaly packet from a sequential action. Most of the pattern based techniques are used static analysis and patterns are generated from the attacked statements. In this paper, we proposed a detection and prevention technique for preventing SQL Injection Attack using AhoCorasick pattern matching algorithm. In this paper, we proposed an overview of the architecture. In the initial stage evaluation, we consider some sample of standard attack patterns and it shows that the proposed algorithm is works well against the SQL Injection Attack.


title: Convolutional gated recurrent networks for video segmentation

abstract: Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video frames and outputs the segmentation of the last frame. Convolutional gated recurrent networks are used for the recurrent part to preserve spatial connectivities in the image. Our proposed method can be applied in both online and batch segmentation. This architecture is tested for both binary and semantic video segmentation tasks. Experiments are conducted on the recent benchmarks in SegTrack V2, Davis, CityScapes, and Synthia. Using recurrent fully convolutional networks improved the baseline network performance in all of our experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis respectively, 5.7% improvement in mean IoU in Synthia and 3.5% improvement in categorical mean IoU in CityScapes. The performance of the RFCN network depends on its baseline fully convolutional network. Thus RFCN architecture can be seen as a method to improve its baseline segmentation network by exploiting spatiotemporal information in videos.


title: Viral Marketing Meets Social Advertising: Ad Allocation with Minimum Regret

abstract: Social advertisement is one of the fastest growing sectors in the digital advertisement landscape: ads in the form of promoted posts are shown in the feed of users of a social networking platform, along with normal social posts; if a user clicks on a promoted post, the host (social network owner) is paid a fixed amount from the advertiser. In this context, allocating ads to users is typically performed by maximizing click-through-rate, i.e., the likelihood that the user will click on the ad. However, this simple strategy fails to leverage the fact the ads can propagate virally through the network, from endorsing users to their followers. ::: ::: In this paper, we study the problem of allocating ads to users through the viral-marketing lenses. We show that allocation that takes into account the propensity of ads for viral propagation can achieve significantly better performance. However, uncontrolled virality could be undesirable for the host as it creates room for exploitation by the advertisers: hoping to tap uncontrolled virality, an advertiser might declare a lower budget for its marketing campaign, aiming at the same large outcome with a smaller cost. ::: ::: This creates a challenging trade-off: on the one hand, the host aims at leveraging virality and the network effect to improve advertising efficacy, while on the other hand the host wants to avoid giving away free service due to uncontrolled virality. We formalize this as the problem of ad allocation with minimum regret, which we show is NP-hard and inapproximable w.r.t. any factor. However, we devise an algorithm that provides approximation guarantees w.r.t. the total budget of all advertisers. We develop a scalable version of our approximation algorithm, which we extensively test on four real-world data sets, confirming that our algorithm delivers high quality solutions, is scalable, and significantly outperforms several natural baselines.


title: Spurious group differences due to head motion in a diffusion MRI study

abstract: Diffusion-weighted MRI (DW-MRI) has become a popular imaging modality for probing the microstructural properties of white matter and comparing them between populations in vivo. However, the contrast in DW-MRI arises from the microscopic random motion of water molecules in brain tissues, which makes it particularly sensitive to macroscopic head motion. Although this has been known since the introduction of DW-MRI, most studies that use this modality for group comparisons do not report measures of head motion for each group and rely on registration-based correction methods that cannot eliminate the full effects of head motion on the DW-MRI contrast. In this work we use data from children with autism and typically developing children to investigate the effects of head motion on differences in anisotropy and diffusivity measures between groups. We show that group differences in head motion can induce group differences in DW-MRI measures, and that this is the case even when comparing groups that include control subjects only, where no anisotropy or diffusivity differences are expected. We also show that such effects can be more prominent in some white-matter pathways than others, and that they can be ameliorated by including motion as a nuisance regressor in the analyses. Our results demonstrate the importance of taking head motion into account in any population study where one group might exhibit more head motion than the other.


title: A Pointwise Approach to Training Dependency Parsers from Partially Annotated Corpora

abstract: We introduce a word-based dependency parser for Japanese that can be trained from partially annotated corpora, allowing for effective use of available linguistic resources and reduction of the costs of preparing new training data. This is especially important for domain adaptation in a real-world situation. We use a pointwise approach where each edge in the dependency tree for a sentence is estimated independently. Experiments on Japanese dependency parsing show that this approach allows for rapid training and achieves accuracy comparable to state-of-the-art dependency parsers trained on fully annotated data.


