title: Mixing Body-Part Sequences for Human Pose Estimation

abstract: In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such methods perform well on certain body parts, e.g., head, their performance on lower arms, i.e., elbows and wrists, remains poor. We present a new approximate scheme with two steps dedicated to pose estimation. First, our approach takes into account temporal links with subsequent frames for the less-certain parts, namely elbows and wrists. Second, our method decomposes poses into limbs, generates limb sequences across time, and recomposes poses by mixing these body part sequences. We introduce a new dataset "Poses in the Wild", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion. We experimentally compare our method with recent approaches on this new dataset as well as on two other benchmark datasets, and show significant improvement.


title: Generation of automatic test cases with mutation analysis and hybrid genetic algorithm

abstract: Software testing is one of the most important and expensive (in term of time and cost) phase of software development life cycle. Over the past few decades a lot of research has been done on automatic software testing process but due to dynamic memory allocation, software is very much unpredictable in behavior. Different Meta heuristic algorithms are applied for improving the efficiency of software testing process. Mutation testing is a one kind of the software testing in which some mutants are injected intently in the program/software and tester finds these mutants during testing process [4]. In this paper a Hybrid Genetic Algorithm is proposed for generating test data automatically using data flow testing approach for mutation testing.


title: Myths and Legends of the Baldwin Effect

abstract: This position paper argues that the Baldwin effect is widely ::: misunderstood by the evolutionary computation community. The ::: misunderstandings appear to fall into two general categories. ::: Firstly, it is commonly believed that the Baldwin effect is ::: concerned with the synergy that results when there is an evolving ::: population of learning individuals. This is only half of the story. ::: The full story is more complicated and more interesting. The Baldwin ::: effect is concerned with the costs and benefits of lifetime ::: learning by individuals in an evolving population. Several ::: researchers have focussed exclusively on the benefits, but there ::: is much to be gained from attention to the costs. This paper explains ::: the two sides of the story and enumerates ten of the costs and ::: benefits of lifetime learning by individuals in an evolving population. ::: Secondly, there is a cluster of misunderstandings about the relationship ::: between the Baldwin effect and Lamarckian inheritance of acquired ::: characteristics. The Baldwin effect is not Lamarckian. A Lamarckian ::: algorithm is not better for most evolutionary computing problems than ::: a Baldwinian algorithm. Finally, Lamarckian inheritance is not a ::: better model of memetic (cultural) evolution than the Baldwin effect.


title: Outsourcing information systems: drawing lessons from a banking case study

abstract: Financial and costs benefits are often put forward as the reasons why organisations decide to outsource. Emerging patterns and trends indicate that today's outsourcing decisions are often motivated by factors other than cost. Thus, the decision-making process is more complex than it may at first appear. This paper presents findings from a case study from an organisation in the UK banking sector that was motivated to outsource aspects of its information technology/information system (IT/IS). The underlying motives and decision-making process that influenced the bank outsource its IT/IS are presented and discussed. Findings from the case study suggest political perspectives, as well as human and organisational issues influenced the bank's strategic decision-making to outsource certain aspects of its business. An examination of the case study findings suggests that cost alone is not always responsible for decisions to outsource, as it was found the bank's outsourcing decision was driven by a series of complex, interrelated motives in a bid to reduce the risks and uncertainties of managing its own technology. Considering the complex nature of the outsourcing process a frame of reference that can be used to assist managers with their decision to outsource IT/IS is propagated. The case study is used to present an organisation's experiences as to how and why it decided to outsource its IS and thus offers a learning opportunity for other organisations facing similar difficulties. In addition, the case study findings highlight the need to focus greater attention on discriminating between the short and long-term consequences of IT/IS decision-making.


title: Mechatronic design of NAO humanoid

abstract: This article presents the mechatronic design of the autonomous humanoid robot called NAO that is built by the French company Aldebaran-Robotics. With its height of 0.57 m and its weight about 4.5 kg, this innovative robot is lightweight and compact. It distinguishes itself from existing humanoids thanks to its pelvis kinematics design, its proprietary actuation system based on brush DC motors, its electronic, computer and distributed software architectures. This robot has been designed to be affordable without sacrificing quality and performance. It is an open and easy-to-handle platform. The comprehensive and functional design is one of the reasons that helped select NAO to replace the AIBO quadrupeds in the 2008 RoboCup standard league.


title: Cloud Event Programming Paradigms: Applications and Analysis

abstract: Rapid expansion in cloud event technologies such as Amazon Web Service's Lambda, IBM Bluemix's OpenWhisk, Google Cloud Platform's Cloud Functions, and Microsoft Azure's Functions motivates study of software development in these services and their potential as a disruptive force in commercial cloud technologies. In addition to discussing the current state of cloud event services, this paper presents two real world applications utilizing these platforms: Lambdefy, a library designed to make traditional web application run effectively in AWS Lambda, and a performant media management service designed by Trek10, capable of resizing thousands of images per second. Furthermore, we discuss how cloud event technologies enable and/or limit these applications, motivate new software design paradigms in a cloud event environment, and highlight compelling use case scenarios and barriers to entry for cloud event services. AWS cloud technologies are exclusively used due to their maturity and the recent release of the other platforms, while Node.js and the Serverless Framework are utilized for deployment and application development.


Plant Growth Promoting Rhizobacteria : A Critical Review BS Saharan


Lending Fraud Detection : A Big Data Approach


title: Can Patterns improve i* Modeling? Two Exploratory Studies

abstract: A considerable amount of effort has been placed into the investigation of i* modeling as a tool for early stage requirements engineering. However, widespread adoption of i* models in the requirements process has been hindered by issues such as the effort required to create the models, coverage of the problem context, and model complexity. In this work, we explore the feasibility of pattern application to address these issues. To this end, we perform both an exploratory case study and initial experiment to investigate whether the application of patterns improves aspects of i* modeling. Furthermore, we develop a methodology which guides the adoption of patterns for i* modeling. Our findings suggest that applying model patterns can increase model coverage, but increases complexity, and may increase modeling effort depending on the experience of the modeler. Our conclusions indicate situations where pattern application to i* models may be beneficial.


title: Nonlinear predictive control of an omnidirectional robot dribbling a rolling ball

abstract: This paper focuses on the dribbling control problem of an omnidirectional mobile robot and a rolling ball in the RoboCup middle size domain. Because the ball easily slides away from the robot when the ball moves along a curve, dribbling control is more challenging than the normal mobile robot motion control problem. Based on an introduced reference point with respect to the robot body and a sophisticated planning method of robot pose, the nonlinear predictive control is used to steer the robot to follow the planned poses so as to prevent the ball from leaving the robot. Real-world experiments showed that nonlinear predictive control is capable of solving the pose following problem in a real-time application.


title: A 50-kVA Three-Phase Solid-State Transformer Based on the Minimal Topology: Dyna-C

abstract: The Dynamic Current or Dyna-C is a minimal topology for implementing the bidirectional three-phase solid-state transformer (SST). While only two current-source power conversion stages are employed, the Dyna-C SST has features of voltage step up/down, arbitrary power factors, and frequencies between the input and output terminals. In this paper, a compact 50-kVA three-phase SST based on this minimal topology is designed. More specifically, design considerations and practical implementation techniques are presented. Results from experimental measurements are shown and discussed.


title: Utilización de las TIC por el profesorado universitario como recurso didáctico

abstract: The learning environment at the university stage which is defined by the Bologna Declaration has long indicated the need for major changes, among others, in the teaching methodology and teaching resources used by the university teachers. With this work, we aim to demonstrate some of the results achieved from the implementation of a National Research Project that has been carried out by four Spanish Universities. The objectives of the project aim to describe, through an exploratory study, the methodological situation in which our universities are highlighting their strengths and weaknesses based on the premises of the European Higher Education Area to suggest some strategies in order to facilitate the necessary adaptation. The methodology followed by implementing a questionnaire focused on concrete analysis of the dimension of the Information and Communication Technologies (ICT) reflects the view of university teachers and through the analysis of results allows us to confirm the need for adaptation by the universities for the implementation, with guarantees of the education reform. This article also presents several conclusions and suggestions for educational improvement focused on teaching and methodological training in the use of teaching tools that can promote the suggestions provided by the European Higher Education using ICT, as for example those offered by Web 2.0.


