title: A Secure and Efficient Chaotic Map-Based Authenticated Key Agreement Scheme for Telecare Medicine Information Systems

abstract: Advancement in network technology provides new ways to utilize telecare medicine information systems (TMIS) for patient care. Although TMIS usually faces various attacks as the services are provided over the public network. Recently, Jiang et al. proposed a chaotic map-based remote user authentication scheme for TMIS. Their scheme has the merits of low cost and session key agreement using Chaos theory. It enhances the security of the system by resisting various attacks. In this paper, we analyze the security of Jiang et al.'s scheme and demonstrate that their scheme is vulnerable to denial of service attack. Moreover, we demonstrate flaws in password change phase of their scheme. Further, our aim is to propose a new chaos map-based anonymous user authentication scheme for TMIS to overcome the weaknesses of Jiang et al.'s scheme, while also retaining the original merits of their scheme. We also show that our scheme is secure against various known attacks including the attacks found in Jiang et al.'s scheme. The proposed scheme is comparable in terms of the communication and computational overheads with Jiang et al.'s scheme and other related existing schemes. Moreover, we demonstrate the validity of the proposed scheme through the BAN (Burrows, Abadi, and Needham) logic.


White Blood Cell Segmentation Techniques in Microscopic Images for Leukemia Detection


title: QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns

abstract: Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.


title: Unsupervised Named-Entity Extraction from the Web: An Experimental Study

abstract: The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., ''chemist'' and ''biologist'' are identified as sub-classes of ''scientist''). List Extraction locates lists of class instances, learns a ''wrapper'' for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.


title: Strength Design on Permanent Magnet Rotor in High Speed Motor Using Finite Element Method

abstract: The permanent magnets can be damaged for the tremendous centrifugal stress when the permanent magnet rotor rotates in high-speed. In this paper, the mechanical strength of high-speed permanent magnet rotor was researched. Stability operation mechanical conditions of the surface-mounted permanent magnet rotor were analyzed. The mathematical model between the shrink range of bandage and the maximum operating speed were concluded. The strengths of two kinds of surface-mounted permanent magnet rotors were calculated and compared using contact finite element method. For buried type permanent magnet rotor, the equivalent ring method was put forward to calculate its stress limit. The mathematical relationships between the maximum velocity and iron bridge thickness has been deduced. Using finite element method, the stress distributions was analyzed. The research results have shown that the high speed permanent magnet rotors designed according to the method proposed here had the adequate mechanical strength. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4675 Full Text: PDF


title: Robust visual inertial odometry using a direct EKF-based approach

abstract: In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).


title: Monocular camera localization in 3D LiDAR maps

abstract: Localizing a camera in a given map is essential for vision-based navigation. In contrast to common methods for visual localization that use maps acquired with cameras, we propose a novel approach, which tracks the pose of monocular camera with respect to a given 3D LiDAR map. We employ a visual odometry system based on local bundle adjustment to reconstruct a sparse set of 3D points from image features. These points are continuously matched against the map to track the camera pose in an online fashion. Our approach to visual localization has several advantages. Since it only relies on matching geometry, it is robust to changes in the photometric appearance of the environment. Utilizing panoramic LiDAR maps additionally provides viewpoint invariance. Yet low-cost and lightweight camera sensors are used for tracking. We present real-world experiments demonstrating that our method accurately estimates the 6-DoF camera pose over long trajectories and under varying conditions.


title: Real-time Monocular Object SLAM

abstract: We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: 1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and 2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects. The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization. At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall. We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques.


title: I am the antenna: accurate outdoor AP location using smartphones

abstract: Today's WiFi access points (APs) are ubiquitous, and provide critical connectivity for a wide range of mobile networking devices. Many management tasks, e.g. optimizing AP placement and detecting rogue APs, require a user to efficiently determine the location of wireless APs. Unlike prior localization techniques that require either specialized equipment or extensive outdoor measurements, we propose a way to locate APs in real-time using commodity smartphones. Our insight is that by rotating a wireless receiver (smartphone) around a signal-blocking obstacle (the user's body), we can effectively emulate the sensitivity and functionality of a directional antenna. Our measurements show that we can detect these signal strength artifacts on multiple smartphone platforms for a variety of outdoor environments. We develop a model for detecting signal dips caused by blocking obstacles, and use it to produce a directional analysis technique that accurately predicts the direction of the AP, along with an associated confidence value. The result is Borealis, a system that provides accurate directional guidance and leads users to a desired AP after a few measurements. Detailed measurements show that Borealis is significantly more accurate than other real-time localization systems, and is nearly as accurate as offline approaches using extensive wireless measurements.


title: Artificial Intelligence in XPRIZE DeepQ Tricorder

abstract: The DeepQ tricorder device developed by HTC from 2013 to 2016 was entered in the Qualcomm Tricorder XPRIZE competition and awarded the second prize in April 2017. This paper presents DeepQ»s three modules powered by artificial intelligence: symptom checker, optical sense, and vital sense. We depict both their initial design and ongoing enhancements.


title: Feature Engineering for Predictive Modeling using Reinforcement Learning

abstract: Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.


title: Greedy Attribute Selection

abstract: Abstract Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance. We present a caching scheme that makes attribute hillclimbing more practical computationally. We also compare the results of hillclimbing in attribute space with FOCUS and RELIEF on the two tasks.


title: Link prediction approach to collaborative filtering

abstract: Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.


title: Towards Pleasant Touch: Vibrotactile Grids for Social Touch Interactions

abstract: In this paper, we realize a wearable tactile device that delivers smooth pleasant strokes, those resemble of caressing and calming sensations, on the forearm. In Study 1, we develop a psychophysical model of continuous illusory motion on a discrete vibrotactile array. We use this model to generate a variety of tactile strokes that vary in frequency (quality), amplitude (strength) and duration (speed), and test them on a hedonic scale of pleasant-unpleasant in Study 2. Our results show that low frequency (


title: A semi-direct approach to structure from motion

abstract: Reconstructing three-dimensional structure and motion is often decomposed into two steps: point feature correspondence and three-dimensional reconstruction. This separation often causes gross errors since correspondence relies on the brightness constancy constraint that is local in space and time. Therefore, we advocate the necessity to integrate visual information not only in time (i.e. across different views), but also in space, by matching regions-rather than points-using explicit photometric deformation models. We present an algorithm that integrates 2D region tracking and 3D motion estimation into a closed loop based on an explicit geometric and photometric model, while detecting and rejecting outlier regions that do not fit the model. Our algorithm is recursive and suitable for real-time implementation. Our experiments show that it far exceeds the accuracy and robustness of point feature-based SFM algorithms.


