title: IO: An Interconnected Asset Ontology in Support of Risk Management Processes

abstract: Asset information obtained via infrastructure analysis is essential for developing and establishing risk management. However, information about assets acquired by existing infrastructure analysis processes is often incomplete or lacking in detail, especially concerning their interconnected topology. In this paper, we present the Interconnected-asset Ontology, IO, as a step towards a standardized representation of detailed asset information. The utilization of an asset ontology as a machine-readable representation supports the automation of risk management processes and the standardization of asset information reduces redundant acquisition processes that are often found in practice.


title: A Fuzzy-Genetic Approach to Breast Cancer Diagnosis

abstract: The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.


title: Vehicle-to-Vehicle IEEE 802.11p performance measurements at urban intersections

abstract: During the last few years, vehicle-to-vehicle (V2V) wireless communication has become a key objective for enabling future cooperative safety applications, such as intersection collision warning. In this paper, we present the results of a 5.9 GHz V2V performance measurement campaign at four different urban intersections under NLOS conditions using commercial off-the-shelf wireless interface cards which meet the 802.11p and ITS-G5 specifications. Particularly, we quantify the packet delivery ratio (PDR) and received signal strength indication (RSSI) levels associated with different scenario conditions with respect to vehicle positioning, intersection geometry and traffic density. We determine reliable communication ranges which constitute an important metric for V2V collision avoidance applications.


title: Assessing forecast model performance in an ERP environment

abstract: Purpose – The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment.Design/methodology/approach – The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP®'s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP® is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany.Findings – The findings of the study support the adoption of CFE as a more relevant commercial decision‐making measure than commonly applied statistical forecast measures.Practical implications – The findings of the study provide forecast model selection guidance to SAP®'s 12+ million worldwide users. However, the CFE metric can be adopt...


title: A General Framework for Adversarial Examples with Objectives

abstract: Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains. Most research on adversarial examples takes as its only constraint that the perturbed images are similar to the originals. However, real-world application of these ideas often requires the examples to satisfy additional objectives, which are typically enforced through custom modifications of the perturbation process. In this article, we propose adversarial generative nets (AGNs), a general methodology to train a generator neural network to emit adversarial examples satisfying desired objectives. We demonstrate the ability of AGNs to accommodate a wide range of objectives, including imprecise ones difficult to model, in two application domains. In particular, we demonstrate physical adversarial examples—eyeglass frames designed to fool face recognition—with better robustness, inconspicuousness, and scalability than previous approaches, as well as a new attack to fool a handwritten-digit classifier.


Deep Residual Learning for Image Recognition Supplementary Materials


title: The Application of Trend Following Strategies in Stock Market Trading

abstract: Trend-following (TF) strategies use fixed trading mechanism in order to take advantages from the long-term market moves without regards to the past price performance.In contrast with most prediction tools that stemmed from soft computing such as neural networks to predict a future trend, TF just rides on the current trend pattern to decide on buying or selling. While TF is widely applied in currency markets with a good track record for major currency pairs [1], it is doubtful that if TF can be applied in stock market. In this paper a new TF model that features both strategies of evaluating the trend by static and adaptive rules, is created from simulations and later verified on Hong Kong Hang Seng future indices. The model assesses trend profitability from the statistical features of the return distribution of the asset under consideration. The results and examples facilitate some insights on the merits of using the trend following model.


title: ApoE and Aβ in Alzheimer’s Disease: Accidental Encounters or Partners?

abstract: Among the three human apolipoprotein E (apoE) isoforms, apoE4 increases the risk of Alzheimer's disease (AD). While transporting cholesterol is a primary function, apoE also regulates amyloid-β (Aβ) metabolism, aggregation, and deposition. Although earlier work suggests that different affinities of apoE isoforms to Aβ might account for their effects on Aβ clearance, recent studies indicate that apoE also competes with Aβ for cellular uptake through apoE receptors. Thus, several factors probably determine the variable effects apoE has on Aβ. In this Review, we examine biochemical, structural, and functional studies and propose testable models that address the complex mechanisms underlying apoE-Aβ interaction and how apoE4 may increase AD risk and also serve as a target pathway for therapy.


title: A Flexible Framework for Hypothesis Testing in High-dimensions

abstract: Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples ($p>n$) and assume that the high-dimensional parameters vector is $s_0$ sparse. We develop a general and flexible $\ell_\infty$ projection statistic for hypothesis testing in this model. Our framework encompasses testing whether the parameter lies in a convex cone, testing the signal strength, testing arbitrary functionals of the parameter, and testing adaptive hypothesis. We show that the proposed procedure controls the type I error under the standard assumption of $s_0 (\log p)/\sqrt{n}\to 0$, and also analyze the power of the procedure. Our numerical experiments confirms our theoretical findings and demonstrate that we control false positive rate (type I error) near the nominal level, and have high power.


title: Python, performance, and natural language processing

abstract: We present a case study of Python-based workflow for a data-intensive natural language processing problem, namely word classification with vector space model methodology. Problems in the area of natural language processing are typically solved in many steps which require transformation of the data to vastly different formats (in our case, raw text to sparse matrices to dense vectors). A Python implementation for each of these steps would require a different solution. We survey existing approaches to using Python for high-performance processing of large volumes of data, and we propose a sample solution for each step for our case study (aspectual classification of Russian verbs), attempting to preserve both efficiency and user-friendliness. For the most computationally intensive part of the workflow we develop a prototype distributed implementation of co-occurrence extraction module using IPython.parallel cluster.


title: FatCBST: Concurrent Binary Search Tree with Fatnodes

abstract: Effective design of concurrent tree implementation plays a major role in improving the scalability of applications in a multicore environment. We introduce a concurrent binary search tree with fatnodes (FatCBST) and present algorithms to perform basic operations on it. Unlike a simple node with single value, a fatnode consists of a set of values. FatCBST concept allows a thread to perform update operations on an existing fatnode without changing the tree structure, making it less disruptive to other threads' operations. Fatnodes help to take advantage of the spatial locality in the cache hierarchy and also reduce the height of the concurrent binary search tree. Our FatCBST implementation allows multiple threads to perform update operations on the same existing fatnode at the same time. Experimental results show that the FatCBST implementations that have small fatnode sizes provide better throughput for high and medium contention workloads; and large fatnode sizes provide better throughput for low contention workloads, as compared to the current state-of-the-art implementations.


title: Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor

abstract: Can we tell a good professor from their students’ comments? And are there differences between what is considered to be a good professor by different student groups? We use a large corpus of student evaluations collected from the RateMyProfessors website, covering different institutions, disciplines, and cultures, and perform several comparative experiments and analyses aimed to answer these two questions. Our results indicate that (1) we can reliably classify good professors from poor professors with an accuracy of over 90 %, and (2) we can separate the evaluations made for good professors by different groups with accuracies in the range of 71–89 %. Furthermore, a qualitative analysis performed using topic modeling highlights the aspects of interest for different student groups.


title: Stateful Inter-Packet Signal Processing for Wireless Networking

abstract: Traditional signal processing design (e.g., frequency offset and channel estimation) at a receiver treats each packet arrival as an independent process to facilitate decoding and interpreting packet data. In this paper, we enhance the performance of this process in the wireless network domain. We propose STAteful inter-Packet signaL procEssing (STAPLE), a framework of stateful signal processing residing between the physical and link layers. STAPLE transforms the signal processing procedure into a lightweight stateful process that caches in a small-sized memory table physical and link layer header fields as packet state information. The similarity of such information among packets serves as prior knowledge to further enhance the reliability of signal processing and thus improve the wireless network performance. We implement STAPLE on USRP X300-series devices with adapted configurations for 802.11a/b/g/n/ac and 802.15.4. The STAPLE prototype is of low processing complexity and does not change any wireless standard specification. Comprehensive experimental results show that the benefit from STAPLE is universal in various wireless networks.


