title: PSYCHOLOGICAL SCIENCE Research Article Try It, You’ll Like It The Influence of Expectation, Consumption, and Revelation

abstract: Patrons of a pub evaluated regular beer and “MIT brew” (regular beer plus a few drops of balsamic vinegar) in one of three conditions. One group tasted the samples blind (the secret ingredient was never disclosed). A second group was informed of the contents before tasting. A third group learned of the secret ingredient immediately after tasting, but prior to indicating their preference. Not surprisingly, preference for the MIT brew was higher in the blind condition than in either of the two disclosure conditions. However, the timing of the information mattered substantially. Disclosure of the secret ingredient significantly reduced preference only when the disclosure preceded tasting, suggesting that disclosure affected preferences by influencing the experience itself, rather than by acting as an independent negative input or by modifying retrospective interpretation of the experience.


title: A Wideband Circularly Polarized Magnetoelectric Dipole Antenna

abstract: This letter proposes a novel wideband circularly polarized magnetoelectric dipole antenna. In the proposed antenna, a pair of rotationally symmetric horizontal patches functions as an electric dipole, and two vertical patches with the ground act as an equivalent magnetic dipole. A Γ-shaped probe is used to excite the antenna, and a metallic cavity with two gaps is designed for wideband and good performance in radiation. A prototype was fabricated and measured. The experimental results show that the proposed antenna has an impedance bandwidth of 65% for SWR≤2 from 1.76 to 3.46 GHz, a 3-dB axial-ratio bandwidth of 71.5% from 1.68 to 3.55 GHz, and a stable gain of 8 ± 1 dBi. Good unidirectional radiation characteristic and low back-lobe level are achieved over the whole operating frequency band.


title: Ectomycorrhizae formed by three Japanese truffle species (Tuber japonicum, T. longispinosum, and T. himalayense) on indigenous oak and pine species

abstract: Modern truffle cultivation systems started in Europe in the early 1970s and are now successfully used for several European truffles throughout the world. However, systems for indigenous novel truffle species need to be developed in several regions, especially where truffle cultivation has not been attempted so far, such as in Japan. Recently, two new and one known truffle species that are expected to be edible were reported from Japan: Tuber japonicum, T. longispinosum, and T. himalayense. Here, we conducted mycorrhization trials between these three truffle species and four native tree species in Japan (Quercus acutissima, Q. phillyraeoides, Q. serrata, and Pinus densiflora) using spore suspension and trap-plant seedling techniques under axenic pot culture conditions to understand their potential host ranges and mycorrhizal morphologies and to determine whether these inoculation methods are applicable for mycorrhization of Japanese truffles with native host plants. Of the 12 combinations, nine were successful for mycorrhization, including both oak and pine trees. The T. japonicum mycorrhiza was characterized by short, needle-shaped cystidia without septa, whereas the two black truffles, T. longispinosum and T. himalayense, were indistinguishable from each other because they shared the same morphological and anatomical characters such as brownish, long cystidia with right angle ramification. These features were similar to related black truffle species. The results of the present study indicate that the inoculation method used for European truffles can also be applied for mycorrhization between Japanese truffle species and compatible native pine and/or oak hosts in Japan.


Transition-based Neural RST Parsing with Implicit Syntax Features


title: Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

abstract: This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov-Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the min-cut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial-contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.


title: Will deep brain stimulation increase the incidence of induced psychosis? Post-operation follow-ups for 1 hundred patients from 2004-2017

abstract: Generally regarding as a safe treatment for Parkinson’s disease (PD) for the past 20 years, deep brain stimulation (DBS) is also an example of precision medicine where surgeons need to titrate individual patient’s stimulating electrodes one by one down to the scale of micrometer for the maximum therapeutic effect. In order to prevent operation induced psychiatric complications and minimize any other potential side effects, we have followed 103 patients received this treatment provided by a single surgeon in the same medical institution from 2004 to 2017. We identified each patient complaint from nursing care records and complication data from medical charts during the perioperative hospitalization period to see if any of them correlate statistical significantly with the DBS lead placement procedure. Top five frequent complaints including fever, constipation, nausea, headache, wound pain. The majority of post-operative complaints turned out to be the same as general post-operative / post-anesthesia side effects rather than the DBS operation itself. However, a few rare but critical complications such as post-operative intracranial hemorrhage (ICH), postoperative epidural hematoma (EDH) were identified as well. These patients’ subsequent treatments and prognosis were documented for revising the operating procedure in the future. Our retrospective study reconfirmed that DBS is indeed a relatively safe procedure and improve the life quality of PD patients in general. Hopefully, the through preoperative preparation and careful surgical approach will safeguard the patient’s prognosis.


title: Text Mining: Extraction of Interesting Association Rule with Frequent Itemsets Mining for Korean Language from Unstructured Data

abstract: Text mining is a specific method to extract knowledge from structured and unstructured data. This extracted knowledge from text mining process can be used for further usage and discovery. This paper presents the method for extraction information from unstructured text data and the importance of Association Rules Mining, specifically for of Korean language (text) and also, NLP (Natural Language Processing) tools are explained. Association Rules Mining (ARM) can also be used for mining association between itemsets from unstructured data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining method calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data, select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.


title: Days on Market: Measuring Liquidity in Real Estate Markets

abstract: Days on Market (DOM) refers to the number of days a property is on the active market, which is an important measurement of market liquidity in real estate industry. Indeed, at the micro level, DOM is not only a special concern of house sellers, but also a useful indicator for potential buyers to evaluate the popularity of a house. At the macro level, DOM is an important indicator of real estate market status. However, it is very challenging to measure DOM, since there are a variety of factors which can impact on the DOM of a property. To this end, in this paper, we aim to measure real estate liquidity by examining multiple factors in a holistic manner. A special goal is to predict the DOM of a given property listing. Specifically, we first extract key features from multiple types of heterogeneous real estate-related data, such as house profiles and geo-social information of residential communities. Then, based on these features, we develop a multi-task learning based regression approach for predicting the DOM of real estates. This approach can effectively learn district-aware models for different property listings by considering multiple factors. Finally, we conduct extensive experiments on real-world real estate data collected in Beijing and develop a prototype system for practical use. The experimental results clearly validate the effectiveness of the proposed approach for measuring liquidity in real estate markets.


title: Video and learning: a systematic review (2007--2017)

abstract: Video materials have become an integral part of university learning and teaching practice. While empirical research concerning the use of videos for educational purposes has increased, the literature lacks an overview of the specific effects of videos on diverse learning outcomes. To address such a gap, this paper presents preliminary results of a large-scale systematic review of peer-reviewed empirical studies published from 2007-2017. The study synthesizes the trends observed through the analysis of 178 papers selected from the screening of 2531 abstracts. The findings summarize the effects of manipulating video presentation, content and tasks on learning outcomes, such as recall, transfer, academic achievement, among others. The study points out the gap between large-scale analysis of fine-grained data on video interaction and experimental findings reliant on established psychological instruments. Narrowing this gap is suggested as the future direction for the research on video-based learning.


title: Artificial Intelligence as an Effective Classroom Assistant

abstract: The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.


title: DTV lip-sync test using time-indexed audio and video signals without effect on program

abstract: A new lip-synchronization (lip-sync) test method using audio and video signals for DTV is presented. The proposed method does not interfere with or have any effect on the program being broadcast, as the time-indexed lip-sync test signals (TILTS) are embedded in a hidden region. The video TILTS is embedded into transient effect areas outside the active pixel region, making it invisible on-screen. Experimental results confirm that the time difference between audio and video signals can be easily measured at any time from the TILTS in the decoder outputs.


