title: An overview of ontologies and data resources in medical domains

abstract: Abstract The ongoing rapid growth of diversity of data and their wide use to solve different complex tasks requires more sophisticated techniques of knowledge management and automated reasoning. Recent research efforts resulted in a significant number of semantic reference systems enriched with vocabularies, thesauri, terminologies, and ontologies. The extensive use of ontologies in the mainstream computer science has spread to many other branches of knowledge. These branches are included in a new approach to building modern intelligent systems, reusing and sharing pieces of declarative knowledge. In the meanwhile, a lot of effort has been made to produce standard ontologies for medicine and biology. This paper brings an overview and presentation of the state of the art in terminologies, ontologies and important resources/systems and tools for industry and academia in medicine and biology. It could be useful for researchers involved in multidisciplinary and interdisciplinary research areas and projects that include medicine, biology, and information technology.


title: Multi-modal Mean-Fields via Cardinality-Based Clamping

abstract: Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.


Please see the final published version for citation purposesp. 2 Thinking across Cultures: Implications for Dual Processes


title: Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality

abstract: Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of instantaneous input signal-to-noise ratios. In this paper, we present a new approach to improve the adaptivity of such algorithms enabling them to perform robustly across a wide range of input signal and noise types. Our methodology is based on the dynamic control of algorithmic parameters via reinforcement learning. Specifically, we model the noise-suppression module as a black box, requiring no knowledge of the algorithmic mechanics except a simple feedback from the output. We utilize this feedback as the reward signal for a reinforcement-learning agent that learns a policy to adapt the algorithmic parameters for every incoming audio frame (16 ms of data). Our preliminary results show that such a control mechanism can substantially increase the overall performance of the underlying noise-suppression algorithm; 42% and 16% improvements in output SNR and MSE, respectively, when compared to no adaptivity.


title: Massive MIMO Design for 5G Networks: An Overview on Alternative Antenna Configurations and Channel Model Challenges

abstract: With the growth of mobile data application and the ultimate expectations of 5G technology, the need to expand the capacity of the wireless networks is inevitable. Massive MIMO technique is currently taking a major part of the ongoing research, and expected to be the key player in the new cellular technologies. This papers presents an overview of the major aspects related to massive MIMO design including, antenna array general design, configuration, and challenges, in addition to advanced beamforming techniques and channel modeling and estimation issues affecting the implementation of such systems.


title: Data Mining of Printed-Circuit Board Defects

abstract: This paper discusses an industrial case study in which data mining has been applied to solve a quality engineering problem in electronics assembly. During the assembly process, solder balls occur underneath some components of printed circuit boards. The goal is to identify the cause of solder defects in a circuit board using a data mining approach. Statistical process control and design of experiment approaches did not provide conclusive results. The paper discusses features considered in the study, data collected, and the data mining solution approach to identify causes of quality faults in an industrial application.


title: Unraveling and Learning Workflow Models from Interleaved Event Logs

abstract: Business process mining is to extract process knowledge from a system's log in order to reconstruct workflow models. Existing approaches treat a log record as an instance of one workflow model. They do not deal with interleaved logs, where each log record is a mixture of multiple workflow traces. However, such an interleaved log is typical for many systems especially web-based ones where all the user-system interaction traces are recorded and maintained by a web server. Dealing with interleaved logs is challenging due to the lack of prior knowledge of workflow models and noises contained in the log data. In this paper, we propose a two-phase workflow learning process. During the first phase, we use a probabilistic approach to learn the links between operations and the hidden workflow models. We consider a workflow model as a probabilistic distributions over operations and derive it through likelihood maximization. This allows us to identify the membership of an operation to a workflow model, which can be used to unravel a log record and generate a set of workflow instances from it. During the second phase, the sequential patterns between operations within each workflow model are derived from all its instances. We have conducted a comprehensive experimental study, which indicates the effectiveness of the proposed solution.


title: Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories

abstract: We study the problem of classifying actions of human subjects using depth movies generated by Kinect or other depth sensors. Representing human body as dynamical skeletons, we study the evolution of their (skeletons’) shapes as trajectories on Kendall’s shape manifold. The action data is typically corrupted by large variability in execution rates within and across subjects and, thus, causing major problems in statistical analyses. To address that issue, we adopt a recently-developed framework of Su et al. [1] , [2] to this problem domain. Here, the variable execution rates correspond to re-parameterizations of trajectories, and one uses a parameterization-invariant metric for aligning, comparing, averaging, and modeling trajectories. This is based on a combination of transported square-root vector fields (TSRVFs) of trajectories and the standard Euclidean norm, that allows computational efficiency. We develop a comprehensive suite of computational tools for this application domain: smoothing and denoising skeleton trajectories using median filtering, up- and down-sampling actions in time domain, simultaneous temporal-registration of multiple actions, and extracting invertible Euclidean representations of actions. Due to invertibility these Euclidean representations allow both discriminative and generative models for statistical analysis. For instance, they can be used in a SVM-based classification of original actions, as demonstrated here using MSR Action-3D, MSR Daily Activity and 3D Action Pairs datasets. Using only the skeletal information, we achieve state-of-the-art classification results on these datasets.


title: Classifying human-robot interaction: an updated taxonomy

abstract: This paper extends taxonomy of human-robot interaction (HRI) introduced in 2002 to include additional categories as well as updates to the categories from the original taxonomy. New classifications include measures of the social nature of the task (human interaction roles and human-robot physical proximity), task type, and robot morphology.


title: Bounded Model Checking of Hybrid Systems for Control

abstract: A bounded LTL model checking algorithm to check the properties of hybrid systems is presented. The proposed algorithm can also be applied to control systems as counterexamples of a negated goal contain information to achieve the original goal. This approach is different than existing abstraction-based techniques. While many of the latter approaches build a control strategy after partitioning a state space, the proposed approach constructs a necessary set of constraints and computes a possibly optimal control input on the fly. The bounded LTL semantics of this paper is more expressive than those of bounded reachability: in addition to the finite computation paths that the reachability checkers can handle, the proposed algorithm can check infinite paths ending with a loop. Furthermore, the LTL operators provide a convenient and expressive way of writing complicated specifications.


title: High Performance Architecture for Flow-Table Lookup in SDN on FPGA

abstract: We propose Range-based Ternary Search Tree (RTST), a tree-based approach for flow-table lookup in SDN network. RTST builds upon flow-tables in SDN switches to provide a fast lookup among flows. We present a parallel multi-pipeline architecture for implementing RTST that benefits from high throughput and low latency. The proposed RTST and architecture achieve a memory efficiency of 1 byte of memory for each byte of flow. We also present a set of techniques to support dynamic updates. Experimental results show that RTST can be used to improve the performance of flow-lookup. It achieves a throughput of 670 Million Packets Per Second (MPPS), for a 1 K 15-tuple flow-table, on a state-of-the-art FPGA.