title: Reconstitution of maturating and regulatory lymphocyte subsets after cord blood and BMT in children

abstract: Some clinical characteristics of cord blood transplantation (CBT) might be explained by specificities in the reconstitution of immune subsets differing by their maturation stage or their implication in GVHD, tolerance or immune responses against tumor or infectious agents. Here, we compare the immune reconstitution of several of these subsets after CBT and BMT. B-cell count recovery was faster after CBT. There was no difference in the recovery of CD4(+) and CD8(+) cell counts. There was no difference either in the frequency of several subsets: CD45RO(+) memory, and CD45RA(+) naïve cells within the CD4(+) T-cell compartment, CD27(+) among B cells, CD56(bright), NKG2A(+), and KIR(+) cells among natural killer (NK) cells, CD25(+)FOXP3(+) regulatory T cells and invariant NKT cells. The proportion of the thymic naïve CD31(+)CD45RA(+)CD4(+) T cells was lower after CBT at 6 months post-transplant, and was still below normal at 1 year in both groups. NK-cell expansion was more sustained after CBT, with fewer double-negative NKG2A(-)KIR(-) hyporesponsive cells and more double-positive NKG2A(+)KIR(+) hyper-responsive NK cells. These results, therefore, indicate that further research to improve CBT outcome should try to improve thymopoieisis and take advantage of the sustained NK-cell reconstitution.


title: Online controlled experiments at large scale

abstract: Web-facing companies, including Amazon, eBay, Etsy, Facebook, Google, Groupon, Intuit, LinkedIn, Microsoft, Netflix, Shop Direct, StumbleUpon, Yahoo, and Zynga use online controlled experiments to guide product development and accelerate innovation. At Microsoft's Bing, the use of controlled experiments has grown exponentially over time, with over 200 concurrent experiments now running on any given day. Running experiments at large scale requires addressing multiple challenges in three areas: cultural/organizational, engineering, and trustworthiness. On the cultural and organizational front, the larger organization needs to learn the reasons for running controlled experiments and the tradeoffs between controlled experiments and other methods of evaluating ideas. We discuss why negative experiments, which degrade the user experience short term, should be run, given the learning value and long-term benefits. On the engineering side, we architected a highly scalable system, able to handle data at massive scale: hundreds of concurrent experiments, each containing millions of users. Classical testing and debugging techniques no longer apply when there are billions of live variants of the site, so alerts are used to identify issues rather than relying on heavy up-front testing. On the trustworthiness front, we have a high occurrence of false positives that we address, and we alert experimenters to statistical interactions between experiments. The Bing Experimentation System is credited with having accelerated innovation and increased annual revenues by hundreds of millions of dollars, by allowing us to find and focus on key ideas evaluated through thousands of controlled experiments. A 1% improvement to revenue equals more than $10M annually in the US, yet many ideas impact key metrics by 1% and are not well estimated a-priori. The system has also identified many negative features that we avoided deploying, despite key stakeholders' early excitement, saving us similar large amounts.


title: Automated Selection of Materialized Views and Indexes in SQL Databases

abstract: Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a non-trivial task. A judicious choice must be cost-driven and influenced by the workload experienced by the system. Although there has been work in materialized view selection in the context of multidimensional (OLAP) databases, no past work has looked at the problem of building an industry-strength tool for automated selection of materialized views and indexes for SQL workloads. In this paper, we present an end-to-end solution to the problem of selecting materialized views and indexes. We describe results of extensive experimental evaluation that demonstrate the effectiveness of our techniques. Our solution is implemented as part of a tuning wizard that ships with Microsoft SQL Server 2000.


title: Scaling Semantic Parsers with On-the-Fly Ontology Matching

abstract: We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to represent in a fixed target ontology. For example, even simple phrases such as ‘daughter’ and ‘number of people living in’ cannot be directly represented in Freebase, whose ontology instead encodes facts about gender, parenthood, and population. In this paper, we introduce a new semantic parsing approach that learns to resolve such ontological mismatches. The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Experiments demonstrate state-of-the-art performance on two benchmark semantic parsing datasets, including a nine point accuracy improvement on a recent Freebase QA corpus.


Semi-Supervised Recursive Autoencoder


title: Database Security: Past, Present, and Future

abstract: Database security has received a great deal of attention since the mid-1970s starting from discretionary security for System R and Ingres to access control models to multilevel database systems to security for emerging data management systems to data privacy to privacy-preserving data mining to data mining for security applications to big data security and privacy. This paper describes the developments in database security since 1975 and provides a vision for the future.


title: Nanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions

abstract: Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.


title: Internet gaming disorder in early adolescence: Associations with parental and adolescent mental health

abstract: Abstract Background Internet gaming disorder (IGD) has been included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Currently, associations between IGD in early adolescence and mental health are largely unexplained. In the present study, the relation of IGD with adolescent and parental mental health was investigated for the first time. Methods We surveyed 1095 family dyads (an adolescent aged 12–14 years and a related parent) with a standardized questionnaire for IGD as well as for adolescent and parental mental health. We conducted linear (dimensional approach) and logistic (categorical approach) regression analyses. Results Both with dimensional and categorical approaches, we observed statistically significant associations between IGD and male gender, a higher degree of adolescent antisocial behavior, anger control problems, emotional distress, self-esteem problems, hyperactivity/inattention and parental anxiety (linear regression model: corrected R 2 =0.41, logistic regression model: Nagelkerke's R 2 =0.41). Conclusions IGD appears to be associated with internalizing and externalizing problems in adolescents. Moreover, the findings of the present study provide first evidence that not only adolescent but also parental mental health is relevant to IGD in early adolescence. Adolescent and parental mental health should be considered in prevention and intervention programs for IGD in adolescence.


title: Advantages and Disadvantages of Scenario Approaches for Strategic Foresight

abstract: Scenarios, as a prime technique of future studies, have long been used by government planners, corporate managers and military analysts as powerful tools to aid in decision making in the face of uncertainty. The idea behind them is to establish thinking about possible futures which can minimise surprises and broaden the span of managers' thinking about different possibilities. Today the question of what scenarios are is unclear except with regard to one point - they have become extremely popular. This paper attempts to shed light on differences in scenario approaches. It will describe the origin of scenarios and the development of different understandings and purposes for managers. Categories are developed to compare the different ways scenarios are performed. Finally, the advantages and disadvantages of scenario approaches are analysed.


title: Recommendation system for automatic design of magazine covers

abstract: In this paper, we present a recommendation system for the automatic design of magazine covers. Our users are non-designer designers: individuals or small and medium businesses who want to design without hiring a professional designer while still wanting to create aesthetically compelling designs. Because a design should have a purpose, we suggest a number of semantic features to the user, e.g., "clean and clear," "dynamic and active," or "formal," to describe the color mood for the purpose of his/her design. Based on these high level features and a number of low level features, such as the complexity of the visual balance in a photo, our system selects the best photos from the user's album for his/her design. Our system then generates several alternative designs that can be rated by the user. Consequently, our system generates future designs based on the user's style. In this fashion, our system personalizes the designs of a user based on his/her preferences.


title: The pastoral crowd: Exploring self‐hosted crowdfunding using activity theory and social capital

abstract: As crowdfunding technologies mature, designers and practitioners are continuing to discover new paradigms for fund-raising activities. One such paradigm seeks to complement or replace third-party crowdfunding websites by embedding crowdfunding technologies directly into fund seekers' personal websites. This promises more control and customisation for fund seekers yet also distances fund-seeking activities from the established crowds on websites such as Kickstarter and Indiegogo. Consequently, fund seekers adopting this paradigm must be capable of gathering and sustaining a suitable crowd that does not already exist in any one location. However, not all organisations are likely to possess the social resources to meet this challenge. Of those that do, little is known about how they might leverage these resources or the manner in which specific resources have an impact. Thus, the objective of this study is to explore the social resources that enable self-hosted crowdfunding activities. In particular, this research models these activities by leveraging 2 a priori theoretical lenses: activity theory and social capital theory. These are applied to analyse an extreme case of self-hosted crowdfunding, the funding of Star Citizen. Observations from this case are used to develop a propositional model that links different types of social capital with specific elements of self-hosted crowdfunding.