title: Barrage relay networks: System & protocol design

abstract: Barrage relay networks (BRNs) are mobile ad hoc networks designed from the ground up to meet the demands of tactical edge communications. The fundamental building block of BRNs is not a point-to-point wireless link, but rather a rapid and robust broadcast mechanism that employs an autonomous cooperative communications scheme. Following a summary of basic BRN concepts, this paper demonstrates how the efficient barrage broadcast mechanism can be contained for unicast traffic via controlled barrage regions (CBRs). In particular, a protocol for CBR establishment is defined and formally verified.


title: Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation

abstract: Compared to Multilayer Neural Networks with real weights, Binary Multilayer Neural Networks (BMNNs) can be implemented more efficiently on dedicated hardware. BMNNs have been demonstrated to be effective on binary classification tasks with Expectation BackPropagation (EBP) algorithm on high dimensional text datasets. In this paper, we investigate the capability of BMNNs using the EBP algorithm on multiclass image classification tasks. The performances of binary neural networks with multiple hidden layers and different numbers of hidden units are examined on MNIST. We also explore the effectiveness of image spatial filters and the dropout technique in BMNNs. Experimental results on MNIST dataset show that EBP can obtain 2.12% test error with binary weights and 1.66% test error with real weights, which is comparable to the results of standard BackPropagation algorithm on fully connected MNNs.


title: A user profile model for intelligent delivery of spatial information

abstract: Conventional spatial information delivery is system-oriented, and users have to adapt to the system and complete the ::: delivery of spatial information. Two disadvantages exist in the delivery service mode: (1) users' personalized ::: requirements cannot be satisfied; (2) Initiative delivery service cannot be provided for users. In this paper, we firstly ::: classify conventional user profile models into four categories. After analyzing the limitations of these user profile models, ::: we conclude that conventional user profile models are not suitable for the application of spatial information intelligent ::: delivery. Then, aiming to the objectives of spatial information intelligent delivery, we propose a user profile model of ::: spatial information, and give the building method of the model. Also, we put forward a similarity measure method ::: between spatial information and user profile. In order to verify the availability of the user profile model, we present an ::: example of spatial information intelligent delivery. The experimental results show that the proposed user profile model ::: can reflect user profile quite well.


title: Enhanced vector field visualization via Lagrangian accumulation

abstract: Abstract In this paper, we revisit the Lagrangian accumulation process that aggregates the local attribute information along integral curves for vector field visualization. Similar to the previous work, we adopt the notation of the Lagrangian accumulation field or A field for the representation of the accumulation results. In contrast to the previous work, we provide a more in-depth discussion on the properties of A fields and the meaning of the patterns exhibiting in A fields. In particular, we revisit the discontinuity in the A fields and provide a thorough explanation of its relation to the flow structure and the additional information about the flow that it may reveal. In addition, other remaining questions about the A field, such as its sensitivity to the selection of integration time, are also addressed. Based on these new insights, we demonstrate a number of enhanced flow visualizations aided by the accumulation framework and the A fields, including a new A field guided ribbon placement, an A field guided stream surface seeding and the visualization of particle-based flow data. To further demonstrate the generality of the accumulation framework, we extend it to the non-integral geometric curves (i.e., streak lines), which enable us to reveal information about the flow behavior other than those revealed by the integral curves. Finally, we introduce the Eulerian accumulation, which can reveal different flow behaviors than those revealed by Lagrangian accumulation. In summary, we believe that Lagrangian accumulation and the resulting A fields offer a valuable means to investigate flow behavior complementary to the current state-of-the-art techniques.


title: Web Mining for Open Source Intelligence

abstract: Web mining for open source intelligence is the retrieval, extraction and analysis of information from on-line Internet sites. There are two separate applications areas this paper will review, namely live news-monitoring and targeted topic based data mining. Most newspapers and news agencies have Web sites with live updates on unfolding events, opinions and perspectives on world events. Most governments monitor news reports to feel the pulse of public opinion, and for early warning of emerging crises. The Joint Research Centre has developed significant experience in Internet content monitoring through its work on media monitoring (EMM) for the European Commission. EMM forms the core of the Commission's daily press monitoring service. Intelligence services and law enforcement agencies also require specific site monitoring and topic monitoring, and EMM technology has been applied to the wider Internet for this purpose. The software extracts and downloads all the textual content from monitored sites and applies information extraction techniques. These tools help analysts process large amounts of documents to derive structured data. Lastly the visualisation of the extracted data is important for analysts to identify patterns and trends derived from both news reports and Web mining.


title: An Effective Control Method for Quasi-Z-Source Cascade Multilevel Inverter-Based Grid-Tie Single-Phase Photovoltaic Power System

abstract: An effective control method, including system-level control and pulsewidth modulation for quasi-Z-source cascade multilevel inverter (qZS-CMI) based grid-tie photovoltaic (PV) power system is proposed. The system-level control achieves the grid-tie current injection, independent maximum power point tracking (MPPT) for separate PV panels, and dc-link voltage balance for all quasi-Z-source H-bridge inverter (qZS-HBI) modules. The complete design process is disclosed. A multilevel space vector modulation (SVM) for the single-phase qZS-CMI is proposed to fulfill the synthetization of the step-like voltage waveforms. Simulation and experiment based on a seven-level prototype are carried out to validate the proposed methods.


Microservices for Scalability


title: IoT Based Airport Parking System

abstract: The proliferation of technology paves way to new kind of devices that can communicate with other devices to produce output mostly on wireless communication. Wirelessly communicating embedded devices are brought to one another in a single link over Internet called IoT (Internet of Things). If all objects and people in daily life were equipped with identifiers, computers could manage and inventory them. Besides using RFID, the tagging of things may be achieved through such technologies as near field communication, barcodes, QR codes and digital watermarking. Here new method of using embedded technology to provide such application, Arduino is used as an embedded controller to interface Ethernet shield with a PC/Laptop to provide IoT over Ethernet. A user can use this parking service in the airport scenario provided by airport authority with user ID and password. Whenever a user need to check the vehicle in the parking lot, uses the ID and password to logon into the airport web link and view the status of the car in the parking lot using IoT. IoT Based Airport Parking System is discussed here to implement Arduino environment as IoT application.


title: Understanding HRM–Firm Performance Linkages: The Role of the “Strength” of the HRM System

abstract: Theory building has lagged on the intermediate linkages responsible for the relationship between HRM and firm performance. We introduce the construct “strength of the HRM system” and describe the meta-features of an HRM system that result in a strong organizational climate, analogous to Mischel's “strong situation,” in which individuals share a common interpretation of what behaviors are expected and rewarded. The strength of the HRM system can help explain how individual employee attributes accumulate to affect organizational effectiveness.


abstract: Real-Time Campus University Bus Tracking Mobile Application is a mobile application to help campus members detect the current location of the bus in real-time. Real-Time Campus University Bus Tracking Mobile Application is a hybrid mobile application. However, for this development, it is developed for Android user only. It can show updated estimation time arrival and the number of persons inside the bus. This project using two devices embedded inside the bus, which are GPS Tracker device and IoT people counter device. All devices will transmit the data into cloud database which is Firebase. Real-Time Campus University Bus Tracking Mobile Application is developed as a platform for user to receive the data transmitted from database. Other than that, Student will know the time arrival of the bus and the current quantity of people inside the bus to lead them avoid wasted time knowing that they wait for the bus that pack of passenger. The student also able to make complaint and feedback via the platform. Furthermore, this project using PhoneGap as a tool platform to develop the application. The GPS Tracker device using Arduino and IoT people counter using Raspberry PI to transmit data. Overall this project using the reusability techniques and Agile method to complete all the system which it is involved four interactions to make it full system work as expected.


title: Control of autonomous vehicles at an unsignalized intersection

abstract: Safety and optimal traffic flow are important priorities in intelligent transportation systems. This paper presents a collaborative method for collision avoidance between autonomous vehicles at an unsignalized intersection. The collision conditions are expressed in terms of the vehicles' speed ratios. The problem is then formulated and solved as an optimization problem with the safety constraints represented by the speed ratios. The cost function is defined in terms of the deviation from the current speeds. Both pairwise and three vehicle formulations are discussed. The proposed formulation allows to easily scale the solution to higher number of vehicles. The method is highly effective in resolving conflicts and achieving smooth flow. The results are formally proven and illustrated using simulation.


