title: A scrum-based approach to CMMI maturity level 2 in web development environments

abstract: Scrum has become one of the most popular agile methodologies, either alone or combined with other agile practices. Besides, CMMI (Capability Maturity Model Integration) is accepted as a suitable model to measure the maturity of the organizations when developing or acquiring software. Although these two approaches are often considered antagonist, the use of an agile approach to reach certain CMMI maturity levels may result beneficial to organizations that develop Web systems, since they would take the advantages of both approaches. In Web community, this union may be very interesting, because agile approaches fits with the special needs of Web development, and they could be a useful tool for companies getting a certain grade of maturity. This work analyzes the goals of CMMI maturity level 2 and the feasibility of achieving them using the practices proposed by Scrum, trying to assess whether the use of this methodology is suitable for meeting the CMMI generic and specific goals or not. Finally, and based on this analysis, this paper raises a possible extension of Scrum, based on agile techniques, to accommodate the CMMI maturity level 2.


title: Learning robot grasping from 3-D images with Markov Random Fields

abstract: Learning to grasp novel objects is an essential skill for robots operating in unstructured environments. We therefore propose a probabilistic approach for learning to grasp. In particular, we learn a function that predicts the success probability of grasps performed on surface points of a given object. Our approach is based on Markov Random Fields (MRF), and motivated by the fact that points that are geometrically close to each other tend to have similar grasp success probabilities. The MRF approach is successfully tested in simulation, and on a real robot using 3-D scans of various types of objects. The empirical results show a significant improvement over methods that do not utilize the smoothness assumption and classify each point separately from the others.


Development of an Integration Platform Metamodel


title: Characterizing the Activity Factor in NBTI Aging Models for Embedded Cores

abstract: In deeply scaled CMOS technologies, device aging causes cores performance parameters to degrade over time. While accurate models to efficiently assess these degradation exist for devices and circuits, no reliable model for processor cores has gained strong acceptance in the literature. In this work, we propose a methodology for deriving an NBTI aging model for embedded cores. Based on an accurate characterization on the netlist of the core, we were able to (1) prove the independence of the aging on the workload (i.e., executed instructions), and (2) calculate an equivalent average constant aging factor that justifies the use of the baseline model template. We derived and assessed the proposed model by using a RISC-like processor core implemented in a 45nm process technology as a reference architecture, achieving a maximum error of 2.2% against simulated data on the core netlist.


title: Learning doubly sparse transforms for image representation

abstract: The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.


title: Improved pregnancy rate in human in vitro fertilization with the use of a medium based on the composition of human tubal fluid

abstract: Significantly more mouse zygotes developed to blastocysts in culture in a medium formulated on the composition of human tubal fluid (HTF) than in modified Tyrode's medium (T6). In a randomized 2×2 factorial trial of human in vitro fertilization that compared the two media and culture under oil versus culture in loosely capped tubes, significantly more clinical pregnancies (30% of 60 transfers) were obtained with HTF medium than with T6 medium (11% of 53 transfers). Decreasing the K + content of HTF medium to that present in T6 medium significantly decreased the number of mouse zygotes that developed in culture. Modifying Ca + + levels had no effect. It is therefore likely that the higher K + content in HTF medium is primarily responsible for the superiority of HTF medium over T6 medium, but other differences in the composition of the two media could contribute to the results observed.


title: Iterative multiuser receiver in sparse code multiple access systems

abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%.


title: Determining Semantic Similarity among Entity Classes from Different Ontologies

abstract: Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.


title: Permanent-magnet, direct-drive, starter/alternator machine with weakened flux linkage for constant-power operation over extremely wide speed range

abstract: As consequence of the considerable increase of the electrical power demand in vehicles, the adoption of a combined direct-drive starter/alternator system is being seriously pursued and a new generation of vehicle alternators delivering power up to 6 kW over the entire range of the engine speed is soon expected for use with connection to a 42 V bus. The surface permanent magnet (SPM) machines offer many of the features sought for such future automotive power generation systems, and thereby a substantial improvement in the control of their output voltage would allow the full exploitation of their attractive characteristics in the direct-drive starter/alternator application without significant penalties otherwise resulting on the machine-fed power converter. Concerning that, this paper reports on the original solution adopted in a proof-of-concept axial-flux permanent magnet machine (AFPM) prototype to provide weakening of the flux linkage with speed and thereby achieve constant-power operation over a wide speed range. The principle being utilized is introduced and described, including design dimensions and experimental data taken from the proof-of-concept machine prototype.


title: A Semantic Loss Function for Deep Learning with Symbolic Knowledge

abstract: This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that our semantic loss function effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.


title: Answering questions with an n-gram based passage retrieval engine

abstract: In this paper, we present a Question Answering system based on redundancy and a Passage Retrieval method that is specifically oriented to Question Answering. We suppose that in a large enough document collection the answer to a given question may appear in several different forms. Therefore, it is possible to find one or more sentences that contain the answer and that also include tokens from the original question. The Passage Retrieval engine is almost language-independent since it is based on n-gram structures. Question classification and answer extraction modules are based on shallow patterns.


title: Effects of 2D geometric transformations on visual memory

abstract: In order to provide well-grounded guidelines for interface design, we systematically examined the effects of 2D geometric transformations and background grids on visual memory. We studied scaling, rotation, rectangular fisheye, and polar fisheye transformations. Based on response time and accuracy results, we found a no-cost zone for each transformation type within which performance is unaffected. Results indicated that scaling had no effect down to at least 20% reduction. Rotation had a no-cost zone of up to 45 degrees, after which the response time increased to 5.4 s from the 3.4 s baseline without significant drop in accuracy. Interestingly, polar fisheye transformations had less effect on accuracy than their rectangular counterparts. The presence of grids extended these zones and significantly improved accuracy in all but the fisheye polar transformations. Our results therefore provided guidance on the types and levels of nonlinear transformations that could be used without affecting performance, and provided insights into the roles of transformations and grids on visual memory.


title: MAFRA — A MApping FRAmework for Distributed

abstract: Ontologies as means for conceptualizing and structuring domain knowledge within a community of interest are seen as a key to realize the Semantic Web vision. However, the decentralized nature of the Web makes achieving this consensus across communities difficult, thus, hampering efficient knowledge sharing between them. In order to balance the autonomy of each community with the need for interoperability, mapping mechanisms between distributed ontologies in the Semantic Web are required. In this paper we present MAFRA, an interactive, incremental and dynamic framework for mapping distributed ontologies.


title: Impression Management Using Typeface Design

abstract: Abstract This article develops empirically based guidelines to help managers select typefaces that affect strategically valued impressions. The authors discuss the potential trade-offs among the impressions created by typeface (e.g., pleasing, engaging, reassuring, prominent). The selection of typeface can be simplified with the use of six underlying design dimensions: elaborate, harmony, natural, flourish, weight, and compressed.


title: Aspect-oriented programming

abstract: Aspect-oriented programming (AOP) is a technique for improving separation of concerns in software design and implementation. AOP works by providing explicit mechanisms for capturing the structure of crosscutting concerns. This tutorial shows how to use AOP to implement crosscutting conerns in a concise modular way. It works with AspectJ, a seamless aspect-oriented extension to the Java(tm) programming language, and with AspectC, an aspect-oriented extension to C in the style of AspectJ. It also includes a description of their underlying model, in terms of which a wide range of AOP languages can be understood.


title: Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

abstract: Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset.


title: Commensal bacteria at the interface of host metabolism and the immune system

abstract: The mammalian gastrointestinal tract, the site of digestion and nutrient absorption, harbors trillions of beneficial commensal microbes from all three domains of life. Commensal bacteria, in particular, are key participants in the digestion of food, and are responsible for the extraction and synthesis of nutrients and other metabolites that are essential for the maintenance of mammalian health. Many of these nutrients and metabolites derived from commensal bacteria have been implicated in the development, homeostasis and function of the immune system, suggesting that commensal bacteria may influence host immunity via nutrient- and metabolite-dependent mechanisms. Here we review the current knowledge of how commensal bacteria regulate the production and bioavailability of immunomodulatory, diet-dependent nutrients and metabolites and discuss how these commensal bacteria-derived products may regulate the development and function of the mammalian immune system.