title: A Generic Approach for Escaping Saddle points

abstract: A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance.


title: Analysis of Face Recognition Under Varying Facial Expression: A Survey IAJIT First Online Publication

abstract: Automatic face recognition is one of the most emphasizing dilemmas in diverse of potential relevance like in different surveillance systems, security systems, authentication or verification of individual like criminals etc. Adjoining of dynamic expression in face causes a broad range of discrepancies in recognition systems. Facial expression not only exposes the sensation or passion of any person but can also be used to judge his/her mental views and psychosomatic aspects. This paper is based on a complete survey of face recognition conducted under varying facial expressions. In order to analyze different techniques, motion-based, model-based and muscles-based approaches have been used in order to handle the facial expression and recognition catastrophe. The analysis has been completed by evaluating various existing algorithms while comparing their results in general. It also expands the scope for other researchers for answering the question of effectively dealing with such problems.


title: Global warming and extinctions of endemic species from biodiversity hotspots.

abstract: Global warming is a key threat to biodiversity, but few researchers have assessed the magnitude of this threat at the global scale. We used major vegetation types (biomes) as proxies for natural habitats and, based on projected future biome distributions under doubled-CO2 climates, calculated changes in habitat areas and associated extinctions of endemic plant and vertebrate species in biodiversity hotspots. Because of numerous uncertainties in this approach, we undertook a sensitivity analysis of multiple factors that included (1) two global vegetation models, (2) different numbers of biome classes in our biome classification schemes, (3) different assumptions about whether species distributions were biome specific or not, and (4) different migration capabilities. Extinctions were calculated using both species-area and endemic-area relationships. In addition, average required migration rates were calculated for each hotspot assuming a doubled-CO2 climate in 100 years. Projected percent extinctions ranged from <1 to 43% of the endemic biota (average 11.6%), with biome specificity having the greatest influence on the estimates, followed by the global vegetation model and then by migration and biome classification assumptions. Bootstrap comparisons indicated that effects on hotpots as a group were not significantly different from effects on random same-biome collections of grid cells with respect to biome change or migration rates; in some scenarios, however, botspots exhibited relatively high biome change and low migration rates. Especially vulnerable hotspots were the Cape Floristic Region, Caribbean, Indo-Burma, Mediterranean Basin, Southwest Australia, and Tropical Andes, where plant extinctions per hotspot sometimes exceeded 2000 species. Under the assumption that projected habitat changes were attained in 100 years, estimated global-warming-induced rates of species extinctions in tropical hotspots in some cases exceeded those due to deforestation, supporting suggestions that global warming is one of the most serious threats to the planet's biodiversity.


Fisheye Menus


title: Low-calorie sweeteners and body weight and composition: a meta-analysis of randomized controlled trials and prospective cohort studies123

abstract: Background: Replacement of caloric sweeteners with lower- or nocalorie alternatives may facilitate weight loss or weight maintenance by helping to reduce energy intake; however, past research examining low-calorie sweeteners (LCSs) and body weight has produced mixed results. Objective: The objective was to systematically review and quantitatively evaluate randomized controlled trials (RCTs) and prospective cohort studies, separately, that examined the relation between LCSs and body weight and composition. Design: A systematic literature search identified 15 RCTs and 9 prospective cohort studies that examined LCSs from foods or beverages or LCSs consumed as tabletop sweeteners. Meta-analyses generated weighted mean differences in body weight and composition values between the LCS and control groups among RCTs and weighted mean correlations for LCS intake and these parameters among prospective cohort studies. Results: In RCTs, LCSs modestly but significantly reduced all outcomes examined, including body weight (20.80 kg; 95% CI: 21.17, 20.43), body mass index [BMI (in kg/m 2 ): 20.24; 95% CI: 20.41, 20.07], fat mass (21.10 kg; 95% CI: 21.77, 20.44), and waist circumference (20.83 cm; 95% CI: 21.29, 20.37). Among prospective cohort studies, LCS intake was not associated with body weight or fat mass, but was significantly associated with slightly higher BMI (0.03; 95% CI: 0.01, 0.06). Conclusions: The current meta-analysis provides a rigorous evaluation of the scientific evidence on LCSs and body weight and composition. Findings from observational studies showed no association between LCS intake and body weight or fat mass and a small positive association with BMI; however, data from RCTs, which provide the highest quality of evidence for examining the potentially causal effects of LCS intake, indicate that substituting LCS options for their regular-calorie versions results in a modest weight loss, and may be a useful dietary tool to improve compliance with weight loss or weight maintenance plans. Am J Clin Nutr doi: 10.3945/ajcn. 113.082826.


title: Structure learning in random fields for heart motion abnormality detection

abstract: Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpret leading to high intra-observer variability. Previous work indicates that in order to approach this problem, the interactions between the different heart regions and their overall influence on the clinical condition of the heart need to be considered. To do this, we propose a method for jointly learning the structure and parameters of conditional random fields, formulating these tasks as a convex optimization problem. We consider block-L1 regularization for each set of features associated with an edge, and formalize an efficient projection method to find the globally optimal penalized maximum likelihood solution. We perform extensive numerical experiments comparing the presented method with related methods that approach the structure learning problem differently. We verify the robustness of our method on echocardiograms collected in routine clinical practice at one hospital.


title: Power-efficient sum of absolute differences architecture using adder compressors

abstract: The calculation of the Sum of Absolute Differences (SAD) is one of the most time-consuming operations of the video encoder compatible with the new High Efficiency Video Coding (HEVC) standard. SAD hardware architecture employs an adder tree to accumulate the coefficients from absolute difference between two video blocks. This paper proposes the exploration of the different adder compressors structures in the SAD hardware architecture. The architectures were synthesized to 45nm standard cells. Synthesis results show that SAD architecture with adder compressors with Kogge-Stone adders in the recombination line reduces power dissipation in 60.8% on average when compared with SAD architecture using conventional adders from the synthesis tool.


title: Surviving the Web: A Journey into Web Session Security

abstract: In this article, we survey the most common attacks against web sessions, that is, attacks that target honest web browser users establishing an authenticated session with a trusted web application. We then review existing security solutions that prevent or mitigate the different attacks by evaluating them along four different axes: protection, usability, compatibility, and ease of deployment. We also assess several defensive solutions that aim at providing robust safeguards against multiple attacks. Based on this survey, we identify five guidelines that, to different extents, have been taken into account by the designers of the different proposals we reviewed. We believe that these guidelines can be helpful for the development of innovative solutions approaching web security in a more systematic and comprehensive way.


