title: An improved MPPT technique for high gain DC-DC converter using model predictive control for photovoltaic applications

abstract: This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.


title: Test of Page-Hinckley, an approach for fault detection in an agro-alimentary production system

abstract: The increasingly important automation of the manufacturing processes has made into evidence the needs in dependability for the installations. To ensure the industrial process dependability, the establishment of a monitoring system is primordial whose role is to recognize and to indicate in real time the behavior anomalies starting from information available on the system. Indeed the function of monitoring of a system is to detect, locate and diagnose the faults, which can affect its performances and its dependability. The objective of these communication consists of the study and the conception of a detection module based on the techniques of static analysis and modelling, the matter is of establishing the operations which starting from the data coming from the industrial system make it possible to detect the abnormal situations in order to prevent or to reduce the dysfunction risks. Thus, the study consists in developing a detection module of the dysfunction for the diagnosis tool system. Our study is interested particularly at the stage of fault detection, which precedes any stage of diagnosis, based on the application of the test of Page-Hinckley for a system of pasteurization of agro-alimentary production system.


title: Series Clutch Actuators for safe physical human-robot interaction

abstract: This paper presents the design, implementation and control of a device intented to mechanically improve the safety of serial robots interacting with humans. The device consists of an electronically adjustable torque limiter placed in series with each actuator, referred to as a Series Clutch Actuator (SCA). By appropriately adjusting the limit torques according to the robot's configuration, the maximum static force that the robot can apply to its environment at the Tool Centre Point (TCP) can be limited to a prescribed safe level. If a limit torque is exceeded, the SCA slips and an emergency stop is triggered while the inertia located upstream from the SCA in the kinematic chain is mechanically disconnected. A method is presented to determine the optimal limit torques that maximize the isotropically achievable force (which can be applied in all directions without triggering any SCA) while satisfying the safe force limit. An approach to optimize the pose of a redundant robot in order to maximize the isotropically achievable force while preserving a safe maximum force threshold is also proposed. The design and fabrication of a torque limiter using a large number of friction discs is presented. Finally, the mechanisms are implemented into a 4-DOF redundant serial arm and preliminary experimental results are presented.


title: Tight approximation algorithms for maximum general assignment problems

abstract: A separable assignment problem ( SAP ) is defined by a set of bins and a set of items to pack in each bin; a value, fij, for assigning item j to bin i; and a separate packing constraint for each bin---i.e., for each bin, a family of subsets of items that fit in to that bin. The goal is to pack items into bins to maximize the aggregate value. This class of problems includes the maximum generalized assignment problem ( GAP )1 and a distributed caching problem (DCP) described in this paper. ::: ::: Given a β-approximation algorithm for finding the highest value packing of a single bin, we give ::: ::: ::: A polynomial-time LP-rounding based ((1-1/e)β)-approximation algorithm. ::: A simple polynomial-time local search (β/(β + 1)-e)-approximation algorithm, for any e > 0. ::: ::: ::: ::: Therefore, for all examples of SAP that admit an approximation scheme for the single-bin problem, we obtain an LP-based algorithm with (1-1/e-e)-approximation and a local search algorithm with (½-e)-approximation guarantee. Furthermore, for cases in which the subproblem admits a fully polynomial approximation scheme (such as for GAP ), the LP-based algorithm analysis can be strengthened to give a guarantee of 1-1/e. The best previously known approximation algorithm for GAP is a ½-approximation by Shmoys and Tardos and Chekuri and Khanna. Our LP algorithm is based on rounding a new linear programming relaxation, with a provably better integrality gap. ::: ::: To complement these results, we show that SAP and DCP cannot be approximated within a factor better than 1-1/e unless NP ⊆ DTIME(nO(log log n)), even if there exists a polynomial-time exact algorithm for the single-bin problem. ::: ::: We extend the (1-1/e)-approximation algorithm to a constant-factor approximation algorithms for a nonseparable assignment problem with applications in maximizing revenue for budget-constrained combinatorial auctions and the AdWords assignment problem. We generalize the local search algorithm to yield a ½-e approximation algorithm for the maximum k-median problem with hard capacities.


title: Finding needles in the haystack: search and candidate generation

abstract: A key phase in the DeepQA architecture is Hypothesis Generation, in which candidate system responses are generated for downstream scoring and ranking. In the IBM Watson™ system, these hypotheses are potential answers to Jeopardy!™ questions and are generated by two components: search and candidate generation. The search component retrieves content relevant to a given question from Watson's knowledge resources. The candidate generation component identifies potential answers to the question from the retrieved content. In this paper, we present strategies developed to use characteristics of Watson's different knowledge sources and to formulate effective search queries against those sources. We further discuss a suite of candidate generation strategies that use various kinds of metadata, such as document titles or anchor texts in hyperlinked documents. We demonstrate that a combination of these strategies brings the correct answer into the candidate answer pool for 87.17% of all the questions in a blind test set, facilitating high end-to-end question-answering performance.


title: RFID-Based 3-D Positioning Schemes

abstract: This research focuses on RFID-based 3-D positioning schemes, aiming to locate an object in a 3-dimensional space, with reference to a predetermined arbitrary coordinates system, by using RFID tags and readers. More specifically, we consider a hexahedron which may be a shipping container, a storage room, or other hexahedral shape spaces. A number of RFID tags and/or readers with known locations are deployed as reference nodes. We propose two positioning schemes, namely, the active scheme and the passive scheme. The former scheme locates an RFID reader. For example, it may be employed to locate a mobile person who is equipped with an RFID reader or an object that is approached by an RFID reader. The passive scheme locates an RFID tag, which is attached to the target object. Both approaches are based on a Nelder-Mead nonlinear optimization method that minimizes the error objective functions. We have carried out analyses and extensive simulations to evaluate the proposed schemes. Our results show that both schemes can locate the targets with acceptable accuracy. The active scheme usually results in smaller errors and has a lower hardware cost compared to its passive counterpart. On the other hand, the passive scheme is more efficient when locating multiple targets simultaneously. The effectiveness of our proposed approaches is verified experimentally using the IDENTEC RFID kits.


title: Pedestrian Prediction by Planning Using Deep Neural Networks

abstract: Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed planning. For this, we infer a mixture density function for possible destinations. We use these destinations as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The patterns are learned by a fully convolutional network operating on maps of the environment. We show that this entire system can be modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.


Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning


title: Biomechanical analysis by chiropractic radiography: Part III. Lack of effect of projectional distortion on Gonstead vertebral endplate lines.

abstract: Projectional distortion is known to produce artifactual dysrelationships between images of osseous segments on X-ray films. According to Gonstead, lines constructed parallel to the adjacent vertebral endplates will converge if a lateral flexion malposition is present and will be parallel if no such subluxation exists. To test the accuracy of these lines, we mounted two vertebrae on supports so that the superior vertebra could be laterally flexed by a known amount upon the fixed inferior vertebra. The specimen was X rayed with no flexion or with various degrees of right or left lateral flexion of the superior vertebra. Lateral and inferior off-centering, rotation about the z axis, and object-film distance were also varied systematically. In every instance, off-centering produced no measurable effect on the position of the constructed Gonstead lines. We therefore conclude that these lines can be confidentially used to indicate structural dysrelationship during lateral flexion. No correction for projectional distortion appears to be necessary.