Reinforcement Learning and Causal Models


title: Object Detection using Image Processing

abstract: An Unmanned Ariel vehicle (UAV) has greater importance in the army for border security. The main objective of this article is to develop an OpenCV-Python code using Haar Cascade algorithm for object and face detection. Currently, UAVs are used for detecting and attacking the infiltrated ground targets. The main drawback for this type of UAVs is that sometimes the object are not properly detected, which thereby causes the object to hit the UAV. This project aims to avoid such unwanted collisions and damages of UAV. UAV is also used for surveillance that uses Voila-jones algorithm to detect and track humans. This algorithm uses cascade object detector function and vision. train function to train the algorithm. The main advantage of this code is the reduced processing time. The Python code was tested with the help of available database of video and image, the output was verified.


title: Water phantom backscatter factors for x-rays in the 60 kV to 300 kV region.

abstract: Experimentally determined values of x-ray backscatter factors are presented in this paper. Measurements were made for x-rays generated at voltages between 60 kV and 300 kV and three water phantoms different in shape and size. To study the influence of the irradiation geometry on the backscatter factors, the measurements were performed for different photon beam field diameters at the phantom front face, at a fixed source-to-phantom distance of 1 m. An ionization chamber of volume 1 cm3 was used for dose measurement. Measured results are analysed and discussed in comparison with measured and calculated values given in the cited references.


title: Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

abstract: In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures.


title: A 7.1-fJ/Conversion-Step 88-dB SFDR SAR ADC With Energy-Free “Swap To Reset”

abstract: The digital-to-analog converter (DAC) in SAR anolog-to-digital converters (ADCs) is often dominant for both power consumption and linearity. Dedicated switching schemes can save power, but mostly focus on conversion energy, whereas the DAC reset can consume significant energy as well. This paper presents an energy-free DAC reset scheme, “swap to reset,” for charge-redistribution SAR ADCs. It is widely applicable to existing low-power switching schemes. Additionally, to limit complexity while maintaining most of the energy savings, it can be utilized for the MSBs of the DAC only while the LSBs use conventional reset. To demonstrate the scheme, it is applied to the 2 MSBs of a 12-b SAR ADC using a split-monotonic DAC in 65-nm CMOS, resulting in an energy saving of 33% for the DAC or 18% for the whole ADC. Besides the “swap to reset,” a rotation is also applied to the 2 MSBs, hence enhancing the linearity to 88-dB spurious free dynamic range. The SAR ADC operates at 0.8-V power supply and 40 kS/s, achieving an signal to noise and distortion ratio of 64.2 dB and a Figure of Merit of 7.1-fJ/conversion step.


title: Role of Artificial Intelligence & Expert System in: Business Competitiveness

abstract: Today’s global economy requires increased attention to the issue of business competitiveness. Business information system or Artificial Intelligence and expert system raise the competitiveness of enterprises in the global market. Business intelligence as the basis for the development and application in business information is becoming an important information technology framework that can help organization to manage, develop and communicate their intangible assets, such as information and knowledge based economy. Competency Based Management (CBM) has become vital to any firm’s strategic position and organizational decision making. The role of Artificial intelligence & Expert System (ES) is to provide a knowledge based information system that is expected to have human attributes in order to replicate human capacity in ethical decision making. In this paper a holistic framework is proposed to review the ES approach that will be practically feasible for organizational settings. It will also provide executives and scholars with pragmatic understanding about integrating knowledge management strategy and technologies in business processes for successful performance. It is based on the psychological conceptions of human competence and performance in the workplace. Using an ES approach to Educational Institute with CBM will be able to more effectively use their limited resources to reap the more benefits from their investments in both people and technology. The idea of robotic domestic workers is still farfetched but companies are making progress even here. There is already a Robot Vacuum Cleaner marketed by Electrolux and doubtless improved systems with better functionality.


title: Automated market-making in the large: the gates hillman prediction market

abstract: We designed and built the Gates Hillman Prediction Market (GHPM) to predict the opening day of the Gates and Hillman Centers, the new computer science buildings at Carnegie Mellon University. The market ran for almost a year and attracted 169 active traders who placed almost 40,000 bets with an automated market maker. Ranging over 365 possible opening days, the market's event partition size is the largest ever elicited in any prediction market by an order of magnitude. A market of this size required new advances, including a novel span-based elicitation interface. The results of the GHPM are important for two reasons. First, we uncovered two flaws of current automated market makers: spikiness and liquidity-insensitivity, and we develop the mathematical underpinnings of these flaws. Second, the market provides a valuable corpus of identity-linked trades. We use this data set to explore whether the market reacted to or anticipated official communications, how self-reported trader confidence had little relation to actual performance, and how trade frequencies suggest a power law distribution. Most significantly, the data enabled us to evaluate two competing hypotheses about how markets aggregate information, the Marginal Trader Hypothesis and the Hayek Hypothesis; the data strongly support the former.


title: Different approaches to community detection

abstract: A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.


A Brief History of Cloud Offload: A Personal Journey from Odyssey Through Cyber Foraging to Cloudlets


HARMONIC MAASS FORMS, MOCK MODULAR FORMS, AND QUANTUM MODULAR FORMS


title: Handwritten Digit Classification using Higher Order Singular Value Decomposition

abstract: In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one.


title: Adaptive Beamforming with Per-Antenna Feedback for Multi-Cell Cooperative Networks

abstract: Beamforming is a signal processing technique that enables antenna arrays to create directional signals, increasing transmitter or receiver gain. We propose a new adaptive user antenna beamforming technique for Multi-cell Cooperative Networks which simultaneously communicates with multiple available BSs and RSs using the MS’s multiple antennas. We show that the proposed adaptive beamforming technique outperforms distributed beamforming by increasing the data rate with less degradation of the BER.


title: Robust Statistical Face Frontalization

abstract: Recently, it has been shown that excellent results can be achieved in both facial landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D facial data. In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only. By observing that the frontal facial image is the one having the minimum rank of all different poses, an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix l1 norm is solved. The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions. The relevant experiments have been conducted on 8 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.


title: Content-centric networking: is that a solution for upcoming vehicular networks?

abstract: In this paper we propose an innovative networking architecture for content retrieval and distribution in Vehicular Ad hoc NETworks (VANETs), that leverages the recently proposed Content-Centric Networking (CCN) paradigm. It is based on named contents instead of IP addresses, and seems an attractive opportunity to handle the dynamic, short-lived and intermittent connectivity typical of VANETs. Achieved results show that the proposed architecture significantly outperforms the legacy TCP/IP protocol suite, thus confirming the potentialities of content-centric as a promising networking solution for upcoming VANETs.


title: SUPPLY CHAIN MANAGEMENT IN INDIAN AUTOMOTIVE INDUSTRY : COMPLEXITIES, CHALLENGES AND WAY AHEAD

abstract: The Indian automotive industry, comprising vehicle and component manufacturers, has grown steadily since the economic liberalization of the early 1990’s. The arrival of major global auto companies has galvanised the domestic sector into adopting Supply Chain best practices. This has enhanced competitiveness leading to a quantum growth in exports. However, the Indian automotive industry has to operate in an unique environment further posing challenges to the already complex automobile supply chain. Therefore, a need is felt to continually study supply chain practices in this sector from a contemporary, practitioner’s viewpoint in order to identify key factors of differentiation which would ultimately provide competitive advantage. This paper seeks to understand the present status, complexities and challenges facing the Indian automobile sector. It examines trends such as visibility and innovation, collaboration and supply networks and evolving leadership roles impacting supply chain effectiveness. Strategies for overcoming challenges are presented as also a framework for further study and analysis.


title: A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’

abstract: Bibliometric and "tech mining" studies depend on a crucial foundation--the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of "legacy technology" terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze "Big Data," we confront all three challenges--emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a "Hit-Ratio" that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replicable searches with high recall and satisfactory precision for other emerging technology studies.


title: Recognizing action at a distance

abstract: Our goal is to recognize human action at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatiotemporal volume for each stabilized human figure, and an associated similarity measure to be used in a nearest-neighbor framework. Making use of noisy optical flow measurements is the key challenge, which is addressed by treating optical flow not as precise pixel displacements, but rather as a spatial pattern of noisy measurements which are carefully smoothed and aggregated to form our spatiotemporal motion descriptor. To classify the action being performed by a human figure in a query sequence, we retrieve nearest neighbor(s) from a database of stored, annotated video sequences. We can also use these retrieved exemplars to transfer 2D/3D skeletons onto the figures in the query sequence, as well as two forms of data-based action synthesis "do as I do" and "do as I say". Results are demonstrated on ballet, tennis as well as football datasets.


title: Self-Configuration of Antenna Tilt and Power for Plug & Play Deployed Cellular Networks

abstract: The concept of self-configuration of radio interface parameters is often mentioned as an important building block of emerging wireless systems, such as the 3GPP Long Term Evolution (LTE) system [6]. In this paper, we develop a framework concept and propose specific algorithms for the self-configuration of the antenna tilt and the transmission power of a base station, being the two most important cell parameters that need to be properly configured for the efficient operation of the system. The most important element that our concept builds upon is the use of available terminal measurements to perform the optimization on a running network as opposed to off-line planning combined with drive tests.


title: On the control of long-chain-fatty acid synthesis in isolated intact spinach (Spinacia oleracea) chloroplasts.

abstract: 1. Chloroplasts isolated from spinach leaves by using the low-ionic-strength buffers of Nakatani & Barber [(1977) Biochim. Biophys. Acta.461, 510-512] had higher rates of HCO(3) (-)-dependent oxygen evolution (up to 369mumol/h per mg of chlorophyll) and higher rates of [1-(14)C]acetate incorporation into long-chain fatty acids (up to 1500nmol/h per mg of chlorophyll) than chloroplasts isolated by using alternative procedures. 2. Acetate appeared to be the preferred substrate for fatty acid synthesis by isolated chloroplasts, although high rates of synthesis were also measured from H(14)CO(3) (-) in assays permitting high rats of photosynthesis. Incorporation of H(14)CO(3) (-) into fatty acids was decreased by relatively low concentrations of unlabelled acetate. Acetyl-CoA synthetase activity was present 3-4 times in excess of that required to account for rates of [1-(14)C]acetate incorporation into fatty acids, but pyruvate dehydrogenase was either absent or present in very low activity in spinach chloroplasts. 3. Rates of long-chain-fatty acid synthesis from [1-(14)C]acetate in the highly active chloroplast preparations, compared with those used previously, were less dependent on added cofactors, but showed a greater response to light. The effects of added CoA plus ATP, Triton X-100 and sn-glycerol 3-phosphate on the products of [1-(14)C]acetate incorporation were similar to those reported for less active chloroplast preparations. 4. Endogenous [(14)C]acetyl-CoA plus [(14)C]malonyl-CoA was maintained at a constant low level even when fatty acid synthesis was limited by low HCO(3) (-) concentrations. Endogenous [(14)C]acyl-(acyl-carrier protein) concentrations increased with increasing HCO(3) (-) concentration and higher rates of fatty acid synthesis, but were slightly lower in the presence of Triton X-100. It is proposed that rates of long-chain-fatty acid synthesis in isolated chloroplasts at saturating [1-(14)C]acetate concentrations and optimal HCO(3) (-) concentrations may be primarily controlled by rates of removal of the products of the fatty acid synthetase.


title: Propagation Networks for Model-Based Control Under Partial Observation

abstract: There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. With these innovations, our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieves superior performance on various control tasks. Compared with existing deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to novel, partially observable scenes and tasks.


title: Cross-Domain Based Data Sharing Scheme in Cooperative Edge Computing

abstract: With the development of Internet technology, the demand for openness and sharing of information in the network grows rapidly. Data sharing in one domain can not satisfy users' requirement. What a user needs may be the data in another management domain, so there is an urgent need for a secure way to share data among different domains. At present, some web technology (JSONP, server proxy, cross frame and so on) and attribute-based encryption (ABE) are main methods to solve cross-domain sharing. The main problem cross-domain sharing face is security of data and authentication among different domains. In edge computing model, the cloud service provider is a trust root which links all the edge computing nodes together. Edge computing node is the administrator of one domain, and responsible for data transmission and processing. In this paper, we propose a secure way to share data among different domains by edge computing model, we regard the edge computing node and managed edge equipment as a domain, and all the domains are linked together by the cloud, it is easy to solve the problem of authentication among different domains in this way. Meanwhile, RSA algorithm and ciphertext-policy attribute-based encryption (CP-ABE) are used to ensure the confidentiality of the information and one-to-many sharing of data. Finally, analysis shows that our scheme is secure.


title: The Safety, Cosmetic Outcome, and Patient Satisfaction after Inferior Pedicle Reduction Mammaplasty for Significant Macromastia

abstract: Background: Significant macromastia is socially and physically debilitating. Reduction mammaplasty in these cases carries significant morbidity. Methods: Cases of inferior pedicle reduction mammaplasty performed at the breast unit, King Fahd Hospital, Jeddah, Saudi Arabia, over the last 10 years were reviewed. Inclusion criteria were cases with significant macromastia in which the distance from the supra-sternal notch to the nipple was ≥ 40 cm. Results: There were 26 cases of inferior pedicle reduction mammaplasty done for significant macromastia. The average age was 34.56 years (range, 16–56 years). The average sternal notch to the nipple distance was 43.08 cm (range, 40–49 cm). The average amount of breast tissue removed from the right breast was 1,057.6 g (range, 495–2,450) and from the left breast was 959.4 g (range, 445–2,100). Postoperatively, 4/26 (15.4%) had ecchymosis, 9/26 (34.6%) developed T-junction sloughing, 2/26 (7.7%) had wound infection, and 1/26 (3.8%) had unilateral partial nipple-areola complex ischemia. In 7/26 (26.9%), scars were evident and revision was performed in 4/26 (15.4%) cases. Variable degrees of upper breast flattening and bottoming were seen in most cases; however, these variations were more profound in fatty breasts and longer pedicles. The average follow-up period was 26.04 months (range, 3–68 months). All patients were satisfied with the reduced breast heaviness, but only 19/26 (73.1%) were highly satisfied with the breast shape and scars. Conclusions: In cases of significant macromastia, inferior pedicle reduction mammaplasty is a safe procedure. Evident scars, upper breast flattening, and bottoming adversely affect the level of satisfaction.


title: CHAMELEON: a deep learning meta-architecture for news recommender systems

abstract: Recommender Systems (RS) have became a popular research topic and, since 2016, Deep Learning methods and techniques have been increasingly explored in this area. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. The main contribution of this research was named CHAMELEON, a Deep Learning meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks. As information about users' past interactions is scarce in the news domain, the user context can be leveraged to deal with the user cold-start problem. Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain. Furthermore, external breaking events may temporally attract global readership attention, a phenomenon generally known as concept drift in machine learning. All those characteristics are explicitly modeled on this research by a contextual hybrid session-based recommendation approach using Recurrent Neural Networks. The task addressed by this research is session-based news recommendation, i.e., next-click prediction using only information available in the current user session. A method is proposed for a realistic temporal offline evaluation of such task, replaying the stream of user clicks and fresh articles being continuously published in a news portal. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.


title: Improving Short Text Clustering by Similarity Matrix Sparsification

abstract: Short text clustering is an important but challenging task. We investigate impact of similarity matrix sparsification on the performance of short text clustering. We show that two sparsification methods (the proposed Similarity Distribution based, and k-nearest neighbors) that aim to retain a prescribed number of similarity elements per text, improve hierarchical clustering quality of short texts for various text similarities. These methods using a word embedding based similarity yield competitive results with state-of-the-art methods for short text clustering especially for general domain, and are faster than the main state-of-the-art baseline.


title: Virtual Reality: How Much Immersion Is Enough?

abstract: Solid evidence of virtual reality's benefits has graduated from impressive visual demonstrations to producing results in practical applications. Further, a realistic experience is no longer immersion's sole asset. Empirical studies show that various components of immersion provide other benefits - full immersion is not always necessary. The goal of immersive virtual environments (VEs) was to let the user experience a computer-generated world as if it were real - producing a sense of presence, or "being there," in the user's mind.


title: SQLProb: a proxy-based architecture towards preventing SQL injection attacks

abstract: SQL injection attacks (SQLIAs) consist of maliciously crafted SQL inputs, including control code, used against Database-connected Web applications. To curtail the attackers' ability to generate such attacks, we propose an SQL Proxy-based Blocker (SQLProb). SQLProb harnesses the effectiveness and adaptivity of genetic algorithms to dynamically detect and extract users' inputs for undesirable SQL control sequences. Compared to state-of-the-art protection mechanisms, our method does not require any code changes on either the client, the web-server or the back-end database. Rather, our system uses a proxy that seamlessly integrates with existing operational environments offering protection to front-end web servers and back-end databases. To evaluate the overhead and the detection performance of our system, we implemented a prototype of SQLProb which we tested using real SQL attacks. Our experimental results show that we can detect all SQL injection attacks while maintaining very low resource utilization.


A New Approach to School Management: Determination of Student Related Risks According to the Internal Control.


title: Middleware for Internet of Things: A Study

abstract: The significant progress in smart devices has lead to the shifting of actual Internet to the Internet of Things (IoT). Internet of Things is invading our real lives. In this new Internet vision, trillions of smart dynamic objects and areas will be connected to the Internet and interact between each others to achieve common applications. This new Internet vision requires suitable IoT infrastructure that offers the main Internet of Things functionalities to facilitate the development of IoT applications. Middleware is the software layer between technological and application layers. It is mainly used to afford common services and functions to applications and to abstract implementation details to ease more complex application development. In this paper, we review the main challenges facing Internet of Things middleware and survey the most well known middleware that have been proposed in this field.


title: Arousal and consumer in-store behavior

abstract: Abstract From a psychophysiological point of view, arousal is a fundamental feature of behavior. As reported in different empirical studies based on insights from theories of consumer behavior, store atmosphere should evoke phasic arousal reactions to attract consumers. Most of these empirical investigations used verbal scales to measure consumers’ perceived phasic arousal at the point-of-sale (POS). However, the validity of verbal arousal measurement is questioned; self-reporting methods only allow a time-lagged measurement. Furthermore, the selection of inappropriate items to represent perceived arousal is criticized, and verbal reports require some form of cognitive evaluation of perceived arousal by the individual, who might (in a non-measurement condition) not even be aware of the arousal. By contrast, phasic electrodermal reaction (EDR) has proven to be the most appropriate and valid indicator for measuring arousal [W. Boucsein, Physiologische Grundlagen und Mesmethoden der dermalen Aktivitat. In: F. Rosler (Ed.), Enzyklopadie der Psychologie, Bereich Psychophysiologie, Band 1: Grundlagen and Methoden der Psychophysiologie, Kapitel, Vol. 7, Hogrefe, Gottingen, 2001, pp. 551–623] that could be relevant to behavior. EDR can be recorded simultaneously to the perception of stimuli. Furthermore, telemetric online device can be used, which enables physiological arousal measurement while participants can move freely through the store and perform the assigned task in the experiments. The present paper delivers insights on arousal theory and results from empirical studies using EDR to measure arousal at the POS.


title: Lane identification and ego-vehicle accurate global positioning in intersections

abstract: This paper proposes a method for achieving accurate ego-vehicle global localization with respect to an approaching intersection; the method is based on the data alignment of the information from two input systems: a Sensorial Perception system, on-board of the ego-vehicle, and an a priori digital map. For this purpose an Extended Digital Map is proposed that contains the detailed information about the intersection infrastructure: detailed landmarks accurately measured and positioned on the map. The data alignment mechanism is thus based on superimposing the sensorial detected landmarks with the corresponding, correctly positioned map landmarks stored in the new Extended Digital Map. The data Alignment Algorithm requires as input, beside the information from the two input systems, the ego-vehicle driving lane. This information is inferred by using a probabilistic approach in the form of a Bayesian Network; the uncertain and noisy character of the sensorial data require such a probabilistic approach in the quest of the ego-lane.


title: Classifying Particle Semantics In English Verb-Particle Constructions

abstract: Previous computational work on learning the semantic properties of verb-particle constructions (VPCs) has focused on their compositionality, and has left unaddressed the issue of which meaning of the component words is being used in a given VPC. We develop a feature space for use in classification of the sense contributed by the particle in a VPC, and test this on VPCs using the particle up. The features that capture linguistic properties of VPCs that are relevant to the semantics of the particle outperform linguistically uninformed word co-occurrence features in our experiments on unseen test VPCs.