Dealing with pragmatic and implicit information in an ICALL system: the PILE´FACE example


Abstractive Multi-Document Summarization : An Overview


title: Algorithmic trading with Markov chains

abstract: An order book consists of a list of all buy and sell offers, represented by price and quantity, available to a market agent. The order book changes rapidly, within fractions of a second, due to new ...


Why Haven't We Mastered Alignment? The Importance of the Informal Organization Structure


Designing recommender systems for tourism., The


title: An overview of methods and tools for ontology learning from texts

abstract: Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.


title: Individual entrepreneurial orientation role in shaping reactions to new technologies

abstract: Significant progress has been made over the last two decades in explaining and predicting user acceptance of novel technologies at work. Although knowledge voids remain, substantial theoretical and empirical support has accumulated in favor of the powerful and parsimonious explanation provided by the Technology Acceptance Model (TAM). One factor that may be related to the adoption of new technologies, but has remained unexplored in the TAM literature, is the proclivity to behave entrepreneurially, known nominally as individual entrepreneurial orientation. The purpose of the present study is to investigate the role of individual entrepreneurial orientation in facilitating technology adoption under mandated conditions. Predictions are tested using survey data collected from knowledge workers required to use a new technology. Research directions and limitations are discussed.


title: A novel Ka-band 1 to 8 power divider/combiner

abstract: In this paper, a 1-to-8 traveling wave power divider/combiner design at Ka-band is presented. This power divider/combiner is composed of eight units coupling power from rectangular waveguide to microstrip lines. Coupling is achieved through longitudinal slots in the broad wall of the waveguide. Shorting posts inside the waveguide are used to adjust the coupling values. Experiments on the eight-way passive divider/combiner back-to-back design demonstrates a minimum overall insertion loss of 1.8 dB at 32.5 GHz with a 3 dB bandwidth of 15%.


title: Coping with ERP-related contextual issues in SMEs: a vendor's perspective

abstract: Abstract From an ERP vendor's perspective, this paper analyzes what can be done to address contextual issues relating to ERP implementation. The case of UFSoft, a Chinese ERP vendor, reveals three strategies that could be useful for achieving fit between ERP systems and adopting organizations. First, ERP systems need to be localized to reflect the local management features. Second, ERP systems should be customizable at a variety of levels. Finally, BPR should be carried out in an incremental manner, taking the dialectic of organizational learning into account. A significant contention is that IS researchers and practitioners should consider companies' stages of growth as a contextual factor when conducting ERP research or implementing ERP systems.


title: OFDM for cognitive radio: merits and challenges

abstract: Cognitive radio is a novel concept that enables wireless systems to sense the environment, adapt, and learn from previous experience to improve the quality of the communication. However, CR requires a flexible and adaptive physical layer in order to perform the required tasks efficiently. In this article, CR systems and their requirement of a physical layer are discussed, and the orthogonal frequency division multiplexing technique is investigated as a candidate transmission technology for CR. The challenges that arise from employing OFDM in CR systems are identified. The cognitive properties of some OFDM-based wireless standards also are discussed to indicate the trend toward a more cognitive radio.


title: Network Store: Exploring Slicing in Future 5G Networks

abstract: In this paper, we provide a revolutionary vision of 5G networks, in which SDN technologies are used for the programmability of the wireless network, and where a NFV-ready network store is provided to Mobile Network Operators (MNO), Enterprises, and Over-The-Top (OTT) third parties. The proposed network serves as a digital distribution platform of programmable Virtualized Network Functions (VNFs) that enables 5G application use-cases. Currently existing application stores, such as Apple's App Store for iOS applications, Google's Play Store for Android, or Ubuntu's Software Center, deliver applications to user specific software platforms. Our vision is to provide a digital marketplace, gathering 5G enabling Network Applications and Network Functions, written to run on top of commodity cloud infrastructures, connected to remote radio heads (RRH). The 5G Network Store will be the same to the network provider as the application store is currently to a software platform.


title: Viewing Progress in Non-photorealistic Rendering through Heinlein’s Lens

abstract: The field of non-photorealistic rendering is reaching a mature state. In its infancy, researchers explored the mimicry of methods and tools used by traditional artists to generate works of art, through techniques like watercolor or oil painting simulations. As the field has moved past mimicry, ideas from artists and artistic techniques have been adapted and altered for performance in the media of computer graphics, creating algorithmic aesthetics such as generative art or the automatic composition of objects in a scene, as well as abstraction in rendering and geometry. With these two initial stages of non-photorealistic rendering well established, the field must find new territory to cover. In this paper, we provide a high level overview of the past and current state of non-photorealistic rendering and call to arms the community to create the areas of research that make computation of non-photorealistic rendering generate never before realized results.


title: 9 Isozyme Analysis of Mycorrhizal Fungi and their Mycorrhiza

abstract: Publisher Summary This chapter describes isozyme methods for the analysis of vesiculararbuscular and ectomycorrhizal fungi and for the identification and quantification of mycorrhizal fungi within plant roots. Present areas of application are reviewed briefly. As vesicular-arbuscular mycorrhizal fungi cannot be cultured axenically, fungal material in the form of resting spores or external and/or internal root mycelium, together with surface-cleaned infected root systems, are routinely analyzed. The chapter further highlights standardization of extracts. To standardize the amount of extracts for quantitative studies, two methods should be considered: dry weight and protein content. Unlike the fungi forming vesicular-arbuscular mycorrhizal associations, it is possible to culture many of the ectomycorrhizal fungi, particularly members of many Ascomycete and Basidiomycete families. The production of taxonomically recognizable fruiting structures (also a source of extractable tissue) makes it possible to isolate dikaryotic and, in certain cases, monokaryotic mycelium from individuals of the same species. The chapter also explains ectomycorrhizal sample preparation.


title: A Survey of Iris Biometrics Research: 2008-2010

abstract: A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010. Research in iris biometrics has expanded so much that, although covering only 3 years and intentionally being selective about coverage, this new survey lists a larger number of references than the inception-through-2007 survey.


title: Parallelize Bubble Sort Algorithm Using OpenMP

abstract: Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. In this paper we implemented the bubble sort algorithm using multithreading (OpenMP). The proposed work tested on two standard datasets (text file) with different size . The main idea of the proposed algorithm is distributing the elements of the input datasets into many additional temporary sub- arrays according to a number of characters in each word. The sizes of each of these sub-arrays are decided depending on a number of elements with the same number of characters in the input array. We implemented OpenMP using Intel core i7-3610QM ,(8 CPUs),using two approaches (vectores of string and array 3D) . Finally, we get the data structure effects on the performance of the algorithm for that we choice the second approach.