title: Perceptions of social media on students' academic engagement in tertiary education

abstract: Social media has been gaining popularity among university students who use social media at higher rates than the general population. Students consequently spend a significant amount of time on social media, which may inevitably have an effect on their academic engagement. Subsequently, scholars have been intrigued to examine the impact of social media on students' academic engagement. Research that has directly explored the use of social media and its impact on students in tertiary institutions has revealed limited and mixed findings, particularly within a South African context; thus leaving a window of opportunity to further investigate the impact that social media has on students' academic engagement. This study therefore aims to investigate the use of social media in tertiary institutions, the impact that the use thereof has on students' academic engagement and to suggest effective ways of using social media in tertiary institutions to improve students' academic engagement from students' perspectives. This study used an interpretivist (inductive) approach in order to determine and comprehend student's perspectives and experiences towards the use of social media and the effects thereof on their academic engagement. A single case study design at Rhodes University was used to determine students' perceptions and data was collected using an online survey. The findings reveal that students use social media for both social and academic purposes. Students further perceived that social media has a positive impact on their academic engagement and suggest that using social media at tertiary level could be advantageous and could enhance students' academic engagement.


title: Virtual machine selection and placement for dynamic consolidation in Cloud computing environment

abstract: Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to reduce the energy consumption and improve physical resource utilization. Determining which VMs should be migrated from an overloaded host directly influences the VM migration time and increases energy consumption for the whole data center, and can cause the service level of agreement (SLA), delivered by providers and users, to be violated. So when designing a VM selection policy, we not only consider CPU utilization, but also define a variable that represents the degree of resource satisfaction to select the VMs. In addition, we propose a novel VM placement policy that prefers placing a migratable VM on a host that has the minimum correlation coefficient. The bigger correlation coefficient a host has, the greater the influence will be on VMs located on that host after the migration. Using CloudSim, we run simulations whose results let draw us to conclude that the policies we propose in this paper perform better than existing policies in terms of energy consumption, VM migration time, and SLA violation percentage.


title: Multimodal Learning for Web Information Extraction

abstract: We consider the problem of extracting text instances of predefined categories from the Web. Instances of a category may be scattered across thousands of independent sources in many different formats with potential noises, which makes open-domain information extraction a challenging problem. Learning syntactic rules like "cities such as _" or "_ is a city" in a semi-supervised manner using a few labeled examples is usually unreliable because 1) high quality syntactic rules are rare and 2) the learning task is usually underconstrained. To address these problems, in this paper we propose to learn multimodal rules to combat the difficulty of syntactic rules. The multimodal rules are learned from information sources of different modalities, which is motivated by an intuition that information that is difficult to disambiguate correctly in one modality may be easily recognized in another. To demonstrate the effectiveness of this method, we have built a sophisticated end-to-end multimodal information extraction system that takes unannotated raw web pages as input, and generates a set of extracted instances as outputs. More specifically, our system learns reliable relationship between multimodal information by multimodal relation analysis on big unstructured data. Based on the learned relationship, we further train a set of multimodal rules for information extraction. Experimental evaluation shows that a greater accuracy for information extraction can be achieved by multimodal learning.


title: Evolution and Revolution in Artificial Intelligence in Education

abstract: The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon? We analyse 47 papers from three years in the history of the Journal of AIED (1994, 2004, and 2014) to identify the foci and typical scenarios that occupy the field of AIED. We use those results to suggest two parallel strands of research that need to take place in order to impact education in the next 25 years: One is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains. The other is a revolutionary process where we argue for embedding our technologies within students’ everyday lives, supporting their cultures, practices, goals, and communities.


title: Foundations of Game-Based Learning.

abstract: In this article we argue that to study or apply games as learning environments, multiple perspectives have to be taken into account. We first define game-based learning and gamification, and then discuss theoretical models that describe learning with games, arguing that playfulness is orthogonal to learning theory. We then review design elements of games that facilitate learning by fostering learners' cognitive, behavioral, affective, and sociocultural engagement with the subject matter. Finally, we discuss the basis of these design elements in cognitive, motivational, affective, and sociocultural foundations by reviewing key theories from education and psychology that are the most pertinent to game-based learning and by describing empirical research on learning with games that has been or should be conducted. We conclude that a combination of cognitive, motivational, affective, and sociocultural perspectives is necessary for both game design and game research to fully capture what games have to offer for...


How to Normalize Co-Occurrence Data ? An Analysis of Some Well-Known Similarity Measures Nees


title: Service-oriented computing

abstract: Service-Oriented Computing (SOC) is a new computing paradigm that utilizes services as the basic constructs to support the development of rapid, low-cost and easy composition of distributed applications even in heterogeneous environments. The promise of Service-Oriented Computing is a world of cooperating services where application components are assembled with little effort into a network of services that can be loosely coupled to create flexible dynamic business processes and agile applications that may span organizations and computing platforms. The subject of Service-Oriented Computing is vast and enormously complex, spanning many concepts and technologies that find their origins in diverse disciplines that are woven together in an intricate manner. In addition, there is a need to merge technology with an understanding of business processes and organizational structures, a combination of recognizing an enterprise's pain points and the potential solutions that can be applied to correct them. The materi...


Effectiveness of Credit Management System on Loan Performance : Empirical Evidence from Micro Finance Sector in Kenya


title: Minimizing RF Performance Spikes in a Cryogenic Orthomode Transducer (OMT)

abstract: The turnstile junction exhibits very low cross-polarization leakage and is suitable for low-noise millimeter-wave receivers. For use in a cryogenic receiver, it is best if the orthomode transducer (OMT) is implemented in waveguide, contains no additional assembly features, and may be directly machined. However, machined OMTs are prone to sharp signal drop-outs that are costly to overall performance since they show up directly as spikes in receiver noise. We explore the various factors contributing to this degradation and discuss how the current design mitigates each cause. Final performance is demonstrated at cryogenic temperatures.


title: Unstructured lumigraph rendering

abstract: We describe an image based rendering approach that generalizes many current image based rendering algorithms, including light field rendering and view-dependent texture mapping. In particular, it allows for lumigraph-style rendering from a set of input cameras in arbitrary configurations (i.e., not restricted to a plane or to any specific manifold). In the case of regular and planar input camera positions, our algorithm reduces to a typical lumigraph approach. When presented with fewer cameras and good approximate geometry, our algorithm behaves like view-dependent texture mapping. The algorithm achieves this flexibility because it is designed to meet a set of specific goals that we describe. We demonstrate this flexibility with a variety of examples.


title: Chemopreventive and Therapeutic Effects of Edible Berries: A Focus on Colon Cancer Prevention and Treatment

abstract: Colon cancer is one of the most prevalent diseases across the world. Numerous epidemiological studies indicate that diets rich in fruit, such as berries, provide significant health benefits against several types of cancer, including colon cancer. The anticancer activities of berries are attributed to their high content of phytochemicals and to their relevant antioxidant properties. In vitro and in vivo studies have demonstrated that berries and their bioactive components exert therapeutic and preventive effects against colon cancer by the suppression of inflammation, oxidative stress, proliferation and angiogenesis, through the modulation of multiple signaling pathways such as NF-κB, Wnt/β-catenin, PI3K/AKT/PKB/mTOR, and ERK/MAPK. Based on the exciting outcomes of preclinical studies, a few berries have advanced to the clinical phase. A limited number of human studies have shown that consumption of berries can prevent colorectal cancer, especially in patients at high risk (familial adenopolyposis or aberrant crypt foci, and inflammatory bowel diseases). In this review, we aim to highlight the findings of berries and their bioactive compounds in colon cancer from in vitro and in vivo studies, both on animals and humans. Thus, this review could be a useful step towards the next phase of berry research in colon cancer.


title: Pulmonary evaluation and prevalence of non-invasive ventilation in patients with amyotrophic lateral sclerosis: a multicenter survey and proposal of a pulmonary protocol

abstract: Abstract In order to evaluate the current standard of care for the management of respiratory failure in patients with amyotrophic lateral sclerosis (ALS), a questionaire was mailed to the Medical Directors of 48 multidisciplinary ALS centers in the United States. Twenty centers reported information on 2357 patients, mean of 124 patients per center. Pulmonary function tests were performed at each visit in 17/20 institutions. Arterial blood gases, maximal expiratory pressures and maximal inspiratory pressures were followed in three centers and serum chloride was monitored in only four centers. The use of non-invasive ventilation (NIV) was extremely variable (range 0–50%) and included 360 patients (15%). The majority of centers used symptoms/signs of hypoventilation and worsening forced vital capacity (FVC) to initiate NIV with no established protocol. A FVC between 20 and 40% was used by most centers to initiate NIV. Due to great variability in the approach to monitoring pulmonary function among ALS centers and the modest effects of current medications to slow disease progression, we propose the use of a structured protocol which can prospectively study the role of NIV in prolonging survival and improving quality of life.