title: Submap matching for stereo-vision based indoor/outdoor SLAM

abstract: Autonomous robots operating in semi- or unstructured environments, e.g. during search and rescue missions, require methods for online on-board creation of maps to support path planning and obstacle avoidance. Perception based on stereo cameras is well suited for mixed indoor/outdoor environments. The creation of full 3D maps in GPS-denied areas however is still a challenging task for current robot systems, in particular due to depth errors resulting from stereo reconstruction. State-of-the-art 6D SLAM approaches employ graph-based optimization on the relative transformations between keyframes or local submaps. To achieve loop closures, correct data association is crucial, in particular for sensor input received at different points in time. In order to approach this challenge, we propose a novel method for submap matching. It is based on robust keypoints, which we derive from local obstacle classification. By describing geometrical 3D features, we achieve invariance to changing viewpoints and varying light conditions. We performed experiments in indoor, outdoor and mixed environments. In all three scenarios we achieved a final 3D position error of less than 0.23% of the full trajectory. In addition, we compared our approach with a 3D RBPF SLAM from previous work, achieving an improvement of at least 27% in mean 2D localization accuracy in different scenarios.


title: Identity-based cryptosystems and signature schemes

abstract: In this paper we introduce a novel type of cryptographic scheme, which enables any pair of users to communicate securely and to verify each other’s signatures without exchanging private or public keys, without keeping key directories, and without using the services of a third party. The scheme assumes the existence of trusted key generation centers, whose sole purpose is to give each user a personalized smart card when he first joins the network. The information embedded in this card enables the user to sign and encrypt the messages he sends and to decrypt and verify the messages he receives in a totally independent way, regardless of the identity of the other party. Previously issued cards do not have to be updated when new users join the network, and the various centers do not have to coordinate their activities or even to keep a user list. The centers can be closed after all the cards are issued, and the network can continue to function in a completely decentralized way for an indefinite period.


title: Mosaic: quantifying privacy leakage in mobile networks

abstract: With the proliferation of online social networking (OSN) and mobile devices, preserving user privacy has become a great challenge. While prior studies have directly focused on OSN services, we call attention to the privacy leakage in mobile network data. This concern is motivated by two factors. First, the prevalence of OSN usage leaves identifiable digital footprints that can be traced back to users in the real-world. Second, the association between users and their mobile devices makes it easier to associate traffic to its owners. These pose a serious threat to user privacy as they enable an adversary to attribute significant portions of data traffic including the ones with NO identity leaks to network users' true identities. To demonstrate its feasibility, we develop the Tessellation methodology. By applying Tessellation on traffic from a cellular service provider (CSP), we show that up to 50% of the traffic can be attributed to the names of users. In addition to revealing the user identity, the reconstructed profile, dubbed as "mosaic," associates personal information such as political views, browsing habits, and favorite apps to the users. We conclude by discussing approaches for preventing and mitigating the alarming leakage of sensitive user information.


title: SOGANG-HEP-206/96, hep-th/9603043 Entropy of the BTZ Black Hole in 2+1 Dimensions

abstract: We semi-classically calculate the entropy of a scalar field in the background of the BTZ black hole, and derive the perimeter law of the entropy. The proper length from the horizon to the ultraviolet cutoff is independent of both the mass and the angular momentum of the black hole. It is shown that the superradiant scattering modes give the sub-leading order contribution to the entropy while the non-superradiant modes give the leading order one, and thus superradiant effect is minor.


title: On the Locality of Codeword Symbols in Non-Linear Codes

abstract: Consider a possibly non-linear (n,K,d)_q code. Coordinate i has locality r if its value is determined by some r other coordinates. A recent line of work obtained an optimal trade-off between information locality of codes and their redundancy. Further, for linear codes meeting this trade-off, structure theorems were derived. In this work we give a new proof of the locality / redundancy trade-off and generalize structure theorems to non-linear codes.


title: Converse Results to the Wiener Attack on RSA

abstract: A well-known attack on RSA with low secret-exponent d was given by Wiener about 15 years ago. Wiener showed that using continued fractions, one can efficiently recover the secret-exponent d from the public key (N,e) as long as d 0? We answer this question in the negative by proving a converse to Wiener's result. Our result shows that, for any fixed e > 0 and all sufficiently large modulus lengths, Wiener's attack succeeds with negligible probability over a random choice of d 1/4 + e. Thus Wiener's success bound d 1/4. The known attacks in this class (by Verheul and Van Tilborg and Dujella) run in exponential time, so it is natural to ask whether there exists an attack in this class with subexponential run-time. Our second converse result answers this question also in the negative.


title: Battery swap station for mobile robot

abstract: Electricity has been widely adopted in electric car, robot, and other systems as a power sources. A lot of robots, namely, fixed or mobile robots are studied, and researches are done to fuse the new & renewable energy with robot technology to solve the energy supply problems. For the mobile robots, the energies are usually supplied through the battery, and this causes the limitations of the activity time, the discontinuation of activity, and the range of activity. Therefore, solving the problems and securing the continuous energy supply scheme for extending the running time of the mobile robots can show the advantages for the robot activity and achievements. In the study, we suggest a scheme for the battery replacing method for the mobile robot.


title: VINE: an open source interactive data visualization tool for neuroevolution

abstract: Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. VINE works seamlessly with a breadth of neuroevolution algorithms, including ES and GA, and addresses the difficulty of observing the underlying dynamics of the learning process through an interactive visualization of the evolving agent's behavior characterizations over generations. As neuroevolution scales to neural networks with millions or more connections, visualization tools like VINE that offer fresh insight into the underlying dynamics of evolution become increasingly valuable and important for inspiring new innovations and applications.


title: Calibrating Features For Semantic Role Labeling

abstract: This paper takes a critical look at the features used in the semantic role tagging literature and show that the information in the input, generally a syntactic parse tree, has yet to be fully exploited. We propose an additional set of features and our experiments show that these features lead to fairly significant improvements in the tasks we performed. We further show that different features are needed for different subtasks. Finally, we show that by using a Maximum Entropy classifier and fewer features, we achieved results comparable with the best previously reported results obtained with SVM models. We believe this is a clear indication that developing features that capture the right kind of information is crucial to advancing the stateof-the-art in semantic analysis.


title: Testing Techniques for Hardware Security

abstract: System security has emerged as a premier design requirement. While there has been an enormous body of impressive work on testing integrated circuits (ICs) desiderata such as manufacturing correctness, delay, and power, there is no reported effort to systematically test IC security in hardware. Our goal is to provide an impetus for this line of research and development by introducing techniques and methodology for rigorous testing of physically unclonable functions (PUFs). Recently, PUFs received a great deal of attention as security mechanisms due to their flexibility to form numerous security protocols and intrinsic resiliency against physical and side channels attacks. We study three classes of PUFs properties to design pertinent test methods: (i) predictability, (ii) sensitivity to component accuracy, and (iii) susceptibility to reverse engineering. As our case studies, we analyze two popular PUF structures, linear and feed-forward, and show that their security is not adequate from several points of view. The technical highlights of the paper are the first non-destructive technique for PUF reverse engineering and a new PUF structure that is capable of passing our security tests.


title: A New Phase-Shifted Full-Bridge Converter With Voltage-Doubler-Type Rectifier for High-Efficiency PDP Sustaining Power Module

abstract: A new phase-shifted full-bridge converter with a voltage-doubler-type rectifier for a high-efficiency power-sustaining module of a plasma display panel is proposed in this paper. The proposed converter employs a voltage-doubler rectifier without an output inductor. Since it does not have an output inductor, the voltage stresses of the rectifier diodes can be clamped at the output voltage level. Thus, since no dissipative resistor-capacitor snubber for rectifier diodes is needed, high-efficiency low-noise output voltage can be realized. Due to the elimination of the large output inductor, it features simpler structure, lower cost, smaller mass, and lighter weight. Furthermore, the proposed converter has wide zero-voltage-switching ranges of lagging leg switches with low current stresses of the primary power switches by using the magnetizing current. In addition, the resonance between the leakage inductor of the transformer and the rectifier capacitors can reduce the current stresses of the rectifier diodes and conduction losses. In this paper, the operational principles, analysis, design considerations, and experimental results are presented.