Comparison of Different Neural Network Training Algorithms with Application to Face Recognition


title: A systematic review of the surgical treatment of large incisional hernia

abstract: PURPOSE ::: Incisional hernia (IH) is one of the most frequent postoperative complications. Of all patients undergoing IH repair, a vast amount have a hernia which can be defined as a large incisional hernia (LIH). The aim of this study is to identify the preferred technique for LIH repair. ::: ::: ::: METHODS ::: A systematic review of the literature was performed and studies describing patients with IH with a diameter of 10 cm or a surface of 100 cm2 or more were included. Recurrence hazards per year were calculated for all techniques using a generalized linear model. ::: ::: ::: RESULTS ::: Fifty-five articles were included, containing 3,945 LIH repairs. Mesh reinforced techniques displayed better recurrence rates and hazards than techniques without mesh reinforcement. Of all the mesh techniques, sublay repair, sandwich technique with sublay mesh and aponeuroplasty with intraperitoneal mesh displayed the best results (recurrence rates of <3.6%, recurrence hazard <0.5% per year). Wound complications were frequent and most often seen after complex LIH repair. ::: ::: ::: CONCLUSIONS ::: The use of mesh during LIH repair displayed the best recurrence rates and hazards. If possible mesh in sublay position should be used in cases of LIH repair.


title: A Dynamic and Self-Adaptive Network Selection Method for Multimode Communications in Heterogeneous Vehicular Telematics

abstract: With the increasing demands for vehicle-to-vehicle and vehicle-to-infrastructure communications in intelligent transportation systems, new generation of vehicular telematics inevitably depends on the cooperation of heterogeneous wireless networks. In heterogeneous vehicular telematics, the network selection is an important step to the realization of multimode communications that use multiple access technologies and multiple radios in a collaborative manner. This paper presents an innovative network selection solution for the fundamental technological requirement of multimode communications in heterogeneous vehicular telematics. To guarantee the QoS satisfaction of multiple mobile users and the efficient utilization and fair allocation of heterogeneous network resources in a global sense, a dynamic and self-adaptive method for network selection is proposed. It is biologically inspired by the cellular gene network, which enables terminals to dynamically select an appropriate access network according to the variety of QoS requirements and to the dynamic conditions of various available networks. The experimental results prove the effectiveness of the bioinspired scheme and confirm that the proposed network selection method provides better global performance when compared with the utility function method with greedy optimization.


Managing strategic alliances: what do we know now, and where do we go from here?


Language Model Based Grammatical Error Correction without Annotated Training Data


title: FSM based Manchester encoder for UHF RFID tag emulator

abstract: The radio frequency identification system (RFID) is becoming one of the most popular system in wireless technologies. The UHF RFID tag emulator is a part of RFID testing tools. The UHF RFID tag emulator would be imitating the behavior of RFID Tag. The UHF RFID tag emulator (860 MHz to 960 MHz) is aimed for testing the RFID systems and also acts as a general-purpose data transport device for other RFID systems. The tag emulator belongs to the EPC class-III (semi-passive) tags, but it implements the Class-1Generation II (C1G2) air interface protocol for communicating with the reader. In this work, we have presented RTL design of Manchester encoder. As motivated by Finite State Machine (FSM) and RTL implementations of encoder are discussed with particular focus to use the RFID Emulator as data transport device and debugging tool. The synthesis result shows that FSM design is efficient (less area and high speed) and it operates at a maximum frequency of 256.54 MHz.


title: Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning

abstract: The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.


title: Trekking Through Siberia: Managing Cold Data in a Memory-Optimized Database

abstract: Main memories are becoming sufficiently large that most OLTP databases can be stored entirely in main memory, but this may not be the best solution. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). It is still more economical to store the coldest records on secondary storage such as flash. This paper introduces Siberia, a framework for managing cold data in the Microsoft Hekaton main-memory database engine. We discuss how to migrate cold data to secondary storage while providing an interface to the user to manipulate both hot and cold data that hides the actual data location. We describe how queries of different isolation levels can read and modify data stored in both hot and cold stores without restriction while minimizing number of accesses to cold storage. We also show how records can be migrated between hot and cold stores while the DBMS is online and active. Experiments reveal that for cold data access rates appropriate for main-memory optimized databases, we incur an acceptable 7-14% throughput loss.


title: Performance evaluation of a fraud detection system based artificial immune system on the cloud

abstract: Fraud is defined as the unlawful and intentional misrepresentation which can lead to actual or potential disadvantage to another individual or group. Fraud detection is a topic applicable to many industries including banking and financial sectors, insurance, law enforcement, and more. Credit card fraud is a major problem in the financial industry 111. Therefore, real time fraud detection is a vital issue. Indeed, we implement the progress of Negative Selection Algorithm an anomaly detection approach in AIS on the cloud. As a result of our experiments, in serial NSA the time of training phase is around 23800s whereas parallel NSA training phase time is 78s. Also, in parallel algorithm the detection rate increased around %50 compared to the serial algorithm. But we concluded false positive rate a little raised that compared to increase the detection rate is almost negligible. Tests are done with real data sets, and all executions are run using mapreduce and apache hadoop.


title: What are you looking for?: an eye-tracking study of information usage in web search

abstract: Web search services are among the most heavily used applications on the World Wide Web. Perhaps because search is used in such a huge variety of tasks and contexts, the user interface must strike a careful balance to meet all user needs. We describe a study that used eye tracking methodologies to explore the effects of changes in the presentation of search results. We found that adding information to the contextual snippet significantly improved performance for informational tasks but degraded performance for navigational tasks. We discuss possible reasons for this difference and the design implications for better presentation of search results.


title: A novel bridgeless single-stage half-bridge AC/DC converter

abstract: This paper proposes a new bridgeless single-stage half-bridge ac-dc converter. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation (APWM) half-bridge dc-dc converter. The proposed converter provides high power factor and direct power conversion from the line voltage to an isolated dc output voltage without using the full-bridge diode rectifier. Conduction losses are lowered with a simple circuit structure. Switching losses are also reduced by achieving zero-voltage switching (ZVS) of the power switches. The effectiveness of the proposed converter is verified on a 250 W (48 V/5.2 A) experimental prototype.


title: Research synthesis in software engineering: A tertiary study

abstract: ContextComparing and contrasting evidence from multiple studies is necessary to build knowledge and reach conclusions about the empirical support for a phenomenon. Therefore, research synthesis is at the center of the scientific enterprise in the software engineering discipline. ObjectiveThe objective of this article is to contribute to a better understanding of the challenges in synthesizing software engineering research and their implications for the progress of research and practice. MethodA tertiary study of journal articles and full proceedings papers from the inception of evidence-based software engineering was performed to assess the types and methods of research synthesis in systematic reviews in software engineering. ResultsAs many as half of the 49 reviews included in the study did not contain any synthesis. Of the studies that did contain synthesis, two thirds performed a narrative or a thematic synthesis. Only a few studies adequately demonstrated a robust, academic approach to research synthesis. ConclusionWe concluded that, despite the focus on systematic reviews, there is limited attention paid to research synthesis in software engineering. This trend needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice.


title: Enhancing spam detection on mobile phone Short Message Service (SMS) performance using FP-growth and Naive Bayes Classifier

abstract: SMS (Short Message Service) is still the primary choice as a communication medium even though nowadays mobile phone is growing with a variety of communication media messenger applications. However, nowadays along with the SMS tariff reduction leads to the increase of SMS spam, as used by some people as an alternative to advertise and fraud. Therefore, it becomes an important issue as it can bug and harm the users and one of its solution is with automatic SMS spam filtering. One of most challenging in SMS spam filtering is its accuracy. In this research we proposed to enhanced SMS spam filtering performance by combining two of data mining task association and classification. FP-growth in association is utilized for mining frequent pattern on SMS and Naive Bayes Classifier is used to classify whether SMS is spam or ham. Training data was using SMS spam collection from previous research. The result of using collaboration of Naive Bayes and FP-Growth performs the highest average accuracy of 98, 506% and 0,025% better than without using FP-Growth for dataset SMS Spam Collection v.1, and improves the precision score; thus, the classification result is more accurate.


title: Developmental Epidemiology of Anxiety Disorders

abstract: The period of childhood through adolescence is the core risk phase for the first occurrence of anxiety that may range from transient mild symptoms to full-blown persistent anxiety disorders. The clinically valid assessment of anxiety symptoms, syndromes, and disorders is crucial to determine the prevalence, incidence, and longitudinal course and outcome of anxiety disorders. Further, identification of risk factors and specific anxiety characteristics that could serve as solid predictors for malignant or more benign course patterns is important for improved early recognition as well as prevention and treatment in this age span. Anxiety is a basic emotion already present in infancy and childhood, with expressions falling on a continuum from mild to severe. Anxiety is not typically pathologic but commonly adaptive when it facilitates avoidance of danger. The organisms’ responses to danger and the underlying brain circuitry engaged by threats reflect these adaptive aspects of anxiety. 1 Read more in the article by Blackford and Pine elsewhere in this issue. Maladaptive and pathologic anxiety is characterized by