A unified view of kernel k-means, spectral clustering and graph cuts


title: A case study of OSPF behavior in a large enterprise network

abstract: Open Shortest Path First (OSPF) is widely deployed in IP networks to manage intra-domain routing. OSPF is a link-state protocol, in which routers reliably flood "Link State Advertisements" (LSAs), enabling each to build a consistent, global view of the routing topology. Reliable performance hinges on routing stability, yet the behavior of large operational OSPF networks is not well understood. In this paper, we provide a case study on the eharacteristics and dynamics of LSA traffic for a large enterprise network. This network consists of several hundred routers, distributed in tens of OSPF areas, and connected by LANs and private lines. For this network, we focus on LSA traffic and analyze: (a) the class of LSAs triggered by OSPF's soft-state refresh, (b) the class of LSAs triggered by events that change the status of the network, and (c) a class of "duplicate" LSAs received due to redundancy in OSPF's reliable LSA flooding mechanism. We derive the baseline rate of refresh-triggered LSAs automatically from network configuration information. We also investigate finer time scale statistical properties of this traffic, including burstiness, periodicity, and synchronization. We discuss root causes of event-triggered and duplicate LSA traffic, as well as steps identified to reduce this traffic (e.g., localizing a failing router or changing the OSPF configuration).


title: Understanding reflective practice.

abstract: The Nursing and Midwifery Council (NMC) requires that nurses and midwives use feedback as an opportunity for reflection and learning, to improve practice. The NMC revalidation process stipulates that practitioners provide examples of how they have achieved this. To reflect in a meaningful way, it is important to understand what is meant by reflection, the skills required, and how reflection can be undertaken successfully. Traditionally, reflection occurs after an event encountered in practice. The authors challenge this perception, suggesting that reflection should be undertaken before, during and after an event. This article provides practical guidance to help practitioners use reflective models to write reflective accounts. It also outlines how the reflective process can be used as a valuable learning tool in preparation for revalidation.


title: A Computational Framework for Influenza Antigenic Cartography

abstract: Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.


title: Web usage mining: A review on process, methods and techniques

abstract: In Current era, internet is playing such a vital role in our everyday life that it is very difficult to survive without it. The World Wide Web (WWW) has influenced a lot to both users (visitors) as well as the web site owners. Enormous growth of World Wide Web increases the complexity for users to browse effectively. To increase the performance of web sites better web site design, web server activities are changed as per users' interests. To achieve this they have to analyze user access pattern which are captured in the form of log files. Web usage mining is a process of analyzing interaction of user with different web application. Web usage mining can be seen as three step process: data pre-processing, pattern discovery and pattern analysis. Due to tremendous use of web, web log files are increase with faster rate and size is also huge. These data are usually noisy and ambiguous hence preprocessing step is essential in mining process. Different pre-processing techniques are data cleaning, user identification, session identification and transaction identification. In this paper, we provide detailed survey of work done so far on data collection and pre-processing stage of web usage mining.


title: Fourier ptychographic reconstruction using Wirtinger flow optimization

abstract: Recently Fourier Ptychography (FP) has attracted great attention, due to its marked effectiveness in leveraging snapshot numbers for spatial resolution in large field-of-view imaging. To acquire high signal-to-noise-ratio (SNR) images under angularly varying illuminations for subsequent reconstruction, FP requires long exposure time, which largely limits its practical applications. In this paper, based on the recently reported Wirtinger flow algorithm, we propose an iterative optimization framework incorporating phase retrieval and noise relaxation together, to realize FP reconstruction using low SNR images captured under short exposure time. Experiments on both synthetic and real captured data validate the effectiveness of the proposed reconstruction method. Specifically, the proposed technique could save around 80% exposure time to achieve similar retrieval accuracy compared to the conventional FP. Besides, we have released our source code for non-commercial use.


title: Analytical Approach to Design Hairpin Windings in High Performance Electric Vehicle Motors

abstract: This work deals with the analysis and design of a hairpin winding. Such a winding type is more and more used in traction motor of electric and hybrid electric vehicle. It allows a higher slot fill factor in comparison with the traditional round wire windings. This yields a high reduction of the Joule losses, when the motor is required to develop high torque, e.g., during accelerations. However, due to the high cross-area section of such rectangular-shape conductors, the current is non-uniformly distributed in the conductors, and this causes a reduction of the benefit of the higher copper area. In addition, due to the high frequency at the higher speeds, parallel paths are often required. Particular care has to be posed when such parallels are designed, with proper transposition of the wires, to avoid a non uniform distribution of the current in the parallel paths. The motor parameters are computed analytically and then validated by means of a Finite Element analysis. Satisfactory results are achieved. The additional losses are computed according to the conductor geometry and the whole winding connections (parallel paths, transposition, and so on) with a satisfactory precision.


title: Metadata management for integration and analysis of earth observation data

abstract: Earth observation technologies have developed rapidly during the last decades. Substantial amounts of earth observation data have been acquired and stored among the literature and databases of various research fields such as climatology, oceanography, agriculture, and ecology. Analysis and integration of such data might produce valuable data products to promote understanding of the global environment, and to solve global environmental issues. However, most institutions store and manage their Earth observation data in their own manner, with little metadata. Scientists have to struggle hard to search for valuable data from data out of their research domain and seek their usage. In this paper we introduce a conceptual model for earth observation data. Utilizing a model to express earth observation item associated with ontologies, The model is an simple quintuple with information extracted from conventional data models, and is used to uniquely determine portions of earth observation data, which enables flexible annotation of earth observation data. We also introduce our systems for metadata management and user interfaces for encouraging user annotations of earth observation data that can help scientists discover and understand useful data that can support their research.


title: Microexpression Identification and Categorization Using a Facial Dynamics Map

abstract: Unlike conventional facial expressions, microexpressions are instantaneous and involuntary reflections of human emotion. Because microexpressions are fleeting, lasting only a few frames within a video sequence, they are difficult to perceive and interpret correctly, and they are highly challenging to identify and categorize automatically. Existing recognition methods are often ineffective at handling subtle face displacements, which can be prevalent in typical microexpression applications due to the constant movements of the individuals being observed. To address this problem, a novel method called the Facial Dynamics Map is proposed to characterize the movements of a microexpression in different granularity. Specifically, an algorithm based on optical flow estimation is used to perform pixel-level alignment for microexpression sequences. Each expression sequence is then divided into spatiotemporal cuboids in the chosen granularity. We also present an iterative optimal strategy to calculate the principal optical flow direction of each cuboid for better representation of the local facial dynamics. With these principal directions, the resulting Facial Dynamics Map can characterize a microexpression sequence. Finally, a classifier is developed to identify the presence of microexpressions and to categorize different types. Experimental results on four benchmark datasets demonstrate higher recognition performance and improved interpretability.


title: Latent Aspect Mining via Exploring Sparsity and Intrinsic Information

abstract: We investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the domain, and predict the user's ratings on each aspect for each review. Another goal is to detect key terms for each aspect. Existing works on predicting aspect ratings fail to handle the aspect sparsity problem in the review texts leading to unreliable prediction. We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner. By considering the user and item side information of review texts, we introduce two latent variables, namely, user intrinsic aspect interest and item intrinsic aspect quality facilitating better modeling of aspect generation leading to improvement on the accuracy and reliability of predicted aspect ratings. Furthermore, we provide an analytical investigation on the Maximum A Posterior (MAP) optimization problem used in our proposed model and develop a new block coordinate gradient descent algorithm to efficiently solve the optimization with closed-form updating formulas. We also study its convergence analysis. Experimental results on the two real-world product review corpora demonstrate that our proposed model outperforms existing state-of-the-art models.