title: A General-Purpose Counting Filter: Making Every Bit Count

abstract: Approximate Membership Query (AMQ) data structures, such as the Bloom filter, quotient filter, and cuckoo filter, have found numerous applications in databases, storage systems, networks, computational biology, and other domains. However, many applications must work around limitations in the capabilities or performance of current AMQs, making these applications more complex and less performant. For example, many current AMQs cannot delete or count the number of occurrences of each input item, take up large amounts of space, are slow, cannot be resized or merged, or have poor locality of reference and hence perform poorly when stored on SSD or disk. This paper proposes a new general-purpose AMQ, the counting quotient filter (CQF). The CQF supports approximate membership testing and counting the occurrences of items in a data set. This general-purpose AMQ is small and fast, has good locality of reference, scales out of RAM to SSD, and supports deletions, counting (even on skewed data sets), resizing, merging, and highly concurrent access. The paper reports on the structure's performance on both manufactured and application-generated data sets. In our experiments, the CQF performs in-memory inserts and queries up to an order-of magnitude faster than the original quotient filter, several times faster than a Bloom filter, and similarly to the cuckoo filter, even though none of these other data structures support counting. On SSD, the CQF outperforms all structures by a factor of at least 2 because the CQF has good data locality. The CQF achieves these performance gains by restructuring the metadata bits of the quotient filter to obtain fast lookups at high load factors (i.e., even when the data structure is almost full). As a result, the CQF offers good lookup performance even up to a load factor of 95%. Counting is essentially free in the CQF in the sense that the structure is comparable or more space efficient even than non-counting data structures (e.g., Bloom, quotient, and cuckoo filters). The paper also shows how to speed up CQF operations by using new x86 bit-manipulation instructions introduced in Intel's Haswell line of processors. The restructured metadata transforms many quotient filter metadata operations into rank-and-select bit-vector operations. Thus, our efficient implementations of rank and select may be useful for other rank-and-select-based data structures.


title: A Performance Study on the VM Startup Time in the Cloud

abstract: One of many advantages of the cloud is the elasticity, the ability to dynamically acquire or release computing resources in response to demand. However, this elasticity is only meaningful to the cloud users when the acquired Virtual Machines (VMs) can be provisioned in time and be ready to use within the user expectation. The long unexpected VM startup time could result in resource under-provisioning, which will inevitably hurt the application performance. A better understanding of the VM startup time is therefore needed to help cloud users to plan ahead and make in-time resource provisioning decisions. In this paper, we study the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace. We analyze the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time. We also study the VM startup time of spot instances in EC2, which show a longer waiting time and greater variance compared to on-demand instances.


title: Design of a Novel In-Pipe Reliable Leak Detector

abstract: Leakage is the major factor for unaccounted losses in every pipe network around the world (oil, gas, or water). In most cases, the deleterious effects associated with the occurrence of leaks may present serious economical and health problems. Therefore, leaks must be quickly detected, located, and repaired. Unfortunately, most state-of-the-art leak detection systems have limited applicability, are neither reliable nor robust, while others depend on the user experience. In this paper, we present a new in-pipe leak detection system. It performs autonomous leak detection in pipes and, thus, eliminates the need for the user experience. This paper focuses on the detection module and its main characteristics. Detection in based on the presence of a pressure gradient in the neighborhood of the leak. Moreover, the proposed detector can sense leaks at any angle around the circumference of the pipe with only two sensors. We validate the concepts by building a prototype and evaluate the system's performance under real conditions in an experimental laboratory setup.


title: Multivariate Maximal Correlation Analysis

abstract: Correlation analysis is one of the key elements of statistics, and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces. ::: ::: We propose MAC, a novel multivariate correlation measure designed for discovering multi-dimensional patterns. It belongs to the powerful class of maximal correlation analysis, for which we propose a generalization to multivariate domains. We highlight the limitations of current methods in this class, and address these with MAC. Our experiments show that MAC outperforms existing solutions, is robust to noise, and discovers interesting and useful patterns.


title: NeC4.5: neural ensemble based C4.5

abstract: Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability. These merits are integrated into a novel decision tree algorithm NeC4.5. This algorithm trains a neural network ensemble at first. Then, the trained ensemble is employed to generate a new training set through replacing the desired class labels of the original training examples with those output from the trained ensemble. Some extra training examples are also generated from the trained ensemble and added to the new training set. Finally, a C4.5 decision tree is grown from the new training set. Since its learning results are decision trees, the comprehensibility of NeC4.5 is better than that of neural network ensemble. Moreover, experiments show that the generalization ability of NeC4.5 decision trees can be better than that of C4.5 decision trees.


title: How Travelers Use Online and Social Media Channels to Make Hotel Choice Decisions. A Comparative Study of Russian Federation and American Tourists’ Online Consumer Behavior

abstract: Over the last decades, the global hospitality and tourism (or H&T) industry has undergone dramatic changes. Among the factors stimulating the growth of the H&T industry, the spread of information and communication technologies (ICTs) and social media should be emphasized. The same trends characterize the Russian H&T industry as well. However, despite the significant role of social media in the Russian H&T industry, there is still a lack of understanding of Russian H&T customers’ behavior. The current study aims to fill this gap and demonstrates how Russian travelers use social media to make hotel choice decisions and share their experiences on social media after their travel is completed. In order to deliver a more thorough revealing of Russian travel consumers’ peculiarities and to discover if there is Russian uniqueness, the online behavior of American and Russian travelers is compared in this paper.


title: Optimal Cell Load and Throughput in Green Small Cell Networks With Generalized Cell Association

abstract: This paper thoroughly explores the fundamental interactions between cell association, cell load, and throughput in a green (energy-efficient) small cell network in which all base stations form a homogeneous Poisson point process (PPP) of intensity $\lambda_{\mathsf{B}}$ and all users form another independent PPP of intensity $\lambda_{\mathsf{U}}$ . Cell voidness, usually disregarded due to rarity in cellular network modeling, is first theoretically analyzed under generalized (channel-aware) cell association (GCA). We show that the void cell probability cannot be neglected any more since it is bounded above by $\exp(-\lambda_{\mathsf{U}}/\lambda_{\mathsf{B}})$ that is typically not small in a small cell network. The accurate expression of the void cell probability for GCA is characterized and it is used to derive the average cell and user throughputs. We learn that cell association and cell load $\lambda_{\mathsf{U}}/\lambda_{\mathsf{B}}$ significantly affect these two throughputs. According to the average cell and user throughputs, the green cell and user throughputs are defined respectively to reflect whether the energy of a base station is efficiently used to transmit information or not. In order to achieve satisfactory throughput with certain level of greenness, cell load should be properly determined. We present the theoretical solutions of the optimal cell loads that maximize the green cell and user throughputs, respectively, and verify their correctness by simulation.


title: The Roles of Dopamine and Noradrenaline in the Pathophysiology and Treatment of Attention-Deficit/Hyperactivity Disorder

abstract: Through neuromodulatory influences over fronto-striato-cerebellar circuits, dopamine and noradrenaline play important roles in high-level executive functions often reported to be impaired in attention-deficit/hyperactivity disorder (ADHD). Medications used in the treatment of ADHD (including methylphenidate, dextroamphetamine and atomoxetine) act to increase brain catecholamine levels. However, the precise prefrontal cortical and subcortical mechanisms by which these agents exert their therapeutic effects remain to be fully specified. Herein, we review and discuss the present state of knowledge regarding the roles of dopamine (DA) and noradrenaline in the regulation of corticostriatal circuits, with a focus on the molecular neuroimaging literature (both in ADHD patients and in healthy subjects). Recent positron emission tomography evidence has highlighted the utility of quantifying DA markers, at baseline or following drug administration, in striatal subregions governed by differential cortical connectivity. This approach opens the possibility of characterizing the neurobiological underpinnings of ADHD (and associated cognitive dysfunction) and its treatment by targeting specific neural circuits. It is anticipated that the application of refined and novel positron emission tomography methodology will help to disentangle the overlapping and dissociable contributions of DA and noradrenaline in the prefrontal cortex, thereby aiding our understanding of ADHD and facilitating new treatments.


title: Modeling Multistep Cyber Attacks for Scenario Recognition

abstract: Efforts toward automated detection and identification of multistep cyber attack scenarios would benefit significantly from a methodology and language for modeling such scenarios. The Correlated Attack Modeling Language (CAML) uses a modular approach, where a module represents an inference step and modules can be linked together to detect multistep scenarios. CAML is accompanied by a library of predicates, which functions as a vocabulary to describe the properties of system states and events. The concept of attack patterns is introduced to facilitate reuse of generic modules in the attack modeling process. CAML is used in a prototype implementation of a scenario recognition engine that consumes first-level security alerts in real time and produces reports that identify multistep attack scenarios discovered in the alert stream.


