query: Designing a Smart Museum: When Cultural Heritage Joins IoT

positive: Usability Dimensions for Mobile Applications-A Review Usability has been increasingly recognized as a significant quality dimension to determine the success of mobile applications. Due to its importance, a number of usability guidelines have been proposed to direct the design of usable applications. The guidelines are intended particularly for desktop and web-based applications. Mobile applications on the other hand are different in many ways from those applications due to the mobility nature of mobile devices. To date, the usability guidelines for mobile applications are very limited. They in fact are isolated, which makes usability evaluation for mobile devices more difficult. This study aims to address this issue by proposing a set of usability dimensions that should be considered for designing and evaluating mobile applications. The dimensions are illustrated as a model that considers four contextual factors: user, environment, technology and task/activity. The model was proposed based on the reviews of previous related studies, which were analyzed by using content analysis approach. Twenty-five dimensions were found from the analysis. The dimensions however were synthesized and prioritized based on their importance towards designing usable mobile applications. As a result, ten most important dimensions were outlined in the model. The model can be used by practitioners and researchers as a guideline to design usable mobile applications and further research can be conducted in the near future.

positive: SMARTMUSEUM: A mobile recommender system for the Web of Data Semantic and context knowledge have been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMARTMUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users’ expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains.

positive: A visitor's guide in an active museum: Presentations, communications, and reflection Technology can play a crucial role in supporting museum visitors and enhancing their overall museum visit experiences. Visitors coming to a museum do not want to be overloaded with information, but to receive the relevant information, learn, and have an overall interesting experience. To serve this goal, a user-friendly and flexible system is needed. The design of such a system poses several challenges that need to be addressed in parallel. The user interface should be intuitive and let the visitors focus on the exhibits, not on the technology. Content and delivery must provide relevant information and at the same time allow visitors to get the level of detail and the perspectives in which they are interested. Personalization may play a key role in providing relevant information to individuals. Yet, since visitors tend to visit the museum in small groups, technology should also contribute to and facilitate during-the-visit communication or post-visit group interaction. The PIL project applied at the Hecht museum extended the research results of the PEACH project and tried to address all of these considerations. Evaluation involving users substantiated several aspects of the design.

positive: Smart cities in Europe Urban performance currently depends not only on the city’s endowment of hard infrastructure (‘physical capital’), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure (‘human and social capital’). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the ‘smart city’ has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city.

positive: The Mobile Sensing Platform: An Embedded Activity Recognition System Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

negative: Verifiable source code documentation in controlled natural language Writing documentation about software internals is rarely considered a rewarding activity. It is highly time-consuming and the resulting documentation is fragile when the software is continuously evolving in a multi-developer setting. Unfortunately, traditional programming environments poorly support the writing and maintenance of documentation. Consequences are severe as the lack of documentation on software structure negatively impacts the overall quality of the software product. We show that using a controlled natural language with a reasoner and a query engine is a viable technique for verifying the consistency and accuracy of documentation and source code. Using ACE, a state-of-the-art controlled natural language, we present positive results on the comprehensibility and the general feasibility of creating and verifying documentation. As a case study, we used automatic documentation verification to identify and fix severe flaws in the architecture of a non-trivial piece of software. Moreover, a user experiment shows that our language is faster and easier to learn and understand than other formal languages for software documentation.

negative: Context-aware query suggestion by mining click-through and session data Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.

negative: Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques A new spectral-spatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.

negative: Nonvolatile memory design based on ferroelectric FETs Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FERAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.

negative: Abstract Semantic Differencing for Numerical Programs Semantic Differencing for Numerical Programs Nimrod Partush Eran Yahav

negative: Comparative genomic analysis identifies structural features of CRISPR-Cas systems in Riemerella anatipestifer Riemerella anatipestifer infection is a contagious disease that has resulted in major economic losses in the duck industry worldwide. This study attempted to characterize CRISPR-Cas systems in the disease-causing agent, Riemerella anatipestifer (R. anatipestifer). The CRISPR-Cas system provides adaptive immunity against foreign genetic elements in prokaryotes and CRISPR-cas loci extensively exist in the genomes of archaea and bacteria. However, the structure characteristics of R. anatipestifer CRISPR-Cas systems remains to be elucidated due to the limited availability of genomic data. To identify the structure and components associated with CRISPR-Cas systems in R. anatipestifer, we performed comparative genomic analysis of CRISPR-Cas systems in 25 R. anatipestifer strains using high-throughput sequencing. The results showed that most of the R. anatipestifer strains (20/25) that were analyzed have two CRISPR loci (CRISPR1 and CRISPR2). CRISPR1 was shown to be flanked on one side by cas genes, while CRISPR2 was designated as an orphan. The other analyzed strains harbored only one locus, either CRISPR1 or CRISPR2. The length and content of consensus direct repeat sequences, as well as the length of spacer sequences associated with the two loci, differed from each other. Only three cas genes (cas1, cas2 and cas9) were located upstream of CRISPR1. CRISPR1 was also shown to be flanked by a 107 bp-long putative leader sequence and a 16 nt-long anti-repeat sequence. Combined with analysis of spacer organization similarity and phylogenetic tree of the R. anatipestifer strains, CRISPR arrays can be divided into different subgroups. The diversity of spacer organization was observed in the same subgroup. In general, spacer organization in CRISPR1 was more divergent than that in CRISPR2. Additionally, only 8 % of spacers (13/153) were homologous with phage or plasmid sequences. The cas operon flanking CRISPR1 was observed to be relatively conserved based on multiple sequence alignments of Cas amino acid sequences. The phylogenetic analysis associated with Cas9 showed Cas9 sequence from R. anatipestifer was closely related to that of Bacteroides fragilis and formed part of the subtype II-C subcluster. Our data revealed for the first time the structural features of R. anatipestifer CRISPR-Cas systems. The illumination of structural features of CRISPR-Cas system may assist in studying the specific mechanism associated with CRISPR-mediated adaptive immunity and other biological functions in R. anatipestifer.