title: Comparison of Event Choreography and Orchestration Techniques in Microservice Architecture

abstract: Microservice Architecture (MSA) is an architectural design pattern which was introduced to solve the challenges involved in achieving the horizontal scalability, high availability, modularity and infrastructure agility for the traditional monolithic applications. Though MSA comes with a large set of benefits, it is challenging to design isolated services using independent Database per Service pattern. We observed that with each micro service having its own database, when transactions span across multiple services, it becomes challenging to ensure data consistency across databases, particularly in case of roll backs. In case of monolithic applications using RDBMS databases, these distributed transactions and roll backs can be handled efficiently using 2 phase commit techniques. These techniques cannot be applied for isolated No-SQL databases in micro services. This research paper aims to address three things: 1) elucidate the challenges with distributed transactions and rollbacks in isolated No-SQL databases with dependent collections in MSA, 2) examine the application of event choreography and orchestration techniques for the Saga pattern implementation, and 3) present the fact-based recommendations on the saga pattern implementations for the use cases.


title: Interference-driven resource management for GPU-based heterogeneous clusters

abstract: GPU-based clusters are increasingly being deployed in HPC environments to accelerate a variety of scientific applications. Despite their growing popularity, the GPU devices themselves are under-utilized even for many computationally-intensive jobs. This stems from the fact that the typical GPU usage model is one in which a host processor periodically offloads computationally intensive portions of an application to the coprocessor. Since some portions of code cannot be offloaded to the GPU (for example, code performing network communication in MPI applications), this usage model results in periods of time when the GPU is idle. GPUs could be time-shared across jobs to "fill" these idle periods, but unlike CPU resources such as the cache, the effects of sharing the GPU are not well understood. Specifically, two jobs that time-share a single GPU will experience resource contention and interfere with each other. The resulting slow-down could lead to missed job deadlines. Current cluster managers do not support GPU-sharing, but instead dedicate GPUs to a job for the job's lifetime. In this paper, we present a framework to predict and handle interference when two or more jobs time-share GPUs in HPC clusters. Our framework consists of an analysis model, and a dynamic interference detection and response mechanism to detect excessive interference and restart the interfering jobs on different nodes. We implement our framework in Torque, an open-source cluster manager, and using real workloads on an HPC cluster, show that interference-aware two-job colocation (although our method is applicable to colocating more than two jobs) improves GPU utilization by 25%, reduces a job's waiting time in the queue by 39% and improves job latencies by around 20%.


title: Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

abstract: One big challenge that hinders the transition of brain–computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.


title: The odd-even turn model for adaptive routing

abstract: This paper presents a model for designing adaptive wormhole routing algorithms for meshes without virtual channels. The model restricts the locations where some turns can be taken so that deadlock is avoided. In comparison with previous methods, the degree of routing adaptiveness provided by the model is more even for different source-destination pairs. The mesh network may benefit from this feature in terms of communication efficiency. Simulation results show that the even adaptiveness provided by the odd-even turn model makes message routing less vulnerable to nonuniform factors such as hot spot traffic. In addition, this property results in a smaller fluctuation of the network performance with respect to different traffic patterns.


title: Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

abstract: In this letter, we investigate an energy efficiency with performance guaranteed problem in mobile-edge computing. The mobile users desire lower energy consumption with better performance of tasks, for that we propose an energy minimizing optimization problem for mobile-edge cloud computing. We apply KKT conditions to solve it, and also present a request offloading scheme for this issue. In particular, the offloading scheme is determined by energy consumption and bandwidth capacity at each time slot. Numerical results demonstrate that our proposed offloading scheme outperforms local computing and entirely offloading method on energy consumption and performance on delay.


title: Bidirectional Long Short-Term Memory Networks for Relation Classification

abstract: Relation classification is an important semantic processing, which has achieved great attention in recent years. The main challenge is the fact that important information can appear at any position in the sentence. Therefore, we propose bidirectional long short-term memory networks (BLSTM) to model the sentence with complete, sequential information about all words. At the same time, we also use features derived from the lexical resources such as WordNet or NLP systems such as dependency parser and named entity recognizers (NER). The experimental results on SemEval-2010 show that BLSTMbased method only with word embeddings as input features is sufficient to achieve state-of-the-art performance, and importing more features could further improve the performance.