title: Cache Design Options for a Clustered Multithreaded Architecture Technical Report # 866 ∗

abstract: The design of the memory hierarchy in a multi-core architecture is a critical component since it must meet the capacity (in terms of bandwidth and low latency) and coordination requirements of multiple threads of control. Most previous designs have assumed either a shared L1 data cache (e.g., simultaneous multithreaded architectures) or L1 caches that are private to each individual processor (e.g., chip multiprocessors (CMPs)) with coherence maintained across the L1s at the L2 level. A shared L1 cache has the benefit of potentially increasing cache capacity for threads with non-uniform working sets but the disadvantage of higher access latency from remote clusters/cores and the potential for conflicts among threads. Private caches have the benefit of lower L1 access latency but the disadvantages of reduced effective cache size and of coherence overhead. In this paper, we focus on the design of the L1 cache as being a critical design component especially for multithreaded/parallel workloads. We identify the factors contributing to reduced performance, and examine ways in which each factor — locality of access, capacity, as well as migratory, multiple-reader, and read-write sharing patterns — can be addressed in a low-overhead fashion. We demonstrate that direct access to remote L1 cache banks provides the flexibility to address thread-specific capacity issues as well as to provide improved sharing support for active read-write sharing. We propose a novel selectively replicating (SR) adaptive protocol to handle locality and capacity issues in addition to recognizing and adapting to migratory, multiple-reader, and read-write sharing access patterns. The SR cache design is able to meet or beat the performance of the coherent cache, improving performance by up to 44% relative to a coherent cache (with an average of 5% across all applications) when using 8 threads.


title: Mathematical modelling of infectious diseases

abstract: INTRODUCTION ::: Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Here, we illustrate these principles in relation to the current H1N1 epidemic. ::: ::: ::: SOURCES OF DATA ::: Many sources of data are used in mathematical modelling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important. ::: ::: ::: AREAS OF AGREEMENT ::: Mathematical models, and the statistical tools that underpin them, are now a fundamental element in planning control and mitigation measures against any future epidemic of an infectious disease. Well-parameterized mathematical models allow us to test a variety of possible control strategies in computer simulations before applying them in reality. ::: ::: ::: AREAS OF CONTROVERSY ::: The interaction between modellers and public-health practitioners and the level of detail needed for models to be of use. ::: ::: ::: GROWING POINTS ::: The need for stronger statistical links between models and data. ::: ::: ::: AREAS TIMELY FOR DEVELOPING RESEARCH ::: Greater appreciation by the medical community of the uses and limitations of models and a greater appreciation by modellers of the constraints on public-health resources.


title: Ultra Low-Power Clocking Scheme Using Energy Recovery and Clock Gating

abstract: A significant fraction of the total power in highly synchronous systems is dissipated over clock networks. Hence, low-power clocking schemes are promising approaches for low-power design. We propose four novel energy recovery clocked flip-flops that enable energy recovery from the clock network, resulting in significant energy savings. The proposed flip-flops operate with a single-phase sinusoidal clock, which can be generated with high efficiency. In the TSMC 0.25-mum CMOS technology, we implemented 1024 proposed energy recovery clocked flip-flops through an H-tree clock network driven by a resonant clock-generator to generate a sinusoidal clock. Simulation results show a power reduction of 90% on the clock-tree and total power savings of up to 83% as compared to the same implementation using the conventional square-wave clocking scheme and flip-flops. Using a sinusoidal clock signal for energy recovery prevents application of existing clock gating solutions. In this paper, we also propose clock gating solutions for energy recovery clocking. Applying our clock gating to the energy recovery clocked flip-flops reduces their power by more than 1000times in the idle mode with negligible power and delay overhead in the active mode. Finally, a test chip containing two pipelined multipliers one designed with conventional square wave clocked flip-flops and the other one with the proposed energy recovery clocked flip-flops is fabricated and measured. Based on measurement results, the energy recovery clocking scheme and flip-flops show a power reduction of 71% on the clock-tree and 39% on flip-flops, resulting in an overall power savings of 25% for the multiplier chip.


title: Mining multi-tag association for image tagging

abstract: Automatic media tagging plays a critical role in modern tag-based media retrieval systems. Existing tagging schemes mostly perform tag assignment based on community contributed media resources, where the tags are provided by users interactively. However, such social resources usually contain dirty and incomplete tags, which severely limit the performance of these tagging methods. In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags associated with information importance for test images. Given an image dataset, all the near-duplicate clusters are discovered. For each near-duplicate cluster, all the tags occurring in the cluster form the cluster's "document". Given a test image, we firstly initialize the candidate tag set from its near-duplicate cluster's document. The candidate tag set is then expanded by considering the implicit multi-tag associations mined from all the clusters' documents, where each cluster's document is regarded as a transaction. To further reduce noisy tags, a visual relevance score is also computed for each candidate tag to the test image based on a new tag model. Tags with very low scores can be removed from the final tag set. Extensive experiments conducted on a real-world web image dataset--NUS-WIDE, demonstrate the promising effectiveness of our approach.


title: A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory

abstract: Since the definition of the Busy Beaver function by Rado in 1962, an interesting open question has been what the smallest value of n for which BB(n) is independent of ZFC set theory. Is this n approximately 10, or closer to 1,000,000, or is it even larger? In this paper, we show that it is at most 7,918 by presenting an explicit description of a 7,918-state Turing machine Z with 1 tape and a 2-symbol alphabet that cannot be proved to run forever in ZFC (even though it presumably does), assuming ZFC is consistent. The machine is based on work of Harvey Friedman on independent statements involving order-invariant graphs. In doing so, we give the first known upper bound on the highest provable Busy Beaver number in ZFC. We also present a 4,888-state Turing machine G that halts if and only if there is a counterexample to Goldbach’s conjecture, and a 5,372-state Turing machine R that halts if and only if the Riemann hypothesis is false. To create G, R, and Z, we develop and use a higher-level language, Laconic, which is much more convenient than direct state manipulation.


title: Mining Process Models from Workflow Logs

abstract: Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.


title: Slicer: auto-sharding for datacenter applications

abstract: Sharding is a fundamental building block of large-scale applications, but most have their own custom, ad-hoc implementations. Our goal is to make sharding as easily reusable as a filesystem or lock manager. Slicer is Google's general purpose sharding service. It monitors signals such as load hotspots and server health to dynamically shard work over a set of servers. Its goals are to maintain high availability and reduce load imbalance while minimizing churn from moved work. ::: ::: In this paper, we describe Slicer's design and implementation. Slicer has the consistency and global optimization of a centralized sharder while approaching the high availability, scalability, and low latency of systems that make local decisions. It achieves this by separating concerns: a reliable data plane forwards requests, and a smart control plane makes load-balancing decisions off the critical path. Slicer's small but powerful API has proven useful and easy to adopt in dozens of Google applications. It is used to allocate resources for web service front-ends, coalesce writes to increase storage bandwidth, and increase the efficiency of a web cache. It currently handles 2-7M req/s of production traffic. The median production Slicer-managed workload uses 63% fewer resources than it would with static sharding.