title: Realtime and Robust Hand Tracking from Depth

abstract: We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data. Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. Those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking.


title: Fast context-aware recommendations with factorization machines

abstract: The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of context-aware methods. We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast context-aware recommendations because the model equation of FMs can be computed in linear time both in the number of context variables and the factorization size. For learning FMs, we develop an iterative optimization method that analytically finds the least-square solution for one parameter given the other ones. Finally, we show empirically that our approach outperforms Multiverse Recommendation in prediction quality and runtime.


title: Multi-channel lidar processing for lane detection and estimation

abstract: Lane recognition is a function which is needed for a variety of driver assistance systems. For example Lane Departure Warning and Lane Keeping rely on information provided by a lane estimation algorithm. One important step of the lane estimation procedure is the extraction of measurements or detections which can be used to estimate the shape of the road or lane. These detections are generated by white lane markers or the road border itself. Lane estimation has for many years been under heavy development using a gray scale camera. Passive camera based systems can be degraded on its performance under certain circumstances, e.g. at dynamic changes of ambient brightness. The fusion with an active sensor can here increase the robustness of these systems significantly. In this paper an approach is presented to detect lane marks using an active light detection and ranging device (lidar). It can be shown that high reflective lane marks can be reliably detected. A polar Lane Detector Grid is used to combine the distance and intensity measurement channel of the lidar sensor.


title: CPW-Fed Aperture-Coupled Slot Dipole Antenna for Tri-Band Operation

abstract: A uniplanar aperture-coupled slot dipole antenna capable of tri-band operation is presented. Design considerations for the three operating frequencies are extensively discussed. The lower and middle operating frequencies can be obtained by varying the lengths of the lower and middle pairs of the protruded slots, respectively, while the upper frequency can be determined by adjusting the lengths of the upper protruded slot pair and the coupling aperture pair. Within each band, slot-dipole-like radiation patterns, low cross-polarized radiations, and moderate antenna gains can be observed.


title: Botnet detection techniques: review, future trends, and issues

abstract: In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.


A Neural Network based Approach to Automatic Post-Editing


title: Saliency, Scale and Image Description

abstract: Many computer vision problems can be considered to consist of two main tasks: the extraction of image content descriptions and their subsequent matching. The appropriate choice of type and level of description is of course task dependent, yet it is generally accepted that the low-level or so called early vision layers in the Human Visual System are context independent. ::: ::: This paper concentrates on the use of low-level approaches for solving computer vision problems and discusses three inter-related aspects of this: saliencys scale selection and content description. In contrast to many previous approaches which separate these tasks, we argue that these three aspects are intrinsically related. Based on this observation, a multiscale algorithm for the selection of salient regions of an image is introduced and its application to matching type problems such as tracking, object recognition and image retrieval is demonstrated.


title: Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions

abstract: Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students' task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that self-explanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6th-grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.


Sentence Alignment using Unfolding Recursive Autoencoders


title: A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

abstract: Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.


title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

abstract: Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.


title: Type-Constrained Representation Learning in Knowledge Graphs

abstract: Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.


title: Neural correlates of consciousness in humans

abstract: The directness and vivid quality of conscious experience belies the complexity of the underlying neural mechanisms, which remain incompletely understood. Recent work has focused on identifying the brain structures and patterns of neural activity within the primate visual system that are correlated with the content of visual consciousness. Functional neuroimaging in humans and electrophysiology in awake mokeys indicate that there are important differences between striate and extrastriate visual cortex in how well neural activity correlates with consciousness. Moreover, recent neuroimaging studies indicate that, in addition to these ventral areas of visual cortex, dorsal prefrontal and parietal areas might contribute to conscious visual experience.


title: Argos: A Novel 3-DoF Parallel Wrist Mechanism

abstract: This article presents a novel parallel spherical mechanism called argos with three rotational degrees of freedom. Design aspects of the first prototype built of the Argos mechanism are discussed. The direct kinematic problem is solved, leading always to four nonsingular configurations of the end effector for a given set of joint angles. The inverse-kinematic problem yields two possible configurations for each of the three pantographs for a given orientation of the end effector. Potential applications of the Argos mechanism are robot wrists, orientable machine tool beds, joy sticks, surgical manipulators, and orientable units for optical components. Another pantograph based new structure named PantoScope having two rotational DoF is also briefly introduced.


title: Texture Characterization for Hepatic Tumor Recognition

abstract: A new approach to texture characterization from dyn amic CT scans of the liver is presented. Images with the same slice position and correspondi ng to three typical acquisition phases are analyzed simultaneously. Thereby texture evolution during the propagation of contrast product is taken into account. The method is applie d to recognizing hepatic primary tumors. Experiments with various sets of texture parameters and two classification methods show that simultaneous analysis of texture parameters derived from three subsequent acquisition moments improves the classification accuracy.


title: Human motion tracking and gait analysis: a brief review of current sensing systems and integration with intelligent environments

abstract: Human motion tracking and gait analysis is becoming a topic of great interest in everyday applications generating a large amount of research into systems that can provide this information in real time at a low cost and with the smallest intrusion level possible. In this line different approaches have been proposed following distinct philosophies usually related to the final application of the system. This paper is aimed on one hand at providing a review of non vision based current systems highlighting how distinct and oriented towards particular applications they are. On the other this paper deals with the problem of integrating such different elements into efficient environmental intelligence applications that can be used as a base for home rehabilitation or automatic elderly assistance. This is addressed by implementing a specific device access layer within a novel distributed agent based ambient intelligence architecture called HI3.


title: Risk Management for Digital Transformation in Architecture Board: A Case Study on Global Enterprise

abstract: This study investigates the solutions incorporated by the Architecture Board in global healthcare enterprise (GHE) to mitigate architecture risks especially in Digital IT areas while proposing and implementing the Adaptive Integrated EA framework, which can be applied in companies promoting IT strategy with Cloud/Mobile IT. The study revealed the distribution of solutions across the architecture domains in Enterprise Architecture covering applications and technologies with Cloud/Mobile IT/Digital IT to mitigate risks. An in-depth analysis of this distribution resulted in practical guidance for companies that consider Risk Management for Digital Transformation while starting up the Architecture Board in Enterprise Architecture with IT strategy covering Digital IT related elements.


title: Operations Research in Passenger Railway Transportation

abstract: In this paper we give an overview of state-of-the-art Operations Research models and techniques used in passenger railway transportation. For each planning phase (strategic, tactical and operational), we describe the planning problems arising there and discuss some models and algorithms to solve them. We do not only consider classical, well-known topics such as timetabling, rolling stock scheduling and crew scheduling, but we also discuss some recently developed topics such as shunting and reliability of timetables. ::: ::: Finally, we focus on several practical aspects for each of these problems at the largest Dutch railway operator, NS Reizigers.


title: Conceptual Model for Security in Next Generation Network

abstract: Due to the growing threats of internet and computer networks, and the relative level of required security, we decided to discuss about security issues and provide an application security model for next generation. Surely the lack of conceptual model for NGN security with application approach caused not having a certain framework as a reference. For this, at first we discuss the applicable program, next generation network and security in next generation network and then present a new plan as a conceptual model to provide security at the application level for next generation network. This Model could be used for Cloud Computing Networks.


title: Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

abstract: We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with the lack of centralized control and local influences, we propose two special cases of local interaction game to study this problem. The first is local altruistic game, in which each user considers the payoffs of itself as well as its neighbors rather than considering itself only. The second is local congestion game, in which each user minimizes the number of competing neighbors. It is shown that with the proposed games, global optimization is achieved with local information. Specifically, the local altruistic game maximizes the network throughput and the local congestion game minimizes the network collision level. Also, the concurrent spatial adaptive play (C-SAP), which is an extension of the existing spatial adaptive play (SAP), is proposed to achieve the global optimum both autonomously as well as rapidly.