title: Automatic Identification Of Word Translations From Unrelated English And German Corpora

abstract: Algorithms for the alignment of words in translated texts are well established. However, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated texts. This task is more difficult, because most statistical clues useful in the processing of parallel texts cannot be applied to non-parallel texts. Whereas for parallel texts in some studies up to 99% of the word alignments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. The current study, which is based on the assumption that there is a correlation between the patterns of word co-occurrences in corpora of different languages, makes a significant improvement to about 72% of word translations identified correctly.


title: Calibration of ZMPT101B voltage sensor module using polynomial regression for accurate load monitoring

abstract: Smart Electricity is quickly developing as the results of advancements in sensor technology. The accuracy of a sensing device is the backbone of every measurement and the fundamental of every electrical quantity measurement is the voltage and current sensing. The sensor calibration in the context of this research means the marking or scaling of the voltage sensor so that it can present accurate sampled voltage from the ADC output using appropriate algorithm. The peakpeak input voltage (measured with a standard FLUKE 115 meter) to the sensor is correlated with the peak-peak ADC output of the sensor using 1 to 5th order polynomial regression, in order to determine the best fitting relationship between them. The arduino microcontroller is used to receive the ADC conversion and is also programmed to calculate the root mean square value of the supply voltage. The analysis of the polynomials shows that the third order polynomial gives the best relationship between the analog input and ADC output. The accuracy of the algorithm is tested in measuring the root mean square values of the supply voltage using instantaneous voltage calculation and peak-peak voltage methods. The error in the measurement is less than 1% in the peak-peak method and less than 2.5% in the instantaneous method for voltage measurements above 50V AC, which is very good for measurements in utility. Therefore, the proposed calibration method will facilitate more accurate voltage and power computing for researchers and designers especially in load monitoring where the applied voltage is 240V or 120V ranges.


title: Proving Theorems about LISP Functions

abstract: We describe some simple heuristics combining evaluation and mathematical induction which we have implemented in a program that automatically proves a wide variety of theorems about recursive LISP functions. The method the program uses to generate induction formulas is described at length. The theorems proved by the program include that REVERSE is its own inverse and that a particular SORT program is correct. Appendix B contains a list of the theorems proved by the program.


title: User acceptance of virtual Worlds

abstract: Virtual Worlds enable new ways of communication, collaboration, and cooperation over the Internet by applying 3D environments and voice over IP technologies. This papers aims at assessing user acceptance of Virtual Worlds by applying the Technology Acceptance Model (TAM). Based on literature review, an extended TAM has been constructed. Based on empirical data from an online-survey with 249 participants, hypotheses about user acceptance and adoption of Virtual Worlds are empirically tested. Our results show that community factors such as communication, collaboration, and cooperation play a pivotal role in means of influencing user intention and acceptance of Virtual Worlds. This paper provides meaningful insights for theory building of user acceptance in the context of Virtual Worlds as it shows that traditional models have to take ‘community’ factors into account. Our findings are also helpful for practitioners to develop new business models in Virtual Worlds.


title: A Taxonomy of Model Transformation

abstract: This article proposes a taxonomy of model transformation, based on the discussions of a working group on model transformation of the Dagstuhl seminar on Language Engineering for Model-Driven Software Development. This taxonomy can be used, among others, to help developers in deciding which model transformation language or tool is best suited to carry out a particular model transformation activity.


title: Skeleton-Based Relational Modeling for Action Recognition

abstract: With the fast development of effective and low-cost human skeleton capture systems, skeleton-based action recognition has attracted much attention recently. Most existing methods use Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) to extract spatio-temporal information embedded in the skeleton sequences for action recognition. However, these approaches are limited in the ability of relational modeling in a single skeleton, due to the loss of important structural information when converting the raw skeleton data to adapt to the CNN or RNN input. In this paper, we propose an Attentional Recurrent Relational Network-LSTM(ARRN-LSTM) to simultaneously model spatial configurations and temporal dynamics in skeletons for action recognition. The spatial patterns embedded in a single skeleton are learned by a Recurrent Relational Network, followed by a multi-layer LSTM to extract temporal features in the skeleton sequences. To exploit the complementarity between different geometries in the skeleton for sufficient relational modeling, we design a two-stream architecture to learn the relationship among joints and explore the underlying patterns among lines simultaneously. We also introduce an adaptive attentional module for focusing on potential discriminative parts of the skeleton towards a certain action. Extensive experiments are performed on several popular action recognition datasets and the results show that the proposed approach achieves competitive results with the state-of-the-art methods.


title: Zero and data reuse-aware fast convolution for deep neural networks on GPU

abstract: Convolution operations dominate the total execution time of deep convolutional neural networks (CNNs). In this paper, we aim at enhancing the performance of the state-of-the-art convolution algorithm (called Winograd convolution) on the GPU. Our work is based on two observations: (1) CNNs often have abundant zero weights and (2) the performance benefit of Winograd convolution is limited mainly due to extra additions incurred during data transformation. In order to exploit abundant zero weights, we propose a low-overhead and efficient hardware mechanism that skips multiplications that will always give zero results regardless of input data (called ZeroSkip). In addition, to leverage the second observation, we present data reuse optimization for addition operations in Winograd convolution (called AddOpt), which improves the utilization of local registers, thereby reducing on-chip cache accesses. Our experiments with a real-world deep CNN, VGG-16, on GPGPU-Sim and Titan X show that the proposed methods, ZeroSkip and AddOpt, achieve 51.8% higher convolution performance than the baseline Winograd convolution. Moreover, even without any hardware modification, AddOpt alone gives 35.6% higher performance on a real hardware platform, Titan X.


title: SCRAP: Architecture for signature-based protection from Code Reuse Attacks

abstract: Code Reuse Attacks (CRAs) recently emerged as a new class of security exploits. CRAs construct malicious programs out of small fragments (gadgets) of existing code, thus eliminating the need for code injection. Existing defenses against CRAs often incur large performance overheads or require extensive binary rewriting and other changes to the system software. In this paper, we examine a signature-based detection of CRAs, where the attack is detected by observing the behavior of programs and detecting the gadget execution patterns. We first demonstrate that naive signature-based defenses can be defeated by introducing special “delay gadgets” as part of the attack. We then show how a software-configurable signature-based approach can be designed to defend against such stealth CRAs, including the attacks that manage to use longer-length gadgets. The proposed defense (called SCRAP) can be implemented entirely in hardware using simple logic at the commit stage of the pipeline. SCRAP is realized with minimal performance cost, no changes to the software layers and no implications on binary compatibility. Finally, we show that SCRAP generates no false alarms on a wide range of applications.


title: Maximum boost control of the Z-source inverter

abstract: This paper explores control methods for the Z-source inverter and their relationships of voltage boost versus modulation index. A maximum boost control is presented to produce the maximum voltage boost (or voltage gain) under a given modulation index. The control method, relationships of voltage gain versus modulation index, and voltage stress versus voltage gain are analyzed in detail and verified by simulation and experiment.


title: Probability Product Kernels

abstract: The advantages of discriminative learning algorithms and kernel machines are combined with generative modeling using a novel kernel between distributions. In the probability product kernel, data points in the input space are mapped to distributions over the sample space and a general inner product is then evaluated as the integral of the product of pairs of distributions. The kernel is straightforward to evaluate for all exponential family models such as multinomials and Gaussians and yields interesting nonlinear kernels. Furthermore, the kernel is computable in closed form for latent distributions such as mixture models, hidden Markov models and linear dynamical systems. For intractable models, such as switching linear dynamical systems, structured mean-field approximations can be brought to bear on the kernel evaluation. For general distributions, even if an analytic expression for the kernel is not feasible, we show a straightforward sampling method to evaluate it. Thus, the kernel permits discriminative learning methods, including support vector machines, to exploit the properties, metrics and invariances of the generative models we infer from each datum. Experiments are shown using multinomial models for text, hidden Markov models for biological data sets and linear dynamical systems for time series data.


