title: Visual event recognition in videos by learning from web data

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


title: Enhancing Q-Learning for Optimal Asset Allocation

abstract: This paper enhances the Q-learning algorithm for optimal asset allocation proposed in (Neuneier, 1996 [6]). The new formulation simplifies the approach by using only one value-function for many assets and allows model-free policy-iteration. After testing the new algorithm on real data, the possibility of risk management within the framework of Markov decision problems is analyzed. The proposed methods allows the construction of a multi-period portfolio management system which takes into account transaction costs, the risk preferences of the investor, and several constraints on the allocation.


title: Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs

abstract: Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking 'slow' and `deeply' by reasoning over the knowledge graph. We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG. We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain. arXiv:1708.03310v2 [cs.AI]


title: Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria

abstract: This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distribution. Splitting criteria that are relatively insensitive to costs (class distributions) are found to perform as well as or better than, in terms of expected misclassification cost, splitting criteria that are cost sensitive. Consequently there are two opposite ways of dealing with imbalance. One is to combine a costinsensitive splitting criterion with a cost insensitive pruning method to produce a decision tree algorithm little affected by cost or prior class distribution. The other is to grow a cost-independent tree which is then pruned in a cost-sensitive manner.


title: Interaction of cell culture with composition effects on the mechanical properties of polycaprolactone-hydroxyapatite scaffolds fabricated via selective laser sintering (SLS)

abstract: Abstract In the current study PCL/HA composites were fabricated using SLS as two- and three-dimensional lattice structures and exposed to a cellular component (MC 3T3 osteoblast-like cells). The main aims were to determine the mechanical differences due to powder composition and to observe the physical and mechanical changes pertaining to cell presence. These structures were characterized by compressive mechanical testing, and the effects of cell culturing and degradation on mechanical properties of the scaffolds with different PCL/HA compositions were determined. Moreover, changes in the scaffold morphology due to the cell culture conditions were determined by μ-CT analysis. Cells steadily grew on the scaffolds for 21 days with preferential distribution around the macropores and initially PCL/HA(15%) composites had higher cell numbers. Removal of loosely sintered parts was observable during the culturing period. Cell culture conditions did not change the compressive moduli significantly but had a distinct effect on compressive strength. For PCL/HA(15%) composites, an initial loss in strength caused by cell culture was reversed by longer cell exposure, with compressive strength of the structures restored to the initial properties (p ≤ 0.05). μ-CT measurements showed widespread morphological changes in the scaffolds, such as a decrease in the roughness of the struts. In general, in the initial period composites with lower HA content (15 wt.%) showed better metabolic activity compared to the higher HA content, however by day 14 the performance of the two compositions was equal. These results suggest that changes in sintering due to the differences in powder composition can have profound effects on the short and long term mechanical properties of the scaffold particularly under cell culture conditions, and this should be closely considered for SLS processing of scaffolds.


Identifying Regional Dialects in On-Line Social Media


title: Science vs Conspiracy: collective narratives in the age of (mis)information

abstract: The large availability of user provided contents on online social media facilitates people aggregation around common interests, worldviews and narratives. However, in spite of the enthusiastic rhetoric about the so called {\em wisdom of crowds}, unsubstantiated rumors -- as alternative explanation to main stream versions of complex phenomena -- find on the Web a natural medium for their dissemination. In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives -- i.e. main stream scientific and alternative news -- are consumed on Facebook. Through a thorough quantitative analysis, we show that distinct communities with similar information consumption patterns emerge around distinctive narratives. Moreover, consumers of alternative news (mainly conspiracy theories) result to be more focused on their contents, while scientific news consumers are more prone to comment on alternative news. We conclude our analysis testing the response of this social system to 4709 troll information -- i.e. parodistic imitation of alternative and conspiracy theories. We find that, despite the false and satirical vein of news, usual consumers of conspiracy news are the most prone to interact with them.


title: Extracting Complex Biological Events with Rich Graph-Based Feature Sets

abstract: We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.


title: Darnet: a deep learning solution for distracted driving detection

abstract: Distracted driving is known to be the leading cause of motor vehicle accidents. With the increase in the number of IoT devices available within vehicles, there exists an abundance of data for monitoring driver behavior. However, designing a system around this goal presents two key challenges - how to concurrently collect data spanning multiple IoT devices, and how to jointly analyze this multimodal input. To that end, we present a unified data collection and analysis framework, DarNet, capable of detecting and classifying distracted driving behavior. DarNet consists of two primary components: a data collection system and an analytics engine. Our system takes advantage of advances in machine learning (ML) to classify driving behavior based on input sensor data. In our system implementation, we collect image data from an inward facing camera, and Inertial Measurement Unit (IMU) data from a mobile device, both located within the vehicle. Using deep learning techniques, we show that DarNet achieves a Top-1 classification percentage of 87.02% on our collected dataset, significantly outperforming our baseline model of 73.88%. Additionally, we address the privacy concerns associated with collecting image data by presenting an alternative framework designed to operate on down-sampled data which produces a Top-1 classification percentage of 80.00%.


title: Process industry supply chains: Advances and challenges

abstract: A large body of work exists in process industry supply chain optimisation. We describe the state of the art of research in infrastructure design, modelling and analysis and planning and scheduling, together with some industrial examples. We draw some conclusions about the degree to which different classes of problem have been solved, and discuss challenges for the future.


title: Audiovisual Fusion: Challenges and New Approaches

abstract: In this paper, we review recent results on audiovisual (AV) fusion. We also discuss some of the challenges and report on approaches to address them. One important issue in AV fusion is how the modalities interact and influence each other. This review will address this question in the context of AV speech processing, and especially speech recognition, where one of the issues is that the modalities both interact but also sometimes appear to desynchronize from each other. An additional issue that sometimes arises is that one of the modalities may be missing at test time, although it is available at training time; for example, it may be possible to collect AV training data while only having access to audio at test time. We will review approaches to address this issue from the area of multiview learning, where the goal is to learn a model or representation for each of the modalities separately while taking advantage of the rich multimodal training data. In addition to multiview learning, we also discuss the recent application of deep learning (DL) toward AV fusion. We finally draw conclusions and offer our assessment of the future in the area of AV fusion.


title: UDP: a programmable accelerator for extract-transform-load workloads and more

abstract: Big data analytic applications give rise to large-scale extract-transformload (ETL) as a fundamental step to transform new data into a native representation. ETL workloads pose significant performance challenges on conventional architectures, so we propose the design of the unstructured data processor (UDP), a software programmable accelerator that includes multi-way dispatch, variable-size symbol support, flexible-source dispatch (stream buffer and scalar registers), and memory addressing to accelerate ETL kernels both for current and novel future encoding and compression. Specifically, UDP excels at branch-intensive and symbol and pattern-oriented workloads, and can offload them from CPUs. To evaluate UDP, we use a broad set of data processing workloads inspired by ETL, but broad enough to also apply to query execution, stream processing, and intrusion detection/monitoring. A single UDP accelerates these data processing tasks 20-fold (geometric mean, largest increase from 0.4 GB/s to 40 GB/s) and performance per watt by a geomean of 1,900-fold. UDP ASIC implementation in 28nm CMOS shows UDP logic area of 3.82mm2 (8.69mm2 with 1MB local memory), and logic power of 0.149W (0.864W with 1MB local memory); both much smaller than a single core. CCS CONCEPTS • Information systems → Extraction, transformation and loading; • Computer systems organization → Parallel architectures; • Hardware → Application specific processors; • Theory of computation → Pattern matching;


title: Changing curriculum policy into practice: the case of physical education in Hong Kong

abstract: This article examines the differences between the formally stated aims of education and the implementation of the school curriculum in order to show the divergence between what is initiated and what emerges as school practice. This implementation problem is examined from the policy-makers' and teachers' perspectives, with specific reference to physical education as a school subject in Hong Kong. First, it examines the existence and intention of educational policy towards curriculum as established by policy-makers. Second, the implementation process is evaluated from the teachers' perspective by employing a framework adopted by Cuban (1998) that utilized effectiveness, popularity, fidelity, adaptability and longevity. The data are derived from a case-study of a physical education programme that included document analysis, and interviews with teachers, parents and students. The case-study provided a subjective perspective of teachers and their work, while the framework assisted in demonstrating the absence ...