title: Dynamic adaptive HTTP streaming of live content

abstract: MPEG has recently released a first public draft of the new Dynamic Adaptive Streaming over HTTP (DASH) specification. The new streaming technique is based on the 3GPP and partly also on the Open IPTV Forum (OIPF) specifications. In this paper, we give an overview about the new DASH specification with a special focus on Live streaming services. In order to re-use existing web content distribution schemes, the new streaming technique provides the live stream as a sequence of files, which are continuously downloaded by the streaming client. This way of streaming introduces new delay components into the system streaming. Live Streaming technology is often used for events like sport events to allow other users to virtually participate. It is generally preferred to minimize the end-to-end delay for live services. In the paper, we identify and analyze the different delay components of the new adaptive HTTP streaming technique and how they contribute to the end-to-end delay of live services. We also discuss the dependencies and system implications when minimizing the end to end delay. The evaluation principles are also applicable for other adaptive HTTP streaming formats.


title: Bridge Structural Safety Assessment Technology Based on Statistic Analysis

abstract: This paper aims to analyze the statistic rules of the live load effect and the degradation effect and propose some ideas on the bridge structural safety assessment on the condition that both effects closely relate to bridges’ structural performance. Besides, the technology of bridge structural safety assessment based on statistic analysis is also supported by a EWMA controlling Figure with double control limits suitable for bridge structures. After the application in the Ma Sang Xi Yangtze River Bridge project, the technology is proved as reliable, visualized, and perspective in application.


Children's collaboration in emergent game environments


title: Performance evaluation of series connected 1700V SiC MOSFET devices

abstract: The low voltage SiC (Silicon carbide) MOSFET (1.2 kV to 1.7 kV) increases the switching frequency limits of a power electronic converter several folds compared to low voltage Si IGBTs. Significant increase in efficiency and power density of voltage source converters can be achieved. However, for medium-voltage high-power converter applications Silicon (Si) devices (4.5 kV and 6.5 kV IGBT) are still dominant. To explore the capability of low voltage SiC devices for medium or high voltage applications, series connection of 1.7 kV/300 A SiC MOSFET modules has been investigated in this paper. A simple RC snubber method has been used for dynamic voltage sharing to offset the turn-off delays due to mismatch on device's characteristics and/or gate signals. Experimental switching characterization with different values of RC snubbers have been carried out to find the optimal RC snubber which gives minimum voltage sharing difference, snubber losses and total semiconductor losses. This paper also intends to show an optimization of the RC snubber for series connection of a limited number of 1.7kV SiC MOSFETs for 6 kV dc bus and for a generalized dc bus voltage.


title: An ant system with steps counter for the job shop scheduling problem

abstract: In this paper, we present an ant system algorithm variant designed to solve the job shop scheduling problem. The proposed approach is based on a recent biological study which showed that natural ants can count their steps when they build the path between the nest and their food source. Experiments using a set of well-known job shop scheduling problems and a comparison against state-of-the-art techniques show that the proposed approach can reduce the number of evaluations performed without a degradation of performance. Additionally, our proposed approach reduces the number of parameters that need to be tuned by the user (specifically the parameters that balance the importance between the pheromone trail and heuristic values), with respect to the original ant system algorithm.


title: A Case Study in Text Mining: Interpreting Twitter Data From World Cup Tweets

abstract: Cluster analysis is a field of data analysis that extracts underlying patterns in data. One application of cluster analysis is in text-mining, the analysis of large collections of text to find similarities between documents. We used a collection of about 30,000 tweets extracted from Twitter just before the World Cup started. A common problem with real world text data is the presence of linguistic noise. In our case it would be extraneous tweets that are unrelated to dominant themes. To combat this problem, we created an algorithm that combined the DBSCAN algorithm and a consensus matrix. This way we are left with the tweets that are related to those dominant themes. We then used cluster analysis to find those topics that the tweets describe. We clustered the tweets using k-means, a commonly used clustering algorithm, and Non-Negative Matrix Factorization (NMF) and compared the results. The two algorithms gave similar results, but NMF proved to be faster and provided more easily interpreted results. We explored our results using two visualization tools, Gephi and Wordle.


title: Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing

abstract: Knowledge bases (KBs) have attracted increasing attention due to its great success in various areas, such as Web and mobile search.Existing KBs are restricted to objective factual knowledge, such as city population or fruit shape, whereas,subjective knowledge, such as big city, which is commonly mentioned in Web and mobile queries, has been neglected. Subjective knowledge differs from objective knowledge in that it has no documented or observed ground truth. Instead, the truth relies on people's dominant opinion. Thus, we can use the crowdsourcing technique to get opinion from the crowd. In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA),for subjective knowledge acquisition powered by crowdsourcing and existing KBs. The acquired knowledge can be used to enrich existing KBs in the subjective dimension which bridges the gap between existing objective knowledge and subjective queries.The main challenge of CoSKA is the conflict between large scale knowledge facts and limited crowdsourcing resource. To address this challenge, in this work, we define knowledge inference rules and then select the seed knowledge judiciously for crowdsourcing to maximize the inference power under the resource constraint. Our experimental results on real knowledge base and crowdsourcing platform verify the effectiveness of CoSKA system.


title: Vision-only autonomous navigation using topometric maps

abstract: This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph - as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.


title: Visualizing Big Data with augmented and virtual reality: challenges and research agenda

abstract: This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification ::: of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.


title: An effective approach to document retrieval via utilizing WordNet and recognizing phrases

abstract: Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.


title: Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

abstract: Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.


On the security and the efficiency of the Merkle signature scheme


title: Technologizing Africa: On the bumpy information highway

abstract: Abstract Although the microcomputer and the Internet continue to advance rapidly in Western cultures, developing nations, especially those in Africa, are lagging behind—a situation that continues to widen the gap between the haves and the have-nots of the world. This article offers the rare opportunity to get a glimpse of Africa on the information highway and documents the use of computers in the educational systems of three African nations: Ghana, Kenya, and Egypt. It documents the progress these countries have made in exposing their citizens to information technology and reveals the challenges they face in closing the digital divide that Africans continue to experience. In presenting how these countries adapt to technological change, one may better understand why Africa's ride on the information highway is rutted.


title: Compact broadband couplers based on the waveguide magic-T junction

abstract: In this paper, a broadband 180° waveguide directional coupler based on a modified magic-T junction is presented. The well-known magic-T waveguide junction is effectively modified and optimized leading to enhanced performance. Moreover, the volume of the proposed enhanced magic-T is similar to the classic junction. The novel magic-T proposed uses an iris in the E port, a single width step in the H port, a simple post in the junction and reduced height in the H and common ports. The bandwidth of the directional coupler can be significantly enlarged with a proper optimization of these four key elements. Different designs are presented covering the full standard rectangular waveguide band, over 40% relative bandwidth, with more than 25 dB return loss in the four ports. In addition, a compact bend with integrated transformer is introduced at the input/output interfaces.