title: Posterior Bone Block for Posterior Instability

abstract: Posterior bone block is one of the surgical procedures proposed in the literature for posterior instability of the shoulder. Prognosis is better in traumatic than in atraumatic instability. Voluntary subluxations with true intentional episodes are a contraindication for surgery. To have an accurate and reproducible technique, we recommend 7 surgical steps: (1) Vertical deltoid approach, (2) Horizontal infraspinatus splitting approach, (3) Medial T-shaped arthrotomy, (4) Joint exploration and treatment of labral lesions, (5) Abrasion of posterior glenoid cortex, (6) Harvesting and preparation of an accurately sized and shaped iliac graft, and (7) Cautious fixation of the graft with 2 low compression screws. Association with capsulorrhaphy and/or glenoid osteotomy may be necessary, respectively, in cases of hyperlaxity and/or excessive glenoid retroversion. The best treatment of patients who have recurrent posterior instability of the shoulder remains controversial. Precise, specific, and durable rehabilitation prior to surgical treatment has been universally recommended. If this program fails to relieve the patient’s symptoms, a great variety of surgical procedures have been proposed: soft-tissue procedures (posterior capsulorrhaphy, reverse Putti-Platt, biceps tendon transfer), osteotomy of the glenoid, rotational osteotomy of the humerus, and posterior bone block. Various combinations of these procedures have also been proposed. Posterior bone block is not the most popular procedure that has been recommended in this pathology but it is our preferred method, alone or in combination with posterior capsulorrhaphy and/or glenoid osteotomy.


title: Adaptive Parallel Iterative Deepening Search

abstract: Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. ::: ::: Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the EUREKA system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, EUREKA uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. EUREKA has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that EUREKA outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.


title: pHost: distributed near-optimal datacenter transport over commodity network fabric

abstract: The importance of minimizing flow completion times (FCT) in datacenters has led to a growing literature on new network transport designs. Of particular note is pFabric, a protocol that achieves near-optimal FCTs. However, pFabric's performance comes at the cost of generality, since pFabric requires specialized hardware that embeds a specific scheduling policy within the network fabric, making it hard to meet diverse policy goals. Aiming for generality, the recent Fastpass proposal returns to a design based on commodity network hardware and instead relies on a centralized scheduler. Fastpass achieves generality, but (as we show) loses many of pFabric's performance benefits. We present pHost, a new transport design aimed at achieving both: the near-optimal performance of pFabric and the commodity network design of Fastpass. Similar to Fastpass, pHost keeps the network simple by decoupling the network fabric from scheduling decisions. However, pHost introduces a new distributed protocol that allows end-hosts to directly make scheduling decisions, thus avoiding the overheads of Fastpass's centralized scheduler architecture. We show that pHost achieves performance on par with pFabric (within 4% for typical conditions) and significantly outperforms Fastpass (by a factor of 3.8×) while relying only on commodity network hardware.


Towards soft optimization techniques for parallel cognitive applications


title: Visualizing Data using t-SNE

abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.


title: Digital forensics trends and future

abstract: Nowadays, rapid evolution of computers and mobile phones has caused these devices to be used in criminal activities. Providing appropriate and sufficient security measures is a difficult job due to complexity of devices which makes investigating crimes involving these devices even harder. Digital forensic is the procedure of investigating computer crimes in the cyber world. Many researches have been done in this area to help forensic investigation to resolve existing challenges. This paper attempts to look into trends of applications of digital forensics and security at hand in various aspects and provide some estimations about future research trends in this area.


title: The pls Package: Principal Component and Partial Least Squares Regression in R

abstract: The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.


title: High Capacity Colored Two Dimensional codes

abstract: Barcodes enable automated work processes without human intervention, and are widely deployed because they are fast and accurate, eliminate many errors and often save time and money. In order to increase the data capacity of barcodes, two dimensional (2D) code were developed; the main challenges of 2D codes lie in their need to store more information and more character types without compromising their practical efficiency. This paper proposes the High Capacity Colored Two Dimensional (HCC2D) code, a new 2D code which aims at increasing the space available for data, while preserving the strong reliability and robustness properties of QR. The use of colored modules in HCC2D poses some new and non-trivial computer vision challenges. We developed a prototype of HCC2D, which realizes the entire PrintS the experiment results showed that HCC2D codes obtain data densities close to HCCB and strong robustness similar to QR.


title: Prediction of User's Web-Browsing Behavior: Application of Markov Model

abstract: Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.


title: The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment.

abstract: This study examined the process of how socioeconomic status, specifically parents’ education and income, indirectly relates to children’s academic achievement through parents’ beliefs and behaviors. Data from a national, cross-sectional study of children were used for this study. The subjects were 868 8‐12-year-olds, divided approximately equally across gender (436 females, 433 males). This sample was 49% non-Hispanic European American and 47% African American. Using structural equation modeling techniques, the author found that the socioeconomic factors were related indirectly to children’s academic achievement through parents’ beliefs and behaviors but that the process of these relations was different by racial group. Parents’ years of schooling also was found to be an important socioeconomic factor to take into consideration in both policy and research when looking at school-age children.


title: Surprisal-Driven Feedback in Recurrent Networks

abstract: Recurrent neural nets are widely used for predicting temporal data. Their inherent deep feedforward structure allows learning complex sequential patterns. It is believed that top-down feedback might be an important missing ingredient which in theory could help disambiguate similar patterns depending on broader context. In this paper, we introduce surprisal-driven recurrent networks, which take into account past error information when making new predictions. This is achieved by continuously monitoring the discrepancy between most recent predictions and the actual observations. Furthermore, we show that it outperforms other stochastic and fully deterministic approaches on enwik8 character level prediction task achieving 1.37 BPC.


title: An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective

abstract: With the prevalence of smartphones, app markets such as Apple App Store and Google Play has become the center stage in the mobile app ecosystem, with millions of apps developed by tens of thousands of app developers in each major market. This paper presents a study of the mobile app ecosystem from the perspective of app developers. Based on over one million Android apps and 320,000 developers from Google Play, we analyzed the Android app ecosystem from different aspects. Our analysis shows that while over half of the developers have released only one app in the market, many of them have released hundreds of apps. We classified developers into different groups based on the number of apps they have released, and compared their characteristics. Specially, we have analyzed the group of aggressive developers who have released more than 50 apps, trying to understand how and why they create so many apps. We also investigated the privacy behaviors of app developers, showing that some developers have a habit of producing apps with low privacy ratings. Our study shows that understanding the behavior of mobile developers can be helpful to not only other app developers, but also to app markets and mobile users.


title: Optimizing airport gate assignment with operational safety constraints

abstract: This paper makes a comprehensive review of existing approaches and presents an optimization model for the airport gate assignment problem (AGAP) considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (robust assignment) while dealing with the problem of aircraft size mismatching with its gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the validity of the model and algorithm.


title: Have Your Cake and Eat it Too? Managing Knowledge in Hybrid Agile-Traditional Development Projects

abstract: This research examines how knowledge management elements of systems development projects are adapted to correspond with the increasing use of agile practices. Using a single, longitudinal case study, we draw on interview data from an initial CRM implementation that used a traditional approach, followed by a second project phase employing a hybrid agile-traditional approach. Oriented using the concept of ambidexterity, our findings suggest that some knowledge management elements remained traditional, others became strongly agile, and some integrated both approaches together in a hybrid manner. Our study aids practitioners by providing insights into the opportunities and pitfalls of managing knowledge within hybrid development projects. For researchers, this paper applies the concept of ambidexterity in an agile development context as it relates to knowledge management. Our findings extend the literature on the incremental trade-offs that companies face when attempting to simultaneously explore and exploit two development approaches.