Antibacterial and cytotoxic activities of Spondias pinnata (Linn. f.) Kurz fruit extract


title: Exploring the influential reviewer, review and product determinants for review helpfulness

abstract: Helpfulness of online reviews is a multi-faceted concept. The reviews are usually ranked on the basis of perceived helpful votes and aid in making purchase decisions for online customers. This study extends the prior work done for review helpfulness by considering not only the influential characteristics of reviews but also incorporates influential indicators of reviewer and product category. Influential factor based new features (product, reviewer and review) are proposed to predict the helpfulness of online reviews by using five ML methods. The experimental analysis on a real-life review dataset shows that the hybrid set of proposed features deliver the best predictive performance. In addition, the reviewer and the review category features introduced in this research exhibit better predictive performance as a standalone model. Findings show that reviews which have large number of comments, large values of sentiment and polarity scores receive more helpful votes. The reviewer activity length and recency are statistically significant predictors for helpfulness prediction. In addition, number of question answered, ratio of positive reviews and average rating per review are also significant variables of product type. The findings of this study highlight the number of implications for research and provide new insights to retailers for efficient ranking and organization of consumer reviews for online users.


國中讀前段班有差嗎？能力分班對學習成就影響的反事實分析 Does It Make a Difference in Being Assigned to High-Ability Groups in Junior High? A Counterfactual Analysis of the Effects of Ability Grouping on Students’ Academic Achievement in Taiwan


title: PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

abstract: This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.


title: Discovering functional zones using bus smart card data and points of interest in Beijing

abstract: Cities comprise various functional zones, including residential, educational, commercial zones, etc. It is important for urban planners to identify different functional zones and understand their spatial structure within the city in order to make better urban plans. In this chapter, we used 77,976,010 bus smart card records of Beijing City in one week in April 2008 and converted them into two-dimensional time series data of each bus platform, Then, through data mining and previous studies on citizens’ trip behavior, we established the DZoF (discovering zones of different functions) model based on SCD (smart card Data) and POIs (points of interest), and pooled the results at the TAZ (traffic analysis zone) level. The results suggested that DZoF model and cluster analysis based on dimension reduction and EM (expectation-maximization) algorithm can identify functional zones that well match the actual land uses in Beijing. The methodology in the present research can help urban planners and the public understand the complex urban spatial structure and contribute to the academia of urban geography and urban planning.


title: Reactive power capability of the wind turbine with Doubly Fed Induction Generator

abstract: With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.


title: Exhaustive search for small fully absorbing sets and the corresponding low error-floor decoder

abstract: This work provides an exhaustive search algorithm for finding small fully absorbing sets (FASs) of arbitrary low-density parity-check (LDPC) codes. In particular, given any LDPC code, the problem of finding all FASs of size less than t is formulated as an integer programming problem, for which a new branch-&-bound algorithm is devised. New node selection and the tree-trimming mechanisms are designed to further enhance the efficiency of the algorithm. The proposed algorithm is capable of finding all FASs of size ≤ 11 with no larger than 2 induced odd-degree check nodes for LDPC codes of length ≤ 1000. The resulting exhaustive list of small FASs is then used to devise a new post-processing decoder. Numerical results show that by taking advantage of the exhaustive list of small FASs, the proposed decoder significantly lowers the error floor for codes of practical lengths and outperforms the state-of-the-art low-error-floor decoders.


title: Type-Safe Delegation for Run-Time Component Adaptation

abstract: The aim of component technology is the replacement of large monolithic applications with sets of smaller software components, whose particular functionality and interoperation can be adapted to users' needs. However, the adaptation mechanisms of component software are still limited. Most proposals concentrate on adaptations that can be achieved either at compile time or at link time. Current support for dynamic component adaptation, i.e. unanticipated, incremental modifications of a component system at run-time, is not sufficient. ::: ::: This paper proposes object-based inheritance (also known as delegation) as a complement to purely forwarding-based object composition. It presents a type-safe integration of delegation into a class-based object model and shows how it overcomes the problems faced by forwarding-based component interaction, how it supports independent extensibility of components and unanticipated, dynamic component adaptation.


title: Do Bots impact Twitter activity

abstract: The WWW has seen massive growth in population of automated programs (bots) for a variety of exploits on online social networks (OSNs). In this paper we extend on our previous work to study the affects of bots on Twitter. By setting up a bot account on Twitter and conducting analysis on a click logs dataset from our web server, we show that despite bots being in smaller numbers, they exercise a profound impact on content popularity and activity on Twitter.


Towards Integrating Husserlian Phenomenology with Cognitive Neuroscience of Consciousness


title: LossRadar: Fast Detection of Lost Packets in Data Center Networks

abstract: Packet losses are common in data center networks, may be caused by a variety of reasons (e.g., congestion, blackhole), and have significant impacts on application performance and network operations. Thus, it is important to provide fast detection of packet losses independent of their root causes. We also need to capture both the locations and packet header information of the lost packets to help diagnose and mitigate these losses. Unfortunately, existing monitoring tools that are generic in capturing all types of network events often fall short in capturing losses fast with enough details and low overhead. Due to the importance of loss in data centers, we propose a specific monitoring system designed for loss detection. We propose LossRadar, a system that can capture individual lost packets and their detailed information in the entire network on a fine time scale. Our extensive evaluation on prototypes and simulations demonstrates that LossRadar is easy to implement in hardware switches, achieves low memory and bandwidth overhead, while providing detailed information about individual lost packets. We also build a loss analysis tool that demonstrates the usefulness of LossRadar with a few example applications.


title: P4FPGA: A Rapid Prototyping Framework for P4

abstract: This paper presents P4FPGA, a new tool for developing and evaluating data plane applications. P4FPGA is an open-source compiler and runtime. The compiler extends the P4.org reference compiler with a custom backend that generates FPGA code. P4FPGA supports different architecture configurations, depending on the needs of the particular application. We have benchmarked several representative P4 programs, and our experiments show that code generated by P4FPGA runs at line-rate at all packet sizes with latencies comparable to commercial ASICs. By combining high-level programming abstractions offered by P4 with a flexible and powerful hardware target, P4FPGA allows developers to rapidly prototype and deploy new data plane applications.


title: Design for social media engagement: Insights from elderly care assistance

abstract: Abstract This paper explores an alternative view of design as an emergent process of engagement and learning, as opposed to the traditional view of systems design as a problem-solving tool. We focus on digitally enabled elderly support networks as an innovative approach to the design of elderly care assistance through social media. Our research project is conducted in the context of an Italian health-care institution and provides the empirical backdrop illustrating the ensemble artefact, the four design principles and the pragmatic outcomes of personalized elderly care interventions. These contributions add both practical and theoretical guidance and learning regarding social-media engagement in elderly care assistance. Further, we illustrate how the design of personalized services through social-media technologies may challenge traditional organizational boundaries and transform the relationship between internal and external stakeholders. Based on our findings, the conclusion is that personalized elderly care assistance emerges as a socio-technical construction of social media.


title: Sentence based semantic similarity measure for blog-posts

abstract: Blogs-Online digital diary like application on web 2.0 has opened new and easy way to voice opinion, thoughts, and like-dislike of every Internet user to the World. Blogosphere has no doubt the largest user-generated content repository full of knowledge. The potential of this knowledge is still to be explored. Knowledge discovery from this new genre is quite difficult and challenging as it is totally different from other popular genre of web-applications like World Wide Web (WWW). Blog-posts unlike web documents are small in size, thus lack in context and contain relaxed grammatical structures. Hence, standard text similarity measure fails to provide good results. In this paper, specialized requirements for comparing a pair of blog-posts is thoroughly investigated. Based on this we proposed a novel algorithm for sentence oriented semantic similarity measure of a pair of blog-posts. We applied this algorithm on a subset of political blogosphere of Pakistan, to cluster the blogs on different issues of political realm and to identify the influential bloggers.