Classification of Dengue Outbreak Using Data Mining Models


ESTABLISHING AN INFRASTRUCTURE FOR VALUE CHAIN COLLABORATION IN THE NORWEGIAN OIL INDUSTRY - A CASE STUDY


title: Capparis Spinosa L. promotes anti-inflammatory response in vitro through the control of cytokine gene expression in human peripheral blood mononuclear cells

abstract: BACKGROUND ::: Capparis Spinosa L. is an aromatic plant growing wild in dry regions around the Mediterranean basin. Capparis Spinosa was shown to possess several properties such as antioxidant, antifungal, and anti-hepatotoxic actions. In this work, we aimed to evaluate immunomodulatory properties of Capparis Spinosa leaf extracts in vitro on human peripheral blood mononuclear cells (PBMCs) from healthy individuals. ::: ::: ::: RESULTS ::: Using MTT assay, we identified a range of Capparis Spinosa doses, which were not toxic. Unexpectedly, we found out that Capparis Spinosa aqueous fraction exhibited an increase in cell metabolic activity, even though similar doses did not affect cell proliferation as shown by CFSE. Interestingly, Capparis Spinosa aqueous fraction appeared to induce an overall anti-inflammatory response through significant inhibition of IL-17 and induction of IL-4 gene expression when PBMCs were treated with the non toxic doses of 100 and/or 500 μg/ml. Phytoscreening analysis of the used Capparis Spinosa preparations showed that these contain tannins; sterols, alkaloids; polyphenols and flavonoids. Surprisingly, quantification assays showed that our Capparis Spinosa preparation contains low amounts of polyphenols relative to Capparis Spinosa used in other studies. This Capparis Spinosa also appeared to act as a weaker scavenging free radical agent as evidenced by DPPH radical scavenging test. Finally, polyphenolic compounds including catechin, caffeic acid, syringic acid, rutin and ferulic acid were identified by HPLC, in the Capparis spinosa preparation. ::: ::: ::: CONCLUSION ::: Altogether, these findings suggest that our Capparis Spinosa preparation contains interesting compounds, which could be used to suppress IL-17 and to enhance IL-4 gene expression in certain inflammatory situations. Other studies are underway in order to identify the compound(s) underlying this effect.


title: Differences of opinion and the cross section of stock returns

abstract: We provide evidence that stocks with higher dispersion in analysts’ earnings forecasts earn lower future returns than otherwise similar stocks. This effect is most pronounced in small stocks and stocks that have performed poorly over the past year. Interpreting dispersion in analysts’ forecasts as a proxy for differences in opinion about a stock, we show that this evidence is consistent with the hypothesis that prices will ref lect the optimistic view whenever investors with the lowest valuations do not trade. By contrast, our evidence is inconsistent with a view that dispersion in analysts’ forecasts proxies for risk. IN THIS PAPER WE ANALYZE THE ROLE of dispersion in analysts’ earnings forecasts in predicting the cross section of future stock returns. We find that stocks with higher dispersion in analysts’ earnings forecasts earn significantly lower future returns than otherwise similar stocks. In particular, a portfolio of stocks in the highest quintile of dispersion underperforms a portfolio of stocks in the lowest quintile of dispersion by 9.48 percent per year. This effect is strongest in small stocks, and stocks that have performed poorly over the past year. Our results are robust to various risk-adjustment techniques, and are inconsistent with an interpretation of dispersion in analysts’ forecasts as a proxy for risk. We postulate that dispersion in analysts’ earnings forecasts can be viewed as a proxy for differences of opinion among investors. Differences of opinion are typically modeled via dogmatic beliefs or asymmetric information sets, and have been included in numerous models that relax the standard


title: Computer Generated Islamic Star Patterns

abstract: Islamic star patterns are a beautiful and highly geometric art form. Many analyses have been done of their complex structure and symmetries. We pick up one line of analysis based on placing stars and rosettes in a formation dictated by a tiling of the plane, and develop a software implementation of the technique. We discuss the construction of the designs and show some computer-generated results.


abstract: We introduce a new method to combine the output probabilities of convolutional neural networks which we call Weighted Convolutional Neural Network Ensemble. Each network has an associated weight that makes networks with better performance have a greater influence at the time to classify in relation to networks that performed worse. This new approach produces better results than the common method that combines the networks doing just the average of the output probabilities to make the predictions. We show the validity of our proposal by improving the classification rate on a common image classification benchmark.


title: Comparative Analysis of DES, AES, RSA Encryption Algorithms

abstract: As the increasing growth of the computing technology and network technology,it also increases data storage demands. Data Security has become a crucial issue in electronic communication. Secret writing has come up as a solution, and plays a vital role in data security system. It uses some algorithms to scramble data into unreadable text which might be only being decrypted by party those having the associated key. These algorithms consume a major amount of computing resources such as memory and battery power and computation time. This paper accomplishes comparative analysis of encryption standardsDES, AES and RSA considering various parameters such as computation time, memory usages. A cryptographic tool is used for performing experiments. Experiments results are given to analyses the effectiveness ofsymmetric andasymmetricalgorithms.


title: Falsification Attacks against WPA-TKIP in a Realistic Environment

abstract: SUMMARY In this paper, we propose two new falsification attacks against Wi-Fi Protected Access Temporal Key Integrity Protocol (WPATKIP). A previous realistic attack succeeds only for a network that supports IEEE 802.11e QoS features by both an access point (AP) and a client, and it has an execution time of 12–15min, in which it recovers a message integrity code (MIC) key from an ARP packet. Our first attack reduces the execution time for recovering a MIC key. It can recover the MIC key within 7–8min. Our second attack expands its targets that can be attacked. This attack focuses on a new vulnerability of QoS packet processing, and this vulnerability can remove the condition that the AP supports IEEE 802.11e. In addition, we discovered another vulnerability by which our attack succeeds under the condition that the chipset of the client supports IEEE 802.11e even if the client disables this standard through the OS. We demonstrate that chipsets developed by several kinds of vendors have the same vulnerability.