Sales Role Play: An Online Simulation.


title: Ten Benefits of Testing and Their Applications to Educational Practice

abstract: Abstract Testing in school is usually done for purposes of assessment, to assign students grades (from tests in classrooms) or rank them in terms of abilities (in standardized tests). Yet tests can serve other purposes in educational settings that greatly improve performance; this chapter reviews 10 other benefits of testing. Retrieval practice occurring during tests can greatly enhance retention of the retrieved information (relative to no testing or even to restudying). Furthermore, besides its durability, such repeated retrieval produces knowledge that can be retrieved flexibly and transferred to other situations. On open-ended assessments (such as essay tests), retrieval practice required by tests can help students organize information and form a coherent knowledge base. Retrieval of some information on a test can also lead to easier retrieval of related information, at least on delayed tests. Besides these direct effects of testing, there are also indirect effects that are quite positive. If students are quizzed frequently, they tend to study more and with more regularity. Quizzes also permit students to discover gaps in their knowledge and focus study efforts on difficult material; furthermore, when students study after taking a test, they learn more from the study episode than if they had not taken the test. Quizzing also enables better metacognitive monitoring for both students and teachers because it provides feedback as to how well learning is progressing. Greater learning would occur in educational settings if students used self-testing as a study strategy and were quizzed more frequently in class.


title: A survey on unmanned aerial vehicle collision avoidance systems

abstract: Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.


title: r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches

abstract: We start by asking an interesting yet challenging question, "If an eyewitness can only recall the eye features of the suspect, such that the forensic artist can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig. 1), can advanced computer vision techniques help generate the whole face image?" A more generalized question is that if a large proportion (e.g., more than 50%) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated. Existing face completion and generation methods either do not conduct domain transfer learning or can not handle large missing area. For example, the inpainting approach tends to blur the generated region when the missing area is large (i.e., more than 50%). In this paper, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high-fidelity in cross domains. We propose the recursive generation by bidirectional transformation networks (r-BTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and the cross domain challenge make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.


Stochastic tracking of 3D human figures using 2D image motion


title: Has the bug really been fixed?

abstract: Software has bugs, and fixing those bugs pervades the software engineering process. It is folklore that bug fixes are often buggy themselves, resulting in bad fixes, either failing to fix a bug or creating new bugs. To confirm this folklore, we explored bug databases of the Ant, AspectJ, and Rhino projects, and found that bad fixes comprise as much as 9% of all bugs. Thus, detecting and correcting bad fixes is important for improving the quality and reliability of software. However, no prior work has systematically considered this bad fix problem, which this paper introduces and formalizes. In particular, the paper formalizes two criteria to determine whether a fix resolves a bug: coverage and disruption. The coverage of a fix measures the extent to which the fix correctly handles all inputs that may trigger a bug, while disruption measures the deviations from the program's intended behavior after the application of a fix. This paper also introduces a novel notion of distance-bounded weakest precondition as the basis for the developed practical techniques to compute the coverage and disruption of a fix. To validate our approach, we implemented Fixation, a prototype that automatically detects bad fixes for Java programs. When it detects a bad fix, Fixation returns an input that still triggers the bug or reports a newly introduced bug. Programmers can then use that bug-triggering input to refine or reformulate their fix. We manually extracted fixes drawn from real-world projects and evaluated Fixation against them: Fixation successfully detected the extracted bad fixes.


title: Consumerization and IT Department Conflict

abstract: After years of banning consumer devices (e.g., iPads and iPhone) and applications (e.g., DropBox, Evernote, iTunes) organizations are allowing employees to use their consumer tools in the workplace. This IT consumerization phenomenon will have serious consequences on IT departments which have historically valued control, security, standardization and support (Harris et al. 2012). Based on case studies of three organizations in different stages of embracing IT consumerization, this study identifies the conflicts IT consumerization creates for IT departments. All three organizations experienced similar goal and behavior conflicts, while identity conflict varied depending upon the organizations’ stage implementing consumer tools (e.g., embryonic, initiating or institutionalized). Theoretically, this study advances IT consumerization research by applying a role conflict perspective to understand consumerization’s impact on the IT department.


title: Discovering Environmental Attitude and Lifestyle Segmentation of Green Consumers: a Conceptual Model for Research

abstract: The primary purpose of this research is to reviews the literature on green consumers segments with different profiles. It utilizes two constructs–environmental attitude and lifestyle- in the context of electric appliances products in Thailand. This research employs an empirical study by means of the questionnaire survey method to verify the hypotheses. Data will be collected by random sampling from working aged male and female customers who purchased electric appliances. The data will be analyzed using factor analysis, cluster analysis, discriminant and ANOVA. The usefulness of this research was identifying groups of similar customers and potential customers; to prioritize the groups to address; to understand their behavior; and to respond with appropriate marketing strategies.


title: Salient Object Detection by Lossless Feature Reflection

abstract: Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new structural loss function to learn clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.


title: Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells

abstract: In this paper, we present a big-data self organizing network (Bi-SON) framework aiming to optimize energy efficiency of ultra-dense small cells. Although small cell can enhance the capacity of cellular mobile networks, ultra-dense small cells suffer from severe interference and poor energy efficiency. The self organizing network (SON) can automatically manage and optimize the system performance. However, current SON-enable mechanisms mostly focus on indoor femtocells. Our proposed Bi-SON suggests a data flow framework from data collection, analysis and optimization to reconfiguration. We adopt the statistics analysis approach to determine the optimal system parameters to improve the energy efficiency of a huge number of outdoor small cells. The Bi-SON mechanism periodically collects the management data of small cells, e.g. transmission power, reference signal receiving power and the number of users per cell. We find that simple sorting and filtering data analysis from huge number of small cells can already effectively find the almost optimal solution. Our simulation results show that Bi-SON can improve throughput and energy efficiency by 50% and 135% respectively, compared to the scheme without energy saving approach.