title: DETERMINANTS OF THE CONCEPT OF CONTINUOUS IMPROVEMENT IN MANUFACTURING COMPANY - CASE STUDY

abstract: The article concerns the application of the continuous improvement concept and its evolution taking place in a manufacturing company from its implementation to the achievement of the maturity phase. It presents a theoretical analysis of the continuous improvement concept from the Deming’s definition to modern interpretations. Next, the results of research on the identification and analysis of the factors determining the realization of this concept are discussed in a company producing electronic components and the results of its application on the three levels of the organization are presented, such as: management, team and individual.


title: Classification of Aerial Photogrammetric 3D Point Clouds

abstract: Abstract. We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.


title: Feature Extraction or Feature Selection for Text Classification: A Case Study on Phishing Email Detection

abstract: Dimensionality reduction is generally performed when high dimensional data like text are classified. This can be done either by using feature extraction techniques or by using feature selection techniques. This paper analyses which dimension reduction technique is better for classifying text data like emails. Email classification is difficult due to its high dimensional sparse features that affect the generalization performance of classifiers. In phishing email detection, dimensionality reduction techniques are used to keep the most instructive and discriminative features from a collection of emails, consists of both phishing and legitimate, for better detection. Two feature selection techniques - Chi-Square and Information Gain Ratio and two feature extraction techniques - Principal Component Analysis and Latent Semantic Analysis are used for the analysis. It is found that feature extraction techniques offer better performance for the classification, give stable classification results with the different number of features chosen, and robustly keep the performance over time.


title: GRIP: Scalable 3D global routing using Integer Programming

abstract: We propose GRIP, a scalable global routing technique via Integer Programming (IP). GRIP optimizes wirelength and via cost without going through a layer assignment phase. GRIP selects the route for each net from a set of candidate routes that are generated based on an estimate of congestion generated by a linear programming pricing phase. To achieve scalability, the original IP is decomposed into smaller ones corresponding to balanced rectangular subregions on the chip. We introduce the concept of a floating terminal for a net, which allows flexibility to route long nets going through multiple subregions. We also use the IP to plan the routing of long nets, detouring them from congested subregions. For ISPD 2007 benchmarks, we obtain 3.9% and 11.3% average improvement in wirelength and via cost for the 2D and 3D versions respectively, compared to the best results reported in the open literature.


title: A CNS‐permeable Hsp90 inhibitor rescues synaptic dysfunction and memory loss in APP‐overexpressing Alzheimer's mouse model via an HSF1‐mediated mechanism

abstract: A CNS-permeable Hsp90 inhibitor rescues synaptic dysfunction and memory loss in APP-overexpressing Alzheimer’s mouse model via an HSF1-mediated mechanism


title: Analysis of transcriptome and metabolome profiles alterations in fatty liver induced by high-fat diet in rat.

abstract: Excessive energy intake greatly contributes to the development of nonalcoholic fatty liver disease (NAFLD) in modern society. To better understand the comprehensive mechanisms of NAFLD development, we investigated the metabolic alterations of rats with NAFLD induced by high-fat diet (HFD). Male Wistar rats were fed a HFD or standard chow for control. After 16 weeks, rat serum was collected for biochemical measurement. The rats' livers were resected and subjected to histology inspection and gene expression analysis with complementary DNA microarray and metabolic analysis with gas chromatography-mass spectroscopy. In HFD rats, the serum cholesterol, triglycerides, glucose, and insulin contents were increased; and the total cholesterol and triglycerides in the livers were also significantly increased. Complementary DNA microarray analysis revealed that 130 genes were regulated by HFD. Together with real-time reverse transcriptase polymerase chain reaction, lipid metabolism regulatory members like sterol regulatory element binding factor 1 and stearoyl-coenzyme A desaturase 1 had up-regulation, whereas others like peroxisome proliferator-activated receptor, carnitine palmitoyltransferase 1, and 3-hydroxy-3-methylglutaryl-coenzyme A reductase had repressed expression, in HFD rat livers. Metabolomic analysis showed that tetradecanoic acid, hexadecanoic acid, and oleic acid had elevation and arachidonic acid and eicosapentaenoic acid had decreased content in HFD rat livers. Amino acids including glycine, alanine, aspartic acid, glutamic acid, and proline contents were decreased. The integrative results from transcriptomic and metabolomic studies revealed that, in HFD rat livers, fatty acid utilization through beta-oxidation was inhibited and lipogenesis was enhanced. These observations facilitated our understanding of the pathways involved in the development of NAFLD induced by HFD.


title: Lightweight Cryptography and DPA Countermeasures: A Survey

abstract: The dawning Ubiquitous Computing age demands a new attacker model for the myriads of pervasive computing devices used: since a potentially malicious user is in full control over the pervasive device, additionally to the cryptographic attacks the whole field of physical attacks has to be considered. Most notably are here so-called side channel attacks, such as Differential Power Analysis (DPA) attacks. At the same time, the deployment of pervasive devices is strongly cost-driven, which prohibits expensive countermeasures. In this article we survey a broad range of countermeasures and discuss their suitability for ultra-constrained devices, such as passive RFID-tags. We conclude that adiabatic logic countermeasures, such as 2N-2N2P and SAL, seem to be promising candidates, because they increase the resistance against DPA attacks while at the same time lowering the power consumption of the pervasive device.


title: A Knowledge-Assisted Visual Malware Analysis System: Design, Validation, and Reflection of KAMAS

abstract: IT-security experts engage in behavior-based malware analysis in order to learn about previously unknown samples of malicious software (malware) or malware families. For this, they need to find and categorize suspicious patterns from large collections of execution traces. Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues. To close this gap, we designed and developed KAMAS, a knowledge-assisted visualization system for behavior-based malware analysis. This paper is a design study that describes the design, implementation, and evaluation of the prototype. We report on the validation of KAMAS with expert reviews, a user study with domain experts and focus group meetings with analysts from industry. Additionally, we reflect on the acquired insights of the design study and discuss the advantages and disadvantages of the applied visualization methods. An interesting finding is that the arc-diagram was one of the preferred visualization techniques during the design phase but did not provide the expected benefits for finding patterns. In contrast, the seemingly simple looking connection line was described as supportive in finding the link between the rule overview table and the rule detail table which are playing a central role for the analysis in KAMAS.


title: On Approximate Inference for Generalized Gaussian Process Models

abstract: A generalized Gaussian process model (GGPM) is a unifying framework that encompasses many existing Gaussian process (GP) models, such as GP regression, classification, and counting. In the GGPM framework, the observation likelihood of the GP model is itself parameterized using the exponential family distribution (EFD). In this paper, we consider efficient algorithms for approximate inference on GGPMs using the general form of the EFD. A particular GP model and its associated inference algorithms can then be formed by changing the parameters of the EFD, thus greatly simplifying its creation for task-specific output domains. We demonstrate the efficacy of this framework by creating several new GP models for regressing to non-negative reals and to real intervals. We also consider a closed-form Taylor approximation for efficient inference on GGPMs, and elaborate on its connections with other model-specific heuristic closed-form approximations. Finally, we present a comprehensive set of experiments to compare approximate inference algorithms on a wide variety of GGPMs.