title: Energy demands in competitive soccer.

abstract: In elite outfield players, the average work rate during a soccer match, as estimated from variables such as heart rate, is approximately 70% of maximal oxygen uptake ( max). This corresponds to an ...


title: Modeling and Simulation of BLDC Motor Using Soft Computing Techniques

abstract: In this paper a soft computing technique PSIM is used for the performance simulation of the BLDC motor. With the help of its user friendly approach and some basic theories, the corresponding PSIM models for the BLDC can easily be constructed. The simulation results of BLDC motor performance can critically evaluate under various input parameters in the PSIM environment. The accuracy of this approach can also be verified by comparing the simulation results in PSIM with the response obtained from the BLDC motor established results.


title: Wheal reactions in human skin after injection of leukotrienes B4, C4, D4 and E4.

abstract: Intradermal injections of 1-50 pmol leukotriene B4, C4, D4 and E4, (LTB4, LTC4, LTD4 and LTE4) were performed in healthy subjects and patients with recurrent urticaria. A wheal and erythema were seen 15 minutes after injection and were most marked after LTC4 and LTD4. LTE4 also produced a wheal whereas the reaction to LTB4 did not significantly differ from the saline control. When mixed with histamine no potentiation of the wheals was noted and no significant difference in reaction was observed between the healthy controls and those with urticaria.


title: Encoding Markov Logic Networks in Possibilistic Logic

abstract: Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.


A study of translation edit rate with targeted human annotation


title: Big data analytics to improve cardiovascular care: promise and challenges

abstract: The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.


title: An SAO-based text mining approach to building a technology tree for technology planning

abstract: A technology tree (TechTree) is a branching diagram that expresses relationships among product components, technologies, or functions of a technology in a specific technology area. A TechTree identifies strategic core technologies and is a useful tool to support decision making in a given market environment for organizations with specified capabilities. However, existing TechTrees generally overemphasize qualitative and expert-dependent knowledge rather than incorporating quantitative and objective information. In addition, the traditional process of developing a TechTree requires vast amounts of information, which costs considerably in terms of time, and cannot provide integrated information from a variety of technological perspectives simultaneously. To remedy these problems, this research presents a text mining approach based on Subject-Action-Object (SAO) structures; this approach develops a TechTree by extracting and analyzing SAO structures from patent documents. The extracted SAO structures are categorized by similarities, and are identified by the type of technological implications. To demonstrate the feasibility of the proposed approach, we developed a TechTree regarding Proton Exchange Fuel Cell technology.


title: Pathway and network analysis of cancer genomes

abstract: International Cancer Genome Consortium members review and recommend approaches to pathway and network analysis to uncover molecular processes that contribute to tumor biology.


title: Collective Spammer Detection in Evolving Multi-Relational Social Networks

abstract: Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in evolving multi-relational social networks. We model a social network as a time-stamped multi-relational graph where vertices represent users, and edges represent different activities between them. To identify spammer accounts, our approach makes use of structural features, sequence modelling, and collective reasoning. We leverage relational sequence information using k-gram features and probabilistic modelling with a mixture of Markov models. Furthermore, in order to perform collective reasoning and improve the predictive power of a noisy abuse reporting system, we develop a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable. We use Graphlab Create and Probabilistic Soft Logic (PSL) to prototype and experimentally evaluate our solutions on internet-scale data from Tagged.com. Our experiments demonstrate the effectiveness of our approach, and show that models which incorporate the multi-relational nature of the social network significantly gain predictive performance over those that do not.


title: Video Google: Efficient visual search of videos

abstract: We describe an approach to object retrieval which searches for and localizes all the occurrences of an object in a video, given a query image of the object. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject those that are unstable. Efficient retrieval is achieved by employing methods from statistical text retrieval, including inverted file systems, and text and document frequency weightings. This requires a visual analogy of a word which is provided here by vector quantizing the region descriptors. The final ranking also depends on the spatial layout of the regions. The result is that retrieval is immediate, returning a ranked list of shots in the manner of Google. We report results for object retrieval on the full length feature films 'Groundhog Day' and 'Casablanca'.


title: Latte: a language, compiler, and runtime for elegant and efficient deep neural networks

abstract: Deep neural networks (DNNs) have undergone a surge in popularity with consistent advances in the state of the art for tasks including image recognition, natural language processing, and speech recognition. The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance. In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance. Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domain-specific and general optimizations, and emits efficient machine code for heterogeneous architectures. Latte also includes a communication runtime for distributed memory data-parallelism. Using networks described using Latte, we demonstrate 3-6x speedup over Caffe (C++/MKL) on the three state-of-the-art ImageNet models executing on an Intel Xeon E5-2699 v3 x86 CPU.


title: Group value and intention to use - A study of multi-agency disaster management information systems for public safety

abstract: This paper examines and extends the theory of information systems success in the context of large-scale disaster management (DM) for public safety. In the recent past, various evaluation reports on DM efforts have concluded that information quality and system quality are major hurdles for efficient and effective multi-agency DM and are critical antecedents for information systems (IS) success. In contrast to the wealth of literature on IS success in profit-oriented business environments, research regarding drivers of public sector IS success is scarce. This research develops and empirically tests a model that explains IS usage intention as a reflective measure of IS success in the public sector DM domain. In this paper, the effects of the expected value of IS for the entire group of collaborating DM agencies, task support, user satisfaction, and three specific information/service quality dimensions on usage intention are examined. Data was collected from emergency responders using a questionnaire survey method during multi-agency, cross-national DM exercises at the Dutch-German border. The results of the data analysis revealed that expected group value is a key determinant of intention to use in the public sector DM domain. The data analysis also showed that perceived task support only has an indirect effect, through user satisfaction, on the usage intention. These findings imply that previously suggested IS success models for business environments are likely to fall short in their explanatory power and applicability for highly volatile complex disaster environments that require immediate coordinated responses from a large number of organizations. Possible directions for future research are also discussed along with other findings and implications.


title: Design of embedded web server and its implementation of CGI

abstract: The resource of embedded device is limited,and the capacity of RAM and ROM is very small,so it is difficult to implement the web server function under the resource limited situation. Based on the analysis of HTTP1.1,the features of the embedded web server and its designing idea are given,and simplified software architecture for embedded web server is also introduced. The implement strategy of CGI for embedded web server is described in detail. Finally,the HTTP connection is successfully achieved with C language program.


title: TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

abstract: In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users’ preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world data sets database systems and logic programming (DBLP) and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.


title: A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

abstract: We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.


title: A data pattern-tolerant adaptive equalizer using spectrum balancing method

abstract: This brief presents a data-pattern-tolerant adaptive equalizer using the spectrum balancing method. In addition to a high-frequency boost control loop, this equalizer has a corner frequency control loop to guarantee its accurate adaptation for various data patterns and data rates. Measured results show that the jitter of the eye is reduced by a maximum of 37% when compared to the previous spectrum balancing equalizer. The chip is fabricated in a 0.18-?m CMOS process, and the equalizer core occupies 0.35 mm2 and consumes 85 mW.