title: Optimal Approximation with Sparsely Connected Deep Neural Networks

abstract: We derive fundamental lower bounds on the connectivity and the memory requirements of deep neural networks guaranteeing uniform approximation rates for arbitrary function classes in $L^2(\mathbb{R}...


title: Supporting users to take informed decisions on privacy settings of personal devices

abstract: Today, personal information has never been this prone to risk given the current advancement in technologies especially on personal devices. These devices are able to provide services to individuals; however, they also collect huge amount of personal information which may be used to infer sensitive private information. Among these personal devices, fitness trackers have the potential to capture the most personal user information. We conducted an analysis on fitness trackers and built a case study based on Fitbit wearables, its Android application, and the third party applications that provide further services by accessing Fitbit data and exchanging data with its application, given the user's permission. Specifically, we analyzed the case of Lose It! third party application. Then, we applied a framework for user privacy protection in the IoT, which we have defined in our previous work, to this specific case and validated our design choices using controlled experiments. The contribution of the paper is twofold: showing the risks for privacy due to the possible correlation of shared data to infer undisclosed personal information and presenting an approach to support users in managing privacy configuration settings. The ultimate aim of this study is to outline new challenges for IoT development by (i) emphasizing the need to protect users against inference attacks coming from the supposedly trusted third parties and (ii) making the process of information sharing more informative and the users more aware of the related risks.


title: A digitally tuned reconfigurable patch antenna for IoT devices

abstract: In this paper, a reconfigurable patch antenna design operating at 868MHz for Low Power Wide Area Network (LoRa), 1.58 GHz for Global Positioning System (GPS) and 2.4 GHz for Bluetooth is presented. The design is reconfigurable thanks to the integration of a digitally tunable capacitor (DTC). The DTC capacitance changes according to a 5 bits' sequence command. A microcontroller is used to assign the required sequence, and thus leading to frequency reconfiguration. The antenna is a circular patch with a gain that varies from 0.2 dB to 6 dB based on the desired frequency of operation and the reconfiguration state. The antenna is designed for internet of things (IoT) applications.


title: Visualization of large ISO-Surfaces based on nested clip-boxes

abstract: A process of extrusion molding plasticized vulcanizable or cross-linkable rubber or synthetic resin materials, comprising the steps of heating said materials to an elevated temperature in a short time immediately before a molding die and vulcanizing or cross-linking the materials in the manner of maintaining said elevated temperature substantially while the materials are passing through the die or for a while after they have passed through the die, thereby obtaining mold goods.


title: Edge detection in medical images with quasi high-pass filter based on local statistics

abstract: Abstract We developed a robust, quasi high-pass filter for edge detection in medical images. The kernel-based algorithm of our detector was similar to that of conventional edge detectors. The proposed edge detector has a mathematical form of local variance and is adaptive in nature. The mathematical formulation of the detector was exploited and re-expressed as a quadratic form of the Toeplitz matrix. The detector has a highly structured internal architecture with abundant spatial isotropic symmetricity. The proposed operator can greatly reduce problems frequently encountered in edge detection including fragmentation, position dislocation, and thinness loss. The detector is robust to noise and can efficiently extract crucial edge features. We named this new operator as the WL operator (Wang and Lin). The performance of the WL operator was compared to that of other edge detectors by using Pratt’s figure of merits. In addition, the performance was confirmed with experts by using visual analog scale scores. The results obtained using the WL operator for different medical imaging modalities including X-ray, CT, and MRI are promising. Therefore, the WL operator warrants further investigation.


Draft for second edition Prototyping Tools and Techniques


title: Focused matrix factorization for audience selection in display advertising

abstract: Audience selection is a key problem in display advertising systems in which we need to select a list of users who are interested (i.e., most likely to buy) in an advertising campaign. The users' past feedback on this campaign can be leveraged to construct such a list using collaborative filtering techniques such as matrix factorization. However, the user-campaign interaction is typically extremely sparse, hence the conventional matrix factorization does not perform well. Moreover, simply combining the users feedback from all campaigns does not address this since it dilutes the focus on target campaign in consideration. To resolve these issues, we propose a novel focused matrix factorization model (FMF) which learns users' preferences towards the specific campaign products, while also exploiting the information about related products. We exploit the product taxonomy to discover related campaigns, and design models to discriminate between the users' interest towards campaign products and non-campaign products. We develop a parallel multi-core implementation of the FMF model and evaluate its performance over a real-world advertising dataset spanning more than a million products. Our experiments demonstrate the benefits of using our models over existing approaches.


title: FairPlay: Fraud and Malware Detection in Google Play

abstract: Fraudulent behaviors in Google Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.


title: Discrete Deep Feature Extraction: A Theory and New Architectures

abstract: First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and B\"olcskei, 2015. This paper considers the discrete case, introduces new convolutional neural network architectures, and proposes a mathematical framework for their analysis. Specifically, we establish deformation and translation sensitivity results of local and global nature, and we investigate how certain structural properties of the input signal are reflected in the corresponding feature vectors. Our theory applies to general filters and general Lipschitz-continuous non-linearities and pooling operators. Experiments on handwritten digit classification and facial landmark detection---including feature importance evaluation---complement the theoretical findings.


title: Convergence of Stochastic Gradient Descent for PCA

abstract: We consider the problem of principal component analysis (PCA) in a streaming stochastic setting, where our goal is to find a direction of approximate maximal variance, based on a stream of i.i.d. data points in $\reals^d$. A simple and computationally cheap algorithm for this is stochastic gradient descent (SGD), which incrementally updates its estimate based on each new data point. However, due to the non-convex nature of the problem, analyzing its performance has been a challenge. In particular, existing guarantees rely on a non-trivial eigengap assumption on the covariance matrix, which is intuitively unnecessary. In this paper, we provide (to the best of our knowledge) the first eigengap-free convergence guarantees for SGD in the context of PCA. This also partially resolves an open problem posed in \cite{hardt2014noisy}. Moreover, under an eigengap assumption, we show that the same techniques lead to new SGD convergence guarantees with better dependence on the eigengap.


Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or PolyWorld: Life in a new context


Autonomous Precision Pouring from Unknown Symmetric Containers


title: Examining the relational benefits of improved interfirm information processing capability in buyer-supplier dyads

abstract: Information Systems research has studied how buyers and suppliers can benefit from improved information visibility in supply chains characterized by uncertainty. However, the relation-specific information processing solutions that provide visibility can only be exploited if the two firms engage in sufficient coordination efforts. This work takes a nuanced look at how dyadic benefits are derived in the supply chain. Drawing on the information processing view, resource-based view, and transaction cost theory, this study explicates how buyer performance can result from buyer's use of relation-specific information processing solutions and supplier's relational responses. Two interfirm information processing solutions are proposed and examined: the use of IT-based systems for planning and control, and the use of relational (normative) contracts. Based on a sample of 144 manufacturing firms, eight of the nine proposed research hypotheses receive empirical support using PLS analysis. The findings suggest that as buyers and suppliers utilize the IT and relational solutions, they induce relation-specific responses represented as supplier's business process investments and modification flexibility, which in turn lead to positive buyer outcomes. The results help us gain a more granular understanding on how relation-specific interfirm information processing solutions can lead to performance through enhanced interfirm governance capabilities.