title: Exercise-Induced Hormone Elevations Are Related to Muscle Growth

abstract: Partial least squares regression structural equation modeling (PLS-SEM) was used to examine relationships between the endocrine response to resistance exercise and muscle hypertrophy in resistance-trained men. Pretesting (PRE) measures of muscle size (thickness and cross-sectional area) of the vastus lateralis and rectus femoris were collected in 26 resistance-trained men. Participants were randomly selected to complete a high-volume (VOL, n = 13, 10–12RM, 1-minute rest) or high-intensity (INT, n = 13, 3–5RM, 3-minute rest) resistance training program. Blood samples were collected at baseline, immediately postexercise, 30-minute, and 60-minute postexercise during weeks 1 (week 1) and 8 (week 8) of training. The hormonal responses (testosterone, growth hormone [22 kD], insulin-like growth factor-1, cortisol, and insulin) to each training session were evaluated using area-under-the-curve (AUC) analyses. Relationships between muscle size (PRE), AUC values (week 1 + week 8) for each hormone, and muscle size (POST) were assessed using a consistent PLS-SEM algorithm and tested for statistical significance (p <= 0.05) using a 1,000 samples consistent bootstrapping analysis. Group-wise comparisons for each relationship were assessed through independent t-tests. The model explained 73.4% (p < 0.001) of variance in muscle size at POST. Significant pathways between testosterone and muscle size at PRE (p = 0.043) and muscle size at POST (p = 0.032) were observed. The ability to explain muscle size at POST improved when the model was analyzed by group (INT: R2 = 0.882; VOL: R2 = 0.987; p < 0.001). No group differences in modal quality were found. Exercise-induced testosterone elevations, independent of the training programs used in this study, seem to be related to muscle growth.


title: An evolutionary approach to solve the dynamic multihop ridematching problem

abstract: The multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators MACGeO, which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers vehicles and riders passengers. Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required.


title: Human detection using Histogram of oriented gradients and Human body ratio estimation

abstract: Recent research has been devoted to detecting people in images and videos. In this paper, a human detection method based on Histogram of Oriented Gradients (HoG) features and human body ratio estimation is presented. We utilized the discriminative power of HoG features for human detection, and implemented motion detection and local regions sliding window classifier, to obtain a rich descriptor set. Our human detection system consists of two stages. The initial stage involves image preprocessing and image segmentation, whereas the second stage classifies the integral image as human or non-human using human body ratio estimation, local region sliding window method and HoG Human Descriptor. Subsequently, it increases the detection rate and reduces the false alarm by deducting the overlapping window. In our experiments, DaimlerChrysler pedestrian benchmark data set is used to train a standard descriptor and the results showed an overall detection rate of 80% above.


Designing Neural Networks Using Genetic Algorithms with Graph Generation System.


title: Multiparticipant chat analysis: A survey

abstract: We survey research on the analysis of multiparticipant chat. Multiple research and applied communities (e.g., AI, educational, law enforcement, military) have interest in this topic. After introducing some context, we describe relevant problems and how these have been addressed using AI techniques. We also identify recent research trends and unresolved issues that could benefit from more attention.


title: LivDet 2013 Fingerprint Liveness Detection Competition 2013

abstract: A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if a biometric measure is authentic. The Fingerprint Liveness Detection Competition (LivDet) goal is to compare both software-based (Part 1) and hardware-based (Part 2) fingerprint liveness detection methodologies and is open to all academic and industrial institutions. Submissions for the third edition were much more than in the previous editions of LivDet demonstrating a growing interest in the area. We had nine participants (with eleven algorithms) for Part 1 and two submissions for Part 2.


title: XHOUND: Quantifying the Fingerprintability of Browser Extensions

abstract: In recent years, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users' online activity, using increasingly intrusive and sophisticated techniques. Among these, browser fingerprinting has received the most attention since it allows trackers to uniquely identify users despite the clearing of cookies and the use of a browser's private mode. In this paper, we investigate and quantify the fingerprintability of browser extensions, such as, AdBlock and Ghostery. We show that an extension's organic activity in a page's DOM can be used to infer its presence, and develop XHound, the first fully automated system for fingerprinting browser extensions. By applying XHound to the 10,000 most popular Google Chrome extensions, we find that a significant fraction of popular browser extensions are fingerprintable and could thus be used to supplement existing fingerprinting methods. Moreover, by surveying the installed extensions of 854 users, we discover that many users tend to install different sets of fingerprintable browser extensions and could thus be uniquely, or near-uniquely identifiable by extension-based fingerprinting. We use XHound's results to build a proof-of-concept extension-fingerprinting script and show that trackers can fingerprint tens of extensions in just a few seconds. Finally, we describe why the fingerprinting of extensions is more intrusive than the fingerprinting of other browser and system properties, and sketch two different approaches towards defending against extension-based fingerprinting.


title: Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset

abstract: The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset is publicly available today that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region. In this paper, we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400- ${\rm km}^{2}$ region around the city of Koln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.


title: Business intelligence success: The roles of BI capabilities and decision environments

abstract: This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing needs of the organization. Our findings suggest that technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success, regardless of the decision environment. However, the decision environment does influence the relationship between BI success and capabilities, such as the extent to which BI supports flexibility and risk in decision making.


title: I Couldn't Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions

abstract: Determining when conversational participants agree or disagree is instrumental for broader conversational analysis; it is necessary, for example, in deciding when a group has reached consensus. In this paper, we describe three main contributions. We show how different aspects of conversational structure can be used to detect agreement and disagreement in discussion forums. In particular, we exploit information about meta-thread structure and accommodation between participants. Second, we demonstrate the impact of the features using 3-way classification, including sentences expressing disagreement, agreement or neither. Finally, we show how to use a naturally occurring data set with labels derived from the sides that participants choose in debates on createdebate.com. The resulting new agreement corpus, Agreement by Create Debaters (ABCD) is 25 times larger than any prior corpus. We demonstrate that using this data enables us to outperform the same system trained on prior existing in-domain smaller annotated datasets.


title: Cross domain distribution adaptation via kernel mapping

abstract: When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of target-domain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains, including text categorization and web page ratings. In particular, it can achieve around 10% higher accuracy than other approaches for the text categorization problem. The source code and datasets are available from the authors.


title: EEG-Based Estimation and Classification of Mental Fatigue

abstract: Mental fatigue was associated with increased power in frontal theta (θ) and parietal alpha (α) EEG rhythms. A statistical classifier can use these effects to model EEG-fatigue relationships accurately. Participants (n = 22) solved math problems on a computer until either they felt exhausted or 3 h had elapsed. Pre- and post-task mood scales showed that fatigue increased and energy decreased. Mean response times rose from 6.7 s to 7.9 s but accuracy did not change significantly. Mean pow- er spectral densities or PSDs of θ and α bands rose by 29% and 44%, respectively. A kernel partial least squares classifier trained to classify PSD coefficients (1 - 18 Hz) of single 13-s EEG segments from alert or fatigued task periods was 91% to 100% accurate. For EEG segments from other task periods, the classifier outputs tracked the time course of the development of mental fatigue. By this measure, most subjects became substantially fatigued after 60 min of task performance. However, the trajectories of individual classifier outputs showed that EEG signs of developing fa- tigue were present in all subjects after 15 - 30 minutes of task performance. The results show that EEG can track the development of mental fatigue over time with accurate updates on a time scale a short as 13 seconds. In addition, the results agree with the notion that growing mental fatigue produces a shift away from executive and attention networks to default mode and is accompanied by a shift in alpha frequency to the lower alpha band.


title: Central Problems in the Management of Innovation

abstract: Innovation is defined as the development and implementation of new ideas by people who over time engage in transactions with others within an institutional order. This definition focuses on four basic factors new ideas, people, transactions, and institutional context. An understanding of how these factors are related leads to four basic problems confronting most general managers: 1 a human problem of managing attention, 2 a process problem in managing new ideas into good currency, 3 a structural problem of managing part-whole relationships, and 4 a strategic problem of institutional leadership. This paper discusses these four basic problems and concludes by suggesting how they fit together into an overall framework to guide longitudinal study of the management of innovation.


Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification


title: Meaningful maps with object-oriented semantic mapping

abstract: For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation.


title: Stacked Learning to Search for Scene Labeling

abstract: Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space. In this paper, a stacked learning to search method is proposed to address scene labeling tasks. We design a simplified search process consisting of a sequence of ranking functions, which are learned based on a stacked learning strategy to prevent over-fitting. Our method is able to encode rich prior knowledge by incorporating a variety of local and global scene features. In addition, we estimate a labeling confidence map to further improve the search efficiency from two aspects: first, it constrains the search space more effectively by pruning out low-quality solutions based on confidence scores and second, we employ the confidence map as an additional ranking feature to improve its prediction performance and thus reduce the search steps. Our approach is evaluated on both semantic segmentation and geometric labeling tasks, including the Stanford Background , Sift Flow , Geometric Context , and NYUv2 RGB-D data set. The competitive results demonstrate that our stacked learning to search method provides an effective alternative paradigm for scene labeling.


title: Estimation accuracy of a vector-controlled frequency converter used in the determination of the pump system operating state

abstract: Pump, compressor and fan systems often have a notable energy savings potential, which can be identified by monitoring their operation for instance by a frequency converter and model-based estimation methods. In such cases, sensorless determination of the system operating state relies on the accurate estimation of the motor rotational speed and shaft torque, which is commonly available in vector- and direct-torque-controlled frequency converters. However, frequency converter manufacturers seldom publish the expected estimation accuracies for the rotational speed and shaft torque. In this paper, the accuracy of these motor estimates is studied by laboratory measurements for a vector-controlled frequency converter both in the steady and dynamical states. In addition, the effect of the flux optimization feature on the estimation accuracy is studied. Finally, the impact of erroneous motor estimates on the flow rate estimation is demonstrated in the paper.


title: iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE

abstract: Identifying interaction between drug and protein is a crucial challenge in drug discovery, which can lead the researchers to develop novel drug compounds or new target proteins for the existing drugs. The determination of drug-target interactions (DTIs) is an extremely time-consuming, costly, and tedious task with wet-lab experiments. To date, multiple computational techniques have been presented to simplify the drug discovery process, but a huge number of interactions are still undiscovered. Furthermore, a class imbalance is a critical challenge regarding this experiment which can significantly degrade the classification accuracy that has not been effectively addressed yet. In this paper, we proposed a novel high-throughput computational model, called iDTi-CSsmoteB, for identification of DTIs based on drug chemical structures and protein sequences. More specifically, the protein sequence is extracted through position-specific scoring matrix (PSSM)-Bigram, amphiphilic pseudo amino acid composition (AM-PseAAC) and dipeptide PseAAC descriptors which represents evolutionary and sequence information. The drug chemical structure is represented as a molecular substructure fingerprint (MSF) which describes the existence of the functional fragments or groups. Finally, we used the over-sampling SMOTE technique to overcome the imbalance issue of the datasets and applied XGBoost algorithm as a classifier to predict DTIs. To evaluate the performance of iDTi-CSsmoteB, several experiments have been conducted on four benchmark datasets, namely, enzyme, ion channel, GPCR, and nuclear receptor based on fivefold cross validation. The experimental analysis exhibits that our model outperforms similar methods in terms of area under the ROC (auROC) curve. In addition, our achieved results indicate the effectiveness of the feature extraction techniques, balancing methods, and classifier for predicting the DTIs which can provide substance for new drug development. iDTi-CSsmoteB webserver is available online at http://idticssmoteb-uestc.me/.


title: Modelling User's Theory of AI's Mind in Interactive Intelligent Systems

abstract: Many interactive intelligent systems, such as recommendation and information retrieval systems, treat users as a passive data source. Yet, users form mental models of systems and instead of passively providing feedback to the queries of the system, they will strategically plan their actions within the constraints of the mental model to steer the system and achieve their goals faster. We propose to explicitly account for the user's theory of the AI's mind in the user model: the intelligent system has a model of the user having a model of the intelligent system. We study a case where the system is a contextual bandit and the user model is a Markov decision process that plans based on a simpler model of the bandit. Inference in the model can be reduced to probabilistic inverse reinforcement learning, with the nested bandit model defining the transition dynamics, and is implemented using probabilistic programming. Our results show that improved performance is achieved if users can form accurate mental models that the system can capture, implying predictability of the interactive intelligent system is important not only for the user experience but also for the design of the system's statistical models.


A Qualitative Multi Case Study to Explore the Root Causes of Small Business Failure


Effect of tear/crack on soybean (Glycine max) seed coat, physiological quality and pathology of the seed


title: Auditable Zerocoin

abstract: Bitcoin is the first widely adopted decentralized digitale-cash system. All Bitcoin transactions that include addresses of senders and receivers are stored in the public blockchain which could cause privacy problems. The Zerocoin protocol hides the link between individual Bitcoin transactions without adding trusted third parties. However such an untraceable remittance system could cause illegal transfers such as money laundering. In this paper we address this problem and propose an auditable decentralized e-cash scheme based on the Zerocoin protocol. Our scheme allows designated auditors to extract link information from Zerocoin transactions while preventing other users including miners from obtaining it. Respecting the mind of the decentralized system, the auditor doesn't have other authorities such as stopping transfers, confiscating funds, and deactivating accounts. A technical contribution of our scheme is that a coin sender embeds audit information with a non-interactive zeroknowledge proof of knowledge (NIZKP). This zero-knowledge prevents malicious senders from embedding indiscriminate audit information, and we construct it simply using only the standard Schnorr protocol for discrete logarithm without zk-SNARKs or other recent techniques for zero-knowledge proof.


title: A lexiconizing framework of feature-based opinion mining in tourism industry

abstract: Among of the travel agency business in Thailand, Agoda (www.agoda.com) has boomed in recent years with the number of online agents offering for hotels booking. When customers need to make decision, they typically explore by investigating the opinions attached with each hotel in online agent. This paper proposes a framework of feature-based opinion mining by using scores which essentially relies on the usage of two main lexiconizing levels, features and polar words. An approach for extracting features and polar words from textual opinion is based on syntactic pattern analysis. The evaluation is performed with existing opinions and compared the statistical resulted scores with the existing scores of each hotel. The proposed scoring method is proven for the effectiveness of the score from Agoda and could facilitate the further text retrieval application development for the benefit of automatic customer's opinion detection.


title: High-temperature SiC power module with integrated SiC gate drivers for future high-density power electronics applications

abstract: This paper presents the testing results of an all-silicon carbide (SiC) intelligent power module (IPM) for use in future high-density power electronics applications. The IPM has high-temperature capability and contains both SiC power devices and SiC gate driver integrated circuits (ICs). The high-temperature capability of the SiC gate driver ICs allows for them to be packaged into the power module and be located physically close to the power devices. This provides a distinct advantage by reducing the gate driver loop inductance, which promotes high frequency operation, while also reducing the overall volume of the system through higher levels of integration. The power module was tested in a bridgeless-boost converter to showcase the performance of the module in a system level application. The converter was initially operated with a switching frequency of 200 kHz with a peak output power of approximately 5 kW. The efficiency of the converter was then evaluated experimentally and optimized by increasing the overdrive voltage on the SiC gate driver ICs. Overall a peak efficiency of 97.7% was measured at 3.0 kW output. The converter s switching frequency was then increased to 500 kHz to prove the high frequency capability of the power module was then pushedmore » to its limits and operated at a switching frequency of 500 kHz. With no further optimization of components, the converter was able to operate under these conditions and showed a peak efficiency of 95.0% at an output power of 2.1 kW.« less


title: Graph Indexing: Tree + Delta >= Graph

abstract: Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs. As a result, it is of special interest to process graph containment queries effectively on large graph databases. Given a graph database G, and a query raph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (Δ) on demand, without a costly graph mining process beforehand. Our study verifies that (Tree+Δ) is a better choice than graph for indexing purpose, denoted (Tree+Δ ≥Graph), to address the graph containment query problem. It has two implications: (1) the index construction by (Tree+Δ) is efficient, and (2) the graph containment query processing by (Tree+Δ) is efficient. Our experimental studies demonstrate that (Tree+Δ) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graph-based indexing methods: gIndex and C-Tree, in graph containment query processing.