title: Using Surveys and Web-Scraping to Select Tools for Software Testing Consultancy

abstract: We analyzed findings from data collected utilizing surveys and Web-scraping, to support Knowit Oy, a software testing consultation company, in the process of selecting the right tools for software testing & test automation. We conducted two surveys (2013 & 2016) among (mostly Finnish) software professionals to acquire criteria and a list of tools used for software testing in industry. Considering all our data sources Selenium was the most popular pure tool, while Robot Framework was the most referenced tool (latter survey). According to the surveys Jenkins and Sikuli have the highest increase in popularity (or familiarity). Top referred criteria for selection were usability, functionality, maintainability and available support for a tool. While Knowit considers it best to utilize traditional surveys, Web-scraping is seen as cost effective support for such instruments. To get comprehensive picture and to gain knowledge of the tools in markets multiple sources should be used.


title: Evaluating topic coherence measures

abstract: Topic models extract representative word sets - called topics - from word counts in documents without requiring any semantic annotations. Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics. Studies of topic coherence so far are limited to measures that score pairs of individual words. For the first time, we include coherence measures from scientific philosophy that score pairs of more complex word subsets and apply them to topic scoring.


title: Scrum and CMMI Going from Good to Great

abstract: Projects combining agile methods with CMMI combine adaptability with predictability to better serve large customer needs. The introduction of Scrum at Systematic, a CMMI Level 5 company, doubled productivity and cut defects by 40% compared to waterfall projects in 2006 by focusing on early testing and time to fix builds. Systematic institutionalized Scrum across all projects and used data driven tools like story process efficiency to surface Product Backlog impediments. This allowed them to systematically develop a strategy for a second doubling in productivity. Two teams have achieved a sustainable quadrupling of productivity compared to waterfall projects. We discuss here the strategy to bring the entire company to that level. Our experiences shows that Scrum and CMMI together bring a more powerful combination of adaptability and predictability than either one alone and suggest how other companies can combine them to achieve Toyota level performance – 4 times the productivity and 12 times the quality of waterfall teams.


title: ReSurf: Reconstructing web-surfing activity from network traffic

abstract: More and more applications and services move to the web and this has led to web traffic amounting to as much as 80% of all network traffic. At the same time, most traffic classification efforts stop once they correctly label a flow as web or HTTP. In this paper, we focus on understanding what happens “under the hood” of HTTP traffic. Our first contribution is ReSurf, a systematic approach to reconstruct web-surfing activity starting from raw network data with more than 91% recall and 95% precision over four real network traces. Our second contribution is an extensive analysis of web activity across these traces. By utilizing ReSurf, we study web-surfing behaviors in terms of user requests and transitions between websites (e.g. the click-through history of following hyperlinks). A surprising result is the prevalence of advertising and tracking services that are being accessed during web-surfing that are without the user's explicit consent. In our traces, we found that with 90% chance a user will access such a service after just three user requests (or “clicks”). We believe that our methodology and findings provide valuable insights into modern traffic that can allow: (a) network administrators to better manage and protect their networks, (b) traffic regulators to protect the rights of on-line users, and (c) researchers to better understand the evolution of the traffic from modern websites.


title: Itraconazole in the treatment of superficial cutaneous and mucosal Candida infections.

abstract: Superficial cutaneous and mucosal Candida infections are common and widely affect both the immunocompromised as well as the immunocompetent populations. Common infections may include Candida paronychia, cutaneous candidiasis, oral candidiasis, vulvovaginal candidiasis, and Candida onychomycosis. Although C albicans has been considered to be the most common pathogen, other Candida species have emerged as potential causes of certain infections. Currently, a variety of antifungal agents is available to treat these infections. They include fluconazole, ketoconazole, and itraconazole. These agents have been widely used to treat fungal infections, including superficial and systemic candidiasis. However, some concerns exist regarding safety associated with long-term use of ketoconazole, and emerging issues of Candida resistance to fluconazole in some patient subsets have been reported. Itraconazole has proven efficacy in treating cutaneous and mucosal Candida infections. Additionally, studies have demonstrated that itraconazole may have increased efficacy and an excellent safety profile when administered in a pulse-dose, or intermittent fashion, for superficial mycotic infections. Itraconazole is an effective agent that warrants consideration when selecting treatment for Candida infections.


title: Korean sign language recognition using EMG and IMU sensors based on group-dependent NN models

abstract: Automatic sign language recognition systems can help many hearing and speech-impaired people communicate with the public. To recognize sign language, the system should first determine the shape of the hand and the movement of the arm. Since sign language consists of a sequence of movements, it is difficult to distinguish a certain gesture from gestures (movements). It also has various lengths of gestures. It is effective to make the fixed length input data (gestures) rather than predefine the length of each gesture for recognition. Furthermore, in order to improve recognition accuracy, the effective way is to exploit multiple heterogeneous sensors (both an electromyography (EMG) sensor and an inertial measurement unit (IMU) sensor) which can produce the redundant information to the same physical variable. Specially, we focus on the fact that EMG signals depends on physical features of people because the amount of muscle and the thickness of the fat layer are different for each person. To address these issues, we propose an automatic recognition method for Korean sign language, which based on a sensor fusion technology and group-dependent Neural Network (NN) models. Our approach on group-dependent NN models is to separate the models so that different people can use different models. Finally, the results of recognition show that the proposed method has high accuracy (99.13% of CNN without dropout and 98.1% of CNN with dropout).


title: High-voltage DC transmission: a power electronics workhorse

abstract: Thyristor-based HVDC converter technology is used for highly reliable power transfer across natural or national boundaries or between AC systems designed for different frequencies or incompatible frequency controls. The author discusses the benefits of HVDC transmission and describes HVDC transmission system configurations. The author then describes the use of thyristors in HVDC converters.


title: Antenna-in-package design and module integration for millimeter-wave communication and 5G

abstract: Co-design and integration of RFIC, package, and antennas are critical to enable 5G wireless communications and is particularly challenging at mmWave frequencies. This paper reviews two different aspects of mmWave antenna module packaging and integration for base station and user equipment applications, respectively. We first present the challenges, implementation, and characterization of a 28-GHz phased-array module with 64 dual polarized antennas for 5G base station applications. Next, we describe a compact, low-power, 60-GHz switched-beam transceiver module suitable for handset integration incorporating 4 antennas that supports both normal and end-fire directions for a wide link spatial coverage.


title: The lock holder and the lock waiter pre-emption problems: nip them in the bud using informed spinlocks (I-Spinlock)

abstract: In native Linux systems, spinlock's implementation relies on the assumption that both the lock holder thread and lock waiter threads cannot be preempted. However, in a virtualized environment, these threads are scheduled on top of virtual CPUs (vCPU) that can be preempted by the hypervisor at any time, thus forcing lock waiter threads on other vCPUs to busy wait and to waste CPU cycles. This leads to the well-known Lock Holder Preemption (LHP) and Lock Waiter Preemption (LWP) issues. In this paper, we propose I-Spinlock (for Informed Spinlock), a new spinlock implementation for virtualized environments. Its main principle is to only allow a thread to acquire a lock if and only if the remaining time-slice of its vCPU is sufficient to enter and leave the critical section. This is possible if the spinlock primitive is aware (informed) of its time-to-preemption (by the hypervisor). We implemented I-Spinlock in the Xen virtualization system. We show that our solution is compliant with both para-virtual and hardware virtualization modes. We performed extensive performance evaluations with various reference benchmarks and compared our solution to previous solutions. The evaluations demonstrate that I-Spinlock outperforms other solutions, and more significantly when the number of core increases.


title: Cable suspended robots: design, planning and control

abstract: Cable-suspended robots are structurally similar to parallel actuated robots, but with the fundamental difference that cables can only pull the end-effector but not push it. This feature makes the design, planning and control of cable-suspended robots a lot more challenging compared to their counterparts - parallel-actuated robots. This paper describes the kinematic and dynamic models, work-space, trajectory planning, and feedback controllers for these robots. These results are demonstrated through simulation and experiments on a six degree-of-freedom cable suspended robot.


title: Actions from thoughts

abstract: Real-time direct interfaces between the brain and electronic and mechanical devices could one day be used to restore sensory and motor functions lost through injury or disease. Hybrid brain–machine interfaces also have the potential to enhance our perceptual, motor and cognitive capabilities by revolutionizing the way we use computers and interact with remote environments.


title: LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

abstract: We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. L-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet, but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.