negative: Neural network-based reputation model in a distributed system Current centralized trust models are inappropriate to apply in a large distributed multi-agent system, due to various evaluation models and partial observations in local level reputation management. This paper proposes a distributed reputation management structure, and develops a global reputation model. The global reputation model is a novel application of neural network techniques in distributed reputation evaluations. The experimental results showed that the model has robust performance under various estimation accuracy requirements. More important, the model is adaptive to changes in distributed system structures and in local reputation evaluations.

negative: Infants' preferences for toys, colors, and shapes: sex differences and similarities. Girls and boys differ in their preferences for toys such as dolls and trucks. These sex differences are present in infants, are seen in non-human primates, and relate, in part, to prenatal androgen exposure. This evidence of inborn influences on sex-typed toy preferences has led to suggestions that object features, such as the color or the shape of toys, may be of intrinsically different interest to males and females. We used a preferential looking task to examine preferences for different toys, colors, and shapes in 120 infants, ages 12, 18, or 24 months. Girls looked at dolls significantly more than boys did and boys looked at cars significantly more than girls did, irrespective of color, particularly when brightness was controlled. These outcomes did not vary with age. There were no significant sex differences in infants' preferences for different colors or shapes. Instead, both girls and boys preferred reddish colors over blue and rounded over angular shapes. These findings augment prior evidence of sex-typed toy preferences in infants, but suggest that color and shape do not determine these sex differences. In fact, the direction of influence could be the opposite. Girls may learn to prefer pink, for instance, because the toys that they enjoy playing with are often colored pink. Regarding within sex differences, as opposed to differences between boys and girls, both boys and girls preferred dolls to cars at age 12-months. The preference of young boys for dolls over cars suggests that older boys' avoidance of dolls may be acquired. Similarly, the sex similarities in infants' preferences for colors and shapes suggest that any subsequent sex differences in these preferences may arise from socialization or cognitive gender development rather than inborn factors.

negative: On The Architecture Scheduling Problem Of Industry 4.0 The recently emerged Fourth Industrial Revolution (Industry 4.0) is characterised by the introduction of the new Cyber-Physical System (CPS) concepts and the Internet of Things (IoT) paradigm. These new collaborating computational entities offer a broad range of opportunity to consider with a different perspective. One of the perennial problems of the manufacturing operation is the scheduling problem of typical job-shop manufacturing systems. Starting from a comparison with the typical architecture of an operating systems scheduler module, we introduce a new manufacturing scheduling architecture. Overcoming the typical Full-Hierarchical configuration defined in the ANSI/ISA 95 in favour of a SemiHeterarchical one, the introduced scheduling architecture leads to a mixture of proactive and reactive approach to the Job-shop Scheduling Problem (JSP), taking advantage from both the common decentralised and the centralised methodology. Keywords— Industry 4.0; Cyber-Physical System (CPS); JobShop Scheduling Problem; Manufacturing System; System of

negative: Hardware Intrinsic Security from Physically Unclonable Functions Counterfeiting of goods in general and of electronic goods in particular is a growing concern with a huge impact on the global economy, the society, and the security of its critical infrastructure. Various examples are known where companies suffer from economic and brand damage due to competition with counterfeit goods. In some cases the use of counterfeit components has even led to tragic accidents in which lives were lost. It has also recently become clear that counterfeit products can penetrate the critical and security infrastructure of our modern societies and hence cause a threat to national security. One of the difficulties to deal with this problem stems from the fact that counterfeit goods can originate from sources that are able to make copies that are very hard to distinguish from their legitimate counterpart. A first well-known aspect of counterfeiting is product cloning. A second much less known but increasingly dangerous aspect consists of overproduction of goods. A special, but modern, case of counterfeiting is theft of Intellectual Property such as software and designs. The attractive part from the attackers’ point of view is that it is relatively easy to steal and has a high value without having to do huge investments in research and development. From a high-level point of view one can state that the attack can be thwarted by using encryption and authentication techniques. Device configuration data or embedded software can, for example, be encrypted such that it will only run on the device possessing the correct cryptographic key. Since encrypted data is still easy to copy, it now becomes essential that the secret key is well protected against copying or cloning. In order to deal with these two aspects of counterfeiting, a secret unclonable identifier is required together with strong cryptographic protocols. In this chapter we focus on a new way to address these problems: Hardware Intrinsic Security. It is based on the implementation and generation of secret physically unclonable identifiers used in conjunction with cryptographic techniques such as encryption and