Agro-Economic Analysis of the Use of Glyphosate in Germany


Adaptive Multi-pass Decoder for Neural Machine Translation with Reinforcement Learning


title: Monocular LSD-SLAM integration within AR System Building a system similar to the HoloLens by integrating LSD-SLAM into our AR Oculus Rift stereo engine for realworld camera tracking

abstract: In this paper, we cover the process of integrating Large-Scale Direct Simultaneous Localization and Mapping (LSD-SLAM) algorithm into our existing AR stereo engine, developed for our modified "Augmented Reality Oculus Rift". With that, we are able to track one of our realworld cameras which are mounted on the rift, within a complete unknown environment. This makes it possible to achieve a constant and full augmentation, synchronizing our 3D movement (x, y, z) in both worlds, the real world and the virtual world. The development for the basic AR setup using the Oculus Rift DK1 and two fisheye cameras is fully documented in our previous paper. After an introduction to image-based registration, we detail the LSD-SLAM algorithm and document our code implementing our integration. The AR stereo engine with Oculus Rift support can be accessed via the GIT repository this https URL and the modified LSD-SLAM project used for the integration is available here this https URL


title: 100 MHz, 20 V, 90% efficient synchronous buck converter with integrated gate driver

abstract: This paper describes a synchronous buck converter based on a GaN-on-SiC integrated circuit, which includes a halfbridge power stage, as well as a modified active pull-up gate driver stage. The integrated modified active pull-up driver takes advantage of depletion-mode device characteristics to achieve fast switching with low power consumption. Design principles and results are presented for a synchronous buck converter prototype operating at 100 MHz switching frequency, delivering up to 7 W from 20 V input voltage. Measured power-stage efficiency peaks above 91%, and remains above 85% over a wide range of operating conditions. Experimental results show that the converter has the ability to accurately track a 20 MHz bandwidth LTE envelope signal with 83.7% efficiency.


title: Hashtag Graph Based Topic Model for Tweet Mining

abstract: Mining topics in Twitter is increasingly attracting more attention. However, the shortness and informality of tweets leads to extreme sparse vector representation with a large vocabulary, which makes the conventional topic models (e.g., Latent Dirichlet Allocation) often fail to achieve high quality underlying topics. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTMestablishes word semantic relations, even if they haven't co-occurred within a specific tweet. In addition, we enhance the dependencies of both multiple words and hash tags via latent variables (topics) modeled by HGTM. We illustrate that the user-contributed hash tags could serve as weakly-supervised information for topic modeling, and hash tag relation could reveal the semantic relation between tweets. Experiments on a real-world twitter data set show that our model provides an effective solution to discover more distinct and coherent topics than the state-of-the-art baselines and has a strong ability to control sparseness and noise in tweets.


title: Performance of recommender algorithms on top-n recommendation tasks

abstract: In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall). An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task. Results show that improvements in RMSE often do not translate into accuracy improvements. In particular, a naive non-personalized algorithm can outperform some common recommendation approaches and almost match the accuracy of sophisticated algorithms. Another finding is that the very few top popular items can skew the top-N performance. The analysis points out that when evaluating a recommender algorithm on the top-N recommendation task, the test set should be chosen carefully in order to not bias accuracy metrics towards non-personalized solutions. Finally, we offer practitioners new variants of two collaborative filtering algorithms that, regardless of their RMSE, significantly outperform other recommender algorithms in pursuing the top-N recommendation task, with offering additional practical advantages. This comes at surprise given the simplicity of these two methods.


title: An optimal design methodology for inductive power link with class-E amplifier

abstract: This paper presents a design methodology of a highly efficient power link based on Class-E driven, inductively coupled coil pair. An optimal power link design for retinal prosthesis and/or other implants must take into consideration the allowable safety limits of magnetic fields, which in turn govern the inductances of the primary and secondary coils. In retinal prosthesis, the optimal coil inductances have to deal with the constraints of the coil sizes, the tradeoffs between the losses, H-field limitation and dc supply voltage required by the Class-E driver. Our design procedure starts with the formation of equivalent circuits, followed by the analysis of the loss of the rectifier and coils and the H-field for induced voltage and current. Both linear and nonlinear models for the analysis are presented. Based on the procedure, an experimental power link is implemented with an overall efficiency of 67% at the optimal distance of 7 mm between the coils. In addition to the coil design methodology, we are also presenting a closed-loop control of Class-E amplifier for any duty cycle and any value of the systemQ.


title: An analysis of approximations for maximizing submodular set functions II

abstract: LetN be a finite set andz be a real-valued function defined on the set of subsets ofN that satisfies z(S)+z(T)gez(SxcupT)+z(SxcapT) for allS, T inN. Such a function is called submodular. We consider the problem maxSsubN{a(S):|S|leK,z(S) submodular}. Several hard combinatorial optimization problems can be posed in this framework. For example, the problem of finding a maximum weight independent set in a matroid, when the elements of the matroid are colored and the elements of the independent set can have no more thanK colors, is in this class. The uncapacitated location problem is a special case of this matroid optimization problem. We analyze greedy and local improvement heuristics and a linear programming relaxation for this problem. Our results are worst case bounds on the quality of the approximations. For example, whenz(S) is nondecreasing andz(0) = 0, we show that a ldquogreedyrdquo heuristic always produces a solution whose value is at least 1 –[(K – 1)/K] K times the optimal value. This bound can be achieved for eachK and has a limiting value of (e – 1)/e, where e is the base of the natural logarithm.


The igraph software package for complex network research


title: Photoinduced electron transfer (PET)-probes for the study of enzyme activity at the ensemble and single molecule level

abstract: Fluorescence based enzyme analysis is commonly done by FRET-probes, natural enzyme substrates flanked by two corresponding fluorophores, showing spectral changes upon distance variations between the fluorophores. However, the use of double labeled substrates displays several limitations such as reduction of sensitivity and high background signal accompanied by high costs for synthesis. Therefore, the development of new probes avoiding these factors is of general interest in enzyme research. A promising approach represents smart probes, i.e. singly labeled quenched enzyme substrates that increase fluorescence intensity upon enzymatic cleavage. Smart probes use the fact that certain rhodamine and oxazine dyes are selectively quenched upon contact formation with guanine or tryptophan residues via photoinduced electron transfer (PET). The rapid response time of the probes enables real-time monitoring of enzyme activity in ensemble as well as in single molecule measurements, which is an important prerequisite for the improved understanding of enzyme mechanisms. We present the design of smart probes for the detection of the two hydrolases, DNasel and Carboxypeptidase A (CPA) with respect to stability and substrate specificity in ensemble measurements. Furthermore, we investigate the influence of the attached fluorophore on hydrolysis efficiency in case of CPA and demonstrate first applications of smart probes in single enzyme experiments.


title: Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

abstract: Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.


title: Ultrasound‐guided spinal accessory nerve blockade in the diagnosis and management of trapezius muscle‐related myofascial pain

abstract: Summary ::: We report the first description of ultrasound-guided spinal accessory nerve blockade using single-shot and subsequently continuous infusion (via a perineural catheter) local anaesthetic techniques, for the diagnosis and treatment of myofascial pain affecting the trapezius muscle. A 38-year-old man presented with a two-year history of incapacitating left suprascapular pain after a fall onto his outstretched hand. The history and clinical examination was suggestive of myofascial pain affecting the trapezius muscle. This had been unresponsive to pharmacological therapy, physiotherapy or suprascapular nerve blockade. Following identification of the spinal accessory nerve in the posterior triangle of the neck, we performed ultrasound-guided nerve blocks, first using a single injection of local anaesthetic and subsequently using a continuous infusion via a perineural catheter, to block the nerve and temporarily relieve the patient’s pain. We have demonstrated that the spinal accessory nerve is identifiable in the posterior triangle of the neck and can be blocked successfully using ultrasound guidance. This technique can aid the diagnosis and treatment of myofascial pain originating from the trapezius muscle.


title: Many hands make light the work: The causes and consequences of social loafing.

abstract: Two experiments found that when asked to perform the physically exerting tasks of clapping and shouting, people exhibit a sizable decrease in individual effort when performing in groups as compared to when they perform alone. This decrease, which we call social loafing, is in addition to losses due to faulty coordination of group efforts. Social loafing is discussed in terms of its experimental generality and theoretical importance. The widespread occurrence, the negative consequences for society, and some conditions that can minimize social loafing are also explored. There is an old saying that "many hands make light the work." This saying is interesting for two reasons. First, it captures one of the promises of social life—that with social organization people can fulfill their individual goals more easily through collective action. When many hands are available, people often do not have to work as hard as when only a few are present. The saying is interesting in a second, less hopeful way—it seems that when many hands are available, people actually work less hard than they ought to.


title: Empowering Imbalanced Data in Supervised Learning: A Semi-supervised Learning Approach

abstract: We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.


title: Networks and the best approximation property

abstract: Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well continuous functions (Cybenko 1988, 1989; Funahashi 1989; Stinchcombe and White 1989). We prove that networks derived from regularization theory and including Radial Basis Functions (Poggio and Girosi 1989), have a similar property. From the point of view of approximation theory, however, the property of approximating continuous functions arbitrarily well is not sufficient for characterizing good approximation schemes. More critical is the property ofbest approximation. The main result of this paper is that multilayer perceptron networks, of the type used in backpropagation, do not have the best approximation property. For regularization networks (in particular Radial Basis Function networks) we prove existence and uniqueness of best approximation.


Machine Learning and Knowledge Discovery in Databases


title: Knowledge Semantic Representation: A Generative Model for Interpretable Knowledge Graph Embedding.

abstract: Knowledge representation is an important, long-history topic in AI, and there have been a large amount of work for knowledge graph embedding which projects symbolic entities and relations into low-dimensional, real-valued vector space. However, most embedding methods merely concentrate on data fitting and ignore the explicit semantic expression, leading to uninterpretable representations. Thus, traditional embedding methods have limited potentials for many applications such as question answering, and entity classification. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments justify our model outperforms other state-of-the-art baselines substantially.


title: Sequential Optimal Design of Neurophysiology Experiments

abstract: Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron's activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making real-time adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared to using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing the nonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron's activity.


title: Central and autonomic nervous system interaction is altered by short-term meditation.

abstract: Five days of integrative body-mind training (IBMT) improves attention and self-regulation in comparison with the same amount of relaxation training. This paper explores the underlying mechanisms of this finding. We measured the physiological and brain changes at rest before, during, and after 5 days of IBMT and relaxation training. During and after training, the IBMT group showed significantly better physiological reactions in heart rate, respiratory amplitude and rate, and skin conductance response (SCR) than the relaxation control. Differences in heart rate variability (HRV) and EEG power suggested greater involvement of the autonomic nervous system (ANS) in the IBMT group during and after training. Imaging data demonstrated stronger subgenual and adjacent ventral anterior cingulate cortex (ACC) activity in the IBMT group. Frontal midline ACC theta was correlated with high-frequency HRV, suggesting control by the ACC over parasympathetic activity. These results indicate that after 5 days of training, the IBMT group shows better regulation of the ANS by a ventral midfrontal brain system than does the relaxation group. This changed state probably reflects training in the coordination of body and mind given in the IBMT but not in the control group. These results could be useful in the design of further specific interventions.


title: VARIABLE NEIGHBORHOOD SEARCH FOR ORDER BATCHING IN A WAREHOUSE

abstract: In this paper we address the problem of batching orders in a warehouse, with the objective of minimizing the total travel time. Order batching is an NP-hard optimization problem that is very difficult to solve exactly in practice. Thus, most implemented solutions are based on elementary heuristic methods that perform a relatively limited exploration of the solution space. As an alternative, we propose a heuristic based on variable neighborhood search, where the emphasis is placed on performing an intensive exploration of the most promising regions of the solution space. Simulations are conducted to study the performance of the method with different warehouse configurations, and an exhaustive comparative analysis, which considers all the best known heuristics, is carried out. The results obtained show that the proposed heuristic is competitive and that it provides a suitable method which can be used in practice. Additionally, since the performance of the algorithms depends heavily on factors such as storage policy, routing strategies, or the structure of the orders, we have developed an ANOVA in order to consider the effect of all the above factors on the different methods tested.


title: Graceful and Odd Graceful Labeling of Some Graphs

abstract: In this paper, we prove that the square graph of bistar Bn,n, the splitting graph of Bn,n and the splitting graph of star K1,n are graceful graphs. We also prove that the splitting graph and the shadow graph of bistar Bn,n admit odd graceful labeling.


title: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

abstract: One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99 and NSLKDD benchmark data sets were generated a decade ago. However, numerous current studies showed that for the current network threat environment, these data sets do not inclusively reflect network traffic and modern low footprint attacks. Countering the unavailability of network benchmark data set challenges, this paper examines a UNSW-NB15 data set creation. This data set has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. Existing and novel methods are utilised to generate the features of the UNSWNB15 data set. This data set is available for research purposes and can be accessed from the link.


title: A Machine Learning Approach for Automatic Student Model Discovery

abstract: Student modeling is one of the key factors that affects automated tutoring systems in making instructional decisions. A student model is a model to predict the probability of a student making errors on given problems. A good student model that matches with student behavior patterns often provides useful information on learning task difficulty and transfer of learning between related problems, and thus often yields better instruction. Manual construction of such models usually requires substantial human effort, and may still miss distinctions in content and learning that have important instructional implications. In this paper, we propose an approach that automatically discovers student models using a state-of-art machine learning agent, SimStudent. We show that the discovered model is of higher quality than human-generated models, and demonstrate how the discovered model can be used to improve a tutoring system’s instruction strategy.


title: The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix

abstract: This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the paper develops the theory required to apply them to non-linear orthogonal regression problems. Although a considerable amount of interest has focussed on the application of robust estimation in computer vision, the relative merits of the many individual methods are unknown, leaving the potential practitioner to guess at their value. The second goal is therefore to compare and judge the methods. ::: ::: Comparative tests are carried out using correspondences generated both synthetically in a statistically controlled fashion and from feature matching in real imagery. In contrast with previously reported methods the goodness of fit to the synthetic observations is judged not in terms of the fit to the observations per se but in terms of fit to the ground truth. A variety of error measures are examined. The experiments allow a statistically satisfying and quasi-optimal method to be synthesized, which is shown to be stable with up to 50 percent outlier contamination, and may still be used if there are more than 50 percent outliers. Performance bounds are established for the method, and a variety of robust methods to estimate the standard deviation of the error and covariance matrix of the parameters are examined. ::: ::: The results of the comparison have broad applicability to vision algorithms where the input data are corrupted not only by noise but also by gross outliers.


title: Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery

abstract: Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.


title: Human posture recognition using active contours and radial basis function neural network

abstract: In this paper an automated video surveillance system for human posture recognition using active contours and neural networks is presented. Localization of moving objects in the scene and human posture estimation are key features of the proposed architecture. The system architecture consists of five sequential modules that include the moving target detection process, two levels of segmentation process for interested element localization, features extraction of the object shape and a human posture classification system based on the radial basis functions neural network. Moving objects are detected by using an adaptive background subtraction method with an automatic background adaptation speed parameter and a new fast gradient vector flow snake algorithm for the elements segmentation is proposed. The developed system has been tested for the classification of three different postures such as standing, bending and squatting considering different kinds of feature. Results are promising and the architecture is also useful for the discrimination of human activities.


title: Multi-camera activity correlation analysis

abstract: We propose a novel approach for modelling correlations between activities in a busy public space captured by multiple non-overlapping and uncalibrated cameras. In our approach, each camera view is automatically decomposed into semantic regions, across which different spatio-temporal activity patterns are observed. A novel Cross Canonical Correlation Analysis (xCCA) framework is formulated to detect and quantify temporal and causal relationships between regional activities within and across camera views. The approach accomplishes three tasks: (1) estimate the spatial and temporal topology of the camera network; (2) facilitate more robust and accurate person re-identification; (3) perform global activity modelling and video temporal segmentation by linking visual evidence collected across camera views. Our approach differs from the state of the art in that it does not rely on either intra or inter camera tracking. It therefore can be applied to even the most challenging video surveillance settings featured with severe occlusions and extremely low spatial and temporal resolutions. Its effectiveness is demonstrated using 153 hours of videos from 8 cameras installed in a busy underground station.


title: Predicting user interests from contextual information

abstract: Search and recommendation systems must include contextual information to effectively model users' interests. In this paper, we present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. Post-query navigation and general browsing behaviors far outweigh direct search engine interaction as an information-gathering activity. Therefore we conducted this study with a focus on Website recommendations rather than search results. The five contextual information sources used are: social, historic, task, collection, and user interaction. We evaluate the utility of these sources, and overlaps between them, based on how effectively they predict users' future interests. Our findings demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source. Designers of Website suggestion systems can use our findings to provide improved support for post-query navigation and general browsing behaviors.


Internationalization Plans for American Higher Education Institutions: The Development and Monitoring of Written Commitments to Internationalization.


The relation between 21 st-century skills and digital skills : A systematic literature review


title: Modeling virtual machine performance: challenges and approaches

abstract: Data centers are increasingly employing virtualization and consolidation as a means to support a large number of disparate applications running simultaneously on server platforms. However, server platforms are still being designed and evaluated based on performance modeling of a single highly parallel application or a set of homogenous work-loads running simultaneously. Since most future datacenters are expected to employ server virtualization, this paper takes a look at the challenges of modeling virtual machine (VM) performance on a datacenter server. Based on vConsolidate (a server virtualization benchmark) and latest multi-core servers, we show that the VM modeling challenge requires addressing three key problems: (a) modeling the contention of visible resources (cores, memory capacity, I/O devices, etc), (b) modeling the contention of invisible resources (shared microarchitecture resources, shared cache, shared memory bandwidth, etc) and (c) modeling overheads of virtual machine monitor (or hypervisor) implementation. We take a first step to addressing this problem by describing a VM performance modeling approach and performing a detailed case study based on the vConsolidate benchmark. We conclude by outlining outstanding problems for future work.


title: Vocational Rehabilitation of Individuals with Autism Spectrum Disorder with Virtual Reality

abstract: In this article, a virtual reality system for vocational rehabilitation of individuals with disabilities (VR4VR) is presented. VR4VR uses immersive virtual environments to assess and train individuals with cognitive and physical disabilities. This article focuses on the system modules that were designed and developed for the Autism Spectrum Disorder (ASD) population. The system offers training on six vocational skills that were identified as transferrable to and useful in many common jobs. These six transferable skills are cleaning, loading the back of a truck, money management, shelving, environmental awareness, and social skills. This article presents the VR4VR system, the design considerations for the ASD population, and the findings with a cohort of nine neurotypical individuals (control group) and nine high-functioning individuals with ASD (experiment group) who used the system. Good design practices gathered throughout the study are also shared for future virtual reality applications targeting individuals with ASD. Research questions focused on the effectiveness of the virtual reality system on vocational training of high-functioning individuals with ASD and the effect of distracters on task performance of high-functioning individuals with ASD. Follow-up survey results indicated that for individuals with ASD, there was improvement in all of the trained skills. No negative effects of the distracters were observed on the score of individuals with ASD. The proposed VR4VR system was found by professional job trainers to provide effective vocational training for individuals with ASD. The system turned out to be promising in terms of providing an alternative practical training tool for individuals with ASD.


Dynamic capabilities and strategic management


title: Large-Scale Domain Adaptation via Teacher-Student Learning

abstract: High accuracy speech recognition requires a large amount of transcribed data for supervised training. In the absence of such data, domain adaptation of a well-trained acoustic model can be performed, but even here, high accuracy usually requires significant labeled data from the target domain. In this work, we propose an approach to domain adaptation that does not require transcriptions but instead uses a corpus of unlabeled parallel data, consisting of pairs of samples from the source domain of the well-trained model and the desired target domain. To perform adaptation, we employ teacher/student (T/S) learning, in which the posterior probabilities generated by the source-domain model can be used in lieu of labels to train the target-domain model. We evaluate the proposed approach in two scenarios, adapting a clean acoustic model to noisy speech and adapting an adults speech acoustic model to children speech. Significant improvements in accuracy are obtained, with reductions in word error rate of up to 44% over the original source model without the need for transcribed data in the target domain. Moreover, we show that increasing the amount of unlabeled data results in additional model robustness, which is particularly beneficial when using simulated training data in the target-domain.


title: Stereo vision with Equal Baseline Multiple Camera Set (EBMCS) for obtaining depth maps of plants

abstract: The multi-camera set is applied for obtaining distances to plants.The method for merging disparity maps from different cameras is proposed.The proposed vision system is over 20% more accurate than a stereo camera.The system is designed for autonomous harvesting robots. This paper presents a method of improving the estimation of distances between an autonomous harvesting robot and plants with ripe fruits by using the vision system based on five cameras. The system is called Equal Baseline Multiple Camera Set (EBMCS). EBMCS has some features of a camera matrix and a camera array. EBMCS is regarded as a set of stereo cameras for estimating distances by obtaining disparity maps and depth maps. This paper introduces Exceptions Excluding Merging Method (EEMM) which makes it possible to improve the quality of disparity maps by integrating maps acquired from individual stereo cameras included in EBMCS. The method was tested with eight different stereo matching algorithms including Efficient Large-scale Stereo Matching (ELAS), algorithms implemented in the OpenCV library and algorithms available in Middlebury Stereo Vision Page. Experiments were performed on input data sets which contained images of strawberry plants, cherry trees and redcurrant plants. The bad matching pixels (BMP) metric was used for measuring the error rate in disparity maps used in the distance estimation. The results of experiments showed that, on average, the EEMM merging method used with EBMCS consisting of five cameras reduces the error rate of the distance estimation by 26.55% in comparison to results obtained from stereoscopy based on a single stereo camera. The best results were acquired by using with five cameras a stereo matching algorithm based on a graph cut.