Evolution of Hand Gesture Recognition : A Review


title: A bluetooth toy car control realization by android equipment

abstract: A remote toy car controlled by an android mobile platform with Bluetooth technology is presented in this paper. The system combined with an android mobile platform and a 2-wheel small car which is under control. The mobile platform requests to connect with the small car then sends instructions to control it by calling the android system Bluetooth API. The small car is composed with 2 step motors with their drivers and main MCU chip, connects with and accepts instructions from Android mobile platform through the BT module, meanwhile, status information is sent to the application software. The paper presents the main realization technology and the test examination is also described in detail.


title: Design of Power amplifier for Radio-Frequency Identification application on 13.56 MHz

abstract: Radio-Frequency Identification (RFID) has become a technology segment that growth rapidly in data collecting industry and automatic identification. One of the most important part of RFID system is power amplifier that enable power transfer between reader and transponder for identification purpose. This research is proposed power amplifier class E for RFID application at frequency of 13.56 MHz. It is shown from the results that the proposed power amplifier had a very good performances on return loss both on input and output, gain, stability, and efficiency.


title: A Review of SCADA Anomaly Detection Systems

abstract: The security of critical infrastructures is decreasing due to the apparition of new cyber threats against Supervisory Control and Data Acquisition (SCADA) systems. The evolution they have experienced; the use of standard hardware and software components or the increase of interconnected devices in order to reduce costs and improve efficiency, have contributed to this. This work reviews the research effort done towards the development of anomaly detection for these specific systems. SCADA systems have a number of peculiarities that make anomaly detection perform better than in traditional information and communications technology (ICT) networks. SCADA communications are deterministic, and their operation model is often cyclical. Based on this premise, modeling normal behavior by mining specific features gets feasible.


title: Monitoring moisture of soil using low cost homemade Soil moisture sensor and Arduino UNO

abstract: Persistent increase in population of world is demanding more and more supply of food. Hence there is a significant need of advancement in cultivation to meet up the future food needs. It is important to know moisture levels in soil to maximize the output. But most of farmers cannot afford high cost devices to measure soil moisture. Our research work in this paper focuses on home-made low cost moisture sensor with accuracy. In this paper we present a method to manufacture soil moisture sensor to estimate moisture content in soil hence by providing information about required water supply for good cultivation. This sensor is tested with several samples of soil and able to meet considerable accuracy. Measuring soil moisture is an effective way to determine condition of soil and get information about the quantity of water that need to be supplied for cultivation. Two separate methods are illustrated in this paper to determine soil moisture over an area and along the depth.


title: Ultra-Compact High-Linearity High-Power Fully Integrated DC–20-GHz 0.18-$\mu{\hbox {m}}$ CMOS T/R Switch

abstract: A fully integrated ultra-broadband transmit/receive (T/R) switch has been developed using nMOS transistors with a deep n-well in a standard 0.18-mum CMOS process, and demonstrates unprecedented insertion loss, isolation, power handling, and linearity. The new CMOS T/R switch exploits patterned-ground-shield on-chip inductors together with MOSFET's parasitic capacitances to synthesize artificial transmission lines, which result in low insertion loss over an extremely wide bandwidth. Negative bias to the bulk or positive bias to the drain of the MOSFET devices with floating bulk is used to reduce effects of the parasitic diodes, leading to enhanced linearity and power handling for the switch. Within dc-10, 10-18, and 18-20 GHz, the developed CMOS T/R switch exhibits insertion loss of less than 0.7, 1.0, and 2.5 dB and isolation between 32-60, 25-32, and 25-27 dB, respectively. The measured 1-dB power compression point and input third-order intercept point reach as high as 26.2 and 41 dBm, respectively. The new CMOS T/R switch has a die area of only 230 mumtimes250 mum. The achieved ultra-broadband performance and high power-handling capability, approaching those achieved in GaAs-based T/R switches, along with the full-integration ability confirm the usefulness of switches in CMOS technology, and demonstrate their great potential for many broadband CMOS radar and communication applications


title: Socially compliant mobile robot navigation via inverse reinforcement learning

abstract: Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot's current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrated that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics.