title: There’s No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams

abstract: We present the first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent. By amalgamating reports gathered by voluntary vigilantes and tracked in online forums, we identify 192 scams and categorize them into four groups: Ponzi schemes, mining scams, scam wallets and fraudulent exchanges. In 21 % of the cases, we also found the associated Bitcoin addresses, which enables us to track payments into and out of the scams. We find that at least $11 million has been contributed to the scams from 13 000 distinct victims. Furthermore, we present evidence that the most successful scams depend on large contributions from a very small number of victims. Finally, we discuss ways in which the scams could be countered.


title: Long-term effect of high-intensity laser therapy in the treatment of patients with chronic low back pain: a randomized blinded placebo-controlled trial

abstract: The aim of this study was to compare the effect of high-intensity laser therapy (HILT), alone or combined with exercise, in the treatment of chronic low back pain (CLBP). A total of 72 male patients participated in this study, with a mean (SD) age of 32.81 (4.48) years. Patients were randomly assigned into three groups and treated with HILT plus exercise (HILT + EX), placebo laser plus exercise (PL + EX), and HILT alone in groups 1, 2, and 3, respectively. The outcomes measured were lumbar range of motion (ROM), pain level by visual analog scale (VAS), and functional disability by both the Roland Disability Questionnaire (RDQ) and the Modified Oswestry Disability Questionnaire (MODQ). Statistical analyses were performed to compare the differences between baseline and post-treatment measurements. The level of statistical significance was set as P < 0.05. ROM significantly increased after 4 weeks of treatment in all groups, then significantly decreased after 12 weeks of follow-up, but was still significantly more than the baseline value in groups 1 and 2. VAS, RDQ, and MODQ results showed significant decrease post-treatment in all groups, although the RDQ and MODQ results were not significantly different between groups 2 and 3. HILT combined with exercise appears to be more effective in patients with CLBP than either HLLT alone or placebo laser with exercise.


title: Introspective classification for robot perception

abstract: In robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision and recall have been widely adopted as canonical metrics to quantify the performance of learning algorithms, but for robotics applications involving mission-critical decision making, good performance in relation to these metrics is insufficient. We introduce and motivate the importance of a classifier's introspective capacity: the ability to associate an appropriate assessment of confidence with any test case. We propose that a key ingredient for introspection is a framework's potential to increase its uncertainty with the distance between a test datum its training data. We compare the introspective capacities of a number of commonly used classification frameworks in both classification and detection tasks, and show that better introspection leads to improved decision making in the context of tasks such as autonomous driving or semantic map generation.


title: Semantic Monte-Carlo localization in changing environments using RGB-D cameras

abstract: The localization with respect to a prior map is a fundamental requirement for mobile robots. The commonly used adaptive monte carlo localization (AMCL) can be found on most of the mobile robots ranging from small cleaning robots to large AGVs. While achieving accurate pose estimates in static environments, this algorithm tends to fail in the presence of significant changes. Recently published extensions and alternatives to AMCL observe the environment over longer times while building complex spatio-temporal models. Our approach, in contrast, utilizes object recognition and prior semantic maps to enable robust localization. It exploits the fact that putative changes in the environment can be predicted based on prior semantic knowledge. Our system is experimentally evaluated in a warehouse environment being subject to frequent changes. This emphasizes the importance of our algorithm for challenging industrial applications.


title: SoK: Introspections on Trust and the Semantic Gap

abstract: An essential goal of Virtual Machine Introspection (VMI) is assuring security policy enforcement and overall functionality in the presence of an untrustworthy OS. A fundamental obstacle to this goal is the difficulty in accurately extracting semantic meaning from the hypervisor's hardware level view of a guest OS, called the semantic gap. Over the twelve years since the semantic gap was identified, immense progress has been made in developing powerful VMI tools. Unfortunately, much of this progress has been made at the cost of reintroducing trust into the guest OS, often in direct contradiction to the underlying threat model motivating the introspection. Although this choice is reasonable in some contexts and has facilitated progress, the ultimate goal of reducing the trusted computing base of software systems is best served by a fresh look at the VMI design space. This paper organizes previous work based on the essential design considerations when building a VMI system, and then explains how these design choices dictate the trust model and security properties of the overall system. The paper then observes portions of the VMI design space which have been under-explored, as well as potential adaptations of existing techniques to bridge the semantic gap without trusting the guest OS. Overall, this paper aims to create an essential checkpoint in the broader quest for meaningful trust in virtualized environments through VM introspection.


title: Topic Modeling based on Keywords and Context

abstract: Current topic models often suffer from discovering topics not matching human intuition, unnatural switching of topics within documents and high computational demands. We address these concerns by proposing a topic model and an inference algorithm based on automatically identifying characteristic keywords for topics. Keywords influence topic-assignments of nearby words. Our algorithm learns (key)word-topic scores and it self-regulates the number of topics. Inference is simple and easily parallelizable. Qualitative analysis yields comparable results to state-of-the-art models (eg. LDA), but with different strengths and weaknesses. Quantitative analysis using 9 datasets shows gains in terms of classification accuracy, PMI score, computational performance and consistency of topic assignments within documents, while most often using less topics.


title: IT PROJECT GOVERNANCE IMPLEMENTATION AS INSTITUTIONALIZATION PROCESS: EVIDENCE FROM THE FINANCIAL SERVICES INDUSTRY

abstract: The implementation of IT project governance (ITPG) is a response to the increased regulatory pressure and the high failure rate of IT projects. Although ITPG is common, there is little research on this phenomenon, and very little is known about the forces that drive ITPG or the process by which such governance is implemented in organizations. This paper presents a process theory of ITPG implementation developed from case studies of five large companies in the financial services industry, using the grounded theory method. The model proposes three phases of ITPG implementation: standardization, centralization, and professionalization, and key drivers of each phase. The paper also shows how the proposed process theory is consistent with and can be integrated with institutional theory to demonstrate ITPG as an institutional process.


title: The Multilevel Grid File-A Dynamic Hierarchical Multidimensional File Structure

abstract: We present a new dynamic hashed file organization that solves most problems associated with the directory of the grid file proposed by Nievergelt et al. Our scheme is a multilevel extension of the grid file that supports multiattribute accesses to the file for exact-match, partialmatch, and range queries as well as graceful expansion and shrinkage of the file in a dynamic environment. This new file organization offers a number of advantages over the grid file such as compact representation of the directory, linear growth of the directory in the number of records, easy splitting and merging of the directory, efficient processing of partial-match queries, and finer granularity of the directory. Moreover, it provides a new concept, calledabshuct d , that can be employed in practical database applications.


title: AAA: a modeling language for software production environments

abstract: If a software production environment wants to reap the benefits of reuse on an organizational level, it must itself be designed to optimize each resouce's access to reusable material and to efficiently implement a reuse-centered software development process. One approach to such redesign, the Experience Factory, divides the environment between the resources involved in reuse activities and the resources involved in current projects. There are many variations on the Experience Factory theme, based on the widely varying characteristics and needs of different environments. The Reference Architecture is used as a model for describing these variations in more detail. Besides explaining the Experience Factory and Reference Architecture models, this paper focuses on AAA, a process modeling language enhancement for modeling the configuration of resources, or the organization, of a software production environment. AAA is based on, and is meant to formalize, the Reference Architecture. The need for an organizational modeling methodology arises from research in incremental organizational design, organizational simulation, and experience packaging. As well as describing the syntax, semantics, and motivation of AAA, this paper also describes, at a high level, the AAA execution environment, which allows the modeler to analyze and modify a AAA model, to simulate projects executed by a modeled architecture, and to control processes currently executing in a modeled architecture. The last section of this paper presents a case study that illustrates some of the issues that arise in modeling organizations and demonstrates the modeling power of AAA.


title: Automatic verification of control system implementations

abstract: Software implementations of controllers for physical subsystems form the core of many modern safety-critical systems such as aircraft flight control and automotive engine control. A fundamental property of such implementations is stability, the guarantee that the physical plant converges to a desired behavior under the actions of the controller. We present a methodology and a tool to perform automated static analysis of embedded controller code for stability of the controlled physical system. The design of controllers for physical systems provides not only the controllers but also mathematical proofs of their stability under idealized mathematical models. Unfortunately, since these models do not capture most of the implementation details, it is not always clear if the stability properties are retained by the software implementation, either because of software bugs, or because of imprecisions arising from fixed-precision arithmetic or timing. Our methodology is based on the following separation of concerns. First, we analyze the controller mathematical models to derive bounds on the implementation errors that can be tolerated while still guaranteeing stability. Second, we automatically analyze the controller software to check if the maximal implementation error is within the tolerance bound computed in the first step. We have implemented this methodology in Costan, a tool to check stability for controller implementations. Using Costan, we analyzed a set of control examples whose mathematical models are given in Matlab/Simulink and whose C implementation is generated using Real-Time Workshop. Unlike previous static analysis research, which has focused on proving low-level runtime properties such as absence of buffer overruns or arithmetic overflows, our technique combines analysis of the mathematical controller models and automated analysis of source code to guarantee application-level stability properties.


title: Deconstructing and restyling D3 visualizations

abstract: The D3 JavaScript library has become a ubiquitous tool for developing visualizations on the Web. Yet, once a D3 visualization is published online its visual style is difficult to change. We present a pair of tools for deconstructing and restyling existing D3 visualizations. Our deconstruction tool analyzes a D3 visualization to extract the data, the marks and the mappings between them. Our restyling tool lets users modify the visual attributes of the marks as well as the mappings from the data to these attributes. Together our tools allow users to easily modify D3 visualizations without examining the underlying code and we show how they can be used to deconstruct and restyle a variety of D3 visualizations.


title: Star-QAM Signaling Constellations for Spatial Modulation

abstract: The performance of spatial modulation (SM)-assisted multiple-input-multiple-output (MIMO) communication systems is highly dependent on the specific amplitude/phase modulation (APM) signal constellation adopted. In this paper, we conceive new star-quadrature amplitude modulation (star-QAM)-aided SM schemes. Our goal is to minimize the system's average bit error probability (ABEP). More specifically, a new class of star-QAM constellations is introduced for SM, which is capable of flexibly adapting ring ratios of the amplitude levels. Then, under a specific MIMO configuration and a predetermined transmission rate, a simple and efficient ring-ratio optimization algorithm is proposed to minimize the ABEP. Moreover, to improve further the performance of our star-QAM-aided SM scheme, a diagonal precoding technique is proposed, and a low-complexity minimum-distance-based approach is conceived for extracting the precoding parameters. Our numerical results show that the proposed star-QAM-aided SM arrangement provides beneficial system performance improvements compared with the identical-throughput maximum-minimum distance (MMD) QAM and phase-shift keying (PSK) benchmarkers. Moreover, our precoding scheme is capable of further improving the attainable system performance at a modest feedback requirement.


title: Investigations of Voltage/Reactive Power Sensitivity for Voltage Control and Voltage Stability in Unbalanced Distribution Systems

abstract: Because of increased DG penetration level, maintaining a good voltage profile in distribution systems is challenging. In addition, because of slow expansion of distribution systems and fast growth of load, voltage stability becomes an important issue. Voltage and reactive power sensitivity, dV/dQ, is one of key important factors in maintaining a good voltage profile and analyzing voltage stability problem. In this work, we investigate some fundamental properties and identify several patterns of dV/dQ based on the IEEE 13-node test feeder. The results have important implications on the application of voltage control and on the validity of modal analysis for voltage stability analysis for three-phase unbalanced distribution systems.


title: Stock Price Prediction With Long Short-Term Memory Recurrent Neural Network

abstract: In this paper, we investigate the prediction of daily stock prices of the top five companies in the Thai SET50 index. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is applied to forecast the next daily stock price (High, Low, Open, Close). Deep Belief Network (DBN) is applied to compare the result with LSTM. The test data are CPALL, SCB, SCC, KBANK, and PTT from the SET50 index. The purpose of selecting these five stocks is to compare how the model performs in different stocks with various volatility. There are two experiments of five stocks from the SET50 index. The first experiment compared the MAPE with different length of training data. The experiment is conducted by using training data for one, three, and five-year. PTT and SCC stock give the lowest median value of MAPE error for five-year training data. KBANK, SCB, and CPALL stock give the lowest median value of MAPE error for one-year training data. In the second experiment, the number of looks back and input are varied. The result with one look back and four inputs gives the best performance for stock price prediction. By comparing different technique, the result show that LSTM give the best performance with CPALL, SCB, and KTB with less than 2% error. DBN give the best performance with PTT and SCC with less than 2% error.


title: Monolithic Microfabricated Valves and Pumps by Multilayer Soft Lithography

abstract: Soft lithography is an alternative to silicon-based micromachining that uses replica molding of nontraditional elastomeric materials to fabricate stamps and microfluidic channels. We describe here an extension to the soft lithography paradigm, multilayer soft lithography, with which devices consisting of multiple layers may be fabricated from soft materials. We used this technique to build active microfluidic systems containing on-off valves, switching valves, and pumps entirely out of elastomer. The softness of these materials allows the device areas to be reduced by more than two orders of magnitude compared with silicon-based devices. The other advantages of soft lithography, such as rapid prototyping, ease of fabrication, and biocompatibility, are retained.


title: Non-Invasive Continuous-Time Blood Pressure Estimation from a Single Channel PPG Signal using Regularized ARX Models

abstract: Continuous blood pressure (BP) monitoring can help in preventing hypertension and other cardiovascular diseases. In principle, an indirect non-invasive continuous-time measurement of BP is possible by exploiting the photoplethysmography (PPG) signal, which can be obtained through wearable optical sensor devices. However, a model of the PPG-to-BP dynamical system is needed. In this study, we investigate if autoregressive with exogenous input (ARX) models with kernel-based regularization are suited for the scope. We analyzed 10 PPG time-series acquired on different individuals by a wearable optical sensor and correspondent BP reference values to evaluate feasibility of continuous BP estimation from a single PPG source. This first proof-of-concept study shows promising results in continuous BP estimation during resting states.


title: Percutaneous microwave ablation combined with synchronous transcatheter arterial chemoembolization for the treatment of colorectal liver metastases: results from a follow-up cohort

abstract: The purpose of this study was to retrospectively evaluate the therapeutic efficacy and safety of ultrasound-guided percutaneous microwave ablation (MWA) combined with synchronous transcatheter arterial chemoembolization (TACE) in patients with colorectal liver metastases (CRLM).A retrospective analysis was performed in 30 patients who were treated with ultrasound-guided percutaneous MWA combined with synchronous TACE for colorectal cancer liver metastases from November 2011 to December 2014 in Zhongshan Hospital, Fudan University. The response of the tumor to treatment was evaluated by follow-up computed tomography and/or magnetic resonance imaging. Local tumor control, procedure-related complications, and long-term survival data were analyzed.A total of 30 patients with 43 tumors ranging in size from 1.4 cm to 10.0 cm were analyzed. The patients' mean age was 61.6±10.3 years (range, 44.0-78.0 years). The median follow-up time was 26.5±10.4 months (range, 13.3-50.6 months). The complete ablation rate was 81.4% (35/43 lesions) for CRLM. Complete response was achieved in eight cases (26.7%), and partial response was achieved in 17 cases (56.7%) 1 month after the procedure. The objective response rate (complete response + partial response) was 83.4%. Progression-free survival and overall survival were 5.0 months and 11.0 months, respectively. The 12-month and 24-month survival rates were 46.7% and 25.4%, respectively. A total of 22 patients succumbed during follow-up due to tumor progression. No major complications or perioperative mortalities were recorded.Ultrasound-guided percutaneous MWA combined with synchronous TACE therapy is a safe and effective modality for patients with CRLM.


title: Triangulation positioning system based on a static IR beacon-receiver system

abstract: The primary goal of each of the navigation systems is to determine the object's position in a given reference frame. It can be achieved in many ways such as GPS, celestial navigation, inertial navigation etc. In local positioning systems a very important role is played by triangulation methods. This paper presents a new beacon-based triangulation system, in which both the transmitter and receiver do not contain any mechanical moving parts. Lack of mechanical moving parts makes the system less complex and more robust. The work contains a description of all the necessary steps to determine the object's position in a given reference frame.


title: Architecture of Underwater Acoustic Sensor Networks: A Survey

abstract: Recently, there has been a growing interest in monitoring aqueous environments using underwater acoustic sensor network (UASNs), which is considered as a novel tool in exploring ocean also in small aqueous environments such as lakes, rivers. Network topology is a crucial factor in determining energy consumption, capacity and reliability of a network, it's necessary for us to know the feasible architectures and their characteristics of UASNs. In this paper, we do a survey on the architecture of UASNs, and classify the architecture into three groups using the node characters as a rule. We also analyze the open questions should to be faced in further research works. The architecture of USANs would be important for application network design, topology control, communication protocols and related study.


title: Social Network Privacy for Attribute Disclosure Attacks

abstract: Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected. To address this gap, we introduce an approach to anonymize social networks that have labeled nodes, $\alpha$-proximity, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve $\alpha$-proximity and experimentally validate the quality of the solutions it derives.


Genetic Algorithms, Tournament Selection, and the Effects of Noise.


title: Emergency diagnostic paracentesis to determine intraperitoneal fluid identity discovered on bedside ultrasound of unstable patients.

abstract: Patients presenting with hypotension may be evaluated with a FAST (Focused Abdominal Sonography for Trauma) examination as recent literature has suggested its utility in the unstable patient. Those who are found to have intraperitoneal fluid on the FAST examination may have solid organ injury from unknown trauma, ruptured abdominal aortic aneurysm (AAA), hemorrhaging ovarian cyst, ruptured ectopic pregnancy, or other disease process responsible for intra-periteal blood. However, because ultrasound does not assist in fluid identification, it is possible that the fluid present is not blood, but ascites. Further, a decision may need to be made regarding emergent surgical intervention or blood transfusion vs. aggressive fluid resuscitation or pressors followed by other testing such as computed tomography. This case series illustrates the utility of an ultrasound guided, emergent diagnostic paracentesis in the management of unstable patients found to have a positive FAST examination. Six unstable patients were evaluated with the FAST examination and found to have large quantities of intraperitoneal fluid. In each case the fluid was sampled and proposed management changed due to fluid identification.


title: Background music: Effects on attention performance

abstract: Objective: Previous studies indicate that noise may affect worker attention. However, some background music in the work environment can increase worker satisfaction and productivity. This study compared how music with, and without, lyrics affects human attention. Participants: One hundred and two participants, aged 20-24 years, were recruited into this study. Fifty-six males and 46 females participated in this study. Methods: Background music with, and without lyrics, was tested for effects on listener concentration in attention testing using a randomized controlled trial (RCT) study. Results: The comparison results revealed that background music with lyrics had significant negative effects on concentration and attention. Conclusions :T hefindings suggest that, if background music is played in the work environment, music without lyrics is preferable because songs with lyrics are likely to reduce worker attention and performance.


title: Resting and total energy expenditure in patients with ischemic heart disease.

abstract: Total energy expenditure (TEE) was measured by doubly labeled water in 13 preoperative patients undergoing elective coronary artery surgery and compared to resting energy expenditure (REE) measured by indirect calorimetry (IC) calculated from the Harris-Benedict (HB) formula or from formulas based on midarm circumference and arm muscle circumference. Mean REE measured by IC and calculated from the HB, midarm circumference, arm muscle circumference formulas were 62, 75, 62, and 69%, respectively, of TEE measured by doubly labeled water. REE measured by IC correlated significantly with that predicted by the HB (p = 0.006) but not the anthropometric formulas. The relationship between REE derived from anthropometric predictive formulas and REE measured by IC is altered in ischemic heart disease.


title: Bags of Binary Words for Fast Place Recognition in Image Sequences

abstract: We propose a novel method for visual place recognition using bag of words obtained from accelerated segment test (FAST)+BRIEF features. For the first time, we build a vocabulary tree that discretizes a binary descriptor space and use the tree to speed up correspondences for geometrical verification. We present competitive results with no false positives in very different datasets, using exactly the same vocabulary and settings. The whole technique, including feature extraction, requires 22 ms/frame in a sequence with 26 300 images that is one order of magnitude faster than previous approaches.


title: Sense-aware semantic analysis: a multi-prototype word representation model using Wikipedia

abstract: Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The "sense-specific" prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.


1. INTRODUCTION WORD ORDER, WORD ORDER FLEXIBILITY AND THE LEXICON


title: From Argumentation Mining to Stance Classification

abstract: Argumentation mining and stance classification were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other machine learning approaches for argument tagging which often require large set of labeled data, the proposed model is minimally supervised and merely a one-to-one mapping between the pre-defined argument set and the extracted topics is required. These extracted arguments are subsequently exploited for stance classification. Additionally, a manuallyannotated corpus for stance classification and argument tagging of online news comments is introduced and made available. Experiments on our collected corpus demonstrate the benefits of using topic-modeling for argument tagging. We show that using Non-Negative Matrix Factorization instead of Latent Dirichlet Allocation achieves better results for argument classification, close to the results of a supervised classifier. Furthermore, the statistical model that leverages automatically-extracted arguments as features for stance classification shows promising results.


title: What storytelling can do for information visualization

abstract: A well-told story conveys great quantities of information in relatively few words in a format that is easily assimilated by the listener or viewer. People usually find it easier to understand information integrated into stories than information spelled out in serial lists (such as bulleted items in an overhead slide). Stories are also just more compelling. For example, despite its sketchiness, the story fragment in Figure 1 is loaded with information, following an analysis similar to that of John Thomas of IBM Research [5]. We find that Jim uses technology (a pager and the Internet) and is dedicated to his job. Many other pieces of information can be deduced about Jim and his work, as well as about his relationships with his coworkers, as noted in the right side of the figure. The story does not express all this information explicitly; some is only implied; for example, we can surmise that Jim is probably not at the gym and his attendance at the meeting is important to his boss and coworkers, as well as to his company’s business performance. As in most stories, this one involves uncerFor as long as people have been around, they have used stories to convey information, cultural values, and experiences. Since the invention of writing and the printing press until today, technology and culture have constantly provided new and increasingly sophisticated means to tell stories. More recently, technology, entertainment, and art have converged in the computer. The ancient art of storytelling and its adaptation in film and video can now be used to efficiently convey information in our increasingly computerized world. What Storytelling Can Do for Information Visualization


title: Feature selection for improved phishing detection

abstract: Phishing --- a hotbed of multibillion dollar underground economy --- has become an important cybersecurity problem. The centralized blacklist approach used by most web browsers usually fails to detect zero-day attacks, leaving the ordinary users vulnerable to new phishing schemes; therefore, learning machine based approaches have been implemented for phishing detection. Many existing techniques in phishing website detection seem to include as many features as can be conceived, while identifying a relevant and representative subset of features to construct an accurate classifier remains an interesting issue in this particular application of machine learning. This paper evaluates correlation-based and wrapper feature selection techniques using real-world phishing data sets with 177 initial features. Experiments results show that applying an effective feature selection procedure generally results in statistically significant improvements in the classification accuracies of --- among others --- Naive Bayes, Logistic Regression and Random Forests, in addition to improved efficiency in training time.


title: Where you want to go today (like it or not): leisure practices in Cyberspace.

abstract: Investigates informal leisure uses of technology, including developing home pages, building special interest sites, surfing the Net and participating in electronic communities.


title: Functional quantum biology in photosynthesis and magnetoreception

abstract: Is there a functional role for quantum mechanics or coherent quantum effects in biological processes? While this question is as old as quantum theory, only recently have measurements on biological systems on ultra-fast time-scales shed light on a possible answer. In this review we give an overview of the two main candidates for biological systems which may harness such functional quantum effects: photosynthesis and magnetoreception. We discuss some of the latest evidence both for and against room temperature quantum coherence, and consider whether there is truly a functional role for coherence in these biological mechanisms. Finally, we give a brief overview of some more speculative examples of functional quantum biology including the sense of smell, long-range quantum tunneling in proteins, biological photoreceptors, and the flow of ions across a cell membrane.


title: A Neural Networks filtering mechanism for foreign exchange trading signals

abstract: Neural Networks have been successfully used in several financial applications. In the stock market and foreign exchange domains, Neural Networks have been used with considerable success to predict the future prices of stocks and currency pairs, their rate of return, risk analysis, and several other features that might be of benefit. In this paper, we present a methodology to filter the high-frequency signals of a rule-based foreign exchange trading strategy, through a neural network-based, intelligent selection mechanism. We then compare the results vs. a random selection mechanism and again vs. the overall signal pool, in terms of profit and correctness. We can clearly show that the neural network filtering approach yields a better performance than its random baseline.


title: Learning from Multi-View Structural Data via Structural Factorization Machines

abstract: Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.


title: Vervets revisited: A quantitative analysis of alarm call structure and context specificity

abstract: The alarm calls of vervet monkeys (Chlorocebus pygerythrus) constitute the classic textbook example of semantic communication in nonhuman animals, as vervet monkeys give acoustically distinct calls to different predators and these calls elicit appropriate responses in conspecifics. They also give similar sounding calls in aggressive contexts, however. Despite the central role the vervet alarm calls have played for understanding the evolution of communication, a comprehensive, quantitative analysis of the acoustic structure of these calls was lacking. We used 2-step cluster analysis to identify objective call types and discriminant function analysis to assess context specificity. Alarm calls given in response to leopards, eagles, and snakes could be well distinguished, while the inclusion of calls given in aggressive contexts yielded some overlap, specifically between female calls given to snakes, eagles and during aggression, as well as between male vervet barks (additionally recorded in South Africa) in leopard and aggressive contexts. We suggest that both cognitive appraisal of the situation and internal state contribute to the variation in call usage and structure. While the semantic properties of vervet alarm calls bear little resemblance to human words, the existing acoustic variation, possibly together with additional contextual information, allows listeners to select appropriate responses.


title: Multi-robot coalition formation

abstract: As the community strives towards autonomous multi-robot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system


title: Taking proof-based verified computation a few steps closer to practicality

abstract: Abstract : We describe GINGER, a built system for unconditional, general-purpose, and nearly practical verification of outsourced computation. GINGER is based on PEPPER, which uses the PCP theorem and cryptographic techniques to implement an efficient argument system (a kind of interactive protocol). GINGER slashes the query size and costs via theoretical refinements that are of independent interest; broadens the computational model to include (primitive) floating-point fractions, inequality comparisons, logical operations, and conditional control flow; and includes a parallel GPU-based implementation that dramatically reduces latency.


title: Wireless power transfer with metamaterials

abstract: In this paper, wireless power transfer based on resonant coupling with metamaterials is studied. We show with numerical studies that the coupling between transmitter and receiver can be enhanced, and the power transfer efficiency can be improved by metamaterials. A prototype wireless power transfer system and a metamaterial is designed and built. Experiment results prove the efficiency improvement with the fabricated metamaterial. The system with metamaterial is capable of transferring power wirelessly at roughly double the efficiency of the same system without a metamaterial.


Investigating Syllabic Structure And Its Variation In Speech From French Radio Interviews


title: An arbiter based on-chip droop detector system

abstract: This paper presents a new design of supply droop detector system with a 10-20mV resolution at a very fast sampling rate (3GHz) in IBM 45nm SOI process. The detector system samples noisy supply voltage and stable reference voltages, and these sampled voltages are converted into different propagation delay values by using current starved inverters. Simple arbiters are used to convert these propagation delay differences into digitally reconstructed supply droop voltage. In addition, the detector has only 1 clock latency; a digital value of supply droop voltage is available after one clock cycle. This new droop detector system can greatly assist an adaptive frequency scaling system with a digital control and short latency.


title: Convolutional Neural Networks for Direct Text Deblurring.

abstract: In this work we address the problem of blind deconvolution and denoising. We focus on restoration of text documents and we show that this type of highly structured data can be successfully restored by a convolutional neural network. The networks are trained to reconstruct high-quality images directly from blurry inputs without assuming any specific blur and noise models. We demonstrate the performance of the convolutional networks on a large set of text documents and on a combination of realistic de-focus and camera shake blur kernels. On this artificial data, the convolutional networks significantly outperform existing blind deconvolution methods, including those optimized for text, in terms of image quality and OCR accuracy. In fact, the networks outperform even state-of-the-art non-blind methods for anything but the lowest noise levels. The approach is validated on real photos taken by various devices.


title: Stable user-specific haptic rendering of the virtual wall

abstract: E cient control algorithms are developed to implement sti virtual walls without chatter. An analysis of the complete coupled system comprising controller, interface device, and user's nger underlies the design of a wall algorithm, thus each virtual wall is tailored to a speci c user impedance. The nger is modeled as a static second order impedance with justi cation drawn from empirical studies of limb dynamics available in the literature and from observations of the disparity in time scales between contact instability and volitional control. Compensation is incorporated for the destabilizing e ects of the zero order hold using either model based prediction or design in the digital domain. The destabilizing e ects of asynchronous wall on/o switching times and sampling times are tracked by a special watchdog while deadbeat control is used to periodically eliminate these effects. Extensions are discussed, including on-they system identi cation of the user impedance.


Facial Identification and Classification by Scale Invariance Using Content Based Image Retrieval.


title: Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework

abstract: This paper presents a unified dynamic modeling framework for differential-drive mobile robots (DDMR). Two formulations for mobile robot dynamics are developed; one is based on Lagrangian mechanics, and the other on Newton-Euler mechanics. Major difficulties experienced when modeling non-holonomic systems in both methods are illustrated and design procedures are outlined. It is shown that the two formulations are mathematically equivalent providing a check on their consistency. The presented work leads to an improved understanding of differentialdrive mobile robot dynamics, which will assist engineering students and researchers in the modeling and design of suitable controllers for DDMR navigation and trajectory tracking.


title: Virtual eXtensible Local Area Network (VXLAN): A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks

abstract: This document describes Virtual eXtensible Local Area Network (VXLAN), ::: which is used to address the need for overlay networks within ::: virtualized data centers accommodating multiple tenants. The scheme ::: and the related protocols can be used in networks for cloud service ::: providers and enterprise data centers. This memo documents the ::: deployed VXLAN protocol for the benefit of the Internet community.


title: Designing core ontologies

abstract: One of the key factors that hinders integration of distributed, heterogeneous information systems is the lack of a formal basis for modeling the complex, structured knowledge that is to be exchanged. To alleviate this situation, we present an approach based on core ontologies. Core ontologies are characterized by a high degree of axiomatization and formal precision. This is achieved by basing on a foundational ontology. In addition, core ontologies should follow a pattern-oriented design approach. By this, they are modular and extensible. Core ontologies allow for reusing the structured knowledge they define as well as integrating existing domain knowledge. The structured knowledge of the core ontologies is clearly separated from the domain-specific knowledge. Such core ontologies allow for both formally conceptualize their particular fields and to be flexibly combined to cover the needs of concrete, complex application domains. Over the last years, we have developed three independent core ontologies for events and objects, multimedia annotations and personal information management. In this paper, we present the simultaneous use and integration of our core ontologies at the example of a complex, distributed socio-technical system of emergency response. We describe our design approach for core ontologies and discuss the lessons learned in designing them. Finally, we elaborate on the beauty aspects of our core ontologies.


title: Image classification using HTM cortical learning algorithms

abstract: Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) achieved state-of-the-art performance in image classification. However, only adopting SPM to exploit spatial information is not enough for satisfactory performance. In this paper, we use hierarchical temporal memory (HTM) cortical learning algorithms to extend this LLC & SPM based model. HTM regions consist of HTM cells are constructed to spatial pool the LLC codes. Each cell receives a subset of LLC codes, and adjacent subsets are overlapped so that more spatial information can be captured. Additionally, HTM cortical learning algorithms have two processes: learning phase which make the HTM cell only receive most frequent LLC codes, and inhibition phase which ensure that the output of HTM regions is sparse. The experimental results on Caltech 101 and UIUC-Sport dataset show the improvement on the original LLC & SPM based model.


title: Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis.

abstract: BACKGROUND ::: The lack of a reliable, universally acceptable system for classification of adolescent idiopathic scoliosis has made comparisons between various types of operative treatment an impossible task. Furthermore, long-term outcomes cannot be determined because of the great variations in the description of study groups. ::: ::: ::: METHODS ::: We developed a new classification system with three components: curve type (1 through 6), a lumbar spine modifier (A, B, or C), and a sagittal thoracic modifier (-, N, or +). The six curve types have specific characteristics, on coronal and sagittal radiographs, that differentiate structural and nonstructural curves in the proximal thoracic, main thoracic, and thoracolumbar/lumbar regions. The lumbar spine modifier is based on the relationship of the center sacral vertical line to the apex of the lumbar curve, and the sagittal thoracic modifier is based on the sagittal curve measurement from the fifth to the twelfth thoracic level. A minus sign represents a curve of less than +10 degrees, N represents a curve of 10 degrees to 40 degrees, and a plus sign represents a curve of more than +40 degrees. Five surgeons, members of the Scoliosis Research Society who had developed the new system and who had previously tested the reliability of the King classification on radiographs of twenty-seven patients, measured the same radiographs (standing coronal and lateral as well as supine side-bending views) to test the reliability of the new classification. A randomly chosen independent group of seven surgeons, also members of the Scoliosis Research Society, tested the reliability and validity of the classification as well. ::: ::: ::: RESULTS ::: The interobserver and intraobserver kappa values for the curve type were, respectively, 0.92 and 0.83 for the five developers of the system and 0.740 and 0.893 for the independent group of seven scoliosis surgeons. In the independent group, the mean interobserver and intraobserver kappa values were 0.800 and 0.840 for the lumbar modifier and 0.938 and 0.970 for the sagittal thoracic modifier. These kappa values were all in the good-to-excellent range (>0.75), except for the interobserver reliability of the independent group for the curve type (kappa = 0.74), which fell just below this level. ::: ::: ::: CONCLUSIONS ::: This new two-dimensional classification of adolescent idiopathic scoliosis, as tested by two groups of surgeons, was shown to be much more reliable than the King system. Additional studies are necessary to determine the versatility, reliability, and accuracy of the classification for defining the vertebrae to be included in an arthrodesis.


title: Image super-resolution by estimating the enhancement weight of self example and external missing patches

abstract: Image super-resolution (SR) is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed, but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas.


title: Collision Resolution Protocol for Delay and Energy Efficient LoRa Networks

abstract: Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wide area networks. In particular, LoRaWAN built on LoRa physical layer is gathering increasing interest, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas. While its communication range may go up to 20 km, the achievable bit rates in LoRaWAN are limited to a few kilobits per second. In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions. First, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals. Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer. Secondly, we design a full MAC protocol enabling collision resolution. The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay. These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.


title: Multiscale vessel tracking

abstract: A method is presented that uses a vectorial multiscale feature image for wave front propagation between two or more user defined points to retrieve the central axis of tubular objects in digital images. Its implicit scale selection mechanism makes the method more robust to overlap and to the presence of adjacent structures than conventional techniques that propagate a wave front over a scalar image representing the maximum of a range of filters. The method is shown to retain its potential to cope with severe stenoses or imaging artifacts and objects with varying widths in simulated and actual two-dimensional angiographic images.


title: Modern debugging: the art of finding a needle in a haystack

abstract: Systematic use of proven debugging approaches and tools lets programmers address even apparently intractable bugs.


title: Network Cosmology

abstract: Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.


title: Social desirability is the same in offline, online, and paper surveys: A meta-analysis

abstract: This meta-analysis compares social desirability between paper and computer surveys.Social desirability in computer (Internet/offline) and paper surveys is the same.Some of the large effects found in the past may have been due to sampling error. The aim of this meta-analysis was to compare social desirability scores between paper and computer surveys. Subgroup analyses were conducted with Internet connectivity, level of anonymity, individual or group test setting, possibility of skipping items, possibility of backtracking previous items, inclusion of questions of sensitive nature, and social desirability scale type as moderators. Subgroup analyses were also conducted for study characteristics, namely the randomisation of participants, sample type (students vs. other), and study design (between- vs. within-subjects). Social desirability scores between the two administration modes were compared for 51 studies that included 62 independent samples and 16,700 unique participants. The overall effect of administration mode was close to zero (Cohen's d=0.00 for fixed-effect and d=-0.01 for random-effects meta-analysis). The majority of the effect sizes in the subgroup analyses were not significantly different from zero either. The effect sizes were close to zero for both Internet and offline surveys. In conclusion, the totality of evidence indicates that there is no difference in social desirability between paper-and-pencil surveys and computer surveys. Publication year and sample size were positively correlated (?=.64), which suggests that certain of the large effects that have been found in the past may have been due to sampling error.


Object-Oriented Software Engineering: Measuring and Controlling the Development Process


title: Exosome enrichment by ultracentrifugation and size exclusion chromatography

abstract: Exosomes are a subset of extracellular vesicles (EVs) that have important roles in intercellular communication. They contain and carry bioactive molecules within their membranes which are delivered to target cells. Reproducible isolation and enrichment of these exosomes will aid in evaluation of cellular communication. We present an approach that involved the pre-processing of plasma, combined with ultracentrifugation (UC) and size exclusion chromatography (SEC) to isolate EVs and subsequently enrich exosomes. Four variations of this approach (denoted methods I to IV) were compared. Coupling an ultracentrifugation method with size exclusion chromatography (Method II) provided the best yield by nanoparticle tracking analyses (NTA), the presence of the exosomal markers CD63, Flotillin-1 and TSG-101 (immunoblotting) and showed exosome morphology using transmission electron microscopy (TEM). This method provides an efficient way to enrich the exosomes from blood (plasma), which could be potentially employed for clinical diagnostic assessment and therapeutic intervention.


title: DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

abstract: Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. ::: ::: Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. ::: ::: Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. ::: ::: Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank ::: ::: Contact: nc.ude.naduf@fsuhz ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Plane-based Surface Regularization for Urban 3D Reconstruction

abstract: In this paper, we present a method for regularizing noisy 3D reconstructions, which is especially well suited for scenes containing planar structures like buildings. At horizontal structures, the input model is divided into slices and for each slice, an inside/outside labeling is computed. With the outlines of each slice labeling, we create an irregularly shaped volumetric cell decomposition of the whole scene. Then, an optimized inside/outside labeling of these cells is computed by solving an energy minimization problem. For the cell labeling optimization we introduce a novel smoothness term, where lines in the images are used o improve the regularization result. We show that our approach can take arbitrary dense meshed point clouds as input and delivers well regularized building models, which can be textured afterwards.


title: Event cube: another perspective on business processes

abstract: In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples.


title: Masculinities and Sexualities in Sport and Physical Cultures: Three Decades of Evolving Research

abstract: This article traces the foundation of the study between sport and physical cultures and masculinities and sexualities principally by examining the homophobic zeitgeist by which the academic discipline was formed. I show that the intense homophobia of the mid-1980s waned throughout the 1990s, and that during the new millennia, researchers found more inclusive forms of heterosexuality. Indeed, research on masculinities and homophobia today shows that, even in the traditionally conservative institution of sport, matters have shifted dramatically. This has resulted not only in improved conditions for sexual minorities, but it has also promoted a culture of softer, more tactile and emotional forms of heterosexual masculinities. These studies, alongside those within this special issue of the Journal of Homosexuality, highlight the necessity of developing new ways of theorizing the changing dynamics between masculinities, sexualities, and physical cultures in the next decade.


title: Taming Deadlocks in Multithreaded Programs

abstract: Many real-world multithreaded programs contain deadlock bugs. These bugs should be detected and corrected. Many existing detection strategies are not consistently scalable to handle large-scale applications. Many existing dynamic confirmation strategies may not reveal detectable deadlocks with high probability. And many existing runtime deadlock-tolerant strategies may incur high runtime overhead and may not prevent the same deadlock from re-occurring. This paper presents the current progress of our project on dynamic deadlock detection, confirmation, and resolution. It also describes a test harness framework developed to support our proposed approach.


title: Homomorphic Encryption and Applications

abstract: This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.


title: End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

abstract: In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification.


title: On Computer Vision for Augmented Reality

abstract: We review some recent techniques for 3D tracking and occlusion handling for computer vision-based augmented reality. We discuss what their limits for real applications are, and why object recognition techniques are certainly the key to further improvements.


title: Robust nonlinear adaptive backstepping controller design for three-phase grid-connected solar photovoltaic systems with unknown parameters

abstract: This paper presents a nonlinear control scheme to regulate the dc-link voltage for extracting the maximum power from PV system and the current to control the amount of injected power into the grid. The controller is designed using an adaptive backstepping technique by considering the parameters of the system as totally unknown. The control of power injection into the grid requires the regulation of active and reactive components of the output current of the inverter in order to control active and reactive power, respectively. The proposed controller is adaptive to unknown parameters of grid-connected solar photovoltaic (PV) systems and these parameters are estimated through the adaptation laws while guaranteeing the extraction of maximum power from the PV system and delivering appropriate active and reactive power into the grid. The overall stability of the whole system is analyzed based on the formulation of control Lyapunov functions (CLFs). Finally, the performance of the designed controller is tested on a three-phase grid-connected PV system under changeing environmental conditions and the result is also compared with an existing backstepping controller in terms of improving power quality. Simulation results indicate the robustness of the proposed scheme under changing atmospheric conditions.


title: Biodegradability of polyethylene by bacteria and fungi from Dandora dumpsite Nairobi-Kenya

abstract: This study aimed at isolating and identifying bacteria and fungi with the capacity to degrade low density polyethylene (LDPE). The level of biodegradation of LDPE sheets with bacterial and fungal inoculums from different sampling points of Dandora dumpsite was evaluated under laboratory conditions. Incubation of the LDPE sheets was done for sixteen weeks at 37°C and 28°C for bacteria and fungi respectively in a shaker incubator. Isolation of effective candidates for biodegradation was done based on the recorded biodegradation outcomes. The extent of biodegradation on the polyethylene sheets was assessed by various techniques including weight loss analysis, Fourier Transform Infrared Spectroscopy (FTIR) and GC-MS. Fourier Transform Infra-Red spectroscopy (FTIR) analysis revealed the appearance of new functional groups attributed to hydrocarbon degradation after incubation with the bacteria and fungi. Analysis of the 16S rDNA and 18S rDNA sequences for bacteria and fungi respectively showed that bacteria belonging to genera Pseudomonas, Bacillus, Brevibacillus, Cellulosimicrobium, Lysinibacillus and fungi of genus Aspergillus were implicated as polyethylene degraders. An overall analysis confirmed that fungi are generally better degraders of polyethylene than bacteria. The highest fungal degradation activity was a mean weight reduction of 36.4±5.53% attributed to Aspergillus oryzae strain A5, 1 (MG779508). The highest degradation activity for bacteria was a mean of 35.72± 4.01% and 20.28± 2.30% attributed to Bacillus cereus strain A5,a (MG645264) and Brevibacillus borstelensis strain B2,2 (MG645267) respectively. Genus Aspergillus, Bacillus and Brevibacillus were confirmed to be good candidates for Low Density Poly Ethene bio-degradation. This was further confirmed by the appearance of the aldehyde, ether and carboxyl functional groups after FTIR analysis of the polythene sheets and the appearance of a ketone which is also an intermediary product in the culture media. To improve this degrading capacity through assessment of optimum conditions for microbial activity and enzyme production will enable these findings to be applied commercially and on a larger scale.


title: Mining Analogical Libraries in Q&A Discussions -- Incorporating Relational and Categorical Knowledge into Word Embedding

abstract: Third-party libraries are an integral part of many software projects. It often happens that developers need to find analogical libraries that can provide comparable features to the libraries they are already familiar with. Existing methods to find analogical libraries are limited by the community-curated list of libraries, blogs, or Q&A posts, which often contain overwhelming or out-of-date information. In this paper, we present a new approach to recommend analogical libraries based on a knowledge base of analogical libraries mined from tags of millions of Stack Overflow questions. The novelty of our approach is to solve analogical-libraries questions by combining state-of-the-art word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. We implement our approach in a proof-of-concept web application (https://graphofknowledge.appspot.com/similartech). The evaluation results show that our approach can make accurate recommendation of analogical libraries (Precision@1=0.81 and Precision@5=0.67). Google Analytics of the website traffic provides initial evidence of the potential usefulness of our web application for software developers.


title: An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls

abstract: This paper presents a preference-based method to handle optimization problems with multiple objectives. With an increase in the number of objectives the computational cost in solving a multi-objective optimization problem rises exponentially, and it becomes increasingly difficult for evolutionary multi-objective techniques to produce the entire Pareto-optimal front. In this paper, an evolutionary multi-objective procedure is combined with preference information from the decision maker during the intermediate stages of the algorithm leading to the most preferred point. The proposed approach is different from the existing approaches, as it tries to find the most preferred point with a limited budget of decision maker calls. In this paper, we incorporate the idea into a progressively interactive technique based on polyhedral cones. The idea is also tested on another progressively interactive approach based on value functions. Results are provided on two to five-objective unconstrained as well as constrained test problems.


title: Health literacy: A barrier to pharmacist–patient communication and medication adherence

abstract: Abstract Objectives To present a summary of the existing literature on medication nonadherence, health literacy, and use of written patient information in health care and pharmacy in particular. Data sources Searches of Medline, PubMed, and International Pharmaceutical Abstracts databases were conducted using one or more of the following terms adherence/nonadherence, compliance/noncompliance, printed/written information, literacy, patient education, communication , and health literacy . These terms were combined with the following search terms: drug information, readability, medication/drug, patient, pharmacy/pharmacist , and prescription . References of pertinent articles were hand searched to retrieve additional articles. Data extraction By the author. Data synthesis Articles were grouped and summarized into three broad categories (nonadherence, health literacy, and communicating health information to patients), with an emphasis on the use of written patient information in health care and pharmacy practice in particular. The complexities inherent in nonadherence behavior, health literacy, and patient education are summarized, and suggestions for enhancing medication adherence, especially for patients with low health literacy skills, are provided. Conclusion The health literacy skills of American adults have not changed considerably during the previous decade. This makes use of written patient medication information in pharmacy practice problematic for some patients. Limited health literacy has been associated with poorer health, medication nonadherence, medication errors, higher medical expenses, and increased hospitalization. A need exists for identifying patients with limited health literacy and tailoring medication counseling to their needs.


eview of grid applications with the Zurich 1 MW battery energy torage system


title: Refining the theory of basic individual values.

abstract: We propose a refined theory of basic individual values intended to provide greater heuristic and explanatory power than the original theory of 10 values (Schwartz, 1992). The refined theory more accurately expresses the central assumption of the original theory that research has largely ignored: Values form a circular motivational continuum. The theory defines and orders 19 values on the continuum based on their compatible and conflicting motivations, expression of self-protection versus growth, and personal versus social focus. We assess the theory with a new instrument in 15 samples from 10 countries (N 6,059). Confirmatory factor and multidimensional scaling analyses support discrimination of the 19 values, confirming the refined theory. Multidimensional scaling analyses largely support the predicted motivational order of the values. Analyses of predictive validity demonstrate that the refined values theory provides greater and more precise insight into the value underpinnings of beliefs. Each value correlates uniquely with external variables.


title: Antibiotic Resistance of Pseudomonas aeruginosa in Pneumonia at a Single University Hospital Center in Germany over a 10-Year Period

abstract: BACKGROUND ::: Pseudomonas aeruginosa is a common cause of community-acquired and nosocomial-acquired pneumonia. The development of resistance of P. aeruginosa to antibiotics is increasing globally due to the overuse of antibiotics. This article examines, retrospectively, the antibiotic resistance in patients with community-acquired versus nosocomial-acquired pneumonia caused by P. aeruginosa or multidrug-resistant (MDR) P. aeruginosa. ::: ::: ::: METHODS ::: Data from patients with community-acquired and nosocomial-acquired pneumonia caused by P. aeruginosa and MDR P. aeruginosa were collected from the hospital charts at the HELIOS Clinic, Witten/Herdecke University, Wuppertal, Germany, between January 2004 and August 2014. An antibiogram was created from all study patients with community-acquired and nosocomial-acquired pneumonia caused by P. aeruginosa or MDR P. aeruginosa. ::: ::: ::: RESULTS ::: A total of 168 patients with mean age 68.1 ± 12.8 (113 [67.3% males and 55 [32.7%] females) were identified; 91 (54.2%) had community-acquired and 77 (45.8%) had nosocomial-acquired pneumonia caused by P. aeruginosa. Patients with community-acquired versus nosocomial-acquired pneumonia had a mean age of 66.4 ± 13.8 vs. 70.1 ± 11.4 years [59 vs. 54 (64.8% vs. 70.1%) males and 32 vs. 23 (35.2% vs. 29.9%) females]. They included 41 (24.4%) patients with pneumonia due to MDR P. aeruginosa: 27 (65.9%) community-acquired and 14 (34.1%) nosocomial-acquired cases. P. aeruginosa and MDR P. aeruginosa showed a very high resistance to fosfomycin (community-acquired vs. nosocomial-acquired) (81.0% vs. 84.2%; 0 vs. 85.7%). A similar resistance pattern was seen with ciprofloxacin (35.2% vs. 24.0%; 70.4% vs. 61.5%), levofloxacin (34.6% vs. 24.5%; 66.7% vs. 64.3%), ceftazidime (15.9% vs. 30.9; 33.3% vs. 61.5%), piperacillin (24.2% vs. 29.9%; 44.4% vs. 57.1%), imipenem (28.6% vs. 27.3%; 55.6% vs. 50.0%), piperacillin and tazobactam (23.1% vs. 28.6%; 44.4% vs. 50.0%), tobramycin (28.0% vs. 17.2%; 52.0% vs. 27.3%), gentamicin (26.4% vs. 18.2%; 44.4% vs. 21.4%), and meropenem (20.2% vs. 20.3%; 42.3% vs. 50.0%). An elevated resistance of P. aeruginosa and MDR P. aeruginosa was found for cefepime (11.1% vs. 23.3%; 25.9% vs. 50.0%), and amikacin (10.2% vs. 9.1%; 27.3% vs. 9.1%). Neither pathogen was resistant to colistin (P = 0.574). ::: ::: ::: CONCLUSION ::: While P. aeruginosa and MDR P. aeruginosa were resistant to a variety of commonly used antibiotics, they were not resistant to colistin in the few isolates recovered from patients with pneumonia.


title: Nutrition in Hepatic Encephalopathy

abstract: Protein calorie malnutrition (PCM) is a well-known complication of chronic liver disease (CLD). A major contribution to PCM in CLD is restriction of dietary protein intake. After many decades of injudicious reduction in dietary protein, cirrhotic patients are now prescribed appropriate amounts of protein. PCM in CLD is known to be associated with life-threatening complications. In the general approach to these patients, the initial and most important step for the clinician is to recognize the extent of malnutrition. Most patients tolerate a normal amount of dietary protein without developing hepatic encephalopathy (HE). Oral branched-chain amino acids (BCAAs) have a limited role in HE. Patients who exhibit dietary protein intolerance originally were thought to be best treated with BCAA formulations. Mixed evidence has been reported in multiple studies. In keeping with other reports, this article shows that in animal protein-intolerant patients, even those with advanced cirrhosis, vegetable protein-based diets are well tolerated. Another approach to management of apparent dietary intolerance is to optimize HE treatment with available medications. This article reviews the causes of HE, minimal HE, and PCM; examines nutrition requirements and assessment; and discusses treatment options for malnutrition in HE.


title: Understanding post‐adoption IS usage stages: an empirical assessment of self‐service information systems

abstract: Organisations have invested in self-service information systems (IS) to provide a direct interface for service delivery. Enriching the usage of these systems can provide organisations with immense benefits. However, limited research has been directed towards understanding post-adoption IS usage behaviour in general and specifically in the context of self-service IS. This study proposes post-adoption IS usage behaviour as a broader concept constituting feature level usage of IS, integration of IS in the work system and exploration of new uses of IS. We evaluate how the new conceptualisation can be used to classify users at different stages of self-service IS usage. Further, we examine user perceptions that differentiate among the users situated at different self-service IS usage stages. Data were collected in the context of a self-service Web-based IS to validate the post-adoption IS usage constructs and to examine the proposed thesis. The newly developed conceptual structure and measures for post-adoption IS usage behaviour exhibit strong psychometric properties. The analysis shows three distinct post-adoption IS usage stages and highlights that usefulness, user-initiated learning, ease of use, satisfaction and voluntariness of use differentiate users at the different stages of post-adoption IS usage. The results show that these variables aggregate into value confirmation and learning orientation as two higher-level concepts. Further, we evaluate the predictive efficacy of the research model in classifying users into different post-adoption self-service IS usage stages. Implications are drawn for future research.


title: On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit

abstract: We develop improved algorithms to construct good low-density parity-check codes that approach the Shannon limit very closely. For rate 1/2, the best code found has a threshold within 0.0045 dB of the Shannon limit of the binary-input additive white Gaussian noise channel. Simulation results with a somewhat simpler code show that we can achieve within 0.04 dB of the Shannon limit at a bit error rate of 10/sup -6/ using a block length of 10/sup 7/.


title: Big Data security and privacy: A review

abstract: While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.


title: Document Clustering : Before and After the Singular Value Decomposition

abstract: Document Clustering is an issue of measuring similarity between documents and grouping similar documents together. Information Retrieval (IR) is an issue of comparing query with a collection of documents to locate a set of documents relevant to a particular query. In the vector space IR model, a query is treated as a document which consists of a few terms. Therefore, in both clustering and retrieval we necessarily address issues involving representation of documents and computation of similarities between a set of documents. In the vector space IR model, term-document matrix is computed from a collection of documents using a certain weighting scheme. Latent Semantic Indexing, an efficient vector space retrieval approach, uses Singular Value Decomposition technique to reduce the rank of the original term-document matrix. Theoretically, SVD, a dimensionality reduction technique, performs a term-to-concept mapping, and therefore, conceptual indexing and retrieval is made possible. In this paper, we discuss the clustering of documents by calculating pair-wise similarity between documents using the original term-document matrix and the decomposed term-document matrix. We also report an evaluation method based on the clustering hypothesis and analyze the clustering results.


title: The Pattern Glare Test: a review and determination of normative values

abstract: Pattern glare is characterised by symptoms of visual perceptual distortions and visual stress on viewing striped patterns. People with migraine or Meares–Irlen syndrome (visual stress) are especially prone to pattern glare. The literature on pattern glare is reviewed, and the goal of this study was to develop clinical norms for the Wilkins and Evans Pattern Glare Test. This comprises three test plates of square wave patterns of spatial frequency 0.5, 3 and 12 cycles per degree (cpd). Patients are shown the 0.5 cpd grating and the number of distortions that are reported in response to a list of questions is recorded. This is repeated for the other patterns. People who are prone to pattern glare experience visual perceptual distortions on viewing the 3 cpd grating, and pattern glare can be quantified as either the sum of distortions reported with the 3 cpd pattern or as the difference between the number of distortions with the 3 and 12 cpd gratings, the ‘3–12 cpd difference’. In study 1, 100 patients consulting an optometrist performed the Pattern Glare Test and the 95th percentile of responses was calculated as the limit of the normal range. The normal range for the number of distortions was found to be 3 on the 3 cpd grating or a score of >1 on the 3–12 cpd difference. The literature suggests that these people are likely to have visual stress in everyday life and may therefore benefit from interventions designed to alleviate visual stress, such as precision tinted lenses.


Deep learning for sentiment analysis of movie reviews


title: MR Brain Image Segmentation Based on Self-Organizing Map Network

abstract: Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of magnetic resonance (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on self-organizing feature map (SOFM) network is presented. The algorithm includes spatial constraints by using a Markov Random Field (MRF) model. The MRF term introduces the prior distribution with clique potentials and thus improves the segmentation results without having extra data samples in the training set or a complicated network structure. The simulation results demonstrate that the proposed algorithm is promising.


title: Locomotion for Difficult Terrain

abstract: Most of the earth’s land surface is inaccessible to regular vehicles so there is a need for mobile robots that can handle difficult terrain. Today’s robots are mostly designed for traveling over relatively smooth, level or inclined, surfaces. This survey will however discuss different locomotion systems for mobile robots used in difficult terrain. Only robots that use ground contact for propulsion are considered which means that robots travelling through air or water are not included.


title: Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery

abstract: Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quadratic programs. Our approach considerably outperforms the state of the art on the materials discovery problem, scaling to larger datasets and recovering more precise and physically meaningful decompositions. We also show the effectiveness of our approach for enforcing background knowledge on other application domains.


title: Memory Efficient Kernel Approximation

abstract: The scalability of kernel machines is a big challenge when facing millions of samples due to storage and computation issues for large kernel matrices, that are usually dense. Recently, many papers have suggested tackling this problem by using a low-rank approximation of the kernel matrix. In this paper, we first make the observation that the structure of shift-invariant kernels changes from low-rank to block-diagonal (without any low-rank structure) when varying the scale parameter. Based on this observation, we propose a new kernel approximation algorithm - Memory Efficient Kernel Approximation (MEKA), which considers both low-rank and clustering structure of the kernel matrix. We show that the resulting algorithm outperforms state-of-the-art low-rank kernel approximation methods in terms of speed, approximation error, and memory usage. As an example, on the mnist2m dataset with two-million samples, our method takes 550 seconds on a single machine using less than 500 MBytes memory to achieve 0.2313 test RMSE for kernel ridge regression, while standard Nystrom approximation takes more than 2700 seconds and uses more than 2 GBytes memory on the same problem to achieve 0.2318 test RMSE.


title: Robotics Component Verification on ISS ROKVISS - Preliminary Results for Telepresence

abstract: ROKVISS, Germany's newest space robotics technology experiment, was successfully installed outside at the Russian Service Module of the International Space Station (ISS) during an extravehicular space walk at the end of January 2005. Since February 2005 a two joint manipulator is operated from ground via a direct radio link. The aim of ROKVISS is the in flight verification of highly integrated modular robotic joints as well as the demonstration of different control modes, reaching from high system autonomy to force feedback teleoperation (telepresence mode). The experiment will be operated for at least one year in free space to evaluate and qualify intelligent light weight robotics components under realistic circumstances for maintenance and repair tasks as foreseen in upcoming manned and unmanned space applications in near future. This paper focuses in the telepresence control mode, its technology and first results from the space experiment ROKVISS.


title: Integration of Smart Home Technologies in a Health Monitoring System for the Elderly

abstract: Among older adults, the challenges of maintaining mobility and cognitive function make it increasingly difficult to remain living alone independently. As a result, many older adults are forced to seek residence in costly clinical institutions where they can receive constant medical supervision. A home-based automated system that monitors their health and well- being while remaining unobtrusive would provide them with a more comfortable and independent lifestyle, as well as more affordable care. This paper presents a smart home system for the elderly, developed by the Technology Assisted Friendly Environment for the Third Age (TAFETA) group. It introduces the sensor technologies integrated in the system and develops a framework for the processing and communication of the extracted information. It also considers the acceptability and implications of this technology from the perspective of the potential occupants.


title: What research paradigms have cognitive psychologists used to study “False memory,” and what are the implications of these choices?

abstract: Abstract This research examines the methodologies employed by cognitive psychologists to study “false memory,” and assesses if these methodologies are likely to facilitate scientific progress or perhaps constrain the conclusions reached. A PsycINFO search of the empirical publications in cognitive psychology was conducted through January, 2004, using the subject heading, “false memory.” The search produced 198 articles. Although there is an apparent false memory research bandwagon in cognitive psychology, with increasing numbers of studies published on this topic over the past decade, few researchers (only 13.1% of the articles) have studied false memory as the term was originally intended—to specifically refer to planting memory for an entirely new event that was never experienced in an individual’s lifetime. Cognitive psychologists interested in conducting research relevant to assessing the authenticity of memories for child sexual abuse should consider the generalizability of their research to the planting of entirely new events in memory.


title: SALAD: Achieving Symmetric Access Latency with Asymmetric DRAM Architecture

abstract: Memory access latency has significant impact on application performance. Unfortunately, the random access latency of DRAM has been scaling relatively slowly, and often directly affects the critical path of execution, especially for applications with insufficient locality or memory-level parallelism. The existing low-latency DRAM organizations either incur significant area overhead or burden the software stack with non-uniform access latency. This paper proposes SALAD, a new DRAM device architecture that provides s ymmetric a ccess l atency with a symmetric D RAM bank organizations. Since local banks have lower data transfer time due to their proximity to the I/O pads, SALAD applies high aspect-ratio (i.e., low-latency) mats only to remote banks to offset the difference in data transfer time, thus providing uniformly low access time (tAC) over the whole device. Our evaluation demonstrates that SALAD improves the IPC by 13 percent (10 percent) without any software modifications, while incurring only 6 percent (3 percent) area overhead.


title: Effect of Pitch Type, Pitch Count, and Pitching Mechanics on Risk of Elbow and Shoulder Pain in Youth Baseball Pitchers

abstract: BACKGROUND ::: Joint pain is thought to be an early sign of injury to a pitcher. ::: ::: ::: OBJECTIVE ::: To evaluate the association between pitch counts, pitch types, and pitching mechanics and shoulder and elbow pain in young pitchers. ::: ::: ::: STUDY DESIGN ::: Prospective cohort study. ::: ::: ::: METHODS ::: Four hundred and seventy-six young (ages 9 to 14 years) baseball pitchers were followed for one season. Data were collected from pre- and postseason questionnaires, injury and performance interviews after each game, pitch count logs, and video analysis of pitching mechanics. Generalized estimating equations and logistic regression analysis were used. ::: ::: ::: RESULTS ::: Half of the subjects experienced elbow or shoulder pain during the season. The curveball was associated with a 52% increased risk of shoulder pain and the slider was associated with an 86% increased risk of elbow pain. There was a significant association between the number of pitches thrown in a game and during the season and the rate of elbow pain and shoulder pain. ::: ::: ::: CONCLUSIONS ::: Pitchers in this age group should be cautioned about throwing breaking pitches (curveballs and sliders) because of the increased risk of elbow and shoulder pain. Limitations on pitches thrown in a game and in a season can also reduce the risk of pain. Further evaluation of pain and pitching mechanics is necessary.


title: Body composition and susceptibility to type 2 diabetes: an evolutionary perspective

abstract: Type 2 diabetes is rapidly increasing in prevalence worldwide, in concert with epidemics of obesity and sedentary behavior that are themselves tracking economic development. Within this broad pattern, susceptibility to diabetes varies substantially in association with ethnicity and nutritional exposures through the life-course. An evolutionary perspective may help understand why humans are so prone to this condition in modern environments, and why this risk is unequally distributed. A simple conceptual model treats diabetes risk as the function of two interacting traits, namely 'metabolic capacity' which promotes glucose homeostasis, and 'metabolic load' which challenges glucose homoeostasis. This conceptual model helps understand how long-term and more recent trends in body composition can be considered to have shaped variability in diabetes risk. Hominin evolution appears to have continued a broader trend evident in primates, towards lower levels of muscularity. In addition, hominins developed higher levels of body fatness, especially in females in relative terms. These traits most likely evolved as part of a broader reorganization of human life history traits in response to growing levels of ecological instability, enabling both survival during tough periods and reproduction during bountiful periods. Since the emergence of Homo sapiens, populations have diverged in body composition in association with geographical setting and local ecological stresses. These long-term trends in both metabolic capacity and adiposity help explain the overall susceptibility of humans to diabetes in ways that are similar to, and exacerbated by, the effects of nutritional exposures during the life-course.


title: Design of a Multi-Segmented Magnetic Robot for Hull Inspection

abstract: The use of a hull-climbing robot is proposed to assist hull surveyors in their inspection tasks, reducing cost and risk to personnel. A novel multisegmented hull-climbing robot with magnetic wheels is introduced where multiple two-wheeled modular segments are adjoined by flexible linkages. Compared to traditional rigid-body tracked magnetic robots that tend to detach easily in the presence of surface discontinuities, the segmented design adapts to such discontinuities with improved adhesion to the ferrous surface. Coordinated mobility is achieved with the use of a motion-control algorithm that estimates robot pose through position sensors located in each segment and linkage in order to optimally command each of the drive motors of the system. Self-powered segments and an onboard radio allow for wireless transmission of video and control data between the robot and its operator control unit. The modular-design approach of the system is highly suited for upgrading or adding segments as needed. For example, enhancing the system with a segment that supports an ultrasonic measurement device used to measure hull-thickness of corroded sites can help minimize the number of areas that a surveyor must personally visit for further inspection and repair. Future development efforts may lead to the design of autonomy segments that accept high-level commands from the operator and automatically execute wide-area inspections. It is also foreseeable that with several multi-segmented robots, a coordinated inspection task can take place in parallel, significantly reducing inspection time and cost.


Extended objects


title: Looking at, looking up or keeping up with people?: motives and use of facebook

abstract: This paper investigates the uses of social networking site Facebook, and the gratifications users derive from those uses. In the first study, 137 users generated words or phrases to describe how they used Facebook, and what they enjoyed about their use. These phrases were coded into 46 items which were completed by 241 Facebook users in Study 2. Factor analysis identified seven unique uses and gratifications: social connection, shared identities, content, social investigation, social network surfing and status updating. User demographics, site visit patterns and the use of privacy settings were associated with different uses and gratifications.


title: Interaction of Multinational Corporation and University Science Park

abstract: Along with the entrance of Multinational Corporation and the drive of high technology,various kinds of University Science Parks were set up,with the high-tech and economic strength of the university,and the support of government.This article analyzes interaction of Multinational Corporation and University Science Park,strategy and development direction from the view of Multinational Corporation and University Science Park respectively.It also analyzes interaction between Science Park and Regional Economy with the participation of Multinational Corporation.