title: Smart and green urban solid waste collection system for differentiated collection with integrated sensor networks

abstract: Urban solid waste prevention and recycling are needed at the local scale rather than heavily relying on the treatment and disposal at the regional scale. Differentiated collection is necessary but complicates the current schemes of waste collection. To bridge this gap and construct an efficient, effective, and sustainable scheme, this study developed a smart and green system (SGS) which embodies the in-depth integration of various informatics and the strategy of sustainable waste management. The proposed SGS is able to gather the massive volumes of data and information from different application scenes of waste separation and differentiated collection, with the aid of sensor network technologies to timely detect state parameters throughout a waste collection system and the rate of waste separation and recycling. In-network data processing and cloud computing contribute to managing the communications among all collection nodes, vehicles, and crews in a secure, scalable and highly-available environment. The proposed SGS can deal with well-structured, semi-structured, and even poorly structured problems in a more efficient, collaborative, and resilient way with the aid of a position-navigation-timing system. Integrated three-level SGS architecture with a scalable sensor network is proposed in this study as an instruction of developing SGS for sustainable waste management.


title: Clinical information extraction applications: A literature review

abstract: BACKGROUND ::: With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. ::: ::: ::: OBJECTIVES ::: In this literature review, we present a review of recent published research on clinical information extraction (IE) applications. ::: ::: ::: METHODS ::: A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library. ::: ::: ::: RESULTS ::: A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations. ::: ::: ::: CONCLUSIONS ::: Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.


title: Role of Text Pre-processing in Twitter Sentiment Analysis

abstract: Abstract Ubiquitous nature of online social media and ever expending usage of short text messages becomes a potential source of crowd wisdom extraction especially in terms of sentiments therefore sentiment classification and analysis is a significant task of current research purview. Major challenge in this area is to tame the data in terms of noise, relevance, emoticons, folksonomies and slangs. This works is an effort to see the effect of pre-processing on twitter data for the fortification of sentiment classification especially in terms of slang word. The proposed method of pre-processing relies on the bindings of slang words on other coexisting words to check the significance and sentiment translation of the slang word. We have used n-gram to find the bindings and conditional random fields to check the significance of slang word. Experiments were carried out to observe the effect of proposed method on sentiment classification which clearly indicates the improvements in accuracy of classification.


title: Dual-polarized aperture-coupled wideband microstrip patch antenna with high isolation for C-Band

abstract: An aperture-coupled dual-polarized wideband microstrip antenna with high port-to-port isolation in C-Band is presented in this paper. The proposed antenna contains reflector, substrate, patch and director. By means of four shorting pins the bandwidth increases noticeably. It has 50% bandwidth of -10dB return loss at the 5.25GHz center frequency, and minimum 31.5dB isolation across the band. The reflector helps to improve Front to Back (F/B) ratio of better than 20dB. Moreover, it has a peak gain of 9.2dBi. The structure of the antenna is compact and easy to fabricate. Therefore, it's a suitable candidate for arrays, and could be used in base stations for Wi-Fi applications.


title: Approximate String Matching with Address Bit Errors

abstract: A string Si¾? Σmcan be viewed as a set of pairs S= { (i¾? i , i) : ii¾? { 0,..., mi¾? 1} }. We consider approximate pattern matching problems arising from the setting where errors are introduced to the location component (i), rather than the more traditional setting, where errors are introduced to the content itself (i¾? i ). In this paper, we consider the case where bits of imay be erroneously flipped, either in a consistent or transient manner. We formally define the corresponding approximate pattern matching problems, and provide efficient algorithms for their resolution, while introducing some novel techniques.


title: From a Federated to an Integrated Automotive Architecture

abstract: This paper describes an integrated system architecture for automotive electronic systems based on multicore systems-on-chips (SoCs). We integrate functions from different suppliers into a few powerful electronic control units using a dedicated core for each function. This work is fueled by technological opportunities resulting from recent advances in the semiconductor industry and the challenges of providing dependable automotive electronic systems at competitive costs. The presented architecture introduces infrastructure IP cores to overcome key challenges in moving to automotive multicore SoCs: a time-triggered network-on-a-chip with fault isolation for the interconnection of functional IP cores, a diagnostic IP core for error detection and state recovery, a gateway IP core for interfacing legacy systems, and an IP core for reconfiguration. This paper also outlines the migration from today's federated architectures to the proposed integrated architecture using an exemplary automotive E/E system.


title: LDS-OFDM an Efficient Multiple Access Technique

abstract: In this paper LDS-OFDM is introduced as an uplink multicarrier multiple access scheme. LDS-OFDM uses Low Density Signature (LDS) structure for spreading the symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams up to 400% more than the number of subcarriers (overloaded condition). The performance of LDS-OFDM is evaluated and compared with conventional OFDMA systems over multipath fading channel. Monte Carlo based simulations for various loading conditions indicate significant performance improvement over OFDMA system.


On the Naive Bayes Model for Text Categorization


title: Osteogenesis of Adipose-Derived Stem Cells

abstract: Current treatment options for skeletal repair, including immobilization, rigid fixation, alloplastic materials and bone grafts, have significant limitations. Bone tissue engineering offers a promising method for the repair of bone deficieny caused by fractures, bone loss and tumors. The use of adipose derived stem cells (ASCs) has received attention because of the self-renewal ability, high proliferative capacity and potential of osteogenic differentiation in vitro and in vivo studies of bone regeneration. Although cell therapies using ASCs are widely promising in various clinical fields, no large human clinical trials exist for bone tissue engineering. The aim of this review is to introduce how they are harvested, examine the characterization of ASCs, to review the mechanisms of osteogenic differentiation, to analyze the effect of mechanical and chemical stimuli on ASC osteodifferentiation, to summarize the current knowledge about usage of ASC in vivo studies and clinical trials, and finally to conclude with a general summary of the field and comments on its future direction.


title: Scene text localization using extremal regions and Corner-HOG feature

abstract: This paper presents a text detection method based on Extremal Regions (ERs) and Corner-HOG feature. Local Histogram of Oriented Gradient (HOG) extracted around corners (Corner-HOG) is used to effectively prune the non-text components in the component tree. Experimental results show that the Corner-HOG based pruning method can discard an average of 83.06% of all ERs in an image while preserving a recall of 90.51% of the text components. The remaining ERs are then grouped into text lines and candidate text lines are verified using black-white transition feature and the covariance descriptor of HOG. Experimental results on the 2011 Robust Reading Competition dataset show that the proposed text detection method provides promising performance.


title: Who is .com?: Learning to Parse WHOIS Records

abstract: WHOIS is a long-established protocol for querying information about the 280M+ registered domain names on the Internet. Unfortunately, while such records are accessible in a ``human-readable'' format, they do not follow any consistent schema and thus are challenging to analyze at scale. Existing approaches, which rely on manual crafting of parsing rules and per-registrar templates, are inherently limited in coverage and fragile to ongoing changes in data representations. In this paper, we develop a statistical model for parsing WHOIS records that learns from labeled examples. Our model is a conditional random field (CRF) with a small number of hidden states, a large number of domain-specific features, and parameters that are estimated by efficient dynamic-programming procedures for probabilistic inference. We show that this approach can achieve extremely high accuracy (well over 99%) using modest amounts of labeled training data, that it is robust to minor changes in schema, and that it can adapt to new schema variants by incorporating just a handful of additional examples. Finally, using our parser, we conduct an exhaustive survey of the registration patterns found in 102M com domains.