title: A Model of Data Warehousing Process Maturity

abstract: Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.


title: Solid‐state polymerization of a diacetylene crystal: Thermal, ultraviolet, and γ‐ray polymerization of 2,4‐hexadiyne‐1,6‐diol bis‐(p‐toluene sulfonate)

abstract: Results are presented for the thermal, ultraviolet, and γ-ray polymerization of 2,4-hexadiyne-1,6-diol bis-(p-toluene sulfonate) (PTS). Monomer extraction is used to obtain polymer conversion-vs.-time curves at 30, 50, and 80°C. In agreement with previous work over a narrower temperature range, the curves all display a dramatic autocatalytic effect with an onset at about 10% conversion to polymer. Although the polymerization rate undergoes a 200-fold change over this temperature range, the shape of the conversion curves does not change. These data yield an activation energy (E) of 22.2 ± 0.4 kcal/mole when interpreted in terms of the time required to reach 50% polymer. An annealing technique is used to provide a closer look at the autocatalytic region. In that case, E = 22.5 ± 0.8 kcal/mole is determined from measurements of the time required to go from 10 to 50% polymer at temperatures ranging from 23 to 80°C (a 500-fold change in rate). Thermal polymerization rates measured in the low-conversion limit using a spectroscopic method based on diffuse reflectance yield E = 22.8 ± 0.6 kcal/mole. Thus E is independent of polymer conversion and the autocatalytic effect can be best understood as arising from a large increase in the propagation length of the polymer chains. The autocatalytic effect is shown to be present in both UV and γ-ray polymerization. In the case of γ-ray polymerization, conversion-vs.-time and spectroscopic measurements are consistent with inhomogeneities in the polymer concentration caused by particle tracks. Activation energies for UV and γ-ray polymerization are quite low (2-3 kcal/mole) and confirm that the chain initiation event makes the major energetic contribution to E. The polymerization mechanism is discussed in detail. The photopolymerization experiments can be consistently interpreted with a model based on the triplet excited state of the diacetylene monomer as the chain initiation species.


title: Carpenter: finding closed patterns in long biological datasets

abstract: The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.


title: Simple task-specific bilingual word embeddings

abstract: We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word embeddings. We use a small dictionary of easily-obtainable task-specific word equivalence classes to produce mixed context-target pairs that we use to train off-the-shelf embedding models. Our model has the advantage that it (a) is independent of the choice of embedding algorithm, (b) does not require parallel data, and (c) can be adapted to specific tasks by re-defining the equivalence classes. We show how our method outperforms off-the-shelf bilingual embeddings on the task of unsupervised cross-language partof-speech (POS) tagging, as well as on the task of semi-supervised cross-language super sense (SuS) tagging.


title: Multi-octave band tapered slot EW antenna

abstract: Usually ridged waveguides, log periodic and tapered slot antennas have been extensively used for EW applications. In this paper, design of a multi-octave band tapered slot Vivaldi antenna (62 mm × 31 mm × 1.6 mm) operating in the frequency band of 6–18 GHz is proposed. Various antenna characteristics such as return loss, VSWR, effective gain and radiation patterns are measured. In addition, the responses of the antenna are studied through parametric study for different values of exponential tapering rate (R), slot width (W3)and cavity diameter (ri). The simulation results show that the VSWR increases at higher value of R resulting in poor performance of the antenna. The simulated value of antenna gain at selected frequencies of 6 GHz, 10 GHz, 12 GHz and 18 GHz are 3.2 dB, 7.3 dB, 7.8 dB and 0.4 dB, respectively. The antenna is fabricated using FR4 substrate and measured results are compared with simulation results. It is shown that the simulated results agree well with the measured results. The antenna is designed using HFSS software and concluding remarks are reported.


title: Mechanisms of evoked and induced responses in MEG/EEG

abstract: Abstract Cortical responses, recorded by electroencephalography and magneto-encephalography, can be characterized in the time domain, to study event-related potentials/fields, or in the time–frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and on-going oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time–frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trial-to-trial variations in input and can be attributed to structural perturbations without ambiguity.


title: FOUNDATIONS OF THE PARAFAC PROCEDURE: MODELS AND CONDITIONS FOR AN "EXPLANATORY " MULTIMODAL FACTOR ANALYSIS by

abstract: Simple structure and other common principles of factor rotation do not in general provide strong grounds for attributing explanatory significance to the factors which they select. In contrast, it is shown that an extension of Cattell's principle of rotation to Proportional Profiles (PP) offers a basis for determining explanatory factors for three-way or higher order multi-mode data. Conceptual models are developed for two basic patterns of multi-mode data variation, systemand object-variation, and PP analysis is found to apply in the system-variation case. Although PP was originally formulated as a principle of rotation to be used with classic two-way factor analysis, it is shown to embody a latent three-mode factor model, which is here made explicit and generalized frown two to N "parallel occasions". As originally formulated, PP rotation was restricted to orthogonal factors. The generalized PP model is demonstrated to give unique "correct" solutions with oblique, non-simple structure, and even non-linear factor structures. A series of tests, conducted with synthetic data of known factor composition, demonstrate the capabilities of linear and non-linear versions of the model, provide data on the minimal necessary conditions of uniqueness, and reveal the properties of the analysis procedures when these minimal conditions are not fulfilled. In addition, a mathematical proof is presented for the uniqueness of the solution given certain conditions on the data. Three-mode PP factor analysis is applied to a three-way set of real data consisting of the fundamental and first three formant frequencies of 11 persons saying 8 vowels. A unique solution is extracted, consisting of three factors which are highly meaningful and consistent with prior knowledge and theory concerning vowel quality. The relationships between the three-mode PP model and Tucker's multi-modal model, McDonald's non-linear model and Carroll and Chang's multi-dimensional scaling model are explored.


title: Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flows

abstract: Multi-core end-systems use Receive Side Scaling (RSS) to parallelize protocol processing. RSS uses a hash function on the standard flow descriptors and an indirection table to assign incoming packets to receive queues which are pinned to specific cores. This ensures flow affinity in that the interrupt processing of all packets belonging to a specific flow is processed by the same core. A key limitation of standard RSS is that it does not consider the application process that consumes the incoming data in determining the flow affinity. In this paper, we carry out a detailed experimental analysis of the performance impact of the application affinity in a 40 Gbps testbed network with a dual hexa-core end-system. We show, contrary to conventional wisdom, that when the application process and the flow are affinitized to the same core, the performance (measured in terms of end-to-end TCP throughput) is significantly lower than the line rate. Near line rate performance is observed when the flow and the application process are affinitized to different cores belonging to the same socket. Furthermore, affinitizing the application and the flow to cores on different sockets results in significantly lower throughput than the line rate. These results arise due to the memory bottleneck, which is demonstrated using preliminary correlational data on the cache hit rate in the core that services the application process.


title: Scholarly communication trends in the digital age: Informal scholarly publishing and dissemination, a grounded theory approach

abstract: Purpose – The purpose of this paper is to investigate scholars’ attitudes toward informal publishing and dissemination to provide a view of the challenges and advantages of using such channels. Although considerable research has been carried out in relation to peer-reviewed scholarly publishing, relatively few studies have investigated the adoption of informal scholarly communication platforms in the scholarly publishing process. Design/methodology/approach – The paper deployed a grounded theory approach using semi-structured interviews as a qualitative research tool. A theoretical sample of 40 researchers in 4 universities were interviewed to gather data regarding informal publishing, platforms, factors that affect the researchers’ decision and the use of informal channels in dissemination. Findings – Results of the interviews suggest that there is an increasing trend among researchers toward informal publishing and dissemination throughout the scholarly communication cycle. The paper shows that there ar...


Neural Captioning for the ImageCLEF 2017 Medical Image Challenges


title: Reading the mind in cartoons and stories: an fMRI study of ‘theory of mind’ in verbal and nonverbal tasks

abstract: Previous functional imaging studies have explored the brain regions activated by tasks requiring ‘theory of mind’—the attribution of mental states. Tasks used have been primarily verbal, and it has been unclear to what extent different results have reflected different tasks, scanning techniques, or genuinely distinct regions of activation. Here we report results from a functional magnetic resonance imaging study (fMRI) involving two rather different tasks both designed to tap theory of mind. Brain activation during the theory of mind condition of a story task and a cartoon task showed considerable overlap, specifically in the medial prefrontal cortex (paracingulate cortex). These results are discussed in relation to the cognitive mechanisms underpinning our everyday ability to ‘mind-read’.


title: Finding Buffer Overflow Inducing Loops in Binary Executables

abstract: Vulnerability analysis is one among the important components of overall software assurance practice. Buffer overflow (BoF) is one example of the such vulnerabilities and it is still the root cause of many effective attacks. A general practice to find BoF is to look for the presence of certain functions that manipulate string buffers, like the strcpy family. In these functions, data is moved from one buffer to another, within a loop, without considering destination buffer size. We argue that similar behaviour may also be present in many other functions that are coded separately, and therefore are equally vulnerable. In the present work, we investigate the detection of such functions by finding loops that exhibit similar behaviour. We call such loops Buffer Overflow Inducing Loops (BOIL). We implemented a lightweight static analysis to detect BOILs, and evaluated it on real-world x86 binary executables. The results obtained show that this (simple but yet efficient) vulnerability pattern happens to be very effective in practice to retrieve real vulnerabilities, providing a drastic reduction of the part of the code to be analysed.


title: Subreflectarrays for Reflector Surface Distortion Compensation

abstract: With the increasing interest in the applications of large deployable reflector antennas operating at high frequencies, the requirement on the reflector surface accuracy becomes more demanding. Thermal effects inevitably cause certain reflector surface distortions, thus degrading the overall antenna performance. This paper introduces a novel reflector surface distortion compensation technique using a subreflectarray and presents detailed discussions. A microstrip reflectarray is used as a subreflector, illuminated by a primary feed. By properly adjusting the additional phase shift provided by the subreflectarray, the aperture phase errors caused by the main reflector surface distortions are compensated, resulting in a considerably improved antenna performance. As an example, a distorted 20-m offset parabolic reflector antenna operating at X-band is successfully compensated by a subreflectarray, and the simulation results are compared with those obtained by array feed and shaped subreflector compensation techniques. The microstrip subreflectarray is low-profile, lightweight, and cost-effective. Only one primary feed is required, and a reconfigurable design can be achieved if electronically reconfigurable reflectarray elements are adopted.


title: Bigrams of syntactic labels for authorship discrimination of short texts

abstract: We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Bronte ¨, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long.


title: Spatial-crowd: A big data framework for efficient data visualization

abstract: Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing by scaling up the middleware components and its infrastructure. Then, it enables filtering, fixing, enriching and summarising the acquired dataset, readily available for client interfaces which usually are not scalable or built to manage such large datasets. This framework follows published subscriber model and allows users to subscribe to aggregated data streams instead of requesting data in real time. The framework is tested with data generated by a very large simulated dataset and performance showed a significant data reduction on the client side to enhance data visualisation.


title: Functional connectivity of brain regions for self- and other-evaluation in children, adolescents and adults with autism.

abstract: Developing strong ties between oneself and others lays the foundation for developing social competence. Neuroimaging studies have consistently identified specific cortical midline regions activated during evaluative judgments about the self and others. Individuals with autism spectrum disorder (ASD) process self-relevant information differently from their peers, both behaviorally and at the neural level. We compared resting-state functional connectivity (rsFC) of regions involved in self-referential (e.g. medial prefrontal cortex; mPFC) and other-referential (e.g. posterior cingulate cortex; PCC) processing between neurotypical individuals and individuals with ASD in three age cohorts using regions of interest (ROIs) identified through an activation likelihood estimation meta-analysis. Typically developing children demonstrated greater connectivity within the midline self- and other-referential networks compared with age-matched children with ASD. No group differences in rsFC of mPFC or PCC emerged between typically developing adolescents and adolescents with ASD. Neurotypical adults exhibited stronger rsFC of the PCC with orbitofrontal cortex compared with adults with ASD. Developmental differences in functional connectivity between areas underlying self- and other-referential thought may explain altered developmental trajectories in the understanding of self and others in individuals with ASD.


title: Robocentric map joining: Improving the consistency of EKF-SLAM

abstract: In this paper 1 we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold. We propose a mapping algorithm, Robocentric Map Joining, which improves consistency of the EKFSLAM algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. Simulations and a large-scale indoor/outdoor experiment validate the proposed approach. c 2006 Elsevier B.V. All rights reserved.


title: Correlated link shadow fading in multi-hop wireless networks

abstract: Accurate representation of the physical layer is required for analysis and simulation of multi-hop networking in sensor, ad hoc, and mesh networks. This paper investigates, models, and analyzes the correlations that exist in shadow fading between links in multi-hop networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated fading. We describe a measurement procedure and campaign to measure a large number of multi-hop networks in an ensemble of environments. The measurements show statistically significant correlations among shadowing experienced on different links in the network, with correlation coefficients up to 0.33. We propose a statistical model for the shadowing correlation between link pairs which shows strong agreement with the measurements, and we compare the new model with an existing shadowing correlation model of Gudmundson (1991). Finally, we analyze multi-hop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater.


title: Palletizing Manipulator Design and Control Using Arduino and MATLAB

abstract: Robotic manipulators have many useful industrial applications. This paper describes the design and development of a palletizing manipulator robot system. The mechanical design is presented followed by derivation of the forward and inverse kinematics equations that describe the robot's motion. The control system and codes have been developed using MATLAB and Arduino to drive stepper motors that actuate the manipulator. The robot's motion equations have been tested and verified using MATLAB, LabVIEW and SOLIDWORKS. The simulated results were in good agreement with the actual performance of the robot. Finally, the manipulator's positioning errors were determined and discussed based on a number of repeatability experiments.


title: Lifetime analysis of an automotive electrical motor with hairpin wound stator

abstract: The electrical motors adopted in electric vehicles are subjected to specific stress conditions which determine their insulation lifetime. However, the qualification of automotive electrical motors does not have a specific standard and follows the one commonly used for other electrical machines. In this work, the insulation of a hairpin wound stator has been evaluated employing a multi-factorial aging procedure, with the simultaneous application of thermal and mechanical stresses. Data here presented suggest that the turn to turn insulation is the weakest point of the motor. Moreover, an extrapolation method to obtain the system lifetime at the operating temperature has been used and discussed. The resulting motor reliability of 99.6% for 10000 working hours appears fairly acceptable. Eventually, this work opens to a discussion on a proper qualification standard for automotive electrical motors.


title: Cyber attacks, countermeasures, and protection schemes — A state of the art survey

abstract: Thousands of cyber-attacks (fraudulent online activities to acquire users' sensitive information via email, during online transactions, live video streaming, online gaming and browsing) are launched every day against Internet users across the world. To prevent these attacks, researchers have responded with a number of protection systems. Currently, the methods which cyber-attackers use to conduct attacks is associated with exploiting humans. Such attacks are recorded more frequently than before, and they are more challenging to control. Traditional security countermeasures are unable to prevent breaches targeting the human element. This paper describes the state of the art of cyber security attacks, countermeasures, and protection tools related to everyday online activities. It provides a useful cyber-attack taxonomy and classification which helps to involve in a protection process to identify attacks and measures for cyber security. Existing protection schemes that target the cyber threats and risks are evaluated against three of our criteria for an effective measure: resilience to cyber-attacks' countermeasures; real-time support and needs-based action; and training and educational materials to increase users' awareness of cybercrimes. Potential features of smart solutions to cybercrime are also identified.


title: Satisfiability Solvers

abstract: Publisher Summary The past few years have seen enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a general-purpose tool in areas as diverse as software and hardware verification, automatic test-pattern generation, planning, scheduling, and even challenging problems from algebra. Annual SAT competitions have led to the development of dozens of clever implementations of such solvers, exploration of new techniques, and creation of an extensive suite of real-world instances as well as challenging hand-crafted benchmark problems. Modern SAT solvers provide a black-box procedure that can often solve hard structured problems with over a million variables and several million constraints. This chapter describes the main solution techniques used in modern SAT solvers, classifying them as complete and incomplete methods. It discusses recent insights explaining the effectiveness of these techniques on practical SAT encodings and presents several extensions of the SAT approach currently under development. These extensions further expand the range of applications to include multiagent and probabilistic reasoning.


title: Two-enqueuer queue in Common2

abstract: The question of whether all shared objects with consensus number 2 belong to Common2, the set of objects that can be implemented in a wait-free manner by any type of consensus number 2, was first posed by Herlihy. In the absence of general results, several researchers have obtained implementations for restricted-concurrency versions of FIFO queues. We present the first Common2 algorithm for a queue with two enqueuers and any number of dequeuers.


title: Environment and Crime in the Inner City: Does Vegetation Reduce Crime?

abstract: Although vegetation has been positively linked to fear of crime and crime in a number of settings, recent findings in urban residential areas have hinted at a possible negative relationship: Residents living in “greener” surroundings report lower levels of fear, fewer incivilities, and less aggressive and violent behavior. This study used police crime reports to examine the relationship between vegetation and crime in an inner-city neighborhood. Crime rates for 98 apartment buildings with varying levels of nearby vegetation were compared. Results indicate that although residents were randomly assigned to different levels of nearby vegetation, the greener a building’s surroundings were, the fewer crimes reported. Furthermore, this pattern held for both property crimes and violent crimes. The relationship of vegetation to crime held after the number of apartments per building, building height, vacancy rate, and number of occupied units per building were accounted for.


title: The caudal-lateral inserted transposition flap in reconstructive tumor surgery on the nasal ala.

abstract: Abstract The nasal region represents an important three-dimensional esthetic unit where minimal distortion will be obvious and disturbing. In this regard the reconstruction of the nasal ala in dermatologic tumor surgery may be challenging. We propose the reconstructive option of a caudal-lateral inserted transposition flap for this indication. The principle of this surgical alternative and the long-term outcome is demonstrated in representative cases. The various indications and modifications of this reconstructive principle are discussed and compared to other remodeling alternatives.


title: Neither Snow Nor Rain Nor MITM...: An Empirical Analysis of Email Delivery Security

abstract: The SMTP protocol is responsible for carrying some of users' most intimate communication, but like other Internet protocols, authentication and confidentiality were added only as an afterthought. In this work, we present the first report on global adoption rates of SMTP security extensions, including: STARTTLS, SPF, DKIM, and DMARC. We present data from two perspectives: SMTP server configurations for the Alexa Top Million domains, and over a year of SMTP connections to and from Gmail. We find that the top mail providers (e.g., Gmail, Yahoo, and Outlook) all proactively encrypt and authenticate messages. However, these best practices have yet to reach widespread adoption in a long tail of over 700,000 SMTP servers, of which only 35% successfully configure encryption, and 1.1% specify a DMARC authentication policy. This security patchwork---paired with SMTP policies that favor failing open to allow gradual deployment---exposes users to attackers who downgrade TLS connections in favor of cleartext and who falsify MX records to reroute messages. We present evidence of such attacks in the wild, highlighting seven countries where more than 20% of inbound Gmail messages arrive in cleartext due to network attackers.


title: Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands)

abstract: Estimation of net above ground primary productivity in forest ecosystems by non-destructive means requires the development of allometric equations, to allow prediction of above ground biomass from readily measurable variables such as diameter-at-breast-height (DBH). Equations of this type have not been well developed for trees of the Macaronesian laurel forest. In the present study, we characterized trees of five species (Erica arborea, Ilex canariensis, Laurus azorica, Myrica faya and Persea indica ) in four types of laurel forest in the Garajonay National Park on Gomera Island in the Canary Islands, with the aim of developing appropriate allometric equations. Among forests, within each species, the slope and elevation (yy-axis intersect) of the equations obtained did not vary significantly, so that we were able to pool the data and obtain a single equation for each species. However, considering each species separately, there was significant variation among the slopes and elevations of the equations obtained for each. The difference between DBH-biomass relationships among these species can be attributed to differences in (a) the distribution of biomass among trunk-plus-primary-branches, secondary branches and leaves, and (b) woody tissue density. We applied the new allometric equations to six different forest types in the Garajonay National Park. Above ground biomass ranged from 65,631 to 352,683 kg d.w. ha−1. Comparison of these results with those obtained using a previously published allometric model revealed differences of more than 100% for some forests. When we applied the new allometric equations to data from other forests on the island of Tenerife we obtained differences of more than 20% in above ground biomass which ranged from 159,469 to 310,580 kg d.w. ha−1. We believe that all previous data corresponding to above ground biomass in Macaronesian laurel forest may contain errors, and propose the new equations to be used in the future, and that other one have to become developed for the remaining species.


title: High-Q 3D RF solenoid inductors in glass

abstract: In this paper, we demonstrate the fabrication and characterization of various 3D solenoid inductors using a glass core substrate. Solenoid inductors were fabricated in glass by drilling through holes in glass and semi-additive copper plating for metallization. This topology is compared to similar solenoid structures in terms of Q-factor performance and inductance density. Inductances of 1.8-4.5nH with Q ~ 60 at 1GHz were demonstrated.


title: Considering Bluetooth's Subband Codec ({SBC}) for Wideband Speech and Audio on the Internet

abstract: The Bluetooth Special Interest Group (SIG) has standardized the subband coding (SBC) audio codec to connect headphones via wireless Bluetooth links. SBC compresses audio at high fidelity while having an ultra-low algorithm delay. To make SBC suitable for the Internet, we extend it by using a time and packet loss concealment (PLC) algorithm that is based on ITU's G.711 Appendix I. The design is novel in the aspect of the interface between codec and speech receiver. We developed a new approach on how to distribute the functionality of a speech receiver between codec and application. Our approach leads to easier implementations of high quality VoIP applications. ::: ::: We conducted subjective and objective listening tests of the audio quality of SBC and PLC in order to determine an optimal coding mode and the trade-off between coding mode and packet loss rate. More precisely, we conducted MUSHRA listening tests for selected sample items. These tests results are then compared with the results of multiple objective assessment algorithms (ITU P.862 PESQ, ITU BS.1387-1 PEAQ, Creusere's algorithm). We found out that a combination of the PEAQ basic and advanced values best matches---after third order linear regression---the subjective MUSHRA results . The linear regression has coefficient of determination of R=0.907. By comparison, our individual human ratings show a correlation of about R=0.9 compared to our averaged human rating results. ::: ::: Using the combination of both PEAQ algorithms, we calculate hundred thousands of objective audio quality ratings varying audio content and algorithmic parameters of SBC and PLC. The results show which set of parameters value are best suitable for a bandwidth and delay constrained link. The transmission quality of SBC is enhanced significantly by selecting optimal encoding parameters as compared to the default parameter sets given in the standard. ::: ::: Finally, we present preliminary objective tests results on the comparison of the audio codecs SBC, CELT, APT-X and ULD coding speech and audio transmission. They all allow a mono and stereo transmission of music at ultra-low coding delays (<10ms), which is especially useful for distributed ensemble performances over the Internet.


title: Wind ramp event prediction with parallelized gradient boosted regression trees

abstract: Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy. This paper proposes a classification based approach for estimating the future class of wind ramp event based on certain thresholds. A parallelized gradient boosted regression tree based technique has been proposed to accurately classify the normal as well as rare extreme wind power ramp events. The model has been validated using wind power data obtained from the National Renewable Energy Laboratory database. Performance comparison with several benchmark techniques indicates the superiority of the proposed technique in terms of superior classification accuracy.


title: Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

abstract: Machine learning is now widely used to detect security vulnerabilities in the software, even before the software is released. But its potential is often severely compromised at the early stage of a software project when we face a shortage of high-quality training data and have to rely on overly generic hand-crafted features. This paper addresses this cold-start problem of machine learning, by learning rich features that generalize across similar projects. To reach an optimal balance between feature-richness and generalizability, we devise a data-driven method including the following innovative ideas. First, the code semantics are revealed through serialized abstract syntax trees (ASTs), with tokens encoded by Continuous Bag-of-Words neural embeddings. Next, the serialized ASTs are fed to a sequential deep learning classifier (Bi-LSTM) to obtain a representation indicative of software vulnerability. Finally, the neural representation obtained from existing software projects is then transferred to the new project to enable early vulnerability detection even with a small set of training labels. To validate this vulnerability detection approach, we manually labeled 457 vulnerable functions and collected 30 000+ nonvulnerable functions from six open-source projects. The empirical results confirmed that the trained model is capable of generating representations that are indicative of program vulnerability and is adaptable across multiple projects. Compared with the traditional code metrics, our transfer-learned representations are more effective for predicting vulnerable functions, both within a project and across multiple projects.


title: Semi-Supervised Classification via Local Spline Regression

abstract: This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions. It is smooth, nonlinear, and able to interpolate the scattered data points with high accuracy. Specifically, in each neighborhood, an optimal spline is estimated via regularized least squares regression. With this spline, each of the neighboring data points is mapped to be a class label. Then, the regularized loss is evaluated and further formulated in terms of class label vector. Finally, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on the labeled and unlabeled data. To achieve the goal of semi-supervised classification, an objective function is constructed by combining together the global loss of the local spline regressions and the squared errors of the class labels of the labeled data. In this way, a transductive classification algorithm is developed in which a globally optimal classification can be finally obtained. In the semi-supervised learning setting, the proposed algorithm is analyzed and addressed into the Laplacian regularization framework. Comparative classification experiments on many public data sets and applications to interactive image segmentation and image matting illustrate the validity of our method.


title: Open innovation practices in the development of wind energy supply chain: an exploratory analysis of the literature

abstract: The open innovation approach and its practice in technological innovation projects have been revealed in the literature and observed in practice companies as an old phenomenon. What differentiates its current use is that today its practice occurs in a systematic and formalized form in companies. This paper carried out a bibliographic‑exploratory research about the wind power supply chain and it analyzes how technological innovations made their development possible. The study focuses specifically on exploring within the technological innovations occurred in the chain wind power, as the innovative approach followed by developers organizations. Results indicate that open innovation practices were essential for the success of economic and technological feasibility for the development of such chain, primarily by what happened partnership between the manufacturers of major components (aero generators, towers, blades) wind energy, research institutes, laboratories, universities and federal governments through incentive policies.


title: Quality of Service of Routing Protocols in Wireless Sensor Networks: A Review

abstract: Due to distributed nature, dynamic topology and resources constraints of tiny sensing nodes in wireless sensor networks (WSNs), the quality of service (QoS) support is a challenging issue. However, satisfying the stringent QoS requirements is an open problem. QoS aware protocols for WSNs have gained recently considerable attention of the researchers. In this paper, we focus on the QoS satisfaction in WSNs, basics of QoS support in WSNs, and more importantly challenge, requirements of QoS at each layer. Furthermore, we review the QoS protocols and categorize the QoS aware protocols and elaborate their pros and cons. We also discuss the QoS parameters with respect to each protocol performance parameters. A survey and comprehensive discussion on the QoS aware protocols of WSNs are presented, including their strengths and limitations. Finally, we also survey some computational intelligence (CI) techniques and find the basic requirements of such techniques. Moreover, we study these CI techniques in the light of QoS management and tabulate the level of each CI technique for QoS management. The paper is concluded with open research issues.


Correcting Lens Distortions in Digital Photographs


title: The Experience of Emotion

abstract: Experiences of emotion are content-rich events that emerge at the level of psychological description, but must be causally constituted by neurobiological processes. This chapter outlines an emerging scientific agenda for understanding what these experiences feel like and how they arise. We review the available answers to what is felt (i.e., the content that makes up an experience of emotion) and how neurobiological processes instantiate these properties of experience. These answers are then integrated into a broad framework that describes, in psychological terms, how the experience of emotion emerges from more basic processes. We then discuss the role of such experiences in the economy of the mind and behavior.


title: The NASA automated requirements measurement tool: a reconstruction

abstract: In the late 1990s the National Aeronautics and Space Administration (NASA) Software Assurance Technology Center (SATC) developed a tool to automatically analyze a requirements document and produce a detailed quality report. The report was based on statistical analysis of word frequencies at various structural levels of the document. The Automated Requirements Measurement (ARM) tool was further enhanced to include additional functionality such as custom definitions of quality indicators inputs for document analysis. By 2011 work on the ARM tool was discontinued. This paper describes the reverse-engineering and reproduction of the functionality of ARM. Recreating the functionality of this tool yielded valuable insight into certain quality metrics and provides a benchmark tool for future research. In addition to recreating and working with the ARM tool, this paper explores both existing and potential definitions of quality metrics in requirements specifications. Automated requirements analysis is a convergence of various fields of research, including text mining, quality analysis, and natural language processing. Informed by tangential areas of research in document understanding and data mining, recommendations are made for future areas of research and development in automated requirements analysis.


title: Microstrip feed substrate integrated waveguide cavity slot antenna for 60GHz applications

abstract: Feature Scenario of Substrate integrated waveguide (SIW) is a good solution for implementing centimeter and millimeter wave applications. In this paper, we present microstrip feed SIW cavity backed slot antenna for 60GHz applications which is intended by using Rogers RT/Duriod 5880 with dielectric constant 2.2. The simulation results show that reflection coefficient, VSWR, gain, radiation pattern and obtained 1.55GHz bandwidth with respect to −10 dB reference line.


title: A Political economy of the Middle East

abstract: This integrated, analytic text presents a comprehensive analysis transformation and development of the political economy in the Middle East over the past several decades. In this updated third edition of "A Political Economy of the Middle East," the book retains its focus on the interaction of economic development processes, state systems, and social actors even as it also: Explores the impact of the Arab Spring and subsequent events to the issues raised throughout the textbook in a wholly new chapter;Documents the many changes in demography, education, labor markets, urbanization, water and agriculture, and international labor migration in the Middle East in recent years;Considers the effect of rising oil prices on reinforcement of authoritarian governance in the region;Refines its assessment of the Washington Consensus to provide a more nuanced approach to the issue of the shifting balance of state and market in economic growth and reform;Presents Islamism as a vital force in the region that is nonetheless a vast, diverse social movement with many conflicting participants."


title: Copy, transform, combine: exploring the remix as a form of innovation

abstract: Abstract ::: The reuse of existing knowledge is an indispensable part of the creation of novel ideas. In the creative domain knowledge reuse is a common practice known as “remixing”. With the emergence of open internet-based platforms in recent years, remixing has found its way from the world of music and art to the design of arbitrary physical goods. However, despite its obvious relevance for the number and quality of innovations on such platforms, little is known about the process of remixing and its contextual factors. This paper considers the example of Thingiverse, a platform for the 3D printing community that allows its users to create, share, and access a broad range of printable digital models. We present an explorative study of remixing activities that took place on the platform over the course of six years by using an extensive set of data on models and users. On the foundation of these empirically observed phenomena, we formulate a set of theoretical propositions and managerial implications regarding (1) the role of remixes in design communities, (2) the different patterns of remixing processes, (3) the platform features that facilitate remixes, and (4) the profile of the remixing platform’s users.


Deep Learning Methods on Recommender System: A Survey of State-of-the-art


title: Improving the effectiveness of nutritional information policies: assessment of unconscious pleasure mechanisms involved in food-choice decisions

abstract: The rise in obesity in many countries has led to the emergence of nutritional information policies that aim to change people's diets. Changing an individual's diet is an ambitious goal, since numerous factors influence a person's food-choice decisions, many of which are made unconsciously. These frequently subconscious processes should not be underestimated in food-choice behavior, as they play a major role in food diet composition. In this review, research in cognitive experimental psychology and neuroscience provides the basis for a critical analysis of the role of pleasure in eating behaviors. An assessment of the main characteristics of nutritional policies is provided, followed by recent findings showing that food choices are guided primarily by automatic emotional processes. Neuroimaging and behavioral studies, which provide new insights into the relationships between emotions and food both in lean persons and in persons with eating disorders, are reported as well. Lastly, the argument is presented that future nutritional policies can be more effective if they associate healthy food with eating pleasure.


title: SPEAR: A monopedal robot with Switchable Parallel Elastic actuation

abstract: Inspired by biological systems, compliant elements are introduced in the driving train of legged robots with the purpose of recycling energy. This paper presents the design and control concept of the monopedal robot SPEAR, driven by a novel implementation of a Switchable Parallel Elastic Actuator (S-PEA). At the stance phase, the parallel spring in S-PEA works with the actuator to support body weight and recover energy. The spring is removed from the system at the flight phase to gain precise joint control. The stiffness of the whole leg is also adjustable online either by active control of the motor or by changing the knee angle at touchdown. Experimental results show that the spring recycles part of energy during hopping, reducing the peak torque and peak power of the motor.


title: The decade of exosomal long RNA species: an emerging cancer antagonist

abstract: Exosomes have emerged as a novel approach for the treatment and diagnosis of cancer after RNA content was discovered in exosomes in 2007. As important meditators of intercellular communication, exosomes have become a strong focus of investigation for researchers in the past decade, as witnessed through the exponential increase of research on exosomes. The capability of exosomes to transfer functionally active cargo highlights their importance as promising biomarkers and diagnostic molecules, as well as prospective drug delivery systems. The accessibility of exosomes in nearly all biofluids additionally alludes to its unprecedented ability in various types of cancers due to its extensive impact on tumor formation and progression. This review analyzes the role of exosomal long RNA species, which is comprised of mRNA, lncRNA, and circRNA, in tumor formation and progression, with an emphasis on their potential as future diagnostic biomarkers and treatment vectors in cancer biology. Their alignment with the development of exosomal databases is further examined in this review, in view of the accumulation of studies published on exosomes in the past decade.


title: A Conversational Interface to Improve Medication Adherence: Towards AI Support in Patient's Treatment

abstract: Medication adherence is of utmost importance for many chronic conditions, regardless of the disease type. Engaging patients in self-tracking their medication is a big challenge. One way to potentially reduce this burden is to use reminders to promote wellness throughout all stages of life and improve medication adherence. Chatbots have proven effectiveness in triggering users to engage in certain activity, such as medication adherence. In this paper, we discuss "Roborto", a chatbot to create an engaging interactive and intelligent environment for patients and assist in positive lifestyle modification. We introduce a way for healthcare providers to track patients adherence and intervene whenever necessary. We describe the health, technical and behavioural approaches to the problem of medication non-adherence and propose a diagnostic and decision support tool. The proposed study will be implemented and validated through a pilot experiment with users to measure the efficacy of the proposed approach.


title: Super-Samples from Kernel Herding

abstract: We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rate O(1/T) which is much faster than the usual O(1/T) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.


title: Single-Cell Exome Sequencing and Monoclonal Evolution of a JAK2-Negative Myeloproliferative Neoplasm

abstract: Summary Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2 -negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1 , which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.


title: Fuzzy criticality assessment model for failure modes and effects analysis

abstract: This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper.


title: Ensemble learning for free with evolutionary algorithms?

abstract: Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.


title: IT Services Reference Catalog

abstract: Information Technology Infrastructure Library (ITIL) has become the best and most adopted practice framework to implement IT Service Management (ITSM) within organizations. Service Catalog is a fundamental basis in ITIL adoption because it is the source of information about services. However, organizations often fail in service identification activity, compromising the creation of the service catalog. Moreover, ITIL states how to develop a service catalog nor offers a standard one. In this paper, we propose an IT Service Reference Catalog (ITSRC) as a basis to start creating a Service Catalog or as a reference to adapt. We evaluated the proposal in real-world settings using Design Science Research methodology, and positive results were achieved.


Interactions of Nutrition and Infection


Igbo Diacritic Restoration using Embedding Models


title: Time series forecasting using improved ARIMA

abstract: In recent years, there has been an explosion of interest in forecasting time series databases in different applied areas. Forecasting is one of the main goal's mining of time series databases. Time series forecasting has been shown effective in suitable decision making in various domains. So far, a variety of techniques have been proposed to obtain goal of prediction and analysis of literature this area is in different directions. In this regard, in this paper, there are two goals. First, provide a review. For this goal, this paper classifies previous major works that investigated the forecasting of time series data in different application areas. Second, propose a novel approach to improve ARIMA model by applying a mean of estimation error for time series forecasting. Experimental results indicate that the proposed approach can improve performance in the process of time series data forecasting.


title: A new cell counter based attack against tor

abstract: Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).


title: Economics of Internet of Things: an information market approach

abstract: Internet of Things (IoT) has been proposed to be a new paradigm of connecting devices and providing services to various applications, e.g., transportation, energy, smart cities, and health care. In this paper we focus on an important issue, i.e., the economics of IoT, that can have a great impact on the success of IoT applications. In particular, we adopt and present the information economics approach with its applications in IoT. We first review existing economic models developed for IoT services. Then we outline two important topics of information economics that are pertinent to IoT, i.e., the value of information and proper pricing of information. Finally, we propose a game theoretic model to study the price competition of IoT sensing services. Perspectives on future research directions to apply information economics to IoT are discussed.


title: Challenges and Solutions in Fog Computing Orchestration

abstract: Fog computing, complementary to cloud computing, has recently emerged as a new paradigm that extends the computing infrastructure from the center to the edge of the network. This article explores the design of a fog computing orchestration framework to support IoT applications. In particular, we focus on how the widely adopted cloud computing orchestration framework can be customized to fog computing systems. We first identify the major challenges in this procedure that arise due to the distinct features of fog computing. Then we discuss the necessary adaptations of the orchestration framework to accommodate these challenges.


title: Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems

abstract: Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma's Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by encouraging agents to visit new states or perform new actions in relation to all prior training episodes (which we call across-training novelty). But such algorithms do not consider whether an agent exhibits intra-life novelty: doing something new within the current episode, regardless of whether those behaviors have been performed in previous episodes. We hypothesize that across-training novelty might discourage agents from revisiting initially non-rewarding states that could become important stepping stones later in training. We introduce Deep Curiosity Search (DeepCS), which encourages intra-life exploration by rewarding agents for visiting as many different states as possible within each episode, and show that DeepCS matches the performance of current state-of-the-art methods on Montezuma's Revenge. We further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and Tutankham (many of which are hard exploration games). Surprisingly, DeepCS doubles A2C performance on Seaquest, a game we would not have expected to benefit from intra-life exploration because the arena is small and already easily navigated by naive exploration techniques. In one run, DeepCS achieves a maximum training score of 80,000 points on Seaquest, higher than any methods other than Ape-X. The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods.


title: Speedup Graph Processing by Graph Ordering

abstract: The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support searching including cache-oblivious, and cache-conscious trees. In this paper, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. The approaches dealing with trees are not applicable to graphs which are complex in nature. In this paper, we explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other 9 possible graph orderings (such as the one obtained by METIS) using 8 large real graphs and 9 representative graph algorithms. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.


title: Cellular and Systems Reconsolidation in the Hippocampus

abstract: Cellular theories of memory consolidation posit that new memories require new protein synthesis in order to be stored. Systems consolidation theories posit that the hippocampus has a time-limited role in memory storage, after which the memory is independent of the hippocampus. Here, we show that intra-hippocampal infusions of the protein synthesis inhibitor anisomycin caused amnesia for a consolidated hippocampal-dependent contextual fear memory, but only if the memory was reactivated prior to infusion. The effect occurred even if reactivation was delayed for 45 days after training, a time when contextual memory is independent of the hippocampus. Indeed, reactivation of a hippocampus-independent memory caused the trace to again become hippocampus dependent, but only for 2 days rather than for weeks. Thus, hippocampal memories can undergo reconsolidation at both the cellular and systems levels.


title: 1.7kV high-power IGBT fabrication by bonded-wafer-concept

abstract: Lower losses and higher performance levels at elevated junction temperatures require fabrication processes enabling full design-flexibility for the IGBT buffer and anode to meet application requirements for the ≤1.7kV voltage-range. Here we present the bonded wafer concept that is enabling high thermal stability and soft and high turn-off capability due to full design flexibility for the critical backside layers.


title: A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means

abstract: In today's world, Botnet has become one of the greatest threats to network security. Network attackers, or Botmasters, use Botnet to launch the Distributed Denial of Service (DDoS) to paralyze large-scale websites or steal confidential data from infected computers. They also employ “phishing” attacks to steal sensitive information (such as users’ accounts and passwords), send bulk email advertising, and/or conduct click fraud. Even though detection technology has been much improved and some solutions to Internet security have been proposed and improved, the threat of Botnet still exists. Most of the past studies dealing with this issue used either packet contents or traffic flow characteristics to identify the invasion of Botnet. However, there still exist many problems in the areas of packet encryption and data privacy, simply because Botnet can easily change the packet contents and flow characteristics to circumvent the Intrusion Detection System (IDS). This study combines Particle Swarm Optimization (PSO) and K-means algorithms to provide a solution to remedy those problems and develop, step by step, a mechanism for Botnet detection. First, three important network behaviors are identified: long active communication behavior (ActBehavior), connection failure behavior (FailBehavior), and network scanning behavior (ScanBehavior). These behaviors are defined according to the relevant prior studies and used to analyze the communication activities among the infected computers. Second, the features of network behaviors are extracted from the flow traces in the network layer and transport layer of the network equipment. Third, PSO and K-means techniques are used to uncover the host members of Botnet in the organizational network. This study mainly utilizes the flow traces of a campus network as an experiment. The experimental findings show that this proposed approach can be employed to detect the suspicious Botnet members earlier than the detection application systems. In addition, this proposed approach is easy to implement and can be further used and extended in the campus dormitory network, home networks, and the mobile 3G network.


title: Dynamic classifier selection : Recent advances and perspectives

abstract: An updated taxonomy of Dynamic Selection techniques is proposed.A review of the state-of-the-art dynamic selection techniques is presented.Empirical comparison between 18 dynamic selection techniques is conducted.We discuss about the recent findings and open research question in this field. Multiple Classifier Systems (MCS) have been widely studied as an alternative for increasing accuracy in pattern recognition. One of the most promising MCS approaches is Dynamic Selection (DS), in which the base classifiers are selected on the fly, according to each new sample to be classified. This paper provides a review of the DS techniques proposed in the literature from a theoretical and empirical point of view. We propose an updated taxonomy based on the main characteristics found in a dynamic selection system: (1) The methodology used to define a local region for the estimation of the local competence of the base classifiers; (2) The source of information used to estimate the level of competence of the base classifiers, such as local accuracy, oracle, ranking and probabilistic models, and (3) The selection approach, which determines whether a single or an ensemble of classifiers is selected. We categorize the main dynamic selection techniques in the DS literature based on the proposed taxonomy. We also conduct an extensive experimental analysis, considering a total of 18 state-of-the-art dynamic selection techniques, as well as static ensemble combination and single classification models. To date, this is the first analysis comparing all the key DS techniques under the same experimental protocol. Furthermore, we also present several perspectives and open research questions that can be used as a guide for future works in this domain.


title: Fractal Organization in Healthcare Information Systems

abstract: The paper presents a new solution for a Healthcare Information System (HIS) architecture that take account of the multi-layer hierarchy and the complexity of interactions between entities distributed at different levels, considered as being components of a multi-agent system. The proposed architectural model follows the requirements for interoperability derived from standards that support the concept of software-oriented architecture. In addition, the HIS topology is associated with a novel construct named Fractal Social Organizations (FSO), a class of socio-technical complex systems based on the recursive application of a same building block, the Service-oriented Community (SoC). Finally, a virtual case study describing a FSO for healthcare services is discussed in order to validate the proposed solutions.


title: A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem

abstract: Most combinatorial optimization problems belong to the NP-complete or NP-hard classes, which means that they may require an infeasible processing time to be solved by an exhaustive search method. Thus, less expensive heuristics in respect to the processing time are commonly used. These heuristics can obtain satisfactory solutions in short running times, but there is no guarantee that the optimal solution will be found. Artificial Neural Networks (ANNs) have been widely studied to solve combinatorial problems, presenting encouraging results. This paper proposes some modifications on RABNET-TSP, an immune-inspired self-organizing neural network, for the solution of the Traveling Salesman Problem (TSP). The modified algorithm is compared with other neural methods from the literature and the results obtained suggest that the proposed method is competitive in relation to the other ones, outperforming them in many cases with regards to the quality (cost) of the solutions found, though demanding a greater time for convergence in many cases.


title: A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

abstract: As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.


title: Third Party Inspection as an Alternative to Command and Control Regulation

abstract: This article makes the case for relying on decentralized market-based incentive mechanisms to supplement performance-based regulations for promoting industrial safety. In particular, we examine how third-party inspections, coupled with insurance protection, can encourage firms to reduce their risks from accidents and disasters. Our proposal is thus an example of a management-based regulatory strategy where the locus of decision making is shifted from the regulator to firms, which are now required to do their own planning as to how they will meet a set of standards or regulations. The third-party inspections we have in mind would take the form of a voluntary contractual relationship between the firm and the party auditing the facility in place of relying solely on the regulatory agency as enforcer. In undertaking the inspection, the third party can simply play the role of informing the firm that it is in violation of a regulation or, going one step further, it can assist the firm in developing a program for reducing its risks to comply with a particular standard or regulation. The passage of Section 112(r) of the Clean Air Act Amendments (CAAA) of 1990 offers an opportunity to implement such a program. This legislation created two new federal regulatory programs aimed at preventing releases of hazardous chemicals: the Occupational Health and Safety Administration (OSHA) Process Safety Management (PSM) standard and the U.S Environmental Protection Agency (EPA) Risk Management Program. The OSHA program was enacted in 1992 and requires facilities containing large quantities of highly hazardous chemicals to implement accident prevention and emergency response measures to protect workers. The EPA Risk Management Program regulation, published in 1996, borrowed the same accident prevention concepts and language from PSM but went beyond the OSHA program. It also required facilities to perform a hazard assessment, estimate consequences from accidents, and submit a summary report, called the Risk Management Plan (RMP), to EPA by June 21, 1999. The challenge is how to encourage compliance with these regulations so that firms will be operating in a safer manner. The article proceeds as follow. In the next section we make the case for well-enforced RMPs and then show in Section 3 why firms may not voluntarily comply with this regulation. Section 4 shows analytically that third-party inspections coupled with insurance provide economic incentives for many firms to undertake RMPs voluntarily and encourages the remaining recalcitrant firms to comply with the regulation. Section 5 illustrates implementation challenges in utilizing third-party inspections while at the same time pointing to their success in a variety of different contexts. The concluding section discusses a set of open issues and questions, the need for future studies, and what analysts and practitioners can learn from this work.