negative: Path integral guided policy search 3Sergey Levine is with Google Brain, Mountain View, CA 94043, USA. We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific instances of a task, and uses these to train a complex, high-dimensional global policy that generalizes across task instances. We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization. We show that these contributions enable us to learn deep neural network policies that can directly perform torque control from visual input. We validate the method on a challenging door opening task and a pick-and-place task, and we demonstrate that our approach substantially outperforms the prior LQR-based local policy optimizer on these tasks. Furthermore, we show that on-policy sampling significantly increases the generalization ability of these policies.

negative:
Transcutaneous measurement of glomerular filtration rate using FITC-sinistrin in rats. BACKGROUND
Inulin/sinistrin (I/S) clearance is a gold standard for an accurate assessment of glomerular filtration rate (GFR). Here we describe and validate an approach for a transcutaneous determination of GFR by using fluorescein-isothiocyanate-labelled sinistrin (FITC-S) in rats.

METHODS
Using a small animal imager, fluorescence is measured over the depilated ear of a rat after the injection of FITC-S. The decay curve of fluorescence is used for the calculation of half-life and GFR. The thus obtained transcutaneous data were validated by simultaneously performed enzymatic and fluorometric measurements in plasma of both FITC-S and sinistrin.

RESULTS
The results of enzymatic sinistrin determination versus transcutaneous half-life of FITC-S or plasma fluorescence correlated well with each other (R(2) > 0.90). Furthermore, Bland-Altman analyses proved a good degree of agreement of the three methods used. The measurements performed in healthy animals as well as different models of renal failure demonstrate its appropriateness in a wide range of renal function.

CONCLUSIONS
The transcutaneous method described offers a precise assessment of GFR in small animals. As neither blood and/or urine sampling nor time-consuming lab work is required, GFR can be determined immediately after the clearance procedure is finished. This method, therefore, simplifies and fastens GFR determinations in small lab animals compared to conventional bolus clearance techniques based on blood sampling. A low-cost device for the measurement of transcutaneous fluorescence intensity over time is under construction.

negative: Periodontal diseases. The periodontal diseases are highly prevalent and can affect up to 90% of the worldwide population. Gingivitis, the mildest form of periodontal disease, is caused by the bacterial biofilm (dental plaque) that accumulates on teeth adjacent to the gingiva (gums). However, gingivitis does not affect the underlying supporting structures of the teeth and is reversible. Periodontitis results in loss of connective tissue and bone support and is a major cause of tooth loss in adults. In addition to pathogenic microorganisms in the biofilm, genetic and environmental factors, especially tobacco use, contribute to the cause of these diseases. Genetic, dermatological, haematological, granulomatous, immunosuppressive, and neoplastic disorders can also have periodontal manifestations. Common forms of periodontal disease have been associated with adverse pregnancy outcomes, cardiovascular disease, stroke, pulmonary disease, and diabetes, but the causal relations have not been established. Prevention and treatment are aimed at controlling the bacterial biofilm and other risk factors, arresting progressive disease, and restoring lost tooth support.

negative: Artificial neural networks in time series forecasting: a comparative analysis Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series models. We begin exploring recent developments in time series forecasting with particular emphasis on the use of non-linear models. Thereafter we include a review of recent results on the topic of ANN. The relevance of ANN models for the statistical methods is considered using time series prediction problems. Finally we construct asymptotic prediction intervals for ANN and show how to use prediction intervals to choose the number of nodes in the ANN.

negative: ntology matching with semantic verification ves Automated Semantic Matching of Ontologies with Verification (ASMOV) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure vailable online xxx eywords: ntology ntology alignment ntology matching its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies. © 2009 Elsevier B.V. All rights reserved.

negative: Reputation , Trust , and Rebates : How Online Auction Markets Can Improve Their Feedback Mechanisms Trust and trustworthiness are crucial to the survival of online markets, and reputation systems that rely on feedback from traders help sustain trust. However, in current online auction markets only half of the buyers leave feedback after transactions, and nearly all of it is positive. In this paper, I propose a mechanism whereby sellers can provide rebates to buyers contingent on buyers provision of reports. Using a game theoretical model, I show how the rebate incentive mechanism can increase reporting. In both a pure adverse selection model, and a model with adverse selection and moral hazard, there exists a pooling equilibrium where both good and bad sellers choose the rebate option, even though their true types are revealed through feedback. In the presence of moral hazard, the mechanism induces bad sellers to improve the quality of the contract.