Nanofibrous scaffolds in biomedical applications


SCHEMA EVOLUTION FOR STARS AND SNOWFLAKES


title: Semantic Web Meets Internet of Things and Web of Things

abstract: An ever growing interest and wide adoption of Internet of Things (IoT) and Web technologies are unleashing a true potential of designing a broad range of high-quality consumer applications. Smart cities, smart buildings, and e-health are among various application domains which are currently benefiting and will continue to benefit from IoT and Web technologies in a foreseeable future. Similarly, semantic technologies have proven their effectiveness in various domains and a few among multiple challenges which semantic Web technologies are addressing are to (i) mitigate heterogeneity by providing semantic inter-operability, (ii) facilitate easy integration of data application, (iii) deduce and extract new knowledge to build applications providing smart solutions, and (iv) facilitate inter-operability among various data processes including representation, management and storage of data. ::: ::: In this tutorial, our focus will be on the combination of Web technologies, Semantic Web, and IoT technologies and we will present to our audience that how a merger of these technologies is leading towards an evolution from IoT to Web of Things (WoT) to Semantic Web of Things. This tutorial will introduce the basics of Internet of Things, Web of Things and Semantic Web and will demonstrate tools and techniques designed to enable the rapid development of semantics-based Web of Things applications. One key aspect of this tutorial is to familiarize its audience with the open source tools designed by different semantic Web, IoT and WoT based projects and provide the audience a rich hands-on experience to use these tools and build smart applications with minimal efforts. Thus, reducing the learning curve to its maximum. We will showcase real-world use case scenarios which are designed using semantically-enabled WoT frameworks (e.g. CityPulse, FIESTA-IoT and M3).


title: Continuous analytics over discontinuous streams

abstract: Continuous analytics systems that enable query processing over steams of data have emerged as key solutions for dealing with massive data volumes and demands for low latency. These systems have been heavily influenced by an assumption that data streams can be viewed as sequences of data that arrived more or less in order. The reality, however, is that streams are not often so well behaved and disruptions of various sorts are endemic. We argue, therefore, that stream processing needs a fundamental rethink and advocate a unified approach toward continuous analytics over discontinuous streaming data. Our approach is based on a simple insight - using techniques inspired by data parallel query processing, queries can be performed over independent sub-streams with arbitrary time ranges in parallel, generating partial results. The consolidation of the partial results over each sub-stream can then be deferred to the time at which the results are actually used on an on-demand basis. In this paper, we describe how the Truviso Continuous Analytics system implements this type of order-independent processing. Not only does the approach provide the first real solution to the problem of processing streaming data that arrives arbitrarily late, it also serves as a critical building block for solutions to a host of hard problems such as parallelism, recovery, transactional consistency, high availability, failover, and replication.


title: Acquiring linear subspaces for face recognition under variable lighting

abstract: Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.


title: Novel Empty Substrate Integrated Waveguide for High-Performance Microwave Integrated Circuits

abstract: Over the last years, a great number of substrate integrated circuits has been developed. These new circuits are a compromise between the advantages of classical waveguide technologies, such as high quality factor and low losses, and the advantages of planar circuits, such as low cost and easy compact integration. Although their quality factor and losses are better than for planar circuits, these characteristics are worse than in the case of waveguides, mainly due to the presence of the dielectric substrate. In order to improve the performance of the integrated circuits, a new methodology for manufacturing empty waveguides, without a dielectric substrate, but at the same time completely integrated in a planar substrate, is proposed in this work. A wideband transition with return losses greater than 20 dB in the whole bandwith of the waveguide allows the integration of the empty waveguide into the planar substrate so that the waveguide can be directly accessed with a microstrip line. Therefore, a microwave circuit integrated in a planar substrate, but at the same time with a very high quality factor (measured quality factor is 4.5 times higher than for the same filter in the substrate integrated waveguide), and very low losses is successfully achieved.


title: Semantic Construction Grammar: Bridging the NL / Logic Divide

abstract: In this paper, we discuss Semantic Construction Grammar (SCG), a system developed over the past several years to facilitate translation between natural language and logical representations. Crucially, SCG is designed to support a variety of different methods of representation, ranging from those that are fairly close to the NL structure (e.g. so-called "logical forms"), to those that are quite different from the NL structure, with higher-order and high-arity relations. Semantic constraints and checks on representations are integral to the process of NL understanding with SCG, and are easily carried out due to the SCG's integration with Cyc's Knowledge Base and inference engine [1] , [2].


title: A convolutional neural network cascade for face detection

abstract: In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.


title: RelFinder : Revealing Relationships in RDF Knowledge Bases

abstract: The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.


Speech Recognition Based on Artificial Neural Networks


title: Zone mapping application for precision-farming: a decision support tool for variable rate application

abstract: A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.


title: GA-based dynamic manipulability optimization of a 2-DOF planar parallel manipulator

abstract: Dynamic manipulability is a very important issue that should be considered for problems of parallel manipulator design. In order to study the dynamic optimization of a 2-DOF planar parallel manipulator, which is used in a heavy duty hybrid machine, genetic algorithm (GA) is used in the dynamic manipulability optimization. Based on the kinematic analysis, the dynamic equation of the 2-DOF parallel manipulator is derived using the virtual work principle method. Furthermore, global dynamic manipulability (GDM) is introduced to measure the dynamic performance of the manipulator. Then the dynamic optimization based GA method is analyzed. From the numerical simulation, it is proved that the dynamic optimization considering GDM proposed in this paper can ameliorate the dynamic performance of the parallel manipulator well.


title: Multi-Pattern Boyer-Moore - Horspool Algorithm based Hashing Function for Intrusion Detection System

abstract: Every day the networks are expanding in very large scale and running in very high speed. The Intrusion Detection System (IDS) becomes as an essential part in any new network structure. The IDS is relying on the string matching algorithm to detect any signature attack, but the high speed of the modern networks are preventing the IDS string matching algorithms to work properly. There is a need to develop a robust IDS string matching algorithms to overcome these weaknesses. In this paper, we continue to developing Boyer-Moore Horspool algorithm by adding a new multi-pattern hashing feature to its original structure, which is called MPH-BMH. The developed algorithm is able to reduce the matching time because it can compare a group of pattern at one time. The results show that MPH-BMH is around 50% faster than BMH and QS algorithms in which it can scan and match large number of network packets in the given time and consequently it can be very useful to work in high speed networks. 


RNAit: an automated web-based tool for the selection of RNAi targets in Trypanosoma brucei.