title: Research on computer vision-based for UAV autonomous landing on a ship

abstract: In this paper, a novel approach to UAV's automatic landing on the ship's deck is proposed. We present the design of the cooperative object, and then begin our basic research on UAV autonomous landing on a ship by using computer vision and affine moment invariants. We analyze the infrared radiation images in our experiments by extracting the target from the background and then recognizing it. Also, we calculate the angle of yaw. We study the basic research concerning automatic UAV navigation and landing on the deck. Based on our experiments, the average recognition time is 17.2ms which is obtained through the use of affine moment invariants. This type of speed is expected to improve the reliability and real-time performance of autonomous UAV landing.


title: RevEcoR: an R package for the reverse ecology analysis of microbiomes

abstract: BACKGROUND ::: All species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecological methods can only be applied on a small scale and for relatively well-understood biological systems. Recently, a graph-theory-based algorithm called the reverse ecology approach has been developed that can analyze the metabolic networks of all the species in a microbial community, and predict the metabolic interface between species and their environment. ::: ::: ::: RESULTS ::: Here, we present RevEcoR, an R package and a Shiny Web application that implements the reverse ecology algorithm for determining microbe-microbe interactions in microbial communities. This software allows users to obtain large-scale ecological insights into species' ecology directly from high-throughput metagenomic data. The software has great potential for facilitating the study of microbiomes. ::: ::: ::: CONCLUSIONS ::: RevEcoR is open source software for the study of microbial community ecology. The RevEcoR R package is freely available under the GNU General Public License v. 2.0 at http://cran.r-project.org/web/packages/RevEcoR/ with the vignette and typical usage examples, and the interactive Shiny web application is available at http://yiluheihei.shinyapps.io/shiny-RevEcoR , or can be installed locally with the source code accessed from https://github.com/yiluheihei/shiny-RevEcoR .


title: Efficient Analysis of Frequency-Selective Surfaces by a Simple Equivalent-Circuit Model

abstract: The transmission and reflection properties of frequency-selective surfaces (FSSs) are evaluated through a simple and accurate first-order circuit approach. The approximate analysis, based on the parallel between real structure and a lumped-LC-network counterpart, is also useful for acquiring physical insights into the working principles of frequency-selective surfaces. The first part of the paper describes a technique for computing lumped parameters of the most common frequency-selective-surface elements. The L and C parameters representing a given frequency-selective-surface element are derived only one time, at normal incidence, and stored, so as to form a database. The second part of the paper deals with the derivation of simple relations allowing the generalization of the stored LC couples in the case where the frequency-selective surface is printed or embedded in arbitrarily thick dielectric slabs, when the incident angle is varied from normal incidence, or if a different periodicity with respect to the reference periodicity is adopted. The generalized lumped parameters are included in an equivalent transmission line for computing the response of generic frequency-selective-surface configurations with no additional computational effort. The results obtained through the simplified model presented here are verified by a careful comparison with MoM simulations.


title: Identifying users across social tagging systems

abstract: How much do tagging activities tell about a user? Is it possible to identify people in Delicious based on the tags, which they use in Flickr? In this paper we study those questions and investigate whether users can be identified across social tagging systems. We combine two kinds of information: their user ids and their tags. We introduce and compare a variety of approaches to measure the distance between user profiles for identification. With the best performing combination we achieve, depending on the actual settings, accuracies of between 60% and 80% which demonstrates that the traces of Web 2.0 users can reveal quite much about their identity.


title: But What Do We Actually Know?

abstract: Knowledge bases such as Wikidata, DBpedia, YAGO, or the Google Knowledge Vault collect a vast number of facts about the world. But while quite some facts are known about the world, little is known about how much is unknown. For example, while the knowledge base may tell us that Barack Obama is the father of Malia Obama and Sasha Obama, it does not tell us whether these are all of his children. This is not just an epistemic challenge, but also a practical problem for data producers and consumers. We envision that KBs become annotated with information about their recall on specific topics. We show what such annotations could look like, how they could be obtained, and survey related work.


title: Towards operationalizing complexity leadership : How generative, administrative and community-building leadership practices enact organizational outcomes

abstract: Over five years ago, The Leadership Quarterly published a special issue on complexity to advance a new way of thinking about leadership. In shifting attention away from the individual to the organizing process itself, complexity added an important focus on process and context to leadership and management research. Yet, the complexity approach creates challenges for researchers who must combine or replace individual level constructs—like those built through surveys or factor analysis—with richer theories that investigate networked meso dynamics, multilevel phenomena, emergent processes, and organizational outcomes. To address this challenge, the present analysis draws on theoretical and empirical work over the last several years to identify five specific areas where complexity inspired research has led to new insights about the mechanisms that enable the organization to perform and adapt. It suggests propositions that describe how leadership and management, defined holistically, might activate complexity mechanisms to perform five essential organizing functions.


title: A Taxonomy of Botnet Behavior, Detection, and Defense

abstract: A number of detection and defense mechanisms have emerged in the last decade to tackle the botnet phenomenon. It is important to organize this knowledge to better understand the botnet problem and its solution space. In this paper, we structure existing botnet literature into three comprehensive taxonomies of botnet behavioral features, detection and defenses. This elevated view highlights opportunities for network defense by revealing shortcomings in existing approaches. We introduce the notion of a dimension to denote different criteria which can be used to classify botnet detection techniques. We demonstrate that classification by dimensions is particularly useful for evaluating botnet detection mechanisms through various metrics of interest. We also show how botnet behavioral features from the first taxonomy affect the accuracy of the detection approaches in the second taxonomy. This information can be used to devise integrated detection strategies by combining complementary approaches. To provide real-world context, we liberally augment our discussions with relevant examples from security research and products.


title: Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging

abstract: For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). The architecture of the network only relies on baseline PET studies of AD and normal subjects as the training dataset. Feature extraction and complicated image preprocessing including nonlinear warping are unnecessary for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI patients outperformed conventional feature-based quantification approaches. ROC analyses revealed that performance of CNN-based approach was significantly higher than that of the conventional quantification methods (p < 0.05). Output scores of the network were strongly correlated with the longitudinal change in cognitive measurements. These results show the feasibility of deep learning as a tool for predicting disease outcome using brain images.


title: PEPsal: a Performance Enhancing Proxy designed for TCP satellite connections

abstract: Internet communications with paths that include satellite link face some peculiar challenges, due to the presence of a long propagation wireless channel. In this paper, we propose a Performance Enhancing Proxy (PEP) solution, called PEPsal, which is, to the best of the authors' knowledge, the first open source TCP splitting solution for the GNU/Linux operating systems. PEPsal improves the performance of a TCP connection over a satellite channel making use of the TCP Hybla, a TCP enhancement for satellite networks developed by the authors. The objective of the paper is to present and evaluate the PEPsal architecture, by comparing it with end to end TCP variants (NewReno, SACK, Hybla), considering both performance and reliability issues. Performance is evaluated by making use of a testbed set up at the University of Bologna, to study advanced transport protocols and architectures for internet satellite communications.


title: Neural Bag-of-Features learning

abstract: In this paper, a neural learning architecture for the well-known Bag-of-Features (BoF) model, called Neural Bag-of-Features, is proposed. The Neural BoF model is formulated in two neural layers: a Radial Basis Function (RBF) layer and an accumulation layer. The ability of the Neural BoF model to improve the classification performance is demonstrated using four datasets, including a large-scale dataset, and five different feature types. The gains are two-fold: the classification accuracy increases and, at the same time, smaller networks can be used, reducing the required training and testing time. Furthermore, the Neural BoF natively supports training and classifying from feature streams. This allows the proposed method to efficiently scale to large datasets. The streaming process can also be used to introduce noise and reduce the over-fitting of the network. Finally, the Neural BoF provides a framework that can model and extend the dictionary learning methodology. HighlightsA neural generalization of the Bag-of-Features (BoF) model is introduced.The proposed model supports the discriminant weighting of the feature space.Two incremental algorithms (for training and classification) are proposed.A method for providing visual attention information for the BoF model is introduced.The proposed method is evaluated using four datasets from different domains.