negative: iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally represent D as an n × n image I(D̃) where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to highlight cluster structure in I(D̃) when D contains highly complex clusters. To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT. In addition, an automated VAT (aVAT) method is also proposed to automatically determine the number of clusters from I(D̃). Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.

negative: Deep Learning for Nonlinear Diffractive Imaging Image reconstruction under multiple light scattering is crucial for a number of important applications in cell microscopy and tissue imaging. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and a regularizer is used to enforce the prior on the object. In this letter, We propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network (CNN) for inverting multiple scattering. Simulations show that the proposed formulation is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

negative: Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation [3] Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Leichter, D.K.C.R.I., Wei, A.V.Y., Krupka, D.F.P.K.E., Fitzgibbon, A., Izadi, S.: Accurate, robust, and flexible real-time hand tracking. In: CHI (2015) [4] Oberweger, M., Wohlhart, P., Lepetit, V.: Training a feedback loop for hand pose estimation. In: ICCV (2015) [18] Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. arXiv preprint arXiv:1502.06807 (2015) [19] Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K., Shotton, J.: Opening the black box: Hierarchical sampling optimization for estimating human hand pose. In: ICCV (2015) * Indicates equal contribution. Results T θ

negative: B2W2: N-Way Concurrent Communication for IoT Devices The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

negative: A study of big data processing constraints on a low-power Hadoop cluster Big Data processing with Hadoop has been emerging recently, both on the computing cloud and enterprise deployment. However, wide-spread security exploits may hurt the reputation of public clouds. If Hadoop on the cloud is not an option, an organization has to build its own Hadoop clusters. But having a data center is not worth for a small organization both in terms of building and operating costs. Another viable solution is to build a cluster with low-cost ARM system-on-chip boards. This paper presents a study of a Hadoop cluster for processing Big Data built atop 22 ARM boards. The Hadoop's MapReduce was replaced by Spark and experiments on three different hardware configurations were conducted to understand limitations and constraints of the cluster. From the experimental results, it can be concluded that processing Big Data on an ARM cluster is highly feasible. The cluster could process a 34 GB Wikipedia article file in acceptable time, while generally consumed the power 0.061-0.322 kWh for all benchmarks. It has been found that I/O of the hardware is fast enough, but the power of CPUs is inadequate because they are largely spent for the Hadoop's I/O.

negative: Selective Intervention and Internal Hybrids: Interpreting and Learning from the Rise and Decline of the Oticon Spaghetti Organization Infusing hierarchies with elements of market control has become a much-used way of simultaneously increasing entrepreneurialism and motivation in firms. However, this paper argues that such “internal hybrids,” particularly in their radical forms, are inherently hard to successfully design and implement, because of fundamental credibility problems related to managerial promises to not intervene in delegated decision-making  an incentive problem that is often referred to as the “problem of selective intervention.” This theoretical theme is developed and illustrated, using the case of the world-leading Danish hearing aids producer, Oticon. In the beginning of the 1990s, Oticon became famous for its radical internal hybrid, the ”spaghetti organization.” Recent work has interpreted the spaghetti organization as a radical attempt to foster dynamic capabilities by imposing loose coupling on the organization, neglecting, however, that about a decade later, the spaghetti organization has given way to a more traditional matrix organization. This paper presents an organizational economics interpretation of organizational changes in Oticon, and argues that a strong liability of the spaghetti organization was the above incentive problem. Motivation in Oticon was strongly harmed by selective intervention on the part of top-management Changing the organizational structure was one means of repairing these motivational problems. Refutable implications are developed, both for the understanding of efficient design of internal hybrids, and for the more general issue of the distinction between firms and markets, as well as the choice between internal and external hybrids.

negative: Optimizing 360 video delivery over cellular networks As an important component of the virtual reality (VR) technology, 360-degree videos provide users with panoramic view and allow them to freely control their viewing direction during video playback. Usually, a player displays only the visible portion of a 360 video. Thus, fetching the entire raw video frame wastes bandwidth. In this paper, we consider the problem of optimizing 360 video delivery over cellular networks. We first conduct a measurement study on commercial 360 video platforms. We then propose a cellular-friendly streaming scheme that delivers only 360 videos' visible portion based on head movement prediction. Using viewing data collected from real users, we demonstrate the feasibility of our approach, which can reduce bandwidth consumption by up to 80% based on a trace-driven simulation.

negative: Investigation of safety in human-robot-interaction for a series elastic, tendon-driven robot arm This paper presents the design of the lightweight BioRob manipulator with spring-loaded tendon-driven actuation developed for safe physical human-robot interaction. The safety of the manipulator is analyzed by an analytical worst-case estimation of impact and clamping forces in the absence of collision detection. As intrinsic joint compliance can pose a threat by storing energy, a safety evaluation method is proposed taking the potential energy stored in the elastic actuation into account. The evaluation shows that the robot arm design constrains the worst case clamping forces to only 25 N, while being able to handle loads up to 2 kg, and inherits extremely low impact properties, such as an effective mass of less than 0.4 kg in non near-singular configurations, enabling safe operation even in case of high velocities. The results are validated in simulation and experiments.

negative: Accidental scald burns in sinks. Scald burns to the feet and lower extremities in children are described in the literature as often resulting from forced immersions. This report illustrates 3 cases of burns whose distribution and historical factors identify them as accidental. The location of these accidental burns is similar to those found in inflicted injury, but the patterns were indicative of flowing water burns, not forced immersions. Burns in these locations may be confused with abuse. Medical providers need to be aware of information that may enable them to distinguish the 2 causes. Effective caregiver education regarding the importance of lowering the temperature of water heaters and discouraging play in household sinks is critical to prevent additional tap water scald burn injuries.

negative: Ontologies for Cultural Heritage In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” [33]. On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy, etc., but also historical sources and social theories. Interoperability between various highly specialized systems, integrated information access and information integration increasingly becomes a demand to support research, professional heritage administration, preservation, public curiosity and education. Therefore the sector is characterized by a complex schema integration problem of associating complementary information from various dedicated systems, which can be efficiently addressed by formal ontologies [14,32,33]. There is a proliferation of specialized terminology, but terminology is less used as a means of agreement between experts than as an intellectual tool for hypothesis building based on discriminating phenomena. Automated classification is a long established discipline of archaeology, but few terminological systems are widely accepted. The sector is, however, more focused on establishing knowledge about facts and context in the past than about classes of things and the laws of their behavior. Respectively, the concatenation of related facts by co-reference [56] to particulars, such as things, people, places, periods is a major open issue. Knowledge Organisation Systems (KOS, [62]) describing people and places are employed to a certain degree, and pose similar technical problems as ontologies, but the required scale is very large. In this chapter, we describe how ontologies are and could be employed to improve information management in the cultural heritage sector.

negative: eyond virtual museums : Experiencing immersive virtual reality in eal museums Contemporary museums are much more than places devoted to the placement and the exhibition of collections and artworks; indeed, they are nowadays considered as a privileged means for communication and play a central role in making culture accessible to the mass audience. One of the keys to approach the general public is the use of new technologies and novel interaction paradigms. These means, which bring with them an undeniable appeal, allow curators to modulate the cultural proposal by structuring different courses for different user profiles. Immersive Virtual reality (VR) is probably one of the most appealing and potentially effective technologies to serve this purpose; nevertheless, it is still quite uncommon to find immersive installations in museums. Starting from our 10 years’ experience in this topic, and following an in-depth survey about these technologies and their use in cultural contexts, we propose a classification of VR installations, specifically oriented to cultural heritage applications, based on their features in terms of interaction and immersion. On the basis of this classification, aiming to provide a tool for framing VR systems which would hopefully suggest indications related to costs, usability and quality of the sensorial experience, we analyze a series of live examples of which we point out strengths and weak points. We then summarize the current state and the very next future, identifying the major issues that prevent these technologies from being actually widespread, and outline proposals for a more se of pervasive and effective u

negative: A location-based smart shopping system with IoT technology Localization is one important part of Internet of Things(IoT) where the Location of Everything (LoE) system plays a important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named “location orbital” is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.

negative: Digital Fabrication Techniques for Cultural Heritage: A Survey Digital fabrication devices exploit basic technologies in order to create tangible reproductions of 3D digital models. Although current 3D printing pipelines still suffer from several restrictions, accuracy in reproduction has reached an excellent level. The manufacturing industry has been the main domain of 3D printing applications over the last decade. Digital fabrication techniques have also been demonstrated to be effective in many other contexts, including the consumer domain. The Cultural Heritage is one of the new application contexts and is an ideal domain to test the flexibility and quality of this new technology. This survey overviews the various fabrication technologies, discussing their strengths, limitations, and costs. Various successful uses of 3D printing in the Cultural Heritage are analysed, which should also be useful for other application contexts. We review works that have attempted to extend fabrication technologies in order to deal with the specific issues in the use of digital fabrication in the Cultural Heritage. Finally, we also propose areas for future research.

negative: Managing Context Information for Adaptive Security in IoT Environments Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.

negative: Green Internet of Things for Smart World Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

negative: Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services In a nearby future 5G technologies will connect the world from the largest megacities to the smallest internet of things in an always online fashion. Such a connected hierarchy must combine the smart cities, the smart homes, and the internet of things into one large coherent infrastructure. This paper suggest a four layer model which join and interfaces these elements by deploying technologies such as 5G, internet of things, cloud of things, and distributed artificial intelligence. Many advantages and service possibilities are offered by this new infrastructure such as interconnected internet of things, smart homes with artificial intelligence, and a platform for new combined smart home and smart city services based on big-data.


query: Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

positive: Feature Selection for Unsupervised Learning In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.

positive: Machine learning: a review of classification and combining techniques Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.

positive: Radiomics: extracting more information from medical images using advanced feature analysis. Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

positive: Reduction Techniques for Instance-Based Learning Algorithms Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many instances can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms and other exemplar-based algorithms. Second, it proposes six additional reduction algorithms called DROP1–DROP5 and DEL (three of which were first described in Wilson & Martinez, 1997c, as RT1–RT3) that can be used to remove instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 classification tasks. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.

positive: Decision Tree Induction Based on Efficient Tree Restructuring The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications.

negative: Automatic camera and range sensor calibration using a single shot As a core robotic and vision problem, camera and range sensor calibration have been researched intensely over the last decades. However, robotic research efforts still often get heavily delayed by the requirement of setting up a calibrated system consisting of multiple cameras and range measurement units. With regard to removing this burden, we present a toolbox with web interface for fully automatic camera-to-camera and camera-to-range calibration. Our system is easy to setup and recovers intrinsic and extrinsic camera parameters as well as the transformation between cameras and range sensors within one minute. In contrast to existing calibration approaches, which often require user intervention, the proposed method is robust to varying imaging conditions, fully automatic, and easy to use since a single image and range scan proves sufficient for most calibration scenarios. Experimentally, we demonstrate that the proposed checkerboard corner detector significantly outperforms current state-of-the-art. Furthermore, the proposed camera-to-range registration method is able to discover multiple solutions in the case of ambiguities. Experiments using a variety of sensors such as grayscale and color cameras, the Kinect 3D sensor and the Velodyne HDL-64 laser scanner show the robustness of our method in different indoor and outdoor settings and under various lighting conditions.

negative: A taxonomy for multi-agent robotics A key di culty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system con gurations is essential. To this end, we present a taxonomy that classi es multiagent systems according to communication, computational and other capabilities. We survey existing e orts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

negative: A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit

negative: Design and implementation of server cluster dynamic load balancing based on OpenFlow Nowadays, the Internet is flooded with huge traffic, many applications have millions users, a single server is difficult to bear a large number of clients' access, so many application providers will put several servers as a computing unit to provide support for a specific application, usually people will use distributed computing, load balancing technology to complete the work. A typical load balancing technique is to use a dedicated load balancer to forward the client requests to different servers, this technique requires dedicated hardware support, the hardware is expensive, lacks of flexibility and is easy to become a single point failure. There will be a new solution for load balancing with OpenFlow proposed., this paper mainly studies dynamic load balancing technology in the OpenFlow environment, the Controller collected the server running status through the SNMP protocol, and calculated the aggregated load of the severs according to dynamic load balancing scheduling algorithm, the OpenFlow switch will forward the client's request to the server whose aggregated load is smallest, thus minimize the response time of the web server. In the OpenFlow network environment, using this method can brings high flexibility without additional equipment.

negative: Lexical Analysis for the Webshell Attacks In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.

negative:
Cowden syndrome. BACKGROUND
Cowden syndrome is a rare genodermatosis charactarized by presence of multiple hamartomas. The aim of the study was to specify the clinical, therapeutic and prognostic aspects of Cowden syndrome.

CASES REPORT
Our study included 4 patients with Cowden syndrome, 2 males and 2 females between 14 and 46 years old. Clinical examination of the skin revealed facials papules (4 cases), acral keratosis (1 case), translucent keratotic papules (2 cases). Oral examination revealed papules (4 cases), papillomatosis (4 cases), gingival hypertrophy (4 cases) and scrotal tongue (2 cases). Investigations revealed thyroid lesions (2 cases), fibrocystic disease and lipoma of the breast in 1 case, "glycogenic acanthosis" (1 case), macrocephaly (2 cases), dysmorphic face (1 case) and lichen nitidus (1 case). Oral etretinate and acitretine were temporary efficient in 2 patients. Topical treatment with tretinoin lotion resulted in some improvement in cutaneous, but not mucosal lesions in one patient. No cancer was revealed.

CONCLUSION
The pathognomonic mucocutaneous lesions were found in all patients. However, no degenerative lesions have been revealed. A new association of Cowden syndrome with lichen nitidus was found. Treatment with oral retinoids was efficient on cutaneous lesions.

negative: Managing the knowledge lifecycle: A integrated knowledge management process model The main purpose of this study is to propose an integrated conceptual model for exploring knowledge management process. Reviewing literature on knowledge management and tracing its historical background, definitions and dominant paradigms and analysis 32 frameworks of KM, a relatively integrated model of KM was presented in this paper. The study found many similarities of models and its presents a model which combine all the best features of the models analyzed. A 10-stage integrated model was proposed. These stages include some activities such as knowledge goal setting, identification, acquisition, creation, organization, sharing, evaluation, preservation, retention and updating and KM effectiveness evaluation. The findings can be used by managers in all organizational environments to implementation and auditing of KM practices.

negative: A Smart Cloud Robotic System Based on Cloud Computing Services In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.

negative: Tagging The Web: Building A Robust Web Tagger with Neural Network In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.

negative: A hardware CABAC encoder for HEVC This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.

negative: Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques Cervical cancer is the fourth most common malignant disease in women’s worldwide. In most cases, cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Most medical data sets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used data set, SMOTE is used to solve this problem. The data set consists of 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.

negative: Social media-induced technostress: Its impact on the job performance of it professionals and the moderating role of job characteristics Using social media during work hours for non-work-related reasons is becoming commonplace. Organizations are therefore challenged with identifying and overcoming the consequences of such use. Social media-induced technostress has been identified as an important unintended consequence of using social media at work, as it could negatively impact job performance. This study draws on Person-Environment Fit to investigate the relationship between social media-induced technostress and job performance in IT professionals, and the moderating effect of job characteristics on this relationship. The results indicate that social media-induced technostress is negatively related to job performance and the negative impact of social media-induced technostress is intensified when the job characteristics are low. This work extends the literature on job-stress, social media, technostress, and job characteristics.

negative: Mining long-term search history to improve search accuracy Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.

negative: Cobalt fill for advanced interconnects Cobalt as a material promises to change the conductor landscape in many areas, particularly logic contact and interconnect. In this paper, we focus on Cobalt as the conductor for logic interconnect — a potential Cu replacement. We demonstrate Co fill capability down to 10nm CD, and show that Co-Cu resistivity cross-over will occur below 14nm CD. Single damascene Co-ULK structures are used to establish an optimized metallization stack with robust CMP performance that has electromigration and TDDB reliability better than copper interconnect.

negative: We're in it together: interpersonal management of disclosure in social network services The workload needed for managing privacy and publicness in current social network services (SNSs) is placed on individuals, yet people have few means to control what others disclose about them. This paper considers SNS-users' concerns in relation to online disclosure and the ways in which they cope with these both individually and collaboratively. While previous work has focused mainly on individual coping strategies, our findings from a qualitative study with 27 participants suggest that collaborative strategies in boundary regulation are of additional importance. We present a framework of strategies for boundary regulation that informs both theoretical work and design practice related to management of disclosure in SNSs. The framework considers disclosure as an interpersonal process of boundary regulation, in which people are dependent on what others choose to disclose about them. The paper concludes by proposing design solutions supportive of collaborative and preventive strategies in boundary regulation that facilitate the management of disclosure online.

negative: Object Detection with Active Sample Harvesting The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between populations of samples. In practice, all the samples are not equally important: some examples are trivially classified and do not bring much to the training, while others close to the boundary or misclassified are the ones that truly matter. Similarly, images where the samples originate from are not all rich in informative samples. However, most training procedures select samples and images uniformly or weigh them equally. The common thread of this dissertation is how to efficiently find the informative samples/images for training. Although we never consider all the possible samples “in the world”, our purpose is to select the samples in a smarter manner, without looking at all the available ones. The framework adopted in this work consists in organising the data (samples or images) in a tree to reflect the statistical regularities of the training samples, by putting “similar” samples in the same branch. Each leaf carries a sample and a weight related to the “importance” of the corresponding sample, and each internal node carries statistics about the weights below. The tree is used to select the next sample/image for training, by applying a sampling policy, and the “importance” weights are updated accordingly, to bias the sampling towards informative samples/images in future iterations. Our experiments show that, in the various applications, properly focusing on informative images or informative samples improves the learning phase by either reaching better performances faster or by reducing the training loss faster.

negative:
Behavioral resource-aware model inference Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.
 Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.

negative: High-level visual features for underwater place recognition This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.

negative: Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. The hypothesis was tested of whether neurofeedback training (NFT)--applied in order to increase upper alpha but decrease theta power--is capable of increasing cognitive performance. A mental rotation task was performed before and after upper alpha and theta NFT. Only those subjects who were able to increase their upper alpha power (responders) performed better on mental rotations after NFT. Training success (extent of NFT-induced increase in upper alpha power) was positively correlated with the improvement in cognitive performance. Furthermore, the EEG of NFT responders showed a significant increase in reference upper alpha power (i.e. in a time interval preceding mental rotation). This is in line with studies showing that increased upper alpha power in a prestimulus (reference) interval is related to good cognitive performance.

negative: The Impact of Blogging and Scaffolding on Primary School Pupils' Narrative Writing: A Case Study Narrative writing is a skill that all primary (elementary) school pupils in Singapore are required to develop in their learning of the English language. However, this is an area in which not all pupils excel. This study investigates if the use of blogging and scaffolding can improve pupils’ narrative writing. Data were gathered from 36 primary five (grade five) pupils through pre-post writing tests, reflection sheets, and interviews. The pre-post writing tests were administered before and after the pupils had completed their blogging activities, while the blogs were used to draft their narrative writings and to comment on their peers’ writings. The teacher also used a writing guide that served as a scaffold to help pupils plan their writing on their blogs. Overall, results showed a statistically significant difference of medium effect size between the pre-post test scores. Pupils’ perceptions of using blogs as a tool for writing were also explored. attend school (Huffaker, 2004). In order to have success throughout life, verbal literacy is crucial and especially so from the beginnings of education to the future employment of adults (Cassell, 2004). In Singapore, the primary education system consists of a six-year program: a four-year foundation stage from primary (grade) 1 to 4 and a two-year orientation stage from primary 5 to 6 (Ministry of Education, 2010). One of the overall aims of the Singapore primary education is to give pupils a good grasp of the English Language. Literacy development such DOI: 10.4018/jwltt.2010040101 2 International Journal of Web-Based Learning and Teaching Technologies, 5(2), 1-17, April-June 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. as reading and writing is at the heart of the English Language instructional programme in Singapore schools (Curriculum Planning and Development Division (CPDD), 2001). As indicated in the 2001 English Language Syllabus

negative: Combined software-defined network (SDN) and Internet of Things (IoT) The Internet of Things (IoT) is an impending technology that captured the industries and research interests in a brief period. Presently, the number of Internet-connected devices are estimated at billions and by 2020 many studies expect to have around 50 billion devices that are connected to the Internet. The IoT devices have produced a significant amount of data that could be threatening to the security and directing the flow of data in the network. One of the alternative solutions for problems caused by IoT is to have some kind of solutions that are based on programming and primary control. Software Define Network (SDN) offers a programmable and primary control for the basic network without variability of the current architecture of the network. This paper discusses some solutions for various domains for the combined SDN based on IoT and current directions in studies. The relative analysis of these solutions prepares a simple and brief view of those directions.

negative: Approximation Algorithms There are many variations of local search: Hill-climbing: The name for the basic method described above. Metropolis: Pick a random y ∈ N(x). If f(y) < f(x), set x = y. If f(y) > f(x), set x = y with some probability. Simulated annealing: Like Metropolis, but where the probability of moving to a higher-cost neighbor decreases over time. Tabu search: Like the previous two, but with memory in order to avoid getting stuck in suboptimal regions or in plateaus (“taboo” areas). Parallel search: Do more than one search, and occasionally replace versions that are doing poorly with copies of versions that are doing well. Genetic algorithm: Keep a population of searches that changes over time via “breeding” of the best-performing searches.

negative: Vitamins, minerals, and mood. In this article, the authors explore the breadth and depth of published research linking dietary vitamins and minerals (micronutrients) to mood. Since the 1920s, there have been many studies on individual vitamins (especially B vitamins and Vitamins C, D, and E), minerals (calcium, chromium, iron, magnesium, zinc, and selenium), and vitamin-like compounds (choline). Recent investigations with multi-ingredient formulas are especially promising. However, without a reasonable conceptual framework for understanding mechanisms by which micronutrients might influence mood, the published literature is too readily dismissed. Consequently, 4 explanatory models are presented, suggesting that mood symptoms may be expressions of inborn errors of metabolism, manifestations of deficient methylation reactions, alterations of gene expression by nutrient deficiency, and/or long-latency deficiency diseases. These models provide possible explanations for why micronutrient supplementation could ameliorate some mental symptoms.

negative: NameClarifier: A Visual Analytics System for Author Name Disambiguation In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.

negative: LegUp: high-level synthesis for FPGA-based processor/accelerator systems In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool.

negative: Lung cancer cell identification based on artificial neural network ensembles An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.

negative: A novel approach to CAD system for the detection of lung nodules in CT images Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.

negative: Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.

negative: Lung nodules diagnosis based on evolutionary convolutional neural network Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.

negative: Lung Nodule Detection Using Convolutional Neural Networks We present a novel procedure to apply deep learning techniques to medical image classification. With increasing popularity of computed tomography (CT) lung screening, fully manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung cancer detection is challenging because of the varying size, location, shape, and density of nodules. Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features. Convolutional neural network has demonstrated to learn discriminative visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database. We compare the performance of two 2.5D convolutional neural network models, two 3D convolutional neural network models, and one 3D convolutional neural networks with a 3D spatial transformer network module on the task of detecting lung nodules. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.

negative: Lung nodule detection in CT images using deep convolutional neural networks Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.

negative: Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.