C ∅ C ∅ : A Framework for Building Composable Zero-Knowledge Proofs


title: The use of neural network technology to model swimming performance

abstract: The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. ::: ::: ::: ::: Key points ::: ::: ::: The non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models. ::: ::: ::: The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low. ::: ::: ::: The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs. ::: ::: ::: The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).


title: Audio-Visual and Autogenic Relaxation Alter Amplitude of Alpha EEG Band, Causing Improvements in Mental Work Performance in Athletes

abstract: The aim of the present study was to investigate the effect of regular audio-visual relaxation combined with Schultz’s autogenic training on: (1) the results of behavioral tests that evaluate work performance during burdensome cognitive tasks (Kraepelin test), (2) changes in classical EEG alpha frequency band, neocortex (frontal, temporal, occipital, parietal), hemisphere (left, right) versus condition (only relaxation 7–12 Hz). Both experimental (EG) and age-and skill-matched control group (CG) consisted of eighteen athletes (ten males and eight females). After 7-month training EG demonstrated changes in the amplitude of mean electrical activity of the EEG alpha bend at rest and an improvement was significantly changing and an improvement in almost all components of Kraepelin test. The same examined variables in CG were unchanged following the period without the intervention. Summing up, combining audio-visual relaxation with autogenic training significantly improves athlete’s ability to perform a prolonged mental effort. These changes are accompanied by greater amplitude of waves in alpha band in the state of relax. The results suggest usefulness of relaxation techniques during performance of mentally difficult sports tasks (sports based on speed and stamina, sports games, combat sports) and during relax of athletes.


title: HowNet - a hybrid language and knowledge resource

abstract: HowNet is an online common-sense knowledge base unveiling inter-conceptual relations and inter-attribute relations of concepts as connoting in Chinese and English bilingual lexicons. Since it was released in 1999, HowNet has become more and more popular and been applied in various areas of NLP, related to both Chinese and English. We show a general picture of HowNet, discuss its theory and peculiarities.


title: Neural Networks in Civil Engineering : 1989 − 2000

abstract: The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.


title: Development of Home Energy Management System Using Arduino

abstract: A new method of integrating arduino with relay and transistor for home energy management system is proposed. The arduino is developed as a controller to manage the lamp, fan and airconditioner. It correspond the relay, lamp and fan via PIR sensor, and air conditioner via temperature sensor. The energy consumed by the electrical appliances can be saved by at least 1.5% with the implementation of arduino.


title: Dynamic Multi-View Hashing for Online Image Retrieval

abstract: Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.


title: Impression Management Struggles in Online Dating

abstract: Online dating systems are now widely used to search for romance and yet there is little research on how people use these systems to manage their impressions with potential romantic partners. To address this issue we conducted an interview study of 41 online dating users, revealing that-contrary to prior work-online daters largely do not want to intentionally deceive their online dating partners because they think such lies would quickly be discovered face-to-face. Nevertheless, bad first dates were a norm rather than an exception for this study's participants. In this paper we present various frustrations online daters associate with conveying and forming impressions of potential romantic partners before meeting face-to-face. We discuss the implications of these findings for the design of online dating systems.


title: Meta-Unsupervised-Learning: A supervised approach to unsupervised learning

abstract: We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on several unsupervised problems such as (a) clustering, (b) outlier detection, and (c) similarity prediction under a common umbrella of meta-unsupervised-learning. We also provide rigorous PAC-agnostic bounds to establish the theoretical foundations of our framework, and show that our framing of meta-clustering circumvents Kleinberg's impossibility theorem for clustering.


title: Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics

abstract: The use of online videos is a common practice today amongst education professionals. Interactive features in videos are constantly evolving and a recent trend is the integration of interactive elements and web content into educational videos. The paper (a) provides a roadmap for using open source tools and open internet resources to develop a learning environment where video content is aggregated with interactive elements, educator content and content coming from the web, (b) describes how these open source tools are used for capturing and storing learner activity data, and, (c) presents findings obtained from analyzing learner activity data gathered in an educational setting during an academic year.


title: Semantic Frames to Predict Stock Price Movement

abstract: Semantic frames are a rich linguistic resource. There has been much work on semantic frame parsers, but less that applies them to general NLP problems. We address a task to predict change in stock price from financial news. Semantic frames help to generalize from specific sentences to scenarios, and to detect the (positive or negative) roles of specific companies. We introduce a novel tree representation, and use it to train predictive models with tree kernels using support vector machines. Our experiments test multiple text representations on two binary classification tasks, change of price and polarity. Experiments show that features derived from semantic frame parsing have significantly better performance across years on the polarity task.


title: A Dead-Time-Controlled Gate Driver Using Current-Sense FET Integrated in SiC MOSFET

abstract: In comparison with silicon IGBT, silicon carbide (SiC) MOSFET is expected to reduce the switching loss and the conduction loss and to remove external freewheeling diodes (FWDs). However, its body diode has comparatively high forward voltage; therefore, the diode conduction loss generated during dead time increases. As a result, the loss reduction by the use of the SiC MOSFET can be weakened. This paper proposes a simple dead-time controller integrated in an isolated gate driver in order to reduce the diode conduction loss during the dead time. The proposed method has high speed and high robustness against switching noise by using of a current-sense FET with small parasitic capacitance. As a result, the diode conduction time can be shortened within 0.1 μs. In addition, the proposed dead-time control method can be achieved by existing components, including the current-sense FET, which is currently used for short-circuit current detection in hybrid vehicle applications. The proposed method was applied to a 10 kW boost converter with SiC MOSFET. The experimental results showed 1% higher efficiency of the converter with the proposed dead-time-controlled gate driver compared to that without dead-time control circuit, and the efficiency was the similar level as when a SiC Schottky barrier diode (SBD) was used as FWDs.


Ultrasound System Considerations and their Impact on Front- End Components


title: An unobtrusive vision system to reduce the cognitive burden of hand prosthesis control

abstract: This paper introduces an inexpensive prosthetic hand control system designed to reduce the cognitive burden on amputees. It is designed around a vision-based object recognition system with an embedded camera that automates grasp selection and switching, and an inexpensive mechanomyography (MMG) sensor for hand opening and closing. A prototype has been developed and implemented to select between two different grasp configurations for the Bebionic V2 hand, developed by RSLSteeper. Pick and place experiments on 6 different objects in ‘Power’ and ‘Pinch’ grasps were used to assess feasibility on which to base full system development. Experimentation demonstrated an overall accuracy of 84.4% for grasp selection between pairs of objects. The results showed that it was more difficult to classify larger objects due to their size relative to the camera resolution. The grasping task became more accurate with time, indicating learning capability when estimating the position and trajectory of the hand for correct grasp selection; however further experimentation is required to form a conclusion. The limitation of this involves the use of unnatural reaching trajectories for correct grasp selection. The success in basic experimentation provides the proof of concept required for further system development.


title: Removing systematic errors for exoplanet search via latent causes

abstract: We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.


title: Average channel capacity of composite κ-μ/gamma fading channels

abstract: A new analytical expression is presented for the instantaneous power Probability Density Function (PDF) of receiver signals over composite κ-μ/gamma fading channels. Moreover, the exact expression of channel capacity is derived in the form of an infinite series, while an accurate approximation expression is obtained in closed form. To reveal the implications of the model parameters on capacity, we provide an expression for the case of a high-SNR environment. The relationship of the presented results with previously reported results on generalised-K and K fading channels is also discussed. Finally, numerical and simulation results are presented to prove the correctness of our derived expressions.


Improving the Generalization Capability of HIDMA with DeJong ’ s Gene Expression


title: An Assessment of the Suitability of FPGA-Based Systems for use in Digital Signal Processing

abstract: FPGAs have been proposed as high-performance alternatives to DSP processors. This paper quantitatively compares FPGA performance against DSP processors and ASICs using actual applications and existing CAD tools and devices. Performance measures were based on actual multiplier performance with FPGAs, DSP processors and ASICs. This study demonstrates that FPGAs can provide an order of magnitude better performance than DSP processors and can in many cases approach or exceed ASIC levels of performance.


title: Undangle: early detection of dangling pointers in use-after-free and double-free vulnerabilities

abstract: Use-after-free vulnerabilities are rapidly growing in popularity, especially for exploiting web browsers. Use-after-free (and double-free) vulnerabilities are caused by a program operating on a dangling pointer. In this work we propose early detection, a novel runtime approach for finding and diagnosing use-after-free and double-free vulnerabilities. While previous work focuses on the creation of the vulnerability (i.e., the use of a dangling pointer), early detection shifts the focus to the creation of the dangling pointer(s) at the root of the vulnerability. Early detection increases the effectiveness of testing by identifying unsafe dangling pointers in executions where they are created but not used. It also accelerates vulnerability analysis and minimizes the risk of incomplete fixes, by automatically collecting information about all dangling pointers involved in the vulnerability. We implement our early detection technique in a tool called Undangle. We evaluate Undangle for vulnerability analysis on 8 real-world vulnerabilities. The analysis uncovers that two separate vulnerabilities in Firefox had a common root cause and that their patches did not completely fix the underlying bug. We also evaluate Undangle for testing on the Firefox web browser identifying a potential vulnerability.


DISCO: A System Leveraging Semantic Search in Document Review


title: Learning to recommend with explicit and implicit social relations

abstract: Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our trusted friends for recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework which naturally fuses the users' tastes and their trusted friends' favors together. The proposed framework is quite general, and it can also be applied to pure user-item rating matrix even if we do not have explicit social trust information among users. In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method outperforms state-of-the-art approaches.


title: Using HistCite software to identify significant articles in subject searches of the Web of Science

abstract: HistCite TM is a large-scale computer tool for mapping science. Its power of visualization combines the production of historiographs on the basis of the analysis of co-citations of documents, with the use of bibliometrics specific indicators. The objective of this article is, to present the advantages of the new bibliometrics configuration of HistCite TM (2004) when identifying articles. The analysis of the histograms that produces HistCite TM , in terms of cumulative advantage and aging of the citations. And the comparative study of the results of HistCite TM , in its indicators of amplitude and recognition. Also is examined its treatment of the sampling problems, by formalizing the Kendall method of estimating the robust standard deviation.


title: Mamba - A waterproof snake robot with tactile sensing

abstract: This paper presents the snake robot Mamba, which is a modular, reconfigurable, and waterproof experimental platform developed to support the ongoing research on snake robot locomotion, including underwater locomotion. A novel contribution of the snake robot is its ability to measure environment contact forces acting along its body, which is achieved by isolating the actuator inside each joint module with a custom-designed force/torque sensor. The paper describes the design of this sensor and presents experimental results which illustrate its performance.


title: Cloud computing: Forensic challenges for law enforcement

abstract: Cloud computing is a relatively new concept that offers the potential to deliver scalable elastic services to many. The notion of pay-per use is attractive and in the current global recession hit economy it offers an economic solution to an organizations' IT needs. Computer forensics is a relatively new discipline born out of the increasing use of computing and digital storage devices in criminal acts (both traditional and hi-tech). Computer forensic practices have been around for several decades and early applications of their use can be charted back to law enforcement and military investigations some 30 years ago. In the last decade computer forensics has developed in terms of procedures, practices and tool support to serve the law enforcement community. However, it now faces possibly its greatest challenges in dealing with cloud computing. Through this paper we explore these challenges and suggest some possible solutions.


title: Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

abstract: We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks.


title: Robotic Cutting: Mechanics and Control of Knife Motion

abstract: Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by a knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.


title: Analysis of earthquake body wave spectra for potency and magnitude values: implications for magnitude scaling relations

abstract: We develop a simple methodology for reliable automated estimation of the low-frequency asymptote in seismic body wave spectra of small to moderate local earthquakes. The procedure corrects individual P- and S-wave spectra for propagation and site effects and estimates the seismic potency from a stacked spectrum. The method is applied to >11 000 earthquakes with local magnitudes 0 < M_L < 4 that occurred in the Southern California plate-boundary region around the San Jacinto fault zone during 2013. Moment magnitude M_w values, derived from the spectra and the scaling relation of Hanks & Kanamori, follow a Gutenberg–Richter distribution with a larger b-value (1.22) from that associated with the M_L values (0.93) for the same earthquakes. The completeness magnitude for the M_w values is 1.6 while for M_L it is 1.0. The quantity (M_w – M_L) linearly increases in the analysed magnitude range as M_L decreases. An average earthquake with M_L = 0 in the study area has an M_w of about 0.9. The developed methodology and results have important implications for earthquake source studies and statistical seismology.


title: DETRAC: A New Benchmark and Protocol for Multi-Object Tracking

abstract: In recent years, most effective multi-object tracking (MOT) methods are based on the tracking-by-detection framework. Existing performance evaluations of MOT methods usually separate the target association step from the object detection step by using the same object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effect of object detection accuracy to the overall MOT performance. This is based on a new large-scale DETection and tRACking (DETRAC) benchmark dataset. The DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140 thousand frames and 1.2 million labeled bounding boxes of objects) for both object detection and MOT. We evaluate complete MOT systems constructed from combinations of state-of-the-art target association methods and object detection schemes. Our analysis shows the complex effects of object detection accuracy on MOT performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and target association for comprehensive analysis.


title: Improved Two-Factor Authenticated Key Exchange Protocol

abstract: Quite recently, two smart-card-based passwords authenticated key exchange protocols were proposed by Lee et al. and Hwang et al. respectively. However, neither of them achieves two-factor authentication fully since they would become completely insecure once one factor is broken. To overcome these congenital defects, this study proposes such a secure authenticated key exchange protocol that achieves fully two-factor authentication and provides forward security of session keys. And yet our scheme is simple and reasonably efficient. Furthermore, we can provide the rigorous proof of the security for it.


title: Enhancing Semantic Web by Semantic Annotation: Experiences in Building an Automatic Conference Calendar

abstract: In this paper, we describe a Semantic Web application that builds a customizable conference calendar. In contrast to previous works aiming at manually creating a list of upcoming/current and past conferences, in this work we aim at providing a semantic conference calendar which automatically extracts information from the web using semantic annotation. In this system, to build a calendar, the user simply needs to specify what conferences he/she is interested in. The system finds, extracts, and updates the semantic information from the Web. We propose a unified approach for semantic annotation of the conference calendar. We also present evaluations of our approach on real-world data.


Influence of Scope Creep on Project Success : A Comparative Study between Conventional Approach Verses Agile Approach


title: Secure Anonymous Key Distribution Scheme for Smart Grid

abstract: To fully support information management among various stakeholders in smart grid domains, how to establish secure communication sessions has become an important issue for smart grid environments. In order to support secure communications between smart meters and service providers, key management for authentication becomes a crucial security topic. Recently, several key distribution schemes have been proposed to provide secure communications for smart grid. However, these schemes do not support smart meter anonymity and possess security weaknesses. This paper utilizes an identity-based signature scheme and an identity-based encryption scheme to propose a new anonymous key distribution scheme for smart grid environments. In the proposed scheme, a smart meter can anonymously access services provided by service providers using one private key without the help of the trusted anchor during authentication. In addition, the proposed scheme requires only a few of computation operations at the smart meter side. Security analysis is conducted to prove the proposed scheme is secure under random oracle model.


Cyberbullying Detection System on Twitter