title: Mitigate DDoS attacks in NDN by interest traceback

abstract: Current Internet is reaching the limits of its capabilities due to its function transition from host-to-host communication to content dissemination. Named Data Networking (NDN) – an instantiation of Content-Centric Networking approach, embraces this shift by stressing the content itself, rather than where it locates. NDN tries to provide better security and privacy than current Internet does, and resilience to Distributed Denial of Service (DDoS) is a significant issue. In this paper, we present a specific and concrete scenario of DDoS attack in NDN, where perpetrators make use of NDN’s packet forwarding rules to send out Interest packets with spoofed names as attacking packets. Afterwards, we identify the victims of NDN DDoS attacks include both the hosts and routers. But the largest victim is not the hosts, but the routers, more specifically, the Pending Interest Table (PIT) within the router. PIT brings NDN many elegant features, but it suffers from vulnerability. We propose Interest traceback as a counter measure against the studied NDN DDoS attacks, which traces back to the originator of the attacking Interest packets. At last, we assess the harmful consequences brought by these NDN DDoS attacks and evaluate the Interest traceback counter measure. Evaluation results reveal that the Interest traceback method effectively mitigates the NDN DDoS attacks studied in this paper


title: Variants of travelling salesman problem: A survey

abstract: The Travelling Salesman Problem (TSP) is a well-known NP-hard problem exceedingly studied in the fields of operations research and computer science. In TSP, a salesman wants to visit each of a set of cities exactly once and return to the starting city with minimal distance travelled. The significance of the TSP is that it can be pertained on many practical applications in real life scenario. But, it is not always possible to apply TSP for all the real world applications because of different constraints and also variations of TSP might be desired in such real-life scenarios. Therefore, several variants of TSP have been proposed to manage with the application specific constraints. In this work, a comprehensive study on various categories of TSP variants such as Profit Based, Time Windows based, Maximal Based and Kinetic Based has been studied with respect to the problem formulation and applications.


title: Evolution of e-commerce Web sites: A conceptual framework and a longitudinal study

abstract: Before the 1990s, the digital exchange of information between companies was achieved using electronic data interchange (EDI) and needed agreement between the organizations. The early 1990s saw the commercialization of the Internet and the advent of open computer technology and connectivity became affordable for individuals as well as businesses. The consequence was the World Wide Web. As e-commerce activities extended across businesses, enterprises, and industries, a genre of Web sites emerged allowing the integrative management of business operations. Here, we provide an evolutionary perspective of e-commerce Web sites. We posited that there have been four eras. To chart the evolution of e-commerce Web sites, a conceptual framework was developed to characterize such sites. Based on the framework, we conducted a longitudinal study between 1993 and 2001. The result showed that the proposed four eras were clearly discernible.


Issues to consider in conducting nonsexist psychological research: A guide for researchers.


title: The Statistical Recurrent Unit

abstract: Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU's architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures' hyperparameters for both synthetic and real-world tasks.


title: Hardening COTS software with generic software wrappers

abstract: Numerous techniques exist to augment the security functionality of commercial off-the-shelf (COTS) applications and operating systems, making them more suitable for use in mission-critical systems. Although individually useful, as a group these techniques present difficulties to system developers because they are not based on a common framework which might simplify integration and promote portability and reuse. The paper presents techniques for developing Generic Software Wrappers-protected, non-bypassable kernel-resident software extensions for augmenting security without modification of COTS source. We describe the key elements of our work: our high-level Wrapper Definition Language (WDL), and our framework for configuring, activating, and managing wrappers. We also discuss code reuse, automatic management of extensions, a framework for system-building through composition, platform-independence, and our experiences with our Solaris and FreeBSD prototypes.


title: Communication-constrained multi-AUV cooperative SLAM

abstract: Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.


title: SurvSurf: human retrieval on large surveillance video data

abstract: The volume of surveillance videos is increasing rapidly, where humans are the major objects of interest. Rapid human retrieval in surveillance videos is therefore desirable and applicable to a broad spectrum of applications. Existing big data processing tools that mainly target textual data cannot be applied directly for timely processing of large video data due to three main challenges: videos are more data-intensive than textual data; visual operations have higher computational complexity than textual operations; and traditional segmentation may damage video data's continuous semantics. In this paper, we design SurvSurf, a human retrieval system on large surveillance video data that exploits characteristics of these data and big data processing tools. We propose using motion information contained in videos for video data segmentation. The basic data unit after segmentation is called M-clip. M-clips help remove redundant video contents and reduce data volumes. We use the MapReduce framework to process M-clips in parallel for human detection and appearance/motion feature extraction. We further accelerate vision algorithms by processing only sub-areas with significant motion vectors rather than entire frames. In addition, we design a distributed data store called V-BigTable to structuralize M-clips' semantic information. V-BigTable enables efficient retrieval on a huge amount of M-clips. We implement the system on Hadoop and HBase. Experimental results show that our system outperforms basic solutions by one order of magnitude in computational time with satisfactory human retrieval accuracy.


title: Broadband Circularly Polarized Printed Slot-Monopole Antenna

abstract: A slot and a monopole antenna fabricated on a single substrate can generate circularly polarized radiation over a broad band of frequencies. Appropriate power division network is used to feed the elements from a microstrip transmission line. Radiation pattern of antenna is circularly polarized in the broadside direction. Measured 3-dB axial-ratio (AR) bandwidth is 30%, and the 10-dB S11 bandwidth is 51%.


Improving Users' Demographic Prediction via the Videos They Talk about


title: Subtotal Septal Reconstruction: An Update

abstract: Subtotal septal reconstruction is a surgical technique used to reconstruct severe septal deviations that are not easily repaired using less invasive methods. Patients with identifiable septal fractures across the caudal or dorsal segments of the L-strut are good candidates. These patients may present with deformities such as the deviated nose or saddle nose. Adequate autologous cartilage is needed to reconstruct the septum. The deviated segments of the nasal septum are removed and then reconstructed by replacing the caudal septum with a straight piece of septal cartilage fixated to the nasal spine. The bilateral extended spreader grafts fixated to the remnant dorsal strut is then fixed to the caudal septal replacement graft to reconstitute the L-strut. In some cases, the dorsal strut may overlap the caudal portion of the L-strut to complete the repair. Care must be taken in setting nasal length, projection, rotation, and supratip break. If these parameters are not set very carefully deformity may ensue. Other potential complications include change in the upper lip smile or a crease forming in the upper lip when the patient smiles. This is a complex technique and must be performed with special care to avoid deformity.


title: Automatic Fish Classification System Using Deep Learning

abstract: Fish classification and detection in the wild remains an unsolved problem due to the variation of pose, uncontrolled environmental conditions and the limited amount of training data. We propose a deep neural fish classification system to automatically label fish using a camera without interference from human. In the system, classification is done in two independent branches. One branch, where the fish are detected, aligned, and fed to classifiers, aims to handle the variation of pose and scale of fish and extract discriminative features to distinguish fish. The other branch makes use of context information in the scenario to infer the type of fish, under the assumption that the types of captured fish are correlated with the environmental conditions around such as boat, fish gears, daylight and weather. The preliminary classification prediction made in the two branches is then reweighted and averaged as the final prediction. In the world-wide competition of The Nature Conservancy Fisheries Monitoring" hosted by Kaggle our model achieved top 0.7% rank on the final leaderboard among solutions submitted by 2 293 different competitive teams