title: Where am I: Place instance and category recognition using spatial PACT

abstract: We introduce spatial PACT (principal component analysis of census transform histograms), a new representation for recognizing instances and categories of places or scenes. Both place instance recognition (ldquoI am in Room 113rdquo) and category recognition (ldquoI am in an officerdquo) have been widely researched. Features that have different discriminative power/invariance tradeoff have been used separately for the two tasks. PACT captures local structures of an image through the census transform (CT), while large-scale structures are captured by the strong correlation between neighboring CT values and the histogram. The PCA operation ignores noise in the histogram distribution, computes important ldquoprimitive shapesrdquo, and results in a compact representation. Spatial PACT, a spatial pyramid of PACT, further incorporates global structures in the image. Our experiments demonstrate that spatial PACT outperforms the current state-of-the-art in several place and scene recognition, and shape matching datasets. Besides, spatial PACT is easy to implement. It has nearly no parameter to tune, and evaluates extremely fast.


title: Design and implementation of a real-time tracking and telemetry system for a solar car

abstract: It is very critical for solar racers to achieve real time tracking of their solar vehicle during any race. The system is made of real time visualisation of the video feedback of the car from the escort vehicle, the location of the car on the maps as it is racing and its velocity. The real time video feedback of the race could be retrieved from the cloud for broadcasting purposes. The main advantage of this system is the wireless link communication which was achieved for distance transmission, coupled to a very reliable and robust desktop application for data sensor display purposes.


title: Incentives for Privacy Tradeoff in Community Sensing

abstract: Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications. Yet, reasonable privacy concerns often limit the access to such data streams. How should systems valuate and negotiate access to private information, for example in return for monetary incentives? How should they optimally choose the participants from a large population of strategic users with privacy concerns, and compensate them for information shared? In this paper, we address these questions and present a novel mechanism, SeqTGreedy, for budgeted recruitment of participants in community sensing. We first show that privacy tradeoffs in community sensing can be cast as an adaptive submodular optimization problem. We then design a budget feasible, incentive compatible (truthful) mechanism for adaptive submodular maximization, which achieves near-optimal utility for a large class of sensing applications. This mechanism is general, and of independent interest. We demonstrate the effectiveness of our approach in a case study of air quality monitoring, using data collected from the Mechanical Turk platform. Compared to the state of the art, our approach achieves up to 30% reduction in cost in order to achieve a desired level of utility.


title: Ultrasonic fingerprint sensor based on a PMUT array bonded to CMOS circuitry

abstract: This paper describes a single-chip 591×438 DPI ultrasonic fingerprint sensor. The sensor is based on a piezoelectric micromachined ultrasonic transducer (PMUT) array that is bonded at wafer-level to complementary metal oxide semiconductor (CMOS) signal processing electronics to produce a pulse-echo ultrasonic imager on a chip. To meet the 500 DPI standard for consumer fingerprint sensors, the PMUT pitch was reduced by approximately a factor of two relative to an earlier design. We conducted a systematic design study of the individual PMUT design and array to achieve this scaling while maintaining a high fill-factor. The resulting 110×56 PMUT array, composed of rectangular PMUTs achieved a 51.7% fill-factor, three times greater than that of the previous design. Together with the custom CMOS ASIC, the sensor achieves 75 micron lateral resolution and 150 micron axial resolution over a 4.6 mm × 3.2 mm image.


Theory: the professional dimension.


title: An admittance control scheme for haptic interfaces based on cable-driven parallel mechanisms

abstract: This paper presents a cable-driven parallel mechanism as a haptic interface and its underlying control method. This human-sized, three-degree-of-freedom mechanism has a tetrahedral architecture, four cables and evolves in three-dimensional space. A brief review of the kinematics of the mechanism is presented. Also, an admittance control law coupled with a closed-loop velocity controller is proposed. The control method is then refined by introducing adaptations for smooth surfaces and sharp edges. This control method is then validated by experimental results. Furthermore, the geometry of the mechanism is identified by a method that does not require any other sensor than the motor encoders.


title: A survey of trust in social networks

abstract: Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. In addition to individuals using such networks to connect to their friends and families, governments and enterprises have started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Therefore, trust becomes an essential and important element of a successful social network. In this article, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. We then discuss recent works addressing three aspects of social trust: trust information collection, trust evaluation, and trust dissemination. Finally, we compare and contrast the literature and identify areas for further research in social trust.


title: Discovering important people and objects for egocentric video summarization

abstract: We present a video summarization approach for egocentric or “wearable” camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video — such as the nearness to hands, gaze, and frequency of occurrence — and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results with 17 hours of egocentric data show the method's promise relative to existing techniques for saliency and summarization.


title: PosDB: An Architecture Overview

abstract: PosDB is an engine of a disk-based column-store DBMS designed for processing OLAP queries in a shared nothing environment. It is written completely from scratch and aims to become a platform for studying the distributed query processing in column-stores. This paper presents the first comprehensive description of the system. The presentation begins with the history of column-stores in order to clarify the reasons of their success. Next, the creation of a new system is justified, and an overview of its architecture is given. Finally, all its components are described in detail. Currently, query execution in PosDB is based on the Volcano model with block-oriented processing and late materialization. Various physical operators have been developed for relational operations such as join, aggregation, and selection. Some auxiliary operators were developed to support intraquery parallelism and network communication. Data distribution is achieved using horizontal range partitioning and data replication. The current version of PosDB can execute all queries from the Star Schema Benchmark in both centralized and distributed environments.


Three Perspectives for Evaluating Human-Robot Interaction


Chapter 18 DEPENDENCY-BASED EVALUATION OF MINIPAR


title: Detectives: detecting coalition hit inflation attacks in advertising networks streams

abstract: Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.


abstract: This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.


title: Examining the Relationship between Student Teachers' Self-Efficacy Beliefs and Their Intended Uses of Technology for Teaching: A Structural Equation Modelling Approach.

abstract: This study examines the relationship between computer self-efficacy and intended uses of technology of student teachers (N=1094) at a teacher training institute in Singapore. Self-efficacy was assessed by three factors: Basic Teaching Skills (BTS), Advanced Teaching Skills (ATS), and Technology for Pedagogy (TP), and intended use of technology was measured by two factors: Traditional Use of Technology (TUT) and Constructivist Use of Technology (CUT). Participants responded to a 7-point Likert-type scale for each factor. Analysis was conducted using the structural equation modelling approach and a good model fit was found for both the measurement and structural models. Results showed that significant relationships exist among BTS, TP, TUT, and CUT. However, ATS did not influence TUT and CUT in a significant way. Overall, the results of this study offer some evidence that student teachers’ self-efficacy is a significant influence on whether they use technology in a traditionalist or constructivist way.


title: An automatic tool for the analysis of natural language requirements

abstract: Using automatic tools for the quality analysis of Natural Language (NL) requirements is recognized as a key factor for achieving software quality. Unfortunately few tools and techniques for the NL requirements analysis are currently available. This paper presents a methodology and a tool (called QuARS Quality Analyzer for Requirement Specifications) for analyzing NL requirements in a systematic and automatic way. QuARS allows requirements engineers to perform an initial parsing of the requirements in order to automatically detect potential linguistic defects that could cause interpretation problems at subsequent stages in developing the software. This tool is also able to partially support the consistency and completeness analysis by clustering the requirements according to specific topics.


title: Irregular moving object detecting and tracking based on color and shape in real-time system

abstract: This paper describes an efficient approach for irregular moving object detecting and tracking in real-time system based on color and shape information of the target object from realistic environment. Firstly, the data is gotten from a real-time camera system at a stable frame rate. And then, each frame is processed by using proposed method to detect and track the target object immediately in consecutive frames. Finally, the target position based modifying controlling signal is used to control pan-tilt-zoom camera (PTZ camera) in order to automatically follow the target object. Our experiment results were obtained by using pan-tilt-zoom camera Sony EVI D70 under variety environments in real-time and our algorithm is verified that it can be implemented effectively and accurately at high frame speed, even 29.97 fps.


title: ARIEL: automatic wi-fi based room fingerprinting for indoor localization

abstract: People spend the majority of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or mobile phone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Existing room localization methods, however, require labor-intensive manual annotation of individual rooms. We present ARIEL, a room localization system that automatically learns room fingerprints based on occupants' indoor movements. ARIEL consists of (1) a zone-based clustering algorithm that accurately identifies in-room occupancy "hotspot(s)" using Wi-Fi signatures; (2) a motion-based clustering algorithm to identify inter-zone correlation, thereby distinguishing different rooms; and (3) an energy-efficient motion detection algorithm to minimize the noise of Wi-Fi signatures. ARIEL has been implemented and deployed for real-world testing with 21 users over a 10-month period. Our studies show that it supports room localization with higher than 95% accuracy without requiring labor-intensive manual annotation.


title: Effect of Fe2+ concentration on fermentative hydrogen production by mixed cultures

abstract: Abstract The effect of the Fe 2 + concentrations ranging from 0 to 1500 mg/L on the fermentative hydrogen production from glucose was investigated in batch tests by mixed cultures at 35 °C and initial pH 7.0. The experimental results showed that in certain concentration range, Fe 2 + was able to enhance the hydrogen production rate, the cumulative hydrogen quantity, and the hydrogen yield by the mixed cultures. The maximum cumulative hydrogen quantity of 302.3 mL and the maximum hydrogen yield of 311.2 mL/g glucose were obtained at the Fe 2 + concentration of 300 and 350 mg/L, respectively. The major soluble metabolites produced by the mixed cultures were ethanol, acetic acid, and butyric acid, with little or no propionic acid. The glucose degradation efficiency had the trend to decrease with increasing Fe 2 + concentrations from 0 to 1500 mg/L, but when the Fe 2 + concentrations were lower than 350 mg/L, the glucose degradation efficiency was between 96.25 and 98.78%, which is relatively high and kept unchanged with increasing Fe 2 + concentrations. In certain concentration range, Fe 2 + was able to enhance the biomass production yield. When the Fe 2 + concentrations were between 100 and 750 mg/L, there was a high biomass production yield plateau ranging from 259.2 to 334.2 mg/g glucose. The final pH value had the trend to decrease with increasing Fe 2 + concentrations from 0 to 1000 mg/L, and the lowest final pH value was about 4.3 at the Fe 2 + concentration of 1000 mg/L.


title: Fundamental Movement Skills and Autism Spectrum Disorders

abstract: Delays and deficits may both contribute to atypical development of movement skills by children with ASD. Fundamental movement skills of 25 children with autism spectrum disorders (ASD) (ages 9–12 years) were compared to three typically developing groups using the Test of Gross Motor Development (TGMD-2). The group matched on chronological age performed significantly better on the TGMD-2. Another comparison group matched on movement skill demonstrated children with ASD perform similarly to children approximately half their age. Comparisons to a third group matched on mental age equivalence revealed the movement skills of children with ASD are more impaired than would be expected given their cognitive level. Collectively, these results suggest the movement skills of children with ASD reflect deficits in addition to delays.


title: Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

abstract: We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-to-sequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1 (F-score on AMR-triples). We examine five different approaches to improve this baseline result: (i) reordering AMR branches to match the word order of the input sentence increases performance to 58.3; (ii) adding part-of-speech tags (automatically produced) to the input shows improvement as well (57.2); (iii) So does the introduction of super characters (conflating frequent sequences of characters to a single character), reaching 57.4; (iv) optimizing the training process by using pre-training and averaging a set of models increases performance to 58.7; (v) adding silver-standard training data obtained by an off-the-shelf parser yields the biggest improvement, resulting in an F-score of 64.0. Combining all five techniques leads to an F-score of 71.0 on holdout data, which is state-of-the-art in AMR parsing. This is remarkable because of the relative simplicity of the approach.


title: Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing

abstract: By transforming data into binary representation, i.e., Hashing, we can perform high-speed search with low storage cost, and thus, Hashing has collected increasing research interest in the recent years. Recently, how to generate Hashcode for multimodal data (e.g., images with textual tags, documents with photos, and so on) for large-scale cross-modality search (e.g., searching semantically related images in database for a document query) is an important research issue because of the fast growth of multimodal data in the Web. To address this issue, a novel framework for multimodal Hashing is proposed, termed as Collective Matrix Factorization Hashing (CMFH). The key idea of CMFH is to learn unified Hashcodes for different modalities of one multimodal instance in the shared latent semantic space in which different modalities can be effectively connected. Therefore, accurate cross-modality search is supported. Based on the general framework, we extend it in the unsupervised scenario where it tries to preserve the Euclidean structure, and in the supervised scenario where it fully exploits the label information of data. The corresponding theoretical analysis and the optimization algorithms are given. We conducted comprehensive experiments on three benchmark data sets for cross-modality search. The experimental results demonstrate that CMFH can significantly outperform several state-of-the-art cross-modality Hashing methods, which validates the effectiveness of the proposed CMFH.


title: VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks

abstract: Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.


title: Preliminary Analysis for Geological Characteristics and Metallogenic Regularity of Yaziquan-Weibao Belt in East Kunlun Mountains,Xinjiang

abstract: The Qimantage area lied in east Kunlun Fe-Pb-Zn-Cu-Co-Au-W-Sn polymetallic and salt deposits belt.As limited by higher altitude,lower temperature,lack of oxygen and other poor natural conditions,there ore exploration has little been done in the past.But in Qinghai province,some new ore deposits have been found since 2000.Recently,as the more supports obtainded from government,the more geology research works and ore deposit exploring works have been done. Followed by a series of large or giant ore deposits has been explored,such as Baiganhu W-Sn ore deposit,Dimunalike Fe ore deposit and Weibao Pb-Zn ore deposit,now in this area there has been a large cluster of ferrous and in-ferrous(such as Fe,Cu,Pb,Zn,W,Sn) mineral deposits,and a hot spot of ore deposit exploring has come into existence.And it would be an important metal resource reservoir,if more exploring and research works have been done and more funds have been obtained.Here,based on geological,geophysical and geochemical characteristics of the belt,the metallogenic regularity of Yaziquan-Weibao belt is preliminarily studied,characteristics of geneses of ore deposits are compared and distinguished,and a regional metallogenetic model is pointed out.


title: Patient-reported outcomes (PRO) focused on adverse events (PRO-AEs) in adjuvant and metastatic breast cancer: clinical and translational implications

abstract: Purpose ::: The capture of adequate treatment outcomes and quality of life (QOL) of advanced breast cancer patients in clinical routine represents a great challenge. Patient-reported outcomes (PROs) are data elements directly reported by patients about experiences with care, including symptoms, functional status, or quality of life. There is growing interest in the medical community for the evaluation and implementation of PROs of adverse events (PRO-AEs). Recent interest in PROs in health care has evolved in the context of patient centeredness. Our primary objective was to identify trials that had implemented PRO-AEs in the breast cancer treatment setting, thereby demonstrating its feasibility. We aimed to identify published studies that used patient reports to assess AEs during and after breast cancer treatment, to identify clinician underreported and modifiable AEs that are important to patients, and to analyze the feasibility and usefulness of PRO instrument implementation in everyday oncological practice with special attention given to electronic-based PRO instruments.


title: An Inductive 2-D Position Detection IC With 99.8% Accuracy for Automotive EMR Gear Control System

abstract: In this paper, the analog front end (AFE) for an inductive position sensor in an automotive electromagnetic resonance gear control applications is presented. To improve the position detection accuracy, a coil driver with an automatic two-step impedance calibration is proposed which, despite the load variation, provides the desired driving capability by controlling the main driver size. Also, a time shared analog-to-digital converter (ADC) is proposed to convert eight-phase signals while reducing the current consumption and area to 1/8 of the conventional structure. A relaxation oscillator with temperature compensation is proposed to generate a constant clock frequency in vehicle temperature conditions. This chip is fabricated using a 0.18- $\mu \text{m}$ CMOS process and the die area is 2 mm $\times 1.5$ mm. The power consumption of the AFE is 23.1 mW from the supply voltage of 3.3 V to drive one transmitter (Tx) coil and eight receiver (Rx) coils. The measured position detection accuracy is greater than 99.8 %. The measurement of the Tx shows a driving capability higher than 35 mA with respect to the load change.