Lymphadenectomy in endometrial cancer


title: Text Line Detection for Heterogeneous Documents

abstract: Text line detection is a pre-processing step for automated document analysis such as word spotting or OCR. It is additionally used for document structure analysis or layout analysis. Considering mixed layouts, degraded documents and handwritten documents, text line detection is still challenging. We present a novel approach that targets torn documents having varying layouts and writing. The proposed method is a bottom up approach that fuses words, to globally minimize their fusing distance. In order to improve processing time and further layout analysis, text lines are represented by oriented rectangles. Even though, the method was designed for modern handwritten and printed documents, tests on medieval manuscripts give promising results. Additionally, the text line detection was evaluated on the ICDAR 2009 and ICFHR 2010 Handwriting Segmentation Contest datasets.


title: "I'm so glad I met you": Designing Dynamic Collaborative Support for Young Adult Cancer Survivors

abstract: Young adult cancer survivors-individuals in their 20's and 30's-must cope with complicated informational and emotional needs that differ from those of other age groups. Although young adult cancer survivors are resourceful in finding information and support to help meet those needs, they face three distinct, ongoing challenges during and after the cancer experience: (1) feeling isolated; (2) confronting a sense of mortality; and (3) struggling with changing body image and identity. We present empirical findings from qualitative interviews with young adult cancer survivors that demonstrate how these challenges change throughout the illness experience, complicating a survivor's search for information and support. We also characterize the adaptive behaviors survivors employ to overcome these challenges. Given these findings, we suggest design implications for online spaces young adult survivors use to collaborate, as well as resources about privacy and self-presentation that can best support survivors in making decisions about shar-ing information.


title: Hexuronyl C5-epimerases in alginate and glycosaminoglycan biosynthesis.

abstract: The sugar residues in most polysaccharides are incorporated as their corresponding monomers during polymerization. Here we summarize the three known exceptions to this rule, involving the biosynthesis of alginate, and the glycosaminoglycans, heparin/heparan sulfate and dermatan sulfate. Alginate is synthesized by brown seaweeds and certain bacteria, while glycosaminoglycans are produced by most animal species. In all cases one of the incorporated sugar monomers are being C5-epimerized at the polymer level, from D-mannuronic acid to L-guluronic acid in alginate, and from D-glucuronic acid to L-iduronic acid in glycosaminoglycans. Alginate epimerization modulates the mechanical properties of seaweed tissues, whereas in bacteria it seems to serve a wide range of purposes. The conformational flexibility of iduronic acid units in glycosaminoglycans promotes apposition to, and thus functional interactions with a variety of proteins at cell surfaces and in the extracellular matrix. In the bacterium Azotobacter vinelandii the alginates are being epimerized at the cell surface or in the extracellular environment by a family of evolutionary strongly related modular type and Ca(2+)-dependent epimerases (AlgE1-7). Each of these enzymes introduces a specific distribution pattern of guluronic acid residues along the polymer chains, explaining the wide structural variability observed in alginates isolated from nature. Glycosaminoglycans are synthesized in the Golgi system, through a series of reactions that include the C5-epimerization reaction along with extensive sulfation of the polymers. The single, Ca(2+)-independent, epimerase in heparin/heparan sulfate biosynthesis and the Ca(2+)-dependent dermatan sulfate epimerase(s) also generate variable epimerization patterns, depending on other polymer-modification reactions. The alginate and heparin epimerases appear unrelated at the amino acid sequence level, and have probably evolved through independent evolutionary pathways; however, hydrophobic cluster analysis indicates limited similarity. Seaweed alginates are widely used in industry, while heparin is well established in the clinic as an anticoagulant.


title: Constructing topical hierarchies in heterogeneous information networks

abstract: Many digital documentary data collections (e.g., scientific publications, enterprise reports, news articles, and social media) can be modeled as a heterogeneous information network, linking text with multiple types of entities. Constructing high-quality hierarchies that can represent topics at multiple granularities benefits tasks such as search, information browsing, and pattern mining. In this work, we present an algorithm for recursively constructing multi-typed topical hierarchies. Contrary to traditional text-based topic modeling, our approach handles both textual phrases and multiple types of entities by a newly designed clustering and ranking algorithm for heterogeneous network data, as well as mining and ranking topical patterns of different types. Our experiments on datasets from two different domains demonstrate that our algorithm yields high-quality, multi-typed topical hierarchies.


title: Plasmonic Luneburg and Eaton lenses

abstract: Plasmonics takes advantage of the properties of surface plasmon polaritons, which are localized or propagating quasiparticles in which photons are coupled to the quasi-free electrons in metals. In particular, plasmonic devices can confine light in regions with dimensions that are smaller than the wavelength of the photons in free space, and this makes it possible to match the different length scales associated with photonics and electronics in a single nanoscale device. Broad applications of plasmonics that have been demonstrated to date include biological sensing, sub-diffraction-limit imaging, focusing and lithography and nano-optical circuitry. Plasmonics-based optical elements such as waveguides, lenses, beamsplitters and reflectors have been implemented by structuring metal surfaces or placing dielectric structures on metals to manipulate the two-dimensional surface plasmon waves. However, the abrupt discontinuities in the material properties or geometries of these elements lead to increased scattering of surface plasmon polaritons, which significantly reduces the efficiency of these components. Transformation optics provides an alternative approach to controlling the propagation of light by spatially varying the optical properties of a material. Here, motivated by this approach, we use grey-scale lithography to adiabatically tailor the topology of a dielectric layer adjacent to a metal surface to demonstrate a plasmonic Luneburg lens that can focus surface plasmon polaritons. We also make a plasmonic Eaton lens that can bend surface plasmon polaritons. Because the optical properties are changed gradually rather than abruptly in these lenses, losses due to scattering can be significantly reduced in comparison with previously reported plasmonic elements.


title: Automated tabular itinerary visualization

abstract: Advances in geographic information extraction have exposed previously untapped resources, such as many travel itineraries found in HTML tables and spreadsheets on the Web. In the general sense, itineraries differ from the related concepts of routes and trajectories in that the precise paths between stopping points are of far less importance than the locations of the stopping points and their order. This characteristic allows for some flexibility when visualizing itineraries. A method for automatically generating itinerary visualizations is presented, which utilizes principles from graph-drawing and map labeling, along with additional criteria designed specifically for the itinerary visualization task. We describe a system based on this method that can perform automated layout for arbitrary itineraries at varying scales.


title: Entailment-based Fully Automatic Technique for Evaluation of Summaries

abstract: We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries man- ually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automat- ically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these doc- uments are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Com- paring with the ROUGE 1.5.5 evaluation scores over TAC 2008 (for- merly DUC, conducted by NIST) dataset, the proposed evaluation tech- nique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.


title: Identifying Political Sentiment between Nation States with Social Media

abstract: This paper describes an approach to largescale modeling of sentiment analysis for the social sciences. The goal is to model relations between nation states through social media. Many cross-disciplinary applications of NLP involve making predictions (such as predicting political elections), but this paper instead focuses on a model that is applicable to broader analysis. Do citizens express opinions in line with their home country’s formal relations? When opinions diverge over time, what is the cause and can social media serve to detect these changes? We describe several learning algorithms to study how the populace of a country discusses foreign nations on Twitter, ranging from state-of-theart contextual sentiment analysis to some required practical learners that filter irrelevant tweets. We evaluate on standard sentiment evaluations, but we also show strong correlations with two public opinion polls and current international alliance relationships. We conclude with some political science use cases.


title: Brain–computer interface systems: progress and prospects

abstract: Brain–computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.


title: D-StaR: A Generic Method for Stamp Segmentation from Document Images

abstract: B This paper presents a novel approach, named D-StaR, for stamp segmentation from scanned document images. The presented approach is generic (applicable to stamps of any color, shape, size, and orientation) and based on deep learning. In particular, it uses Fully Convolutional networks for semantic analysis of documents to extract stamps. The presented approach is evaluated on a publicly available stamp dataset. Evaluation results show that the presented approach outperforms the state-of-the-art methods for stamp segmentation and achieves pixel based precision and recall of 87% and 84%, respectively. Deeper analysis of the evaluation reveals that the presented approach can segment both overlapping and non-overlapping stamps, which was always a problem for existing systems in the literature.


title: Feedback Control of Soft Robot Actuators via Commercial Flex Bend Sensors

abstract: Soft robotics is an emerging field that takes advantage of compliant materials and makes use of nonstandard actuators. Flexible fluid actuators (FFAs) use fluid pressure to produce high deformation of elastomeric-based structures. However, closed-loop control of such actuators is still very challenging due to the lack of robust, reliable, and inexpensive sensors that can be integrated onto highly deformable actuator structures, involving very low cost materials and manufacturing. This paper presents a systematic approach to implement the feedback control of FFA-based soft robotic bending modules by using commercial flex bend sensors. A flex bend sensor detects the module curvature in one direction, and its response is processed by an on board microcontroller and sent to the central control system. Such sensor integration enables the closed-loop control of modular robotic architectures, often used in soft robotics. Once integrated with the soft module, the sensor response was calibrated by the use of a ground truth electro-magnetic tracking system in order to characterize its behavior when combined with the relative FFA. A feedback control using a low-pass filter and a proportional-integral controller was designed and used to evaluate the dynamic response and the position accuracy of the integrated module. With such closed-loop control, the module tip is positioned with less than 1 mm accuracy, which can be considered a relevant result in the soft robotics field.


RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application


title: A PSO-based virtual SDN customization for multi-tenant cloud services

abstract: With the development of cloud computing, the current Software Defined Networking (SDN) data center which is based on the Open Source Cloud platform OpenStack cannot satisfy multiple users' service requirements. OpenStack cannot achieve centralized control of the physical network in cloud data center. In order to satisfy the user's flexible control of the data center network, a multi-tenant virtual network customization in cloud data center is proposed in this paper. SDN with the advantages of centralized control and programming is integrated to OpenStack cloud data center. Isolated tenant virtual SDN networks (vSDN) are created in the cloud data center via the virtual network mapping model based on particle swarm optimization (PSO) algorithm. The vSDN network is controlled by tenant's own controller. Virtual network mapping performance, link customization and data transmission performance are tested in this paper. Results show that the virtual network mapping model based on PSO algorithm compared to shortest path (SP) algorithm, greatly improve the utilization rate of the underlying network bandwidth. At the same time, the SDN mode does not reduce the performance of data transmission on OpenStack cloud platform. The virtual network link customization improves the efficiency of data transmission for tenant.


title: Foods, Nutrients, and Dietary Patterns: Interconnections and Implications for Dietary Guidelines

abstract: Dietary guidelines provide evidence-based statements on food choices to meet nutritional requirements and reduce the risk of prevailing chronic disease. They involve a substantial amount of research translation, and their implementation has important health consequences. Foods, however, are complex combinations of nutrients and other compounds that act synergistically within the food and across food combinations. In addition, the evidence base underpinning dietary guidelines accesses research that reflects different study designs, with inherent strengths and limitations. We propose a systematic approach for the review of evidence that begins with research on dietary patterns. This research will identify the combinations of foods that best protect, or appear deleterious to, health. Next, we suggest that evidence be sought from research that focuses on the effects of individual foods. Finally, nutrient-based research should be considered to explain the mechanisms by which these foods and dietary patterns exert their effects, take into account the effects of ingredients added to the food supply, and enable assessments of dietary sufficiency. The consideration of individual nutrients and food components (e.g., upper limits for saturated fat, added sugar, and sodium) provides important benchmarks for evaluating overall diet quality. The concepts of core and discretionary foods (nutrient-rich and nutrient-poor foods, respectively) enable distinctions between foods, and this has implications for the relation between food policy and food manufacturing. In summary, evidence supporting healthy dietary patterns provides the foundation for the development of dietary guidelines. Further reference to individual foods and nutrients follows from the foundation of healthy dietary patterns.


title: Usability inspection methods after 15 years of research and practice

abstract: Usability inspection methods, such as heuristic evaluation, the cognitive walkthrough, formal usability inspections, and the pluralistic usability walkthrough, were introduced fifteen years ago. Since then, these methods, analyses of their comparative effectiveness, and their use have evolved in different ways. In this paper, we track the fortunes of the methods and analyses, looking at which led to use and to further research, and which led to relative methodological dead ends. Heuristic evaluation and the cognitive walkthrough appear to be the most actively used and researched techniques. The pluralistic walkthrough remains a recognized technique, although not the subject of significant further study. Formal usability inspections appear to have been incorporated into other techniques or largely abandoned in practice. We conclude with lessons for practitioners and suggestions for future research.


title: Characteristics of scalability and their impact on performance

abstract: is a desirable attribute of a network, system, or process. Poor scalability can result in poor system performance, necessitating the reengineering or duplication of systems. While scalability is valued, its characteristics and the characteristics that undermine it are usually only apparent from the context. Here, we attempt to define different aspects of scalability, such as structural scalability and load scalability. Structural scalability is the ability of a system to expand in a chosen dimension without major modifications to its architecture. Load scalability is the ability of a system to perform gracefully as the offered traffic increases. It is argued that systems with poor load scalability may exhibit it because they repeatedly engage in wasteful activity, because they are encumbered with poor scheduling algorithms, because they cannot fully take advantage of parallelism, or because they are algorithmically inefficient. We qualitatively illustrate these concepts with classical examples from the literature of operating systems and local area networks, as well as an example of our own. Some of these are accompanied by rudimentary delay analysis.


title: Design and implementation of multicore support for a highly reliable self-repairing μ-kernel OS

abstract: As computers basically have adopted multicore processors, operating systems (OSs) support for these systems have to follow up. Since the impact of the consequences of multi-core failures affect software systems, most schedulers instead of exploiting the on-chip-based communications between cores on one die, they distribute tasks over multiple dies to reduce recovery time in case of single die failure. The full exploitation of multicore features within a die is best met by developing a highly reliable, fault-tolerant OS. MINIX3 is a highly reliable, self-repairing multiserver microkernel OS, but it doesn't support multicore architectures. This paper presents a proposed design and implementation of modifications to MINIX3 to support multicore architectures. Also, the paper presents the results of the experiments that have been conducted to evaluate the modified MINIX3 considering three issues: reliability, the performance of the multicore port, and contention on the locking scheme.


title: A 0.18-µm CMOS current-mode Hall magnetic sensor with very low bias current and high sensitive front-end

abstract: This paper describes a magnetic Hall sensor working in current mode and its electronic interface. The microsystem uses the current spinning technique to compensate for the offset and provides a differential output signal current. The use of a low-noise chopper stabilized operational amplifier enables the integration of the signal current and ensures, after the sensor interface, a voltage sensitivity of 1660 V/A/mT (@ 25 kS/s) with a sensor bias current as low as 12 μA. The current-mode Hall sensor and the analog integrator read-out circuit have been fabricated with a 6-metal 0.18-μm standard CMOS technology. The current spinning technique reduces the offset to less than 50 μT (measured over 10 samples).


title: Gradient Estimation Using Stochastic Computation Graphs

abstract: In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world. Estimating the gradient of this loss function, using samples, lies at the core of gradient-based learning algorithms for these problems. We introduce the formalism of stochastic computation graphs—directed acyclic graphs that include both deterministic functions and conditional probability distributions—and describe how to easily and automatically derive an unbiased estimator of the loss function's gradient. The resulting algorithm for computing the gradient estimator is a simple modification of the standard backpropagation algorithm. The generic scheme we propose unifies estimators derived in variety of prior work, along with variance-reduction techniques therein. It could assist researchers in developing intricate models involving a combination of stochastic and deterministic operations, enabling, for example, attention, memory, and control actions.


title: An improved calibration framework for electromagnetic tracking devices

abstract: Electromagnetic trackers have many favorable characteristics but are notorious for their sensitivity to magnetic field distortions resulting from metal and electronic equipment in the environment. We categorize existing tracker calibration methods and present an improved technique for reducing the static position and orientation errors that are inherent to these devices. A quaternion-based formulation provides a simple and fast computational framework for representing orientation errors. Our experimental apparatus consists of a 6-DOF mobile platform and an optical position measurement system, allowing the collection of full-pose data at nearly arbitrary orientations of the receiver. A polynomial correction technique is applied and evaluated using a Polhemus Fastrak resulting in a substantial improvement of tracking accuracy. Finally, we apply advanced visualization algorithms to give new insight into the nature of the magnetic distortion field.


title: Vietnam spectrum occupancy measurements and analysis for cognitive radio applications

abstract: The rapid growth of demand for wireless transmission has placed great pressure on the scarce radio spectrum due to the fixed spectrum allocation policy and cognitive radio (CR) is considered as a promising solution to such the problem. The main idea behind a cognitive radio network is for the unlicensed users (also called secondary users) to exploit opportunistically the underutilized spectrum licensed to a licensed network (referred to as a primary network). Before investigating the technical and political implications of CR, it is important to know to what extent the licensed bands are temporally unoccupied. In this paper, for the first time, we investigate the spectrum usage pattern in Vietnam in the frequency bands ranging from 20 MHz to 3000 MHz in Ho Chi Minh City and Long An province. The purpose of the measurement is to find how the scarce radio spectrum allocated to different services is utilized in Vietnam and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. These analyses indicate that, on average, the actual spectral usage in all bands is 13.74% and 11.19% for Ho Chi Minh City and Long An province, respectively. The experiment results also show that the spectrum band assigned for analog television (470–806) is the highest occupancy band with 58%.


title: Accuracy in optical tracking with fiducial markers: an accuracy function for ARToolKit

abstract: Optical tracking with fiducial markers is commonly used in augmented reality (AR) systems - AR systems that rely on the ARToolKit (Kato and Billinghurst, 1999) are prominent examples. The information obtained by the tracking subsystem are widely used in AR, e.g. in order to calculate how virtual objects should be located and oriented. The results of extensive accuracy experiments with single markers are reported and made operational by the definition of an accuracy function. The results show a specific distribution of tracking accuracy dependent on distance as well as angle between camera and marker. This insight is applicable for designing the set-up of AR applications in general that rely on optical tracking.


title: Cache QoS: From concept to reality in the Intel® Xeon® processor E5-2600 v3 product family

abstract: Over the last decade, addressing quality of service (QoS) in multi-core server platforms has been growing research topic. QoS techniques have been proposed to address the shared resource contention between co-running applications or virtual machines in servers and thereby provide better isolation, performance determinism and potentially improve overall throughput. One of the most important shared resources is cache space. Most proposals for addressing shared cache contention are based on simulations and analysis and no commercial platforms were available that integrated such techniques and provided a practical solution. In this paper, we will present the first set of shared cache QoS techniques designed and implemented in state-of-the-art commercial servers (the Intel® Xeon® processor E5-2600 v3 product family). We will describe two key technologies: (i) Cache Monitoring Technology (CMT) to enable monitoring of shared cache usage by different applications and (ii) Cache Allocation Technology (CAT) which enables redistribution of shared cache space between applications to address contention. This is the first paper to describing these techniques as they moved from concept to reality, starting from early research to product implementation. We will also present case studies highlighting the value of these techniques using example scenarios of multi-programmed workloads, virtualized platforms in datacenters and communications platforms. Finally, we will describe initial software infrastructure and enabling for industry practitioners and researchers to take advantage of these technologies for their QoS needs.


title: A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction

abstract: The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT $^2$ I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT $^2$ I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT $^2$ I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.


Second Language Acquisition Modeling.


title: Option Pricing Under a Double Exponential Jump Diffusion Model

abstract: This paper aims to extend the analytical tractability of the Black--Scholes model to alternative models with arbitrary jump size distributions. More precisely, we propose a jump diffusion model for asset prices whose jump sizes have a mixed-exponential distribution, which is a weighted average of exponential distributions but with possibly negative weights. The new model extends existing models, such as hyperexponential and double-exponential jump diffusion models, because the mixed-exponential distribution can approximate any distribution as closely as possible, including the normal distribution and various heavy-tailed distributions. The mixed-exponential jump diffusion model can lead to analytical solutions for Laplace transforms of prices and sensitivity parameters for path-dependent options such as lookback and barrier options. The Laplace transforms can be inverted via the Euler inversion algorithm. Numerical experiments indicate that the formulae are easy to implement and accurate. The analytical solutions are made possible mainly because we solve a high-order integro-differential equation explicitly. A calibration example for SPY options shows that the model can provide a reasonable fit even for options with very short maturity, such as one day. ::: ::: This paper was accepted by Michael Fu, stochastic models and simulation.


title: Points of interest recommendation from GPS trajectories

abstract: Recently, points of interest POIs recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from GPS trajectories. We then take into account three different factors popularity, temporal and geographical features that can influence the recommendation score of a POI. We characterize the impacts caused by popularity, temporal and geographical information, by using different scoring functions based on three developed recommendation models. Finally, we combine the three scoring functions together and obtain a unified framework PTG-Recommend for recommending candidate POIs for a mobile user. To the best of our knowledge, this work is the first that considers popularity, temporal and geographical information together. Experimental results on two real-world data sets strongly demonstrate that our framework is robust and effective, and outperforms the baseline recommendation methods in terms of precision and recall.


title: A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

abstract: A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.


How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets


title: Neurocube: a programmable digital neuromorphic architecture with high-density 3D memory

abstract: This paper presents a programmable and scalable digital neuromorphic architecture based on 3D high-density memory integrated with logic tier for efficient neural computing. The proposed architecture consists of clusters of processing engines, connected by 2D mesh network as a processing tier, which is integrated in 3D with multiple tiers of DRAM. The PE clusters access multiple memory channels (vaults) in parallel. The operating principle, referred to as the memory centric computing, embeds specialized state-machines within the vault controllers of HMC to drive data into the PE clusters. The paper presents the basic architecture of the Neurocube and an analysis of the logic tier synthesized in 28nm and 15nm process technologies. The performance of the Neurocube is evaluated and illustrated through the mapping of a Convolutional Neural Network and estimating the subsequent power and performance for both training and inference.


title: Model-free based neural network control with time-delay estimation for lower extremity exoskeleton

abstract: Abstract A model-free based neural network control with time-delay estimation (TDE-MFNNC) for lower extremity exoskeleton is presented in this paper. The lower limb exoskeleton which has 5 DOFs for each leg is established in Solidworks as a virtual prototype which is used as a platform to build the control system in SimMechanics. In an attempt to get the effective tracking trajectory, a neural network and time-delay estimation technique is added to model-free based iPD controller. The proposed controller is simulated and tested on virtual prototype to comparing with PD controller, neural network and model-free based iPD controller. This comparative study validates TDE-MFNNC as more stable and effective than the traditional controllers. Further, the kinematic model of lower limb exoskeleton added to exoskeleton also proves the stability and effectiveness of the proposed controller.


title: Multi-Stage Programming with Explicit Annotations

abstract: We introduce MetaML, a statically-typed multi-stage programming language extending Nielson and Nielson's two stage notation to an arbitrary number of stages. MetaML extends previous work by introducing four distinct staging annotations which generalize those published previously [25, 12, 7, 6]We give a static semantics in which type checking is done once and for all before the first stage, and a dynamic semantics which introduces a new concept of cross-stage persistence, which requires that variables available in any stage are also available in all future stages.We illustrate that staging is a manual form of binding time analysis. We explain why, even in the presence of automatic binding time analysis, explicit annotations are useful, especially for programs with more than two stages.A thesis of this paper is that multi-stage languages are useful as programming languages in their own right, and should support features that make it possible for programmers to write staged computations without significantly changing their normal programming style. To illustrate this we provide a simple three stage example, and an extended two-stage example elaborating a number of practical issues.


title: VNLP: an open source framework for Vietnamese natural language processing

abstract: Natural Language Processing (NLP) for Vietnamese has been researched for more than a decade but still lacks of an open-source NLP pipeline. As the result, researchers have to spend a lot of time on various fundamental tasks before working on the task of interest. Besides, the circumstance holds back text processing technology in Vietnam because an application costs much more money and time to reach a deliverable state. This work is an attempt to solve this issue. By incorporating available open-source software packages and implementing new ones, we have created an open-source, production-ready solution for Vietnamese text processing. Via three experiments, we demonstrated its effectiveness and efficiency. The software has helped us to develop our solution for Vietnamese sentiment analysis and online reputation management and we hope that it will also facilitate research in Vietnamese NLP.


title: RINSE: the real-time immersive network simulation environment for network security exercises

abstract: The RINSE simulator is being developed to support large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of LANs. The simulator must be able to present a realistic rendering of network behavior as attacks are launched and players diagnose events and try counter measures to keep network services operating. We describe the architecture and function of RINSE and outline how techniques like multiresolution traffic modeling and new routing simulation methods are used to address the scalability challenges of this application. We also describe in more detail new work on CPU/memory models necessary for the exercise scenarios and a latency absorption technique that will help when extending the range of client tools usable by the players.


title: Study of aircraft taxi performance for enhancing airport surface traffic control. Manuscript accepted for IEEE-ITS publication

abstract: In view of the ever-increasing air traffic, much attention in air traffic management research has been given to improving arrival and departure efficiency. As air traffic begins and ends at the airport, the issues of taxi delays and ground incursions are becoming more evident. The paper considers the surface-traffic problem at major airports and envisions a collaborative traffic and aircraft control environment where a surface traffic automation system will help coordinate surface traffic movements. Specifically, the paper studies the performance potential of high-precision taxi toward the realization of such an environment. A state-of-the-art nonlinear control system based on feedback linearization is designed for a detailed B-737 aircraft taxi model. The simulation model with the nonlinear control system is evaluated extensively in a scenario representing the demanding situation of an arrival aircraft crossing an adjacent active runway immediately following its own landing. The evaluation establishes the potential of an automated system to achieve high-precision taxi control, including the ability to comply with taxi clearances with tight time margins. Such a high-precision taxi capability reduces the time margin required for clearing taxiing aircraft to cross active runways, thus increasing the opportunity for issuing such clearances, which in turn reduces the need for aircraft to hold short at the runways to wait for the opportunity for crossing. The results from the analyses provide insight into future aircraft operational capabilities toward the design of the envisioned surface traffic automation system. Moreover, the nonlinear control design serves as a preliminary study for future auto-taxi functional development.


title: Exploring gender based influencers using Social Network Analysis

abstract: The social network is dominating the society with interactive collaboration and social interactions. It is very common now to share, post, like and comment on various topic of interest. These interactions has given space for research to explore the social relationships among online users. Predicting the influencers in social network can help us to control the flow of information in them. The objective of this research work is to analyze the likes in Facebook users cover photos to identify the influencers and their gender. Using clustering coefficient, degree analysis and triadic census we were able to identify the influencers and found that men are strong influencers in the network.


title: EPnP: An Accurate O(n) Solution to the PnP Problem

abstract: We propose a non-iterative solution to the PnP problem--the estimation of the pose of a calibrated camera from n 3D-to-2D point correspondences--whose computational complexity grows linearly with n. This is in contrast to state-of-the-art methods that are O(n 5) or even O(n 8), without being more accurate. Our method is applicable for all n?4 and handles properly both planar and non-planar configurations. Our central idea is to express the n 3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera referential, which can be done in O(n) time by expressing these coordinates as weighted sum of the eigenvectors of a 12×12 matrix and solving a small constant number of quadratic equations to pick the right weights. Furthermore, if maximal precision is required, the output of the closed-form solution can be used to initialize a Gauss-Newton scheme, which improves accuracy with negligible amount of additional time. The advantages of our method are demonstrated by thorough testing on both synthetic and real-data.


title: STAP with staggered PRF

abstract: Space-time adaptive processing (STAP) has proven to be an optimum tool for the design of air- and spaceborne MTI radar. STAP has the unique property of compensating for the platform motion induced Doppler spread, thus making detection of slow targets possible. In this article the effect of a staggered PRF on the STAP performance is discussed. The numerical evaluation is based on two different stagger patterns: pseudo-random and quadratic. It is shown that quadratic staggering is equivalent to a constant accelation of the radar platform. In case of perturbations of the platform motion the precise knowledge of the perturbation is required to avoid mismatch between the target echo sequence and the Doppler filter.


title: Discovering user attribute stylistic differences via paraphrasing

abstract: User attribute prediction from social media text has proven successful and useful for downstream tasks. In previous studies, differences in user trait language use have been limited primarily to the presence or absence of words that indicate topical preferences. In this study, we aim to find linguistic style distinctions across three different user attributes: gender, age and occupational class. By combining paraphrases with a simple yet effective method, we capture a wide set of stylistic differences that are exempt from topic bias. We show their predictive power in user profiling, conformity with human perception and psycholinguistic hypotheses, and potential use in generating natural language tailored to specific user traits.


title: Nobody's A Critic: On The Evaluation Of Creative Code Generators - A Case Study In Video Game Design

abstract: Application domains for Computational Creativity projects range from musical composition to recipe design, but despite all of these systems having computational methods in common, we are aware of no projects to date that focus on program code as the created artefact. We present the Mechanic Miner tool for inventing new concepts for videogame interaction which works by inspecting, modifying and executing code. We describe the system in detail and report on an evaluation based on a large survey of people playing games using content it produced. We use this to raise issues regarding the assessment of code as a created artefact and to discuss future directions for Computational Creativity research.


title: An OAuth based authentication mechanism for IoT networks

abstract: Internet of things (IoT) has rapidly become one of the most familiar and perhaps most discussed topic on the research field. The attention for the Internet of Things is mainly due to the new connected products intended to bring greater efficiencies and simplicity to life. Variety of IoT applications lead to equally wide variety of security issues. In this paper, we propose an approach to provide secure authentication mechanism for an IoT network which consists of various kinds of constrained devices using a security manager. This proposed approach protects IoT network from unauthenticated users with security manager using OAuth 2.0 protocol. Moreover, this approach provides flexibility in managing IoT networks. The security manager provides authentication service for multiple IoT networks, which can also help to reduce the cost overhead to maintain secure database in IoT networks.


title: A Static Analyzer for Large Safety-Critical Software

abstract: We show that abstract interpretation-based static program analysis can be made efficient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement of a general purpose static analyzer and later adaptation to particular programs of the family by the end-user through parametrization. This is applied to the proof of soundness of data manipulation operations at the machine level for periodic synchronous safety critical embedded software.The main novelties are the design principle of static analyzers by refinement and adaptation through parametrization (Sect. 3 and 7), the symbolic manipulation of expressions to improve the precision of abstract transfer functions (Sect. 6.3), the octagon (Sect. 6.2.2), ellipsoid (Sect. 6.2.3), and decision tree (Sect. 6.2.4) abstract domains, all with sound handling of rounding errors in oating point computations, widening strategies (with thresholds: Sect. 7.1.2, delayed: Sect. 7.1.3) and the automatic determination of the parameters (parametrized packing: Sect. 7.2).


title: Multi-Domain Learning: When Do Domains Matter?

abstract: We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multidomain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multi-domain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multidomain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.


title: Asking and Answering Questions during a Programming Change Task

abstract: Little is known about the specific kinds of questions programmers ask when evolving a code base and how well existing tools support those questions. To better support the activity of programming, answers are needed to three broad research questions: 1) What does a programmer need to know about a code base when evolving a software system? 2) How does a programmer go about finding that information? 3) How well do existing tools support programmers in answering those questions? We undertook two qualitative studies of programmers performing change tasks to provide answers to these questions. In this paper, we report on an analysis of the data from these two user studies. This paper makes three key contributions. The first contribution is a catalog of 44 types of questions programmers ask during software evolution tasks. The second contribution is a description of the observed behavior around answering those questions. The third contribution is a description of how existing deployed and proposed tools do, and do not, support answering programmers' questions.


title: Gain Enhancement of H-Plane Sectoral Post-Wall Horn Antenna by Connecting Tapered Slots for Millimeter-Wave Communication

abstract: We propose a millimeter-wave antenna to serve as an element antenna for a multi-sector switched-beam antenna, which can be fed by a post-wall waveguide. Several tapered slot antennas are connected to an aperture of an H-plane sectoral post-wall horn antenna whose radiation pattern is narrowed in the H-plane to further narrow the pattern in the E-plane. Moreover, the aperture of each tapered slot antenna is loaded with a dielectric to increase the gain. We designed an antenna whose gain is over 16 dBi in the frequency band of 57-66 GHz. The wideband property is experimentally examined.


title: Model-driven development and evolution of customized user interfaces

abstract: One of the main benefits of model-driven development of User Interfaces (UIs) is the increase in efficiency and consistency when developing multiple variants of a UI. For instance, multiple UIs for different target users, platforms, devices, or for whole product families can be generated from the same abstract models. However, purely generated UIs are not always sufficient as there is often need for customizing the individual UI variants, e.g., due to usability issues or specific customer requirements. In this paper we present a model-driven approach for the development of UI families with systematic support for customizations. The approach supports customizing all aspects of a UI (UI elements, screens, navigation, etc.) and storing the customizations in specific models. As a result, a UI family can be evolved more efficiently because individual UI variants can be re-generated (after some changes have been applied to the family) without losing any previously made customizations. We demonstrate this by thirty highly customized real-world products from a commercial family of web information systems called HIS-GX/QIS.


Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision Agriculture


title: From process logic to business logic - A cognitive approach to business process management

abstract: The unpredictability of business activities means that business process management should provide a way to adapt to change. The traditional workflow approach, based on predefined process logic, offers little support for today's complex and dynamic business environment. Therefore, a cognitive approach is proposed to help manage complex business activities, based on continuous awareness of situations and real-time decisions on activities. In this approach, the business environment is seen as capturing events that occurred and the state of tasks and resources; business logic involving process routing, operational constraints, exception handling and business strategy is used to determine which actions are appropriate for the current situation. By extending process management from process logic to business logic, the methodology offers flexibility, agility and adaptability in complex business process management.


title: Full-field heterodyne interferometry using a complementary metal-oxide semiconductor digital signal processor camera for high-resolution profilometry

abstract: We describe a heterodyne interferometry system based on a complementary metal-oxide semiconductor digital signal processor CMOS-DSP camera that is utilized for full-field optical phase measure- ment using a carrier-based phase retrieval algorithm, with no need for electro-mechanical scanning. Camera characterization test results sup- port the adoption of a single-pixel approach to perform quasi- instantaneous differential phase measurements, which are immune to mechanical vibrations and thermal drifts. We developed an optical con- figuration based on a Mach-Zehnder heterodyne interferometer to per- form a static test on a mirror surface. The profiles of the mirror surface set at two angular positions, the relative displacements in the range of nanometers, and the corresponding tilt angle were determined. © 2007


title: VisOHC: Designing Visual Analytics for Online Health Communities

abstract: Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.


title: Fronto-parietal network: flexible hub of cognitive control

abstract: A recent study shows that the fronto-parietal network (FPN), and subregions therein, alters its functional connectivity with nodes of other networks based on task goals. Moreover, FPN patterns of connectivity not only reflect engagement of specific tasks, but also serve as a code that can be transferred to facilitate learning novel tasks.


title: Using Visual Aids as a Motivational Tool in Enhancing Students Interest in Reading Literary Texts

abstract: This study aims to investigate the teachers perceptions on the use of visual aids (e.g., animation videos, pictures, films and projectors) as a motivational tool in enhancing students interest in reading literary texts. To achieve the aim of the study, the mixed-method approach was used to collect the required data. Therefore, 52 English teachers from seven national secondary schools in Kapit, Sarawak, Malaysia were selected. Five of the respondents were also randomly selected for the interview. The analysis of the data indicated that the majority of the teachers had positive perceptions of the use of visual aids. The use of visual aids enable the teachers to engage their students closely with the literary texts despite of being able to facilitate students of different English proficiency level in reading the texts with interest. This aspect is vital as literature helps to generate students creative and critical thinking skills. Although the teachers had positive attitudes towards the use of visual aids, the study suggests that it will be more interesting and precise if it includes students perceptions as well.


title: New insights into UTSA-16

abstract: Among the metal organic framework materials proposed for CO2 separation, UTSA-16 possesses the highest CO2 volumetric density explained on the basis of favourable interactions between CO2 and structural water molecules in the material, as revealed by neutron diffraction. In this study, UTSA-16 was synthesised and extensively characterised by XRD, TEM combined with EDX analysis and DR-UV-Vis, Raman and FTIR spectroscopies, as well as by TGA measurements. The synthesised material shows XRD patterns, surface area, CO2 capacity and isosteric heat coincident to the ones reported for UTSA-16 in the original papers but a higher thermal stability and a complete removal of water upon activation under mild conditions (363 K). On the basis of EDX and IR measurements, the formula of UTSA-16 used in the present study is proposed to be K2Co3(cit)2. Infrared spectroscopy clearly shows that UTSA-16 described in this work reversibly interacts with water vapor, CO and CO2. The interaction is attributed to K(+) species, which are present as counterions in the pores. At 1 bar and 298 K a fraction of K(+) sites adsorbs 2 CO2 molecules.


title: Practical evaluation and management of atrophic acne scars: tips for the general dermatologist.

abstract: Atrophic acne scarring is an unfortunate, permanent complication of acne vulgaris, which may be associated with significant psychological distress. General dermatologists are frequently presented with the challenge of evaluating and providing treatment recommendations to patients with acne scars. This article reviews a practical, step-by-step approach to evaluating the patient with atrophic acne scars. An algorithm for providing treatment options is presented, along with pitfalls to avoid. A few select procedures that may be incorporated into a general dermatology practice are reviewed in greater detail, including filler injections, skin needling, and the punch excision.