title: An investigation into reactive planning in complex domains

abstract: A model of purely reactive planning is proposed based on the concept of reactive action packages. A reactive action package, or RAP, can be thought of as an independent entity pursuing some goal in competition with many others at execution time. The RAP processing algorithm addresses the problems of execution monitoring and replanning in uncertain domains with a single, uniform representation and control structure. Use of the RAP model as a basis for adaptive strategic planning is also discussed.


title: Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties

abstract: A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.


title: Ways of applying artificial intelligence in software engineering

abstract: As Artificial Intelligence (AI) techniques become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.


title: The Spread of Physical Activity Through Social Networks

abstract: Many behaviors that lead to worsened health outcomes are modifiable, social, and visible. Social influence has thus the potential to foster adoption of habits that promote health and improve disease management. In this study, we consider the evolution of the physical activity of 44.5 thousand Fitbit users as they interact on the Fitbit social network, in relation to their health status. The users collectively recorded 9.3 million days of steps over the period of a year through a Fitbit device. 7,515 of the users also self-reported whether they were diagnosed with a major chronic condition. A time-aggregated analysis shows that ego net size, average alter physical activity, gender, and body mass index (BMI) are significantly predictive of ego physical activity. For users who self-reported chronic conditions, the direction and effect size of associations varied depending on the condition, with diabetic users specifically showing almost a 6-fold increase in additional daily steps for each additional social tie. Subsequently, we consider the co-evolution of activity and friendship longitudinally on a month by month basis. We show that the fluctuations in average alter activity significantly predict fluctuations in ego activity. By leveraging a class of novel non-parametric statistical tests we investigate the causal factors in these fluctuations. We find that under certain stationarity assumptions, non-null causal dependence exists between ego and alter's activity, even in the presence of unobserved stationary individual traits. We believe that our findings provide evidence that the study of online social networks have the potential to improve our understanding of factors affecting adoption of positive habits, especially in the context of chronic condition management.


title: A Novel Wireless Energy Transmission System with Multiple Resonant Coils

abstract: Based on the theory of electromagnetic induction, the classical energy transmission is achieved by using a combination of a full-bridge inverter and a single-phase LC resonant tank. In this paper, a three-phase wireless power transfer (WPT) structure is proposed, by which the electrical energy can be converted into mechanical energy directly. This structure simplifies the energy conversion process between the primary and secondary side. The magnetic field changes with time when the current flows through the coils at both sides. The dynamic magnetic makes the coil in the secondary side generate mechanical energy. Finally, the results of the simulation verify the feasibility of this proposed system and the stability of the force.


title: Professional users handbook for rock bolting

abstract: Keywords: Ancrages passifs ; Ancrages actifs ; Boulonnage Reference Record created on 2004-09-07, modified on 2016-08-08


2-D gel analysis using artificial neural networks


The RSV-asthma link: the emerging story. Introduction.


title: Distributed traffic simulation using DDS-communication based HLA for V2X

abstract: For implementing Cooperative Intelligent Transport Systems (C-ITS), Vehicle-to-X (V2X) communication functions are important. For efficient V2X simulation, integrating heterogeneous simulators and sophisticated inter-simulator network control are required. In this paper, we suggest Data Distribution Service (DDS) communication-based High Level Architecture (HLA) for distributed V2X simulation by replacing HLA network transport into DDS-based communication. For replacing, we design HLA-DDS wrapper for supporting HLA-DDS transformation. This paper also contains implementation and evaluation of the DDS communication-based V2X simulation system to prove our concept. This kind of advanced simulation communication method would enhanced the overall operation of long-term, large-scale V2X simulation.


title: Coupled bayesian sets algorithm for semi-supervised learning and information extraction

abstract: Our inspiration comes from Nell (Never Ending Language Learning), a computer program running at Carnegie Mellon University to extract structured information from unstructured web pages. We consider the problem of semi-supervised learning approach to extract category instances (e.g. country(USA), city(New York)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless, incorrect set of extractions. We believe that this problem can be overcome by simultaneously learning independent classifiers in a new approach named Coupled Bayesian Sets algorithm, based on Bayesian Sets, for many different categories and relations (in the presence of an ontology defining constraints that couple the training of these classifiers). Experimental results show that simultaneously learning a coupled collection of classifiers for random 11 categories resulted in much more accurate extractions than training classifiers through original Bayesian Sets algorithm, Naive Bayes, BaS-all and Coupled Pattern Learner (the category extractor used in NELL).


title: An Improved Battery Characterization Method Using a Two-Pulse Load Test

abstract: It is very important to have the ability to determine the available capacity, the state of charge (SoC), and the state of health (SoH) of a battery; this ensures that the battery has the available power for the system requirements. A battery is aged by charging and discharging cycles; this process degrades the chemical composition of the battery. An undercharged battery has sulphation and stratification effects that shorten the lifetime of the battery. Overcharging causes gassing and water loss. This paper describes a novel two-pulse test to determine the AHC, SoC, and SoH of a valve regulated lead acid (VRLA) and a lithium ion battery. These parameters are related to the voltage drop after each pulse of current discharge. The first pulse stabilizes the battery relative to its previous history, and the second pulse establishes the parameters. The new approach is fully validated by experiment.


title: Maximum a posteriori estimation of height profiles in InSAR imaging

abstract: We present a statistical method to solve the height estimation problem in interferometric synthetic aperture radar (InSAR) applications. It is based on the use of multifrequency SAR raw datasets obtained by partitioning in subbands the available raw data spectrum, and on a Bayesian estimator using Markov random fields to model the a priori distribution of the unknown images. The method allows recovering topographic profiles affected by strong height discontinuities and allows to perform efficient noise rejections.


title: Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey

abstract: Bank failures threaten the economic system as a whole. Therefore, predicting bank financial failures is crucial to prevent and/or lessen the incoming negative effects on the economic system. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply various neural network techniques, support vector machines and multivariate statistical methods to the bank failure prediction problem in a Turkish case, and to present a comprehensive computational comparison of the classification performances of the techniques tested. Twenty financial ratios with six feature groups including capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market risk (CAMELS) are selected as predictor variables in the study. Four different data sets with different characteristics are developed using official financial data to improve the prediction performance. Each data set is also divided into training and validation sets. In the category of neural networks, four different architectures namely multi-layer perceptron, competitive learning, self-organizing map and learning vector quantization are employed. The multivariate statistical methods; multivariate discriminant analysis, k-means cluster analysis and logistic regression analysis are tested. Experimental results are evaluated with respect to the correct accuracy performance of techniques. Results show that multi-layer perceptron and learning vector quantization can be considered as the most successful models in predicting the financial failure of banks.


title: Lingua 115 (2005) 591–610 Subordinating and coordinating discourse relations

abstract: This paper studies the distinction between subordinating and coordinating discourse relations, a distinction that governs the hierarchical structure of discourse. We provide linguistic tests to clarify which discourse relations are subordinating and which are coordinating. We argue that some relations are classified as subordinating or coordinating by default, a default that can be overridden in specific contexts. The distinction between subordinating and coordinating relations thus belongs to the level of information packaging in discourse and not to the level of information content or the semantics of the relations themselves.