title: A meta-synthesis on parenting a child with autism

abstract: BACKGROUND: The lifelong nature of autism in a child has deep implications on parents as they are faced with a range of challenges and emotional consequences in raising the child. The aim of this meta-synthesis was to explore the perspectives of parents in raising a child with autism in the childhood period to gain an insight of the adaptations and beliefs of parents toward autism, their family and social experiences, as well as their perceptions toward health and educational services. METHODS: A systematic search of six databases (PubMed, EMBASE, PsychInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Database of Abstracts of Reviews of Effects [DARE]) was conducted from inception up to September 30, 2014. Full-text English articles of qualitative studies describing parents' perceptions relating to the care of children younger than 12 years of age and diagnosed with a sole disorder of autism were included. RESULTS: A total of 50 eligible articles were appraised and analyzed, identifying four core themes encompassing all thoughts, emotions, and experiences commonly expressed by parents: 1) The Parent, 2) Impact on the Family, 3) Social Impact, and 4) Health and Educational Services. Findings revealed that parents who have a child with autism experienced multiple challenges in different aspects of care, impacting on parents' stress and adaptation. CONCLUSION: Health care provision should be family centered, addressing and supporting the needs of the whole family and not just the affected child, to ensure the family's well-being and quality of life in the face of a diagnosis of autism.


title: The Semantic Web Vision: Where Are We?

abstract: Semantic Web developers have adopted OWL to represent knowledge. OWL, or a language with similar theoretical foundations, will lead the way in the semantic exploration of the Web. Currently, Web-based standards are the preferred way to represent knowledge. Furthermore, respondents mainly use ontologies to allow both humans and computers to understand knowledge and domain models. Web 3.0 can bring a new breed of spectacular applications compared to Web 2.0. with the same magnitude that separates Web 2.0 from Web 1.0.


Anime Artist Analysis based on CNN


title: Come-and-Go Patterns of Group Evolution: A Dynamic Model

abstract: How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups? In this paper, we examine temporal evolution patterns of more than 100 thousands social groups with more than 10 million users. We surprisingly find that the evolution patterns of real social groups goes far beyond the classic dynamic models like SI and SIR. For example, we observe both diffusion and non-diffusion mechanism in the group joining process, and power-law decay in group quitting process, rather than exponential decay as expected in SIR model. Therefore we propose a new model comeNgo, a concise yet flexible dynamic model for group evolution. Our model has the following advantages: (a) unification power: it generalizes earlier theoretical models and different joining and quitting mechanisms we find from observation. (b) succinctness and interpretability: it contains only six parameters with clear physical meanings. (c) accuracy: it can capture various kinds of group evolution patterns preciously and the goodness of fit increase by 58% over baseline. (d) usefulness: it can be used in multiple application scenarios such as forecasting and pattern discovery.


title: Soft Weight-Sharing for Neural Network Compression

abstract: The success of deep learning in numerous application domains created the desire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression. ::: Recent work by Han et al. (2016) propose a pipeline that involves retraining, pruning and quantization of neural network weights, obtaining state-of-the-art compression rates. ::: In this paper, we show that competitive compression rates can be achieved by using a version of "soft weight-sharing" (Nowlan & Hinton, 1991). Our method achieves both quantization and pruning in one simple (re-)training procedure. ::: This point of view also exposes the relation between compression and the minimum description length (MDL) principle.


title: Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

abstract: In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.


title: Attention-based Multimodal Neural Machine Translation

abstract: We present a novel neural machine translation (NMT) architecture associating visual and textual features for translation tasks with multiple modalities. Transformed global and regional visual features are concatenated with text to form attendable sequences which are dissipated over parallel long short-term memory (LSTM) threads to assist the encoder generating a representation for attention-based decoding. Experiments show that the proposed NMT outperform the text-only baseline.


title: Toward a positive theory of consumer choice

abstract: Abstract The economic theory of the consumer is a combination of positive and normative theories. Since it is based on a rational maximizing model it describes how consumers should choose, but it is alleged to also describe how they do choose. This paper argues that in certain well-defined situations many consumers act in a manner that is inconsistent with economic theory. In these situations economic theory will make systematic errors in predicting behavior. Kanneman and Tversey's prospect theory is proposed as the basis for an alternative descriptive theory. Topics discussed are: undeweighting of opportunity costs, failure to ignore sunk costs, scarch behavior choosing not to choose and regret, and precommitment and self-control.


title: A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method

abstract: In this paper, we propose four new variants of the backpropagation algorithm to improve the generalization ability for feedforward neural networks. The basic idea of these methods stems from the Group Lasso concept which deals with the variable selection problem at the group level. There are two main drawbacks when the Group Lasso penalty has been directly employed during network training. They are numerical oscillations and theoretical challenges in computing the gradients at the origin. To overcome these obstacles, smoothing functions have then been introduced by approximating the Group Lasso penalty. Numerical experiments for classification and regression problems demonstrate that the proposed algorithms perform better than the other three classical penalization methods, Weight Decay, Weight Elimination, and Approximate Smoother , on both generalization and pruning efficiency. In addition, detailed simulations based on a specific data set have been performed to compare with some other common pruning strategies, which verify the advantages of the proposed algorithm. The pruning abilities of the proposed strategy have been investigated in detail for a relatively large data set, MNIST, in terms of various smoothing approximation cases.


title: Small-signal analysis of DCM flyback converter in frequency-foldback mode of operation

abstract: The predicted closed-loop bandwidth of the DCM flyback converter in frequency-foldback mode (FFM) of operation obtained by the conventional averaged small-signal model that models the voltage-controlled oscillator (VCO) with a constant gain does not change with the load. However, this prediction is not in agreement with the closed-loop measurements. In this paper, the discrepancy between the conventional averaged small-signal model and measurements is systematically investigated. It is found that the source of this discrepancy is the nonlinearity of the VCO that generates sideband frequency components and, therefore, cannot be modeled as a constant-gain transfer function. By including the effect of the sideband components in the closed loop, a multi-frequency averaged small-signal model of the DCM flyback converter in the FFM which accurately predicts the loop bandwidth behavior with the load is proposed.


title: Challenges for Toxic Comment Classification: An In-Depth Error Analysis

abstract: Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.


title: Diode Characteristics of a Super-Steep Subthreshold Slope PN-Body Tied SOI-FET for Energy Harvesting Applications

abstract: In this paper, the diode characteristics of our newly proposed super-steep subthreshold slope “PN-body tied (PNBT) silicon-on-insulator field-effect transistor” are presented, and compared with conventional diodes. We report that the device possesses super-steep characteristics, low leakage current, and sharp turn-on characteristics, even in the ultralow voltage range (50 mV). These indicate that the PNBT diode can potentially be used in high-efficiency rectification for energy harvesting, particularly in situations where there is ultralow input power. In addition, the hysteresis characteristics and the slight shift of the voltage at zero current are confirmed as specific characteristics of PNBT diodes.


title: Photonic network-on-chip architectures using multilayer deposited silicon materials for high-performance chip multiprocessors

abstract: Integrated photonics has been slated as a revolutionary technology with the potential to mitigate the many challenges associated with on- and off-chip electrical interconnection networks. To date, all proposed chip-scale photonic interconnects have been based on the crystalline silicon platform for CMOS-compatible fabrication. However, maintaining CMOS compatibility does not preclude the use of other CMOS-compatible silicon materials such as silicon nitride and polycrystalline silicon. In this work, we investigate utilizing devices based on these deposited materials to design photonic networks with multiple layers of photonic devices. We apply rigorous device optimization and insertion loss analysis on various network architectures, demonstrating that multilayer photonic networks can exhibit dramatically lower total insertion loss, enabling unprecedented bandwidth scalability. We show that significant improvements in waveguide propagation and waveguide crossing insertion losses resulting from using these materials enables the realization of topologies that were previously not feasible using only the single-layer crystalline silicon approaches.


title: Integration of omics: more than the sum of its parts

abstract: Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.