title: Definition Modeling: Learning to define word embeddings in natural language

abstract: Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a character-level convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.


title: Communicating Awareness and Intent in Autonomous Vehicle-Pedestrian Interaction

abstract: Drivers use nonverbal cues such as vehicle speed, eye gaze, and hand gestures to communicate awareness and intent to pedestrians. Conversely, in autonomous vehicles, drivers can be distracted or absent, leaving pedestrians to infer awareness and intent from the vehicle alone. In this paper, we investigate the usefulness of interfaces (beyond vehicle movement) that explicitly communicate awareness and intent of autonomous vehicles to pedestrians, focusing on crosswalk scenarios. We conducted a preliminary study to gain insight on designing interfaces that communicate autonomous vehicle awareness and intent to pedestrians. Based on study outcomes, we developed four prototype interfaces and deployed them in studies involving a Segway and a car. We found interfaces communicating vehicle awareness and intent: (1) can help pedestrians attempting to cross; (2) are not limited to the vehicle and can exist in the environment; and (3) should use a combination of modalities such as visual, auditory, and physical.


title: Exploring DRAM organizations for energy-efficient and resilient exascale memories

abstract: The power target for exascale supercomputing is 20MW, with about 30% budgeted for the memory subsystem. Commodity DRAMs will not satisfy this requirement. Additionally, the large number of memory chips (>10M) required will result in crippling failure rates. Although specialized DRAM memories have been reorganized to reduce power through 3D-stacking or row buffer resizing, their implications on fault tolerance have not been considered. We show that addressing reliability and energy is a co-optimization problem involving tradeoffs between error correction cost, access energy and refresh power---reducing the physical page size to decrease access energy increases the energy/area overhead of error resilience. Additionally, power can be reduced by optimizing bitline lengths. The proposed 3D-stacked memory uses a page size of 4kb and consumes 5.1pJ/bit based on simulations with NEK5000 benchmarks. Scaling to 100PB, the memory consumes 4.7MW at 100PB/s which, while well within the total power budget (20MW), is also error-resilient.


Rocchio: Relevance Feedback in Learning Classification Algorithms


title: Evaluating aggregated search pages

abstract: Aggregating search results from a variety of heterogeneous sources or verticals such as news, image and video into a single interface is a popular paradigm in web search. Although various approaches exist for selecting relevant verticals or optimising the aggregated search result page, evaluating the quality of an aggregated page is an open question. This paper proposes a general framework for evaluating the quality of aggregated search pages. We evaluate our approach by collecting annotated user preferences over a set of aggregated search pages for 56 topics and 12 verticals. We empirically demonstrate the fidelity of metrics instantiated from our proposed framework by showing that they strongly agree with the annotated user preferences of pairs of simulated aggregated pages. Furthermore, we show that our metrics agree with the majority preference more often than current diversity-based information retrieval metrics. Finally, we demonstrate the flexibility of our framework by showing that personalised historical preference data can be used to improve the performance of our proposed metrics.


title: Immersion and Emotion: Their Impact on the Sense of Presence

abstract: The present study is designed to test the role of immersion and media content in the sense of presence. Specifically, we are interested in the affective valence of the virtual environments. This paper describes an experiment that compares three immersive systems (a PC monitor, a rear projected video wall, and a head-mounted display) and two virtual environments, one involving emotional content and the other not. The purpose of the experiment was to test the interactive role of these two media characteristics (form and content). Scores on two self-report presence measurements were compared among six groups of 10 people each. The results suggest that both immersion and affective content have an impact on presence. However, immersion was more relevant for non-emotional environments than for emotional ones.


Data Indicating Malfunction Sign of Hydroelectric Power Plants


title: Sampling-Based Estimation of the Number of Distinct Values of an Attribute

abstract: We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators in either the database or statistical literature, and is certainly the first to use highlyskewed data of the sort frequently encountered in database applications. Our experiments indicate that a new “hybrid” estimator yields the highest precision on average for a given sampling fraction. This estimator explicitly takes into account the degree of skew in the data and combines a new “smoothed jackknife” estimator with an estimator due to Shlosser. We investigate how the hybrid estimator behaves as we scale up the size of the database.


title: Scaling Reflection Prompts in Large Classrooms via Mobile Interfaces and Natural Language Processing

abstract: We present the iterative design, prototype, and evaluation of CourseMIRROR (Mobile In-situ Reflections and Review with Optimized Rubrics), an intelligent mobile learning system that uses natural language processing (NLP) techniques to enhance instructor-student interactions in large classrooms. CourseMIRROR enables streamlined and scaffolded reflection prompts by: 1) reminding and collecting students' in-situ written reflections after each lecture; 2) continuously monitoring the quality of a student's reflection at composition time and generating helpful feedback to scaffold reflection writing; and 3) summarizing the reflections and presenting the most significant ones to both instructors and students. Through a combination of a 60-participant lab study and eight semester-long deployments involving 317 students, we found that the reflection and feedback cycle enabled by CourseMIRROR is beneficial to both instructors and students. Furthermore, the reflection quality feedback feature can encourage students to compose more specific and higher-quality reflections, and the algorithms in CourseMIRROR are both robust to cold start and scalable to STEM courses in diverse topics.


G4 Multi-constellation Precise Point Positioning Service for High Accuracy Offshore Navigation


title: The Genome of Intoshia linei Affirms Orthonectids as Highly Simplified Spiralians

abstract: Summary Orthonectids are rare parasites of marine invertebrates [1] that are commonly treated in textbooks as a taxon of uncertain affinity [2]. Trophic forms of orthonectids reside in the tissues of their hosts as multinucleated plasmodia, generating short-lived, worm-like ciliated female and male organisms that exit into the environment for copulation [3]. These ephemeral males and females are composed of just several hundred somatic cells and are deprived of digestive, circulatory, or excretory systems. Since their discovery in the 19 th century, the orthonectids were described as organisms with no differentiated cell types and considered as part of Mesozoa, a putative link between multicellular animals and their unicellular relatives. More recently, this view was challenged as the new data suggested that orthonectids are animals that became simplified due to their parasitic way of life [3, 4]. Here, we report the genomic sequence of Intoshia linei , one of about 20 known species of orthonectids. The genomic data confirm recent morphological analysis asserting that orthonectids are members of Spiralia and possess muscular and nervous systems [5]. The 43-Mbp genome of I. linei encodes about 9,000 genes and retains those essential for the development and activity of muscular and nervous systems. The simplification of orthonectid body plan is associated with considerable reduction of metazoan developmental genes, leaving what might be viewed as the minimal gene set necessary to retain critical bilaterian features.


title: Prediction of Workloads in Incident Management Based on Incident Ticket Updating History

abstract: Incident management is one of the most important and burdensome tasks in system management. In order to achieve effective incident management, prediction of the workload needed to solve incidents is quite useful. Using this prediction, we can provide a fair distribution of incident tickets to administrators. In order to predict the workload needed to handle an incident ticket when it arrives, we propose an incident ticket classification method based on text mining (TF-IDF and Naive Bayes). In this approach, we first collect incident tickets with their number of updates as workload indicators. Next, we construct a model representing the relation between the words in incident texts and the incident workload category (easy or difficult) based on Naive Bayes. We then predict a category into which each new incident ticket should be classified using the model. We implemented our method using Hadoop and Mahout library. By conducting the evaluation with incident tickets recorded in an cloud infrastructure for research, we confirmed that our approach can predict the workload of incident tickets with an F-measure of 0.81 in its best case.


title: A comparison of autoencoder and statistical features for cattle behaviour classification

abstract: A study is presented comparing the effectiveness of unsupervised feature representations with handcrafted features ::: for cattle behaviour classification. Precision management of cattle requires the interaction of individual animals to be continuously monitored on the farm. Consequently, classifiers are trained to infer the behaviour of the animals using the observations from the sensors that are fitted upon them. Historically, domain knowledge drives the generation of features for cattle behaviour classifiers. When new behaviours are introduced into the system, however, it is often necessary to modify the feature set; this requires additional design and more data. Autoencoders, on the other hand, can skip this design step by learning a common, unsupervised feature ::: representation for training. Whilst stacked autoencoders successfully represent structured data including speech, language and images, deep networks have not been used to model cattle motion. Hence, we investigate using a stacked autoencoder to learn a feature representation for cattle behaviour classification. Experimental results demonstrate that the autoencoder features perform reasonably well in comparison to the statistical features that are selected using prior knowledge of behaviour motion.


title: Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

abstract: Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with domain specific structural annotations. We further show several use cases of the tool for analyzing specific hidden state properties on datasets containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.


title: Bangla Speech Recognition for Voice Search

abstract: In this work, different Gaussian Mixture Model-Hidden Markov Model(GMM-HMM) based and Deep Neural Network (DNN-HMM) based models have been analyzed for speech recognition in Bangla language to build a voice search module for search engine pipilika 1. A small corpus of 9 hours of speech recordings from 49 different speakers was prepared for this work consisting of a vocabulary of 500 unique words. The lowest Word Error Rate(WER) for (GMM-HMM) based model was 3.96% and for (DNN-HMM) based model was 5.30%. To our best knowledge, this is the lowest WER for Bangla speech recognition for such vocabulary size.


Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings


title: Classification of Ulcerative Colitis Severity in Colonoscopy Videos using CNN

abstract: Ulcerative Colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, we already developed an approach using the image textures in our previous work. But, we found that it could not handle larger number of variations in their patterns. In this paper, we propose a different approach using CNN (Convolutional Neural Network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. We call the proposed approach using CNN as Ulcerative Colitis Severity CNN (UCS-CNN) which utilizes endoscopic domain knowledge and convolutional neural network to classify different UC severity of colonoscopy images. The experimental results show that the proposed UCS-CNN can evaluate the severity of UC reasonably.


title: An Iterative Spanning Forest Framework for Superpixel Segmentation

abstract: Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of image foresting transforms. In this framework, one can choose the most suitable combination of ISF components for a given application-i.e., 1) a seed sampling strategy; 2) a connectivity function; 3) an adjacency relation; and 4) a seed pixel recomputation procedure. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF-based methods to illustrate different choices for those components. These methods are compared with a number of state-of-the-art approaches with respect to effectiveness and efficiency. Experiments are carried out on several datasets containing 2D and 3D objects with distinct texture and shape properties, including a high-level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show ISF-based methods rank consistently among the best for all datasets.


title: Place recognition based on matching of planar surfaces and line segments

abstract: This paper considers the potential of using three-dimensional 3D planar surfaces and line segments detected in depth images for place recognition. A place recognition method is presented that is based on matching sets of surface and line features extracted from depth images provided by a 3D camera to features of the same type contained in a previously created environment model. The considered environment model consists of a set of local models representing particular locations in the modeled environment. Each local model consists of planar surface segments and line segments representing the edges of objects in the environment. The presented method is designed for indoor and urban environments. A computationally efficient pose hypothesis generation approach is proposed that ranks the features according to their potential contribution to the pose information, thereby reducing the time needed for obtaining accurate pose estimation. Furthermore, a robust probabilistic method for selecting the best pose hypothesis is proposed that allows matching of partially overlapping point clouds with gross outliers. The proposed approach is experimentally tested on a benchmark dataset containing depth images acquired in the indoor environment with changes in lighting conditions and the presence of moving objects. A comparison of the proposed method to FAB-MAP and DLoopDetector is reported.


title: Visual place recognition using HMM sequence matching

abstract: Visual place recognition and loop closure is critical for the global accuracy of visual Simultaneous Localization and Mapping (SLAM) systems. We present a place recognition algorithm which operates by matching local query image sequences to a database of image sequences. To match sequences, we calculate a matrix of low-resolution, contrast-enhanced image similarity probability values. The optimal sequence alignment, which can be viewed as a discontinuous path through the matrix, is found using a Hidden Markov Model (HMM) framework reminiscent of Dynamic Time Warping from speech recognition. The state transitions enforce local velocity constraints and the most likely path sequence is recovered efficiently using the Viterbi algorithm. A rank reduction on the similarity probability matrix is used to provide additional robustness in challenging conditions when scoring sequence matches. We evaluate our approach on seven outdoor vision datasets and show improved precision-recall performance against the recently published seqSLAM algorithm.


title: Design, Verification, and Application of New Discrete-Time Recurrent Neural Network for Dynamic Nonlinear Equations Solving

abstract: Recently, the approach based on recurrent neural network (RNN) has been considered a powerful alternative to mathematical problem solving. In this study, a new discrete-time RNN (DTRNN) is proposed and investigated to determine an exact solution of dynamic nonlinear equations. Specifically, the resultant DTRNN model is established for solving dynamic nonlinear equations by utilizing a Taylor-type difference rule. This DTRNN model is then theoretically proven to have an $O(\tau ^4)$ error pattern, where $\tau$ denotes the sampling gap. Comparative numerical results are illustrated to further substantiate the efficacy and superiority of the proposed DTRNN model in comparison with the existing approach. Finally, the proposed DTRNN model is applied to redundant robot manipulators by solving the system of dynamic nonlinear kinematic equations, indicating the application prospect of the proposed model.


title: BotCensor: Detecting DGA-Based Botnet Using Two-Stage Anomaly Detection

abstract: Nowadays, most botnets utilize domain generation algorithms (DGAs) to build resilient and agile command and control (C&C) channels. Specifically, botmasters employ DGAs to dynamically produce a large number of random domains and only register a small subset for their actual C&C servers with the purpose to defend them from takeovers and blacklisting attempts. While many approaches and models have been developed to detect DGA-based botnets, they suffer from several limitations, such as difficulties of DNS traffic collection, low feasibility and scalability, and so forth. In this paper, we present BotCensor, a new system that can determine if a host is infected with certain DGA malware with two-stage anomaly detection. In the first stage, we preliminarily attempt to identify malicious domains using a Markov model, and in the second stage, we re-examine the hosts that requested aforementioned malicious domains using novelty detection algorithms. Our experimental results show that our approach performs very well on identifying previously unknown DGA-generated domains and detects DGA bots with high efficiency and efficacy. Our approach not only can be regarded as security forensics tools, but also can be used to prevent malware infections and spread.


A survey of music recommendation and possible improvements


title: Learning A Deep $\ell_\infty$ Encoder for Hashing

abstract: We investigate the $\ell_\infty$-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning. Based on the Alternating Direction Method of Multipliers (ADMM), we formulate the original convex minimization problem as a feed-forward neural network, named \textit{Deep $\ell_\infty$ Encoder}, by introducing the novel Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as network biases. Such a structural prior acts as an effective network regularization, and facilitates the model initialization. We then investigate the effective use of the proposed model in the application of hashing, by coupling the proposed encoders under a supervised pairwise loss, to develop a \textit{Deep Siamese $\ell_\infty$ Network}, which can be optimized from end to end. Extensive experiments demonstrate the impressive performances of the proposed model. We also provide an in-depth analysis of its behaviors against the competitors.


title: Effects of large doses of olpadronate (dimethyl-pamidronate) on mineral density, cross-sectional architecture, and mechanical properties of rat femurs

abstract: As part of a safety-assessment study, doses of 8, 40, and 200 mg/kg per day, 6 days per week, of sodium olpadronate (dimethyl-APD, Me2-APD) were given by gavage to 10-week-old male and female rats during 27 weeks. Only the 200 mg/kg per day dose provoked toxic effects and a meaningful growth depression, regardless of the animal gender. In male animals, doses of 40 or 200 mg/kg per day improved strength, stiffness, and cross-sectional moment of inertia (CSMI) of femur diaphyses despite the toxic effects observed at the highest dose. Changes in bone mechanical properties were a consequence of those induced in CSMI. Regression analyses showed a treatment-induced improvement in bone modeling (as assessed by CSMI) for the same level of bone material stiffness (as expressed by calculated values of elastic modulus). The high dependency of results on body mass bearing suggested that these effects were exerted through an increase in the efficiency of bone mechanostat. Strikingly, they were not evident in female rats. If not related to a lower bone bioavailability of bisphosphonates in female rats as described by others, this phenomenon may have reflected: (1) their a smaller biomass; and/or (2) a less effective mechanostatic regulation of bone architecture derived from a higher bone material stiffness related to male animals. An increase of BMD with a predominance toward the distal region was observed in all femurs studied. This effect, unrelated to the observed changes in mechanical properties, seems to express a lack of remodeling of primary cartilage or bone tissue.


title: Enumeration of Bradyrhizobium japonicum in soil subjected to high temperature: Comparison of plate count, most probable number and fluorescent antibody techniques

abstract: The survival of BradyrMzobium japonicum strains in autoclaved and non-autoclaved soil at various temperatures for up to 18 days was determined by plate count, plant infectivity-most probable number (MPN) and modified fluorescent antibody (FA) techniques. The high-temperature tolerant strains studied were BR587, NC1005 and NC1033, and the high-temperature intolerant strains studied were NCI028, 3Ilb24 and 311b123. Each strain was exposed individually in a sandy loam soil at 28, 33 or 37°C under humidity controlled conditions. Those strains reported to be tolerant to elevated temperature in pure culture decreased by <101.5 cells g−1 when enumerated by plate count after high-temperature exposure in autoclaved soil. Based on plate counts, the population of two of the three high-temperature intolerant strains added to autoclaved soil decreased by more than 103 cells g−1 soil after exposure to elevated temperatures. In non-autoclaved soil, the population of all inoculated strains decreased after exposure to elevated temperature when enumeration was by the plant infectivity-MPN technique. In autoclaved soil at 28°C, the correlation between plate count and FA enumerations was significant at the 0.05 probability level for four of the six strains (r2 = 0.65). After high-temperature incubation, counts based on the FA technique were not correlated with plate counts of intolerant strains or MPN enumeration of tolerant and intolerant strains (r2 = 0.004). Differential survival of B. japonicum strains in autoclaved soil at high temperature as enumerated by plate counts confirms the designation of temperature tolerance made by Munevar and Wollum (1981). However. MPN enumeration of bradyrhizobia in the non-autoclaved system did not follow the original temperature tolerance classifications. Enumeration by FA procedures did not show a decrease in population after exposure to high temperature as observed with the plate and MPN counts. Thus, the FA technique may not adequately indicate population dynamics in short-term ecological studies.


title: 1 Game Theory in Wireless Networks: A Tutorial

abstract: The behavior of a given wireless station may affect the communication capabilities of a neighboring one, notably because the radio communication channel is usually shared in wireless networks. In this tutorial, we carefully explain how situations of this kind can be modelled by making use of game theory. By leveraging on four simple running examples, we introduce the most fundamental concepts of non-cooperative game theory. This approach should help students and scholars to quickly master this fascinating analytical tool without having to read the existing lengthy, economics-oriented books; it should also assist them in modelling the problems of their own.


title: Driver Drowsiness Detection Using Visual Information On Android Device

abstract: The number of casualties from road accidents keep arising each year. While there are many causes to road accidents, most are surprisingly caused by human errors, such as drowsiness. Therefore, this issue raises an idea for an application which will be able to track a person’s eye movement while driving. The application, called “Driver Drowsiness Detection”, runs on an Android handheld and wearable. The purpose of this application is to alert drivers so that they can be cautioned to pull over and stop driving in a drowsy state. The application “Driver Drow-siness Detection” utilizes Haar-cascade Detection as well as template matching in OpenCV to detect and track the eyes using the front camera of an Android device. Testing has been conducted to ensure that the functionality, behavior, performance and user satisfaction are as expected. Even though, the input received by the application still has several restrictions, specifically in correlation to sufficient lighting and obscurity of the face and eyes area, the application has successfully detected the eye blinks at the angle of 30 to 60 degrees and distance of 20-50 cm, as well as measuring the heart rate.


title: DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction

abstract: We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.


title: Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

abstract: Selection of good particles in cryo-electron micrographs is an important step in the reconstruction of high resolution 3D structures. In this study, we constructed a deep learning-based method to automatically detect particle centers from micrographs. This is a challenging task because of the low signal-to-noise ratio of cryo-EM micrographs, and the size, shape, and grayscale-level differences of particles. We proposed a Fully Convolutional Regression Network (FCRN) that maps the particle image to a continuous distance map that acts like a probability density function of particle centers. This approach is simple, but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experimental results on dataset β-galactosidase (EMPIAR-10017) [1] showed that FCRN outperfomed Faster-RCNN, Apple picker, and RELION’s particle picker. Compared to the ground truth of this dataset, FCRN achieved better picking performance, and 3D structure of those picked particles also had higher resolution.


title: Object Recognition-Based Second Language Learning Educational Robot System for Chinese Preschool Children

abstract: Research in psychology, pedagogy, and physiology has shown that learning a second language will be a great benefit to preschool children. Compared with adults, children have more advantages in learning, such as better pronunciation and intonation. However, because of children’s inattention, the effects of the second language education in many preschools are not ideal in China. To make children learn the second language effectively, it should be integrated into their daily lives and in line with their interests. In this paper, an educational robot system with object recognition technology is introduced, which aims to provide innovative second language learning services for preschool children in China. The proposed system combines object recognition and projection with English teaching and makes objects in daily life more interesting with projected animation to attract children’s attention. On the projection screen, children can interact with a robot by touch or movement and can easily trigger more interactive effects. To evaluate the effectiveness of the proposed system, we conducted an experiment, and the results showed that the system can improve the language learning efficiency for the Chinese preschool children by interacting with objects under proper guidance.


title: Sexual fantasy.

abstract: This article reviews the research literature on sexual fantasy, a central aspect of human sexual behavior. Topics include (a) gender similarities and differences in the incidence, frequency, and content of sexual fantasies and how they relate to sociocultural and sociobiological theories of sexual behavior; (b) the association between frequency or content of sexual fantasies and variables such as age, sexual adjustment and satisfaction, guilt, sexual orientation, personality, and sexual experience; and (c) "deviant" sexual fantasies (i.e., what they are, whether they play a role in the commission of sexual crimes, and whether they can be modified). The article ends with a summary of major findings and suggestions for future research.


title: Music and Art in Conflict Transformation: A Review

abstract: Since the early 1990s there has been an increase in the use of music and arts within a conflict transformation context. In this paper the developments in this field is discussed and the current status and challenges of the field are examined within the context of this issue's theme of the arts and conflict transformation/peace building.


title: Two Methods for Target Localization in Multistatic Passive Radar

abstract: This paper compares two algorithms for three-dimensional target localization from passive radar measurements. The algorithms use bistatic range measurements from multiple transmitter-receiver pairs to calculate the target position. The algorithms derived are based on the methods known for time-difference-of-arrival (TDOA) systems, namely spherical interpolation (SI) and spherical intersection (SX). Both algorithms rely on closed-form equations. A theoretical accuracy analysis of the algorithms is provided. This analysis is verified with Monte-Carlo simulations and a real-life example is presented.


title: Splinting after contracture release for Dupuytren's contracture (SCoRD): protocol of a pragmatic, multi-centre, randomized controlled trial

abstract: BackgroundSplinting as part of the overall post-surgical management of patients after release of Dupuytren's contracture has been widely reported, though there is variation in practice and criteria for using it. The evidence on its effectiveness is sparse, of poor quality and contradictory with studies reporting negative and positive effects.Methods/DesignA multi-centre, pragmatic, randomized, controlled trial is being conducted to evaluate the effect of static night splinting for six months on hand function, range of movement, patient satisfaction and recurrence at 1 year after fasciectomy or dermofasciectomy. Using a centrally administered computer randomization system consented patients will be allocated to one of two groups: i) splint group who will be given a static splint at approximately 10 to 14 days after surgery to be worn for 6 months at night time only as well as hand therapy; ii) non-splint group, who will receive hand therapy only. The primary outcome measure is the patient-reported Disabilities of the Arm, Hand and Shoulder Questionnaire (DASH). Secondary outcomes are total active flexion and extension of fingers, patient satisfaction and recurrence of contracture. Outcome measures will be collected prior to surgery, 3 months, 6 months and 1 year after surgery. Using the DASH as the primary outcome measure, where a difference of 15 points is considered to be a clinically important difference a total of 51 patients will be needed in each group for a power of 90%. An intention-to-treat analysis will be used.DiscussionThis pragmatic randomized controlled trial will provide much needed evidence on the clinical effectiveness of post-operative night splinting in patients who have undergone fasciectomy or dermofasciectomy for Dupuytren's contracture of the hand.Trial RegistrationCurrent Controlled Trials ISRCTN 57079614


title: Role-Based Access Controls: Status, Dissemination, and Prospects for Generic Security Mechanisms

abstract: E-commerce applications have diverse security requirements ranging from business-to-business over business-to-consumer to consumer-to-consumer types of applications. This range of requirements cannot be handled adequately by one single security model although role-based access controls (RBAC) depict a promising fundament for generic high-level security. Furthermore, RBAC is well researched but rather incompletely realized in most of the current backend as well as business layer systems. Security mechanisms have often been added to existing software causing many of the well-known deficiencies found in most software products. However, with the rise of component-based software development security models can also be made available for reuse. Therefore, we present a general-purpose software framework providing security mechanisms such as authentication, access controls, and auditing for Java software development. The framework is called GAMMA (Generic Authorization Mechanisms for Multi-Tier Applications) and offers multiple high-level security models (including the aforementioned RBAC) that may even be used concurrently to cover such diverse security requirements as found within e-commerce environments.


Toward Robust Deep RL via Better Benchmarks : Identifying Neglected Problem Dimensions


title: Deep Reinforcement Learning With Macro-Actions

abstract: Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. We concentrate on macro-actions, and evaluate these on different Atari 2600 games, where we show that they yield significant improvements in learning speed. Additionally, we show that they can even achieve better scores than DQN. We offer analysis and explanation for both convergence and final results, revealing a problem deep RL approaches have with sparse reward signals.


title: Deep Learning Face Representation by Joint Identification-Verification

abstract: The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 features extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 features extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset [11], 99.15% face verification accuracy is achieved. Compared with the best previous deep learning result [20] on LFW, the error rate has been significantly reduced by 67%.


title: The Impacts of Social Media on Bitcoin Performance

abstract: As the world's first completely decentralized digital payment system, the emergence of Bitcoin represents a revolutionary phenomenon in financial markets. This study examines the dynamic relationships between social media and bitcoin performance. We consider the distinct effects of different social media platforms and different user groups subdivided by posting volume. The results suggest that more bullish forum posts have a positive effect on bitcoin returns, and the effect is stronger when we only include the posts by users who are less likely to contribute. In addition, messages on Internet forum have stronger impacts on future bitcoin market measures at a daily frequency, but microblogs’ effects are more significant at an hourly frequency.


title: Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching

abstract: Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.


title: A wireless photostimulator for optogenetics with live animals

abstract: In this paper, we present a wireless photostimulator providing high output power from small 1.5-V Zinc-Air coin battery to stimulate light sensitized neurons. To overcome the limits of such a small energy source e.g. discharging current and voltage, the proposed photostimulator is based on a DC/DC converter and a switched capacitor circuit to provide high voltage (4 V) and high current (maximum 20 mA) to the stimulating μLEDs. These LED driving voltages and currents are modulated by a PWM signal, and designed to provide a stimulation pulsewidth of 5-ms at a period of 500 ms. A charge pump circuit is used to boost the battery voltage level up to 4 V from the aforementioned low-voltage coin battery. The output current of the proposed LED driver circuit is adjustable to provide different output light power levels output. The post layout simulation results in IBM CMRF8SF 130-nm process shows a variation of less than 1 % of the output LED driver current of the across all PVT corners. The efficiency of the proposed photostimulator, including the charge pump and the LED driver, is of 66 %.


title: Crowdfunding: A Spimatic application of digital fandom:

abstract: The digital environment has opened up new spaces for fans to engage with the production process of their favoured texts. However, fan studies have largely neglected the larger temporal structures undergirding this engagement. In this article, I augment studies of digital fandom by utilizing Bruce Sterling’s technosocial concept of the Spime as a means of investigating the relationship forged between the technology of crowdfunding and the affect of particular audiences. The successful Kickstarter campaigns for Rob Thomas and Kristen Bell’s Veronica Mars film and Zach Braff’s film Wish I Was Here, and the failed Kickstarter campaign for Melissa Joan Hart’s film Darci’s Walk of Shame, each illustrate how the entertainers drew (or did not draw) on specific support from the lineage of his or her own fan communities to generate funds and word-of-mouth publicity. These campaigns ultimately illustrate the consequential powers of the fan at multiple nodes within the production process.


title: OMCO: Online Multiple Coflow Scheduling in Optical Circuit Switch

abstract: Coflow is gradually prevalent as a new traffic structure in data centers, which allows applications to convey their application-level semantics into the network. Meanwhile, optical circuit switches (OCS) are increasingly deployed in data centers due to the superiority in hardware, such as high bandwidth and low energy consumption. However, few works in the literature considered both coflow scheduling with OCS. In this paper, we study the coflow scheduling problem in an OCS-based data center network, with an aim to minimize the coflow completion time (CCT). We derive an online algorithm called OMCO to solve this problem. \texttt{OMCO} can not only optimize the circuit utilization but also minimize the number of circuit reconfigurations. Extensive simulations with real-world data traces show that OMCO outperforms other existing solutions dramatically. Compared with a FIFO-based scheme and the shortest- coflow-first heuristic, OMCO reduces the average coflow completion time by up to 61% and 62%, respectively.


title: Energy-Efficient Multiple-Precision Floating-Point Multiplier for Embedded Applications

abstract: Floating-point (FP) multipliers are the main energy consumers in many modern embedded digital signal processing (DSP) and multimedia systems. For lossy applications, minimizing the precision of FP multiplication operations under the acceptable accuracy loss is a well-known approach for reducing the energy consumption of FP multipliers. This paper proposes a multiple-precision FP multiplier to efficiently trade the energy consumption with the output quality. The proposed FP multiplier can perform low-precision multiplication that generates 8-, 14-, 20-, or 26-bit mantissa product through an iterative and truncated modified Booth multiplier. Energy saving for low-precision multiplication is achieved by partially suppressing the computation of mantissa multiplier. In addition, the proposed multiplier allows the bitwidth of mantissa in the multiplicand, multiplier, and output product to be dynamically changed when it performs different FP multiplication operations to further reduce energy consumption. Experimental results show that the proposed multiplier can achieve 59 %, 71 %, 73 %, and 82 % energy saving under 0.1 %, 1 %, 5 %, and 11 % accuracy loss, respectively, for the RGB-to-YUV & YUV-to-RGB conversion when compared to the conventional IEEE single-precision multiplier. In addition, the results also exhibit that the proposed multiplier can obtain more energy reduction than previous multiple-precision iterative FP multipliers.


title: Legible compact calligrams

abstract: A calligram is an arrangement of words or letters that creates a visual image, and a compact calligram fits one word into a 2D shape. We introduce a fully automatic method for the generation of legible compact calligrams which provides a balance between conveying the input shape, legibility, and aesthetics. Our method has three key elements: a path generation step which computes a global layout path suitable for embedding the input word; an alignment step to place the letters so as to achieve feature alignment between letter and shape protrusions while maintaining word legibility; and a final deformation step which deforms the letters to fit the shape while balancing fit against letter legibility. As letter legibility is critical to the quality of compact calligrams, we conduct a large-scale crowd-sourced study on the impact of different letter deformations on legibility and use the results to train a letter legibility measure which guides the letter deformation. We show automatically generated calligrams on an extensive set of word-image combinations. The legibility and overall quality of the calligrams are evaluated and compared, via user studies, to those produced by human creators, including a professional artist, and existing works.


title: Coarse-to-fine review selection via supervised joint aspect and sentiment model

abstract: Online reviews are immensely valuable for customers to make informed purchase decisions and for businesses to improve the quality of their products and services. However, customer reviews grow exponentially while varying greatly in quality. It is generally very tedious and difficult, if not impossible, for users to read though the huge amount of review data. Fortunately, review quality evaluation enables a system to select the most helpful reviews for users' decision-making. Previous studies predict only the overall review utility about a product, and often focus on developing different data features to learn a quality function for addressing the problem. In this paper, we aim to select the most helpful reviews not only at the product level, but also at a fine-grained product aspect level. We propose a novel supervised joint aspect and sentiment model (SJASM), which is a probabilistic topic modeling framework that jointly discovers aspects and sentiments guided by a review helpfulness metric. One key advantage of SJASM is its ability to infer the underlying aspects and sentiments, which are indicative of the helpfulness of a review. We validate SJASM using publicly available review data, and our experimental results demonstrate the superiority of SJASM over several competing models.


title: Introduction Eye localization: a survey

abstract: In this survey we first discuss the importance of eye localization as the initialization step for many face processing techniques, then we further show the influence of the localization precision on the rate achieved by some baseline face recognition techniques. To do so, we give the definition of the objective error measures already known in literature. A brief review of some state-of-the-art eye localization methods is subsequently given, focusing on those articles that tackle the problem of precise localization and that use some objective criterion for performance evaluation. Finally their localization results are compared on a number of different datasets.


title: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

abstract: An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning problem is then formulated in terms of a Gaussian random field on this graph, where the mean of the field is characterized in terms of harmonic functions, and is efficiently obtained using matrix methods or belief propagation. The resulting learning algorithms have intimate connections with random walks, electric networks, and spectral graph theory. We discuss methods to incorporate class priors and the predictions of classifiers obtained by supervised learning. We also propose a method of parameter learning by entropy minimization, and show the algorithm's ability to perform feature selection. Promising experimental results are presented for synthetic data, digit classification, and text classification tasks.


title: Action spectrum for melatonin regulation in humans: Evidence for a novel circadian photoreceptor

abstract: The photopigment in the human eye that transduces light for circadian and neuroendocrine regulation, is unknown. The aim of this study was to establish an action spectrum for lightinduced melatonin suppression that could help elucidate the ocular photoreceptor system for regulating the human pineal gland. Subjects (37 females, 35 males, mean age of 24.5 0.3 years) were healthy and had normal color vision. Full-field, monochromatic light exposures took place between 2:00 and 3:30 A.M. while subjects’ pupils were dilated. Blood samples collected before and after light exposures were quantified for melatonin. Each subject was tested with at least seven different irradiances of one wavelength with a minimum of 1 week between each nighttime exposure. Nighttime melatonin suppression tests (n 627) were completed with wavelengths from 420 to 600 nm. The data were fit to eight univariant, sigmoidal fluence‐response curves (R 2 0.81‐0.95). The action spectrum constructed from these data fit an opsin template (R 2 0.91), which identifies 446‐477 nm as the most potent wavelength region providing circadian input for regulating melatonin secretion. The results suggest that, in humans, a single photopigment may be primarily responsible for melatonin suppression, and its peak absorbance appears to be distinct from that of rod and cone cell photopigments for vision. The data also suggest that this new photopigment is retinaldehyde based. These findings suggest that there is a novel opsin photopigment in the human eye that mediates circadian photoreception.


title: Battery lifetime estimation by means of an analytical continuous-time model

abstract: Analytical battery models such as Rakhmatov-Vrudhula are tipically represented in discrete-time, and then implemented by software. However, by means of Model-based design and automatic code generation, it is possible to generate embedded code directly from a continuous time representation. Therefore, in this work we present an analytical battery model in continuous-time by means of the Laplace transform, which is a suitable representation for integrated development environments for model-based design. We show that the battery lifetime estimation error for our continuous-time representation is less than 1% compared to the discrete-time Rakhmatov-Vrudhula model.


title: Real-time Motion Planning with Dynamic Obstacles

abstract: Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for realtime planning in dynamic environments fail in the highdimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee realtime performance, and extensions to a leading real-time planner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.


title: mobileSpiro: accurate mobile spirometry for self-management of asthma

abstract: Effective management of asthma and other respiratory diseases requires constant monitoring and frequent data collection using a spirometer and longitudinal analysis. However, even after three decades of clinical use, there are very few personalized spirometers available on the market, especially those connecting to smartphones. To address this problem, we have developed mobileSpiro, a portable, low-cost spirometer intended for patient self-monitoring. The mobileSpiro API, and the accompanying Android application, interfaces with the spirometer hardware to capture, process and analyze the data. Our key contributions are automated algorithms on the smartphone which play a technician's role in detecting erroneous patient maneuvers, ensuring data quality, and coaching patients with easy-to-understand feedback, all packaged as an Android app. We demonstrate that mobileSpiro is as accurate as a commercial ISO13485 device, with an inter-device deviation in flow reading of less than 8%, and detects more than 95% of erroneous cough maneuvers in a public CDC dataset.


title: Linear Road: A Stream Data Management Benchmark

abstract: This paper specifies the Linear Road Benchmark for Stream Data Management Systems (SDMS). Stream Data Management Systems process streaming data by executing continuous and historical queries while producing query results in real-time. This benchmark makes it possible to compare the performance characteristics of SDMS' relative to each other and to alternative (e.g., Relational Database) systems. Linear Road has been endorsed as an SDMS benchmark by the developers of both the Aurora [1] (out of Brandeis University, Brown University and MIT) and STREAM [8] (out of Stanford University) stream systems. ::: ::: Linear Road simulates a toll system for the motor vehicle expressways of a large metropolitan area. The tolling system uses "variable tolling" [6, 11, 9]: an increasingly prevalent tolling technique that uses such dynamic factors as traffic congestion and accident proximity to calculate toll charges. Linear Road specifies a variable tolling system for a fictional urban area including such features as accident detection and alerts, traffic congestion measurements, toll calculations and historical queries. After specifying the benchmark, we describe experimental results involving two implementations: one using a commercially available Relational Database and the other using Aurora. Our results show that a dedicated Stream Data Management System can outperform a Relational Database by at least a factor of 5 on streaming data applications.


title: Machine transliteration survey

abstract: Machine transliteration is the process of automatically transforming the script of a word from a source language to a target language, while preserving pronunciation. The development of algorithms specifically for machine transliteration began over a decade ago based on the phonetics of source and target languages, followed by approaches using statistical and language-specific methods. In this survey, we review the key methodologies introduced in the transliteration literature. The approaches are categorized based on the resources and algorithms used, and the effectiveness is compared.


title: Small-Size LTE/WWAN Tablet Device Antenna With Two Hybrid Feeds

abstract: A small-size tablet device antenna formed by a simple folded metal plate excited by a low-band feed (direct feed with matching network) and a high-band feed (gap-coupled feed with matching network) for the LTE/WWAN operation in the 698–960 and 1710–2690 MHz bands is presented. The folded metal plate has a size of 3 $\, \times \,$ 5 $\, \times \,$ 30 mm $^{3}$ and is disposed in a clearance area of 10 $\, \times \,$ 30 mm $^{2}$ (300 mm $^{2}$ ) above the top edge of the device ground plane. The matching network in the low-band feed compensates for additional capacitance for the folded metal plate with a resonant length much less than 0.25 wavelength in the lower band (698–960 MHz) and enhances the antenna's impedance matching to achieve a wide lower band. The matching network in the high-band feed also enhances the antenna's impedance matching to achieve a wide higher band (1710–2690 MHz). In addition, the coupling gap in the high-band feed can reject the low-band frequency. Also, the matching network in the low-band (high-band) feed includes a bandpass circuit which can help reject the high-band (low-band) frequency. Good isolation between the two hybrid feeds can, hence, be obtained, which makes the antenna with simple structure and small size have good radiation performances in both lower and higher bands. Details of the proposed antenna are presented.


title: Low-cost wireless neural recording system and software

abstract: We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25μV rms in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.


title: Bioprinting Cartilage Tissue from Mesenchymal Stem Cells and PEG Hydrogel

abstract: Abstract Bioprinting based on thermal inkjet printing is one of the most attractive enabling technologies for tissue engineering and regeneration. During the printing process, cells, scaffolds , and growth factors are rapidly deposited to the desired two-dimensional (2D) and three-dimensional (3D) locations. Ideally, the bioprinted tissues are able to mimic the native anatomic structures in order to restore the biological functions. In this study, a bioprinting platform for 3D cartilage tissue engineering was developed using a commercially available thermal inkjet printer with simultaneous photopolymerization . The engineered cartilage demonstrated native zonal organization, ideal extracellular matrix (ECM ) composition, and proper mechanical properties. Compared to the conventional tissue fabrication approach, which requires extended UV exposure, the viability of the printed cells with simultaneous photopolymerization was significantly higher. Printed neocartilage demonstrated excellent glycosaminoglycan (GAG) and collagen type II production, which was consistent with gene expression profile. Therefore, this platform is ideal for anatomic tissue engineering with accurate cell distribution and arrangement.


title: Trends in utilization and outcomes of laparoscopic versus open appendectomy.

abstract: Abstract Background Although a number of trials have analyzed the outcomes of laparoscopic versus open appendectomy, the clinical advantages, and cost-effectiveness of laparoscopic appendectomy in the management of acute and perforated appendicitis are still not clearly defined. The aim of this study was to examine utilization and outcomes of laparoscopic versus open appendectomy using a national administrative database of academic medical centers and teaching hospitals. Methods Using ICD-9 diagnosis and procedure codes, we obtained data from the University HealthSystem Consortium Clinical Data Base for all patients who underwent appendectomy for acute and perforated appendicitis between 1999 and 2003 (n = 60,236). Trends in utilization of laparoscopic appendectomy were examined over the 5-year period. The outcomes of laparoscopic and open appendectomy were compared including length of hospital stay, 30-day readmission, complications, observed and expected (risk-adjusted) in-hospital mortality, and costs. Results Overall, 41,085 patients underwent open appendectomy and 19,151 patients underwent laparoscopic appendectomy. The percentage of appendectomy performed by laparoscopy increased from 20% in 1999 to 43% in 2003 (P Conclusions Utilization of laparoscopic appendectomy at academic centers has increased more than two-fold between 1999 and 2003. Patients selected for laparoscopic appendectomy have less advanced appendicitis and have a shorter length of stay and fewer complications without increasing the inpatient care cost.


title: The influence of organizational factors on successful ERP implementation

abstract: Purpose – This study aims to examine organizational factors (i.e. top management support, training and education, enterprise‐wide communication) that may influence the enterprise resource planning system implementation success in Iran.Design/methodology/approach – Empirical data were collected via a survey questionnaire. The questionnaires were distributed to selected managers of companies adopting ERP systems in Iran.Findings – The results indicate that the companies' top management must provide full support and commitment to the project if the system is to be successful. In addition, management must also ensure the plans are communicated and understood by the entire company. Finally it is also illustrated that adequate training and education pertaining to the systems must be given to all users to ensure that they are able to use the system effectively and efficiently thus contributing to their satisfaction which will subsequently influence the implementation success.Research limitations/implications – T...


title: Proposed system for estimating intrinsic value of stock using Monte Carlo simulation

abstract: Stock market prediction has been the bane and goal for investors, and one of the biggest challenges for artificial intelligence (AI) community. National economies have a great impact by the behaviour of their stock markets. Markets have become a more accessible investment tool. Attribute that all stock markets have in common is uncertainty. This uncertainty associated with stock value in short and long term future investments are undesirable. Stock market prediction is instrumental in process of investment. Drawbacks of existing system faces technical difficulties such as estimating share of dividends, state vector while implementing stochastic modelling for risk analysis. To overcome drawbacks of existing system, proposed system makes an effort of generating a combination of more than one lakh seventy thousand scenarios to find intrinsic value of company, displaying results in graphical visualization. A large scenario generation of distinct intrinsic stock value done by the system will provide intrinsic stock value for each scenario. A large set of values for all the input parameters for example high growth value, declining growth value, terminal growth value, return on equity needs to be created, so that possible intrinsic value can be generated by system. The system will calculate statistical indicators like mean, median, mode, skewness and kurtosis of large data set consisting intrinsic value of company. Comparing statistically calculated intrinsic value and current market price, system will be able to add a robust statistical reasoning for investment decision. This reasoning will have no human or emotional biases as there will be no human intervention involved for arriving to the final intrinsic value of stock. Monte Carlo simulation is best suited solution for generating random scenarios that fall in line with Brownian walk motion of stock prices.


title: A Novel Voltage-Feedback Pixel Circuit for AMOLED Displays

abstract: This study presents a novel voltage-modulated pixel circuit for active-matrix organic light-emitting diode (AMOLED) consisting of five n-type thin-film transistors (TFTs), one additional control signal, and one storage capacitor. The proposed circuit, which can be implemented in all-n-type and all-p-type low temperature poly-silicon (LTPS) TFT technologies, successfully compensates for threshold voltage deviation of TFTs and facilitates correction of OLED degradation using a voltage feedback method. Simulation and experimental results for all-n-type TFTs indicate that the proposed pixel circuit reduced the nonuniformity brightness problem effectively by compensating for threshold voltage variation in TFTs and reduced the degradation of emission efficiency in OLEDs.


title: Does restaurant performance meet customers' expectations? An assessment of restaurant service quality using a modified DINESERV approach.

abstract: The purpose of this study is to determine restaurant service quality. The aims are to: assess customers’ expectations and perceptions, establish the significance of difference between perceived and expected service quality, identify the number of dimensions for expectations and perceptions scales of modified DINESERV model, test the reliability of the applied DINESERV model.The empirical research was conducted using primary data. The questionnaire is based on Stevens et al. (1995) and Andaleeb and Conway’s (2006) research. In order to meet survey goals, descriptive, bivariate and multivariate (exploratory factor analysis and reliability analysis) statistical analyses were conducted.The empirical results show that expectations scores are higher than perceptions scores, which indicate low level of service quality. Furthermore, this study identified seven factors that best explain customers’ expectations and two factors that best explain customers’ perceptions regarding restaurant service.The results of this study would help management identify the strengths and weaknesses of service quality and implement an effective strategy to meet the customers’ expectations.


title: Establishing Moore's Law

abstract: The seemingly unshakeable accuracy of Moore's law - which states that the speed of computers; as measured by the number of transistors that can be placed on a single chip, will double every year or two - has been credited with being the engine of the electronics revolution, and is regarded as the premier example of a self-fulfilling prophecy and technological trajectory in both the academic and popular press. Although many factors have kept Moore's law as an industry benchmark, it is the entry of foreign competition that seems to have played a critical role in maintaining the pace of Moore's law in the early VLSI transition. Many different kinds of chips used many competing logic families. DRAMs and microprocessors became critical to the semiconductor industry, yet were unknown during the original formulation of Moore's law


title: Subjective comparison and evaluation of speech enhancement algorithms

abstract: Making meaningful comparisons between the performance of the various speech enhancement algorithms proposed over the years has been elusive due to lack of a common speech database, differences in the types of noise used and differences in the testing methodology. To facilitate such comparisons, we report on the development of a noisy speech corpus suitable for evaluation of speech enhancement algorithms. This corpus is subsequently used for the subjective evaluation of 13 speech enhancement methods encompassing four classes of algorithms: spectral subtractive, subspace, statistical-model based and Wiener-type algorithms. The subjective evaluation was performed by Dynastat, Inc., using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the results of the subjective tests.


title: Render-time procedural per-pixel geometry generation

abstract: We introduce procedural geometry mapping and ray-dependent grammar development for fast and scalable render-time generation of procedural geometric details on graphics hardware. By leveraging the properties of the widely used split grammars, we replace geometry generation by lazy per-pixel grammar development. This approach drastically reduces the memory costs while implicitly concentrating the computations on objects spanning large areas in image space. Starting with a building footprint, the bounding volume of each facade is projected towards the viewer. For each pixel we lazily develop the grammar describing the facade and intersect the potentially visible split rules and terminal shapes. Further geometric details are added using normal and relief mapping in terminal space. Our approach also supports the computation of per-pixel self shadowing on facades for high visual quality. We demonstrate interactive performance even when generating and tuning large cityscapes comprising thousands of facades. The method is generalized to arbitrary mesh-based shapes to provide full artistic control over the generation of the procedural elements, making it also usable outside the context of urban modeling.


title: Can we predict types of code changes? An empirical analysis

abstract: There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.


title: An Automatic System for Unconstrained Video-Based Face Recognition

abstract: Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of face/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), JANUS Challenge Set 6 (CS6) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.


title: Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

abstract: Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers’ habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.


title: BURS automata generation

abstract: A simple and efficient algorithm for generating bottom-up rewrite system (BURS) tables is described. A small code-generator generator implementation produces BURS tables efficiently, even for complex instruction set descriptions. The algorithm does not require novel data structures or complicated algorithmic techniques. Previously published methods for on-the-fly elimination of states are generalized and simplified to create a new method, triangle trimming, that is employed in the algorithm. A prototype implementation, burg, generates BURS tables very efficiently.