title: Virtual energy simulation of induction traction drive test bench

abstract: This paper describes a stationary bench simulating the traction drive of electric transport vehicles in traction, freewheeling and braking modes which is planned to investigate the drive operation with and without a supercapacitor energy storage system. The bench contains two equal coupled AC motors simulating the traction drive of surface electric transport and the traction drive load. Before the physical realization of the bench it is necessary to design its virtual model for simulations in order to make sure that it functions as needed. The simulation of the drive operation modes is performed using Matlab/Simulink software.


title: Video Segmentation by Tracking Many Figure-Ground Segments

abstract: We propose an unsupervised video segmentation approach by simultaneously tracking multiple holistic figure-ground segments. Segment tracks are initialized from a pool of segment proposals generated from a figure-ground segmentation algorithm. Then, online non-local appearance models are trained incrementally for each track using a multi-output regularized least squares formulation. By using the same set of training examples for all segment tracks, a computational trick allows us to track hundreds of segment tracks efficiently, as well as perform optimal online updates in closed-form. Besides, a new composite statistical inference approach is proposed for refining the obtained segment tracks, which breaks down the initial segment proposals and recombines for better ones by utilizing high-order statistic estimates from the appearance model and enforcing temporal consistency. For evaluating the algorithm, a dataset, SegTrack v2, is collected with about 1,000 frames with pixel-level annotations. The proposed framework outperforms state-of-the-art approaches in the dataset, showing its efficiency and robustness to challenges in different video sequences.


title: Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving

abstract: Environment perception for autonomous driving traditionally uses sensor fusion to combine the object detections from various sensors mounted on the car into a single representation of the environment. Non-calibrated sensors result in artifacts and aberration in the environment model, which makes tasks like free-space detection more challenging. In this study, we improve the LiDAR and camera fusion approach of Levinson and Thrun. We rely on intensity discontinuities and erosion and dilation of the edge image for increased robustness against shadows and visual patterns, which is a recurring problem in point cloud related work. Furthermore, we use a gradientfree optimizer instead of an exhaustive grid search to find the extrinsic calibration. Hence, our fusion pipeline is lightweight and able to run in real-time on a computer in the car. For the detection task, we modify the Faster R-CNN architecture to accommodate hybrid LiDAR-camera data for improved object detection and classification. We test our algorithms on the KITTI data set and locally collected urban scenarios. We also give an outlook on how radar can be added to the fusion pipeline via velocity matching.


title: Distributed Representations for Biological Sequence Analysis

abstract: Biological sequence comparison is a key step in inferring the relatedness of various organisms and the functional similarity of their components. Thanks to the Next Generation Sequencing efforts, an abundance of sequence data is now available to be processed for a range of bioinformatics applications. Embedding a biological sequence over a nucleotide or amino acid alphabet in a lower dimensional vector space makes the data more amenable for use by current machine learning tools, provided the quality of embedding is high and it captures the most meaningful information of the original sequences. Motivated by recent advances in the text document embedding literature, we present a new method, called seq2vec, to represent a complete biological sequence in an Euclidean space. The new representation has the potential to capture the contextual information of the original sequence necessary for sequence comparison tasks. We test our embeddings with protein sequence classification and retrieval tasks and demonstrate encouraging outcomes.


Separationndividuation, family cohesion, and adjustment to college: Measurement validation and test of a theoretical model.


title: Pharmacokinetics Evaluation of Mycophenolic Acid and Its Glucuronide Metabolite in Chinese Renal Transplant Recipients Receiving Enteric-Coated Mycophenolate Sodium and Tacrolimus

abstract: Background:The aim of this study was to characterize the pharmacokinetics of mycophenolic acid (MPA) and MPA glucuronide (MPAG) in Chinese renal transplant patients taking enteric-coated mycophenolate sodium (EC-MPS). Limited sampling strategies (LSSs) were developed to estimate the area under the c


title: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

abstract: Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.


title: A floor sensor system for gait recognition

abstract: This paper describes the development of a prototype floor sensor as a gait recognition system. This could eventually find deployment as a standalone system (e.g. a burglar alarm system) or as part of a multimodal biometric system. The new sensor consists of 1536 individual sensors arranged in a 3 m by 0.5 m rectangular strip with an individual sensor area of 3 cm/sup 2/. The sensor floor operates at a sample rate of 22 Hz. The sensor itself uses a simple design inspired by computer keyboards and is made from low cost, off the shelf materials. Application of the sensor floor to a small database of 15 individuals was performed. Three features were extracted : stride length, stride cadence, and time on toe to time on heel ratio. Two of these measures have been used in video based gait recognition while the third is new to this analysis. These features proved sufficient to achieve an 80% recognition rate.


A FRAMEWORK OF INFORMATION SECURITY CULTURE CHANGE 1


title: Evaluation of Nonnormal Process Capability Indices Using Generalized Lambda Distribution

abstract: [This abstract is based on the author's abstract.]Process capability indices (PCIs) used to describe the ability of a manufacturing process to produce items within specified limits are developed under the assumption that the underlying process distribut..


title: The Synthesis of Unpaired Underwater Images Using a Multistyle Generative Adversarial Network

abstract: Underwater image datasets are crucial in underwater vision research. Because of the strong absorption and scattering effects that occur underwater, some ground truth such as the depth map, which can be easily collected in-air, becomes a great challenge in underwater environments. To solve the issues associated with the lack of underwater ground truth, we propose a trainable end-to-end system of an underwater multistyle generative adversarial network (UMGAN) that takes advantage of a cycle-consistent adversarial network (CycleGAN) and conditional generative adversarial networks. This system can generate multiple realistic underwater images from in-air images using a hybrid adversarial system and an unpaired method. Moreover, our model can translate in-air images to underwater images that retain the main content and structural information of the in-air images under specified turbidities or water styles through a style classifier and a conditional vector. Furthermore, we define the color loss and include the structural similarity index measure loss for the system to preserve the content and structure of original in-air images while transferring the backgrounds of the images from air to water. Using UMGAN, we can take advantage of the in-air ground truth and convert the corresponding in-air images into an underwater dataset with multiple water color styles. Our experiments demonstrate that our synthesized underwater images have a high score on image assessment against CycleGAN, WaterGAN, StarGAN, AdaIN, and other state-of-the-art methods. We also show that our synthesized underwater images with in-air depths can be applied to real underwater image depth map estimation.


title: DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds

abstract: As an increasing number of traditional applications migrated to the cloud, achieving resource management and performance optimization in such a dynamic and uncertain environment becomes a big challenge for cloud-based application providers. In particular, job scheduling is a non-trivial task, which is responsible for allocating massive job requests submitted by users to the most suitable resources and satisfying user QoS requirements as much as possible. Inspired by recent success of using deep reinforcement learning techniques to solve AI control problems, in this paper, we propose an intelligent QoS-aware job scheduling framework for application providers. A deep reinforcement learning-based job scheduler is the key component of the framework. It is able to learn to make appropriate online job-to-VM decisions for continuous job requests directly from its experiences without any prior knowledge. Experimental results using synthetic workloads and real-world NASA workload traces show that compared with other baseline solutions, our proposed job scheduling approach can efficiently reduce average job response time (e.g., reduced by 40.4% compared with the best baseline for NASA traces), guarantee the QoS at a high level (e.g., job success rate is higher than 93% for all simulated changing workload scenarios), and adapt to different workload conditions.


title: Vision-based Robotic Arm Imitation by Human Gesture

abstract: One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate. Our idea is based on success of Deep Q-Learning (DQN) algorithm according to reinforcement learning, and then extend to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a learning-based method, combining modified DDPG and visual imitation network. Our approach acquires frames only from a monocular camera, and no need to either construct a 3D environment or generate actual points. The result we expected during training, was that robot would be able to move as almost the same as how human hands did.


title: Extending the Limits of Feature-Based SLAM With B-Splines